CN116932232A - Data processing method of development platform based on BS architecture - Google Patents
Data processing method of development platform based on BS architecture Download PDFInfo
- Publication number
- CN116932232A CN116932232A CN202311200197.9A CN202311200197A CN116932232A CN 116932232 A CN116932232 A CN 116932232A CN 202311200197 A CN202311200197 A CN 202311200197A CN 116932232 A CN116932232 A CN 116932232A
- Authority
- CN
- China
- Prior art keywords
- data
- development platform
- user
- processed
- data processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000011161 development Methods 0.000 title claims abstract description 295
- 238000003672 processing method Methods 0.000 title claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 374
- 238000012360 testing method Methods 0.000 claims abstract description 179
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 128
- 238000005516 engineering process Methods 0.000 claims abstract description 106
- 238000012544 monitoring process Methods 0.000 claims abstract description 81
- 230000009467 reduction Effects 0.000 claims abstract description 80
- 238000000034 method Methods 0.000 claims abstract description 68
- 238000001514 detection method Methods 0.000 claims abstract description 56
- 230000000007 visual effect Effects 0.000 claims abstract description 23
- 230000006870 function Effects 0.000 claims description 52
- 230000005856 abnormality Effects 0.000 claims description 51
- 230000008569 process Effects 0.000 claims description 34
- 238000012937 correction Methods 0.000 claims description 30
- 230000010354 integration Effects 0.000 claims description 26
- 238000004364 calculation method Methods 0.000 claims description 22
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 238000009826 distribution Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012546 transfer Methods 0.000 claims description 8
- 238000013500 data storage Methods 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000035945 sensitivity Effects 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000013522 software testing Methods 0.000 claims description 3
- 230000002123 temporal effect Effects 0.000 claims description 3
- 238000004260 weight control Methods 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 description 8
- 230000002829 reductive effect Effects 0.000 description 7
- 238000003860 storage Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 4
- 238000012800 visualization Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000007635 classification algorithm Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 241001178520 Stomatepia mongo Species 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention relates to the technical field of software development data processing, in particular to a data processing method of a development platform based on a BS architecture. The method comprises the following steps: configuring and anomaly detecting the development platform based on the BS architecture through an integrated configuration technology and an anomaly detection algorithm to obtain a development platform test anomaly result; the method comprises the steps of obtaining an intelligent data processing platform through updating, and accessing the intelligent data processing platform through a client to obtain data to be processed by a user; carrying out noise reduction and backup processing by using a data noise reduction algorithm and a data backup technology to obtain backup user data to be processed; processing by using a data processing algorithm and a task priority algorithm, and performing computing resource monitoring processing by using a self-adaptive monitoring technology to obtain a computing resource load condition; and carrying out distributed scheduling processing based on the load condition of the computing resources, and displaying the processing result to a front-end interface in a visual form. The invention can improve the efficiency and the flexibility of data processing.
Description
Technical Field
The invention relates to the technical field of software development data processing, in particular to a data processing method of a development platform based on a BS architecture.
Background
In modern society, data processing plays a vital role in various industries and fields. With the rapid development of cloud computing and Web technology, a development platform based on a BS architecture becomes the first choice of more and more enterprises and organizations. However, existing data processing methods are limited by local computing resources and stand-alone processing capabilities, often failing to meet the needs of large-scale data processing and distributed collaboration.
Disclosure of Invention
Accordingly, the present invention is directed to a data processing method of a development platform based on BS architecture, so as to solve at least one of the above-mentioned problems.
In order to achieve the above object, a data processing method of a development platform based on BS architecture includes the following steps:
step S1: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform; performing abnormality detection on the test result of the integrated configuration result of the development platform by using an abnormality detection algorithm to obtain a test abnormality result of the development platform;
step S2: updating and configuring a development platform based on a BS architecture according to a development platform test abnormal result to obtain an intelligent data processing platform; accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user; carrying out noise reduction on the user data to be processed by using a data noise reduction algorithm to obtain the user data to be processed after noise reduction;
Step S3: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface; storing and backing up the user to-be-processed data after noise reduction into a rear-end database of a server by using a data backup technology to obtain backed-up user to-be-processed data;
step S4: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data; the query condition of the data processing task is configured through the front-end interface, so that data processing requirement data is obtained; according to the data processing demand data and by utilizing a task priority algorithm, performing priority calculation on the processed user data to obtain user processing task priority data;
step S5: performing computing resource monitoring processing on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition; the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained; and displaying the user data processing result to the front-end interface in a visual form by utilizing a visual technology.
The invention uses the integrated configuration technology to carry out initialization configuration on the development platform based on the BS framework, which comprises the aspects of configuration server side, client side, menu, security policy, database connection and the like, and the development platform can ensure the stability and the security of the system and provide good performance and reliability through correct configuration and initialization. After the development platform is initialized and configured, the test result of the integrated configuration result of the development platform is subjected to abnormality detection processing by using a proper abnormality detection algorithm, and potential problems in the initialization configuration process can be identified and eliminated by the abnormality detection, so that the normal operation of the development platform is ensured. By detecting the abnormality in the test result, configuration errors or inconsistencies can be found early, potential faults and risks are reduced, and guidance is provided for further updating configuration. And then, according to the detected development platform test abnormal result, updating configuration processing is carried out on the development platform based on the BS framework, and the development platform is converted into an intelligent data processing platform. Based on the anomaly detection results, configuration problems are identified and resolved, which may include repairing erroneous settings, updating software versions, adjusting security policies, etc. By updating the configuration process, the development platform can improve stability, performance and security and provide a reliable base environment for subsequent data processing tasks. In addition, the user can access the intelligent data processing platform through the client and upload the data to be processed. The data may be raw data, log files, images, audio, and so forth. By providing an upload function, a user may transfer data to be processed to the intelligent data processing platform for subsequent data processing operations. The client transmits the data to be processed of the user to the intelligent data processing platform, so that accurate input can be provided for subsequent data processing tasks. And, through using the appropriate data noise reduction algorithm to carry out noise reduction processing to the data to be processed of the user, the data noise reduction is a data cleaning and preprocessing technology, and aims to remove noise, abnormal values or inconsistencies in the data so as to improve the quality and accuracy of the data. By noise reduction processing, the intelligent data processing platform can reduce interference in the data and provide more reliable and consistent data to subsequent data processing tasks. And the user can upload the data subjected to noise reduction treatment to the service end of the intelligent data processing platform through the front-end interface. In this way, the intelligent data processing platform can receive and store the user's data to be processed. Meanwhile, the data to be processed of the user after noise reduction is stored and backed up in a rear-end database of the server by using a data backup technology, so that the safety and reliability of the data are ensured. Therefore, the data to be processed of the user can be uploaded to the intelligent data processing platform, and backup data to be processed of the user can be generated for subsequent use. And then, applying a proper data processing algorithm to the backup user data to be processed, and performing data processing operation to obtain the processed user data. Meanwhile, a user can configure query conditions of the data processing task through the front-end interface to specify specific data processing requirements. Based on the demand data, the intelligent data processing platform can use a task priority algorithm to perform priority calculation on the processed user data so as to determine the execution sequence of the processing task and provide basis for subsequent task scheduling. And finally, monitoring and processing the computing resources of the server by using an adaptive monitoring technology to obtain the load condition of the computing resources. Based on the load condition information, the service end performs distributed scheduling processing on the user processing task priority data so as to fully utilize computing resources and provide efficient data processing results. And by using a visualization technology, the intelligent data processing platform can display the user data processing result to the front-end interface in a visual mode, so that a user can intuitively check and analyze the data processing result, the performance and user experience of the intelligent data processing platform can be improved, and the requirements of large-scale data processing and distributed collaboration are met.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of the steps of a data processing method of a development platform based on a BS architecture of the present invention;
FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;
fig. 3 is a detailed step flow chart of step S11 in fig. 2.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
To achieve the above objective, referring to fig. 1 to 3, the present invention provides a data processing method of a development platform based on BS architecture, the method includes the following steps:
step S1: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform; performing abnormality detection on the test result of the integrated configuration result of the development platform by using an abnormality detection algorithm to obtain a test abnormality result of the development platform;
step S2: updating and configuring a development platform based on a BS architecture according to a development platform test abnormal result to obtain an intelligent data processing platform; accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user; carrying out noise reduction on the user data to be processed by using a data noise reduction algorithm to obtain the user data to be processed after noise reduction;
Step S3: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface; storing and backing up the user to-be-processed data after noise reduction into a rear-end database of a server by using a data backup technology to obtain backed-up user to-be-processed data;
step S4: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data; the query condition of the data processing task is configured through the front-end interface, so that data processing requirement data is obtained; according to the data processing demand data and by utilizing a task priority algorithm, performing priority calculation on the processed user data to obtain user processing task priority data;
step S5: performing computing resource monitoring processing on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition; the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained; and displaying the user data processing result to the front-end interface in a visual form by utilizing a visual technology.
In the embodiment of the present invention, please refer to fig. 1, which is a schematic flow chart of steps of a data processing method of a development platform based on BS architecture, in this example, the steps of the data processing method of the development platform based on BS architecture include:
Step S1: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform; performing abnormality detection on the test result of the integrated configuration result of the development platform by using an abnormality detection algorithm to obtain a test abnormality result of the development platform;
the embodiment of the invention carries out initialization configuration processing on the development platform based on the BS architecture by using an integrated configuration technology consisting of a server configuration technology, a client configuration technology and a menu configuration technology, and carries out integrated configuration on each component and module in the server, the client and the menu so as to obtain an integrated configuration result of the development platform. And then, obtaining the development platform integrated configuration test result by carrying out software test on the development platform integrated configuration result. And finally, carrying out abnormality detection processing on the integrated configuration test result of the development platform by constructing a proper abnormality detection algorithm, and finally obtaining the test abnormality result of the development platform.
Step S2: updating and configuring a development platform based on a BS architecture according to a development platform test abnormal result to obtain an intelligent data processing platform; accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user; carrying out noise reduction on the user data to be processed by using a data noise reduction algorithm to obtain the user data to be processed after noise reduction;
According to the embodiment of the invention, the abnormal test result of the development platform is corrected, and the development platform based on the BS framework is updated and configured according to the corrected result, wherein the updated configuration comprises, but is not limited to, configuration adjustment in aspects of a server, a network, a database, user permission and the like, so that the development platform can smoothly perform intelligent data processing operation, and the intelligent data processing platform is obtained. Then, the client is used for accessing the intelligent data processing platform and uploading data which is required to be processed, analyzed or otherwise operated by the user so as to obtain data to be processed by the user. And finally, carrying out noise reduction processing on the user to-be-processed data by constructing a proper data noise reduction algorithm so as to eliminate the influence of a noise source in the user to-be-processed data, and finally obtaining the noise-reduced user to-be-processed data.
Step S3: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface; storing and backing up the user to-be-processed data after noise reduction into a rear-end database of a server by using a data backup technology to obtain backed-up user to-be-processed data;
according to the embodiment of the invention, the user data to be processed after noise reduction is uploaded to the server side of the intelligent data processing platform in the form of a file by using the front-end interface. And then, storing and backing up the received noise-reduced user to-be-processed data into a relational database, a NoSQL database, a MongoDB database and other back-end databases by adopting a backup strategy such as regular backup, incremental backup and the like by using a data backup technology, and finally obtaining the backed-up user to-be-processed data.
Step S4: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data; the query condition of the data processing task is configured through the front-end interface, so that data processing requirement data is obtained; according to the data processing demand data and by utilizing a task priority algorithm, performing priority calculation on the processed user data to obtain user processing task priority data;
according to the embodiment of the invention, an appropriate data processing algorithm is selected according to specific data processing requirements to process the backed-up user data to be processed, wherein the data processing algorithm comprises a feature extraction algorithm, a prediction algorithm, a clustering algorithm, a classification algorithm or other applicable algorithms, and the processing steps of the data processing algorithm are implemented on the backed-up user data to be processed so as to obtain the processed user data. And then, configuring and inputting query conditions of data processing tasks including data range, time window, data type, feature selection and the like by using a front-end interface, and carrying out demand analysis processing on the input query conditions by using an intelligent data processing platform to obtain data processing demand data. And finally, carrying out priority calculation on the processed user data by using the data processing demand data and a proper task priority algorithm, and finally obtaining the user processing task priority data.
Step S5: performing computing resource monitoring processing on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition; the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained; and displaying the user data processing result to the front-end interface in a visual form by utilizing a visual technology.
According to the embodiment of the invention, the load condition of the current computing resource is monitored by using the self-adaptive monitoring technology to monitor and analyze the indexes such as CPU utilization rate, memory occupation, network bandwidth, disk service condition and the like of the intelligent data processing platform service end in real time so as to obtain the load condition of the computing resource. And then, performing distributed scheduling processing procedures such as task allocation, task scheduling, resource management and the like on the user processing task priority data through the server side of the intelligent data processing platform according to the monitored computing resource load condition so as to obtain a user data processing result. Finally, the user data processing result is displayed to the front end interface of the intelligent data processing platform in the visual form of a chart, an image, a report and the like by using a visual technology.
The invention uses the integrated configuration technology to carry out initialization configuration on the development platform based on the BS framework, which comprises the aspects of configuration server side, client side, menu, security policy, database connection and the like, and the development platform can ensure the stability and the security of the system and provide good performance and reliability through correct configuration and initialization. After the development platform is initialized and configured, the test result of the integrated configuration result of the development platform is subjected to abnormality detection processing by using a proper abnormality detection algorithm, and potential problems in the initialization configuration process can be identified and eliminated by the abnormality detection, so that the normal operation of the development platform is ensured. By detecting the abnormality in the test result, configuration errors or inconsistencies can be found early, potential faults and risks are reduced, and guidance is provided for further updating configuration. And then, according to the detected development platform test abnormal result, updating configuration processing is carried out on the development platform based on the BS framework, and the development platform is converted into an intelligent data processing platform. Based on the anomaly detection results, configuration problems are identified and resolved, which may include repairing erroneous settings, updating software versions, adjusting security policies, etc. By updating the configuration process, the development platform can improve stability, performance and security and provide a reliable base environment for subsequent data processing tasks. In addition, the user can access the intelligent data processing platform through the client and upload the data to be processed. The data may be raw data, log files, images, audio, and so forth. By providing an upload function, a user may transfer data to be processed to the intelligent data processing platform for subsequent data processing operations. The client transmits the data to be processed of the user to the intelligent data processing platform, so that accurate input can be provided for subsequent data processing tasks. And, through using the appropriate data noise reduction algorithm to carry out noise reduction processing to the data to be processed of the user, the data noise reduction is a data cleaning and preprocessing technology, and aims to remove noise, abnormal values or inconsistencies in the data so as to improve the quality and accuracy of the data. By noise reduction processing, the intelligent data processing platform can reduce interference in the data and provide more reliable and consistent data to subsequent data processing tasks. And the user can upload the data subjected to noise reduction treatment to the service end of the intelligent data processing platform through the front-end interface. In this way, the intelligent data processing platform can receive and store the user's data to be processed. Meanwhile, the data to be processed of the user after noise reduction is stored and backed up in a rear-end database of the server by using a data backup technology, so that the safety and reliability of the data are ensured. Therefore, the data to be processed of the user can be uploaded to the intelligent data processing platform, and backup data to be processed of the user can be generated for subsequent use. And then, applying a proper data processing algorithm to the backup user data to be processed, and performing data processing operation to obtain the processed user data. Meanwhile, a user can configure query conditions of the data processing task through the front-end interface to specify specific data processing requirements. Based on the demand data, the intelligent data processing platform can use a task priority algorithm to perform priority calculation on the processed user data so as to determine the execution sequence of the processing task and provide basis for subsequent task scheduling. And finally, monitoring and processing the computing resources of the server by using an adaptive monitoring technology to obtain the load condition of the computing resources. Based on the load condition information, the service end performs distributed scheduling processing on the user processing task priority data so as to fully utilize computing resources and provide efficient data processing results. And by using a visualization technology, the intelligent data processing platform can display the user data processing result to the front-end interface in a visual mode, so that a user can intuitively check and analyze the data processing result, the performance and user experience of the intelligent data processing platform can be improved, and the requirements of large-scale data processing and distributed collaboration are met.
Preferably, step S1 comprises the steps of:
step S11: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform;
step S12: software testing is carried out on the integrated configuration result of the development platform through an automatic script and a cloud service technology, and the integrated configuration test result of the development platform is obtained;
step S13: real-time monitoring and deployment are carried out on the development platform integrated configuration test result by utilizing a reference test algorithm, and monitoring data acquisition is carried out, so that the development platform integrated configuration test real-time monitoring data are obtained;
the formula of the benchmark test algorithm is as follows:
；
in the method, in the process of the invention,configuring benchmark test scores of test results for development platform integration, +.>Configuring the number of test results for development platform integration, +.>Is->The weight of the test result is integrated and configured by the development platform, < + >>Is->Performance index of integrated configuration test result of individual development platform, < >>Configuring the average performance index of the test results for all development platform integration,/-for all development platform integration>Monitoring the time length for the benchmark test, < >>And->Are all integral time variables, +.>Is->The development platform integrates collaborative resource load assessment functions related to configuration test results, and the development platform is- >For at the moment +.>Lower->The development platform integrates acquisition indexes of configuration test results, < >>For at the moment +.>Lower->The development platform integrates acquisition indexes of configuration test results, < >>Correction values for the benchmark test scores;
step S14: and carrying out anomaly detection on the real-time monitoring data of the integrated configuration test of the development platform by utilizing an anomaly detection algorithm to obtain a test anomaly result of the development platform.
As an embodiment of the present invention, referring to fig. 2, a detailed step flow chart of step S1 in fig. 1 is shown, in which step S1 includes the following steps:
step S11: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform;
the embodiment of the invention carries out initialization configuration processing on the development platform based on the BS architecture by using an integrated configuration technology consisting of a server configuration technology, a client configuration technology and a menu configuration technology, carries out integrated configuration on each component and each module in the server, the client and the menu, ensures the mutual connectivity and compatibility between the components and the modules, and finally obtains the integrated configuration result of the development platform.
Step S12: software testing is carried out on the integrated configuration result of the development platform through an automatic script and a cloud service technology, and the integrated configuration test result of the development platform is obtained;
According to the embodiment of the invention, the cloud service technology is used for providing test environment and resource support, and then the automatic script is operated in the test environment to carry out software test on the development platform integrated configuration result, so that the development platform integrated configuration test result is finally obtained.
Step S13: real-time monitoring and deployment are carried out on the development platform integrated configuration test result by utilizing a reference test algorithm, and monitoring data acquisition is carried out, so that the development platform integrated configuration test real-time monitoring data are obtained;
according to the embodiment of the invention, a proper benchmark test algorithm is constructed by combining the weight, the performance index, the cooperative resource load evaluation function, the acquisition index and the related parameters, so that the real-time monitoring and deployment are carried out on the integrated configuration test result of the development platform to monitor the performance and stability of the integrated configuration test result, then the data acquisition and arrangement are carried out on the monitoring result, and finally the real-time monitoring data of the integrated configuration test of the development platform are obtained.
The formula of the benchmark test algorithm is as follows:
；
in the method, in the process of the invention,configuring benchmark test scores of test results for development platform integration, +.>Configuring the number of test results for development platform integration, +.>Is->The weight of the test result is integrated and configured by the development platform, < + > >Is->Performance index of integrated configuration test result of individual development platform, < >>Integrating configuration for all development platformsAverage performance index of test results,/->Monitoring the time length for the benchmark test, < >>And->Are all integral time variables, +.>Is->The development platform integrates collaborative resource load assessment functions related to configuration test results, and the development platform is->For at the moment +.>Lower->The development platform integrates acquisition indexes of configuration test results, < >>For at the moment +.>Lower->The development platform integrates acquisition indexes of configuration test results, < >>Correction values for the benchmark test scores;
the invention constructs a formula of a benchmark test algorithm for real-time monitoring and deployment of the integrated configuration test result of the development platform, and the benchmark test algorithm is used for calculating the integrated configuration test junction of each development platformThe standard test score of the result is obtained by comprehensively considering the difference between the performance index and the average performance index of each development platform integrated configuration test result, the score is higher for the result with better performance index, and the score is lower for the result with worse performance index, so that the comprehensive performance evaluation can help to identify the integrated configuration with better performance. In addition, the weight parameters are used for adjusting the importance of different test results, and the weight of the test results of the integrated configuration of different development platforms is set, so that the influence of the test results of the integrated configuration of each development platform in the comprehensive score can be adjusted according to the requirements and the priorities, and the important performance indexes can be ensured to be properly valued in the calculation of the reference test score. The reference test algorithm also considers the mutual influence among different test results by using a cooperative resource load evaluation function, and evaluates the resource load conditions of the different test results by collecting indexes, so that the configuration results of the development platform can be evaluated, and abnormal conditions can be found and processed in time, thereby improving the stability and performance of the development platform. The algorithm function formula fully considers the benchmark test score of the development platform integrated configuration test result The number of development platform integrated configuration test results is +.>First->The weight of the integrated configuration test result of the development platform>First->Performance index of integrated configuration test result of individual development platform>All development platforms integrate the average performance index of configuration test results +.>Reference test monitoring time length->Integration time variable>And->And->Collaborative resource load assessment function related to integrated configuration test result of each development platform>At the moment +.>Lower->Acquisition index of integrated configuration test result of individual development platform>At the moment +.>Lower->Acquisition index of integrated configuration test result of individual development platform>Correction value of benchmark score ∈>Wherein by->The weight of the integrated configuration test result of the development platform>First->Performance index of integrated configuration test result of individual development platform>And average performance index of all development platform integrated configuration test results +.>Constitutes a performance index weight difference function relationship +.>The length of time is also monitored by a benchmark test>Integration time variable>And->And->Collaborative resource load assessment function related to integrated configuration test result of each development platform>At the moment +.>Lower- >Acquisition index of integrated configuration test result of individual development platform>At the moment->Lower->Acquisition index of integrated configuration test result of individual development platform>Constitutes a resource evaluation integral function relationBenchmark test score based on development platform integrated configuration test resultsThe correlation relationship between the parameters forms a functional relationshipThe algorithm formula can realize the real-time monitoring and deployment process of the integrated configuration test result of the development platform, and meanwhile, the correction value of the benchmark test score is adopted>The introduction of the reference test algorithm can be adjusted according to actual conditions, so that the accuracy and the applicability of the reference test algorithm are improved.
Step S14: and carrying out anomaly detection on the real-time monitoring data of the integrated configuration test of the development platform by utilizing an anomaly detection algorithm to obtain a test anomaly result of the development platform.
According to the embodiment of the invention, a proper abnormality detection algorithm is constructed by combining the weight coefficient, the time variation harmonic smoothing parameter, the integral control factor, the time variation trend function, the abnormality detection time control function and the related parameters, so that the abnormality detection processing is carried out on the characteristic data in the development platform integrated configuration test real-time monitoring data, and finally the development platform test abnormality result is obtained.
The invention uses proper integrated configuration technology to perform initialization configuration processing on the development platform based on the BS framework, ensures that the development platform completes correct operation environment configuration before starting operation, and ensures that each component and module are correctly combined and configured by the initialization configuration processing so as to meet the requirement of the development platform, thereby being beneficial to ensuring the normal operation of the function of the development platform and reducing potential errors and problems. Meanwhile, the software test processing is carried out on the integrated configuration result of the development platform obtained by configuration by using an automatic script and cloud service technology, and the test task can be efficiently executed by automatic test, and the normal work of different parts of the development platform is ensured, so that the method is beneficial to timely finding out potential problems and errors and providing accurate test results. In addition, test results of the integrated configuration of the development platform can be obtained, including indexes such as response time, availability and stability, and data support is provided for subsequent optimization and improvement. And then, carrying out real-time monitoring and evaluation on the development platform integrated configuration test result obtained by testing by using a proper reference test algorithm, wherein the reference test algorithm scores the development platform integrated configuration test result by combining a plurality of indexes and weights, and the indexes comprise performance indexes, resource load evaluation functions, acquisition indexes and the like. Through real-time monitoring deployment and data acquisition, the operation data of the development platform can be timely obtained and compared with expected performance, so that the performance and stability of the development platform can be evaluated, and a basis is provided for timely finding potential problems. And finally, performing abnormality detection processing on the development platform integrated configuration test real-time monitoring data by using a proper abnormality detection algorithm so as to detect whether an abnormality exists. The anomaly detection algorithm can monitor the change and regularity of data in real time according to the integrated configuration test of the development platform, identify potential problems and anomalies such as performance degradation, resource utilization anomalies and the like, and is helpful for timely finding and solving potential faults so as to ensure the normal operation of the development platform. Moreover, the test abnormal result of the development platform can be obtained, and appropriate measures can be timely taken to remove and repair the faults.
Preferably, step S11 comprises the steps of:
step S111: constructing an integrated configuration technology through a computer technology, wherein the integrated configuration technology comprises a server configuration technology, a client configuration technology and a menu configuration technology;
step S112: performing database connection and WCF service configuration on a development platform based on a BS architecture by using a server configuration technology to obtain an initial configuration result of a development platform server;
step S113: performing WCF information security compatible configuration on a development platform based on a BS architecture by using a client configuration technology to obtain an initial configuration result of a development platform client;
step S114: carrying out demand menu configuration on a development platform based on a BS architecture by utilizing a menu configuration technology to obtain an initial configuration result of the development platform menu;
step S115: and carrying out integrated environment configuration on the initial configuration result of the development platform server, the initial configuration result of the development platform client and the initial configuration result of the development platform menu to obtain an integrated configuration result of the development platform.
As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S11 in fig. 2 is shown, in which step S11 includes the following steps:
Step S111: constructing an integrated configuration technology through a computer technology, wherein the integrated configuration technology comprises a server configuration technology, a client configuration technology and a menu configuration technology;
the embodiment of the invention constructs an integrated configuration technology consisting of a server configuration technology, a client configuration technology and a menu configuration technology through a computer technology, wherein the server configuration technology is used for configuring database connection information, setting database access authority and configuring parameters and options related to WCF service. The client configuration technique is used to configure WCF security settings for the client. The menu configuration technology is used for configuring functions such as menu structures, menu items, menu rights and the like of the development platform according to the requirements and the functional requirements of users.
Step S112: performing database connection and WCF service configuration on a development platform based on a BS architecture by using a server configuration technology to obtain an initial configuration result of a development platform server;
the embodiment of the invention configures database connection information, sets database access authority and parameters and options related to the configuration of the WCF service on the development platform based on the BS architecture by using a server configuration technology, verifies the correctness of configuration, ensures that the server can be correctly connected to the database and provides the WCF service, and finally obtains the initial configuration result of the development platform server.
Step S113: performing WCF information security compatible configuration on a development platform based on a BS architecture by using a client configuration technology to obtain an initial configuration result of a development platform client;
the embodiment of the invention carries out WCF information safety compatible configuration on the development platform based on the BS framework by using a client configuration technology, configures WCF binding and safety setting of the client so as to ensure safe and reliable communication with the server, and finally obtains the initial configuration result of the development platform client.
Step S114: carrying out demand menu configuration on a development platform based on a BS architecture by utilizing a menu configuration technology to obtain an initial configuration result of the development platform menu;
according to the embodiment of the invention, the requirement menu configuration technology is used for carrying out requirement menu configuration on the development platform based on the BS framework, and the menu structure, menu items, menu authority and other attribute structures of the development platform are designed and configured according to the requirement and functional requirement of a user, so that the initial configuration result of the development platform menu is finally obtained.
Step S115: and carrying out integrated environment configuration on the initial configuration result of the development platform server, the initial configuration result of the development platform client and the initial configuration result of the development platform menu to obtain an integrated configuration result of the development platform.
According to the embodiment of the invention, the initial configuration result of the development platform server side, the initial configuration result of the development platform client side and the initial configuration result of the development platform menu are summarized, corresponding configuration integration and configuration are carried out in an integrated environment according to requirements, the correctness and compatibility of the overall configuration are verified, the configuration of the server side, the client side and the menu can be ensured to cooperate, and finally the integrated configuration result of the development platform is obtained.
The present invention builds an integrated configuration technique by using computer technology, including server configuration techniques, client configuration techniques, and menu configuration techniques. The purpose of these techniques is to implement configuration and integration of a development platform based on BS architecture to meet the functional requirements and user requirements of the development platform. Through the integrated configuration technology, various components and modules of the development platform can be better managed and integrated, so that the expandability and flexibility of the development platform are improved, and a foundation is provided for subsequent configuration and integration work. The database connection and WCF service configuration processing is carried out on the development platform based on the BS framework by using a server configuration technology, and the database connection information is configured, the database access authority is set, and parameters and options related to the WCF service configuration are included. Through the configuration processing, the server side of the development platform can be ensured to have correct database connection and WCF service configuration so as to interact with the database and provide reliable service, which is helpful to ensure the stability and data security of the development platform and provides a foundation for subsequent function development and service operation. WCF information security compatible configuration processing is then performed on the BS architecture based development platform by using client configuration techniques, including configuring the WCF security settings of the client, such as enabling secure transmissions, configuring authentication and authorization mechanisms, and the like. Through the configuration processing, the client of the development platform can be ensured to have safety and compatibility when communicating with the server, and safe information transmission and access control can be performed, so that the sensitive data and user privacy of the development platform can be protected, and the safety and the credibility of the system can be improved. Next, a demand menu configuration process is performed on the BS architecture-based development platform by using a menu configuration technique, which includes configuring a menu structure, menu items, menu rights, and the like of the development platform according to the user's demand and functional requirements. Through the configuration processing, personalized menu display and user authority management can be realized, so that a user-friendly operation interface and good user experience are provided, the work efficiency and satisfaction of a user are improved, and the development platform is more in line with the actual demands of the user. And finally, carrying out integrated environment configuration processing on the initial configuration result of the development platform server side, the initial configuration result of the development platform client side and the initial configuration result of the development platform menu, wherein the processing comprises the steps of integrating and combining the configuration results to ensure that the configuration results can work together correctly in the integrated environment of the development platform. Through the integrated environment configuration processing, inconsistency and conflict can be eliminated, and the consistency and consistency of each configuration are ensured, so that the whole function and performance of the development platform can be ensured to reach expectations, and a foundation is provided for subsequent development and test work.
Preferably, the formula of the abnormality detection algorithm in step S14 is specifically:
；
in the method, in the process of the invention,for abnormality detection algorithm function, ++>Feature data quantity of real-time monitoring data for integrated configuration test of development platform>Integrated configuration test for development platform in real time monitoring data>Personal characteristic data,/->Configuration test for development platform integration in real-time monitoring data>Weight coefficient corresponding to the individual characteristic data, < ->Configuration test for development platform integration in real-time monitoring data>Temporal variation of the individual characteristic data reconciles the smoothing parameters, < >>Configuration test for development platform integration in real-time monitoring data>Integral control factor of individual characteristic data, +.>Is an integral time variable +.>Configuration test for development platform integration in real-time monitoring data>Time-varying trend function of the individual characteristic data, +.>For the second order weight control coefficient, +.>Configuring test real-time monitoring data quantity for development platform integration,/-for>Is->The second derivative weight coefficient of the real-time monitoring data is tested by the integrated configuration of the development platform>For abnormality detection time variable, +.>Is->The development platform integrates an abnormality detection time control function for configuring test real-time monitoring data, and the detection time control function is +. >Is different fromCorrection values of the algorithm function are often detected.
The invention constructs a formula of an abnormality detection algorithm for carrying out abnormality detection processing on the development platform integrated configuration test real-time monitoring data, and the abnormality detection algorithm can process the development platform integrated configuration test real-time monitoring data so as to detect whether an abnormality exists in real time, thereby helping to find an abnormality problem early and adopting proper measures to solve the abnormality so as to ensure the smooth operation of the development platform. By comprehensively analyzing the characteristic data of the real-time monitoring data of the integrated configuration test of the development platform, the weight coefficient and the time change trend of different characteristic data in the integrated configuration test of the development platform can be considered, so that factors in multiple aspects can be comprehensively considered when abnormality is detected, and the method is not limited to single characteristic data. The time variation of the real-time monitoring data of the integrated configuration test of the development platform is modeled by introducing the time variation harmonic smoothing parameter and the integral control factor, so that the evolution trend of the data can be better known, and whether the current state deviates from the normal condition can be judged. The importance of the data is flexibly adjusted by using the weight coefficient, so that the anomaly detection algorithm has stronger adaptability and generalization capability when processing different types of data, and the reliability of anomaly detection is improved. The algorithm function formula fully considers the function of the abnormality detection algorithm Feature data quantity of development platform integrated configuration test real-time monitoring data>Development platform integrated configuration test is in real-time monitoring data +.>Personal characteristic data->Development platform integrated configuration test is in real-time monitoring data +.>Weight coefficient corresponding to the individual characteristic data +.>Development platform integrated configuration test is in real-time monitoring data +.>Temporal variation of the characteristic data and smoothing parameters +.>Development platform integrated configuration test is in real-time monitoring data +.>Integral control factor of individual characteristic data +.>Integration time variable>Development platform integrated configuration test is in real-time monitoring data +.>Time-dependent trend function of the individual characteristic data +.>Second order weight control coefficient->Development platform integrated configuration test real-time monitoring data quantity +.>First->Second derivative weight coefficient of real-time monitoring data of integrated configuration test of individual development platform>Abnormality detection time variable +.>First->Abnormality detection time control function of real-time monitoring data of integrated configuration test of individual development platform>Correction value of abnormality detection algorithm function +.>According to the abnormality detection algorithm function->The correlation relationship between the parameters forms a functional relationship The algorithm formula can realize the abnormality detection processing of the real-time monitoring data of the integrated configuration test of the development platform, and simultaneously, the correction value of the algorithm function is detected by the abnormality>The introduction of the algorithm can be adjusted according to actual conditions, so that the accuracy and the applicability of the anomaly detection algorithm are improved.
Preferably, step S2 comprises the steps of:
step S21: real-time correction is carried out on the test abnormal result of the development platform through a vulnerability restoration tool, so that a test correction result of the development platform is obtained;
according to the embodiment of the invention, the special bug repairing tool is used for scanning the testing abnormal result of the development platform so as to detect potential bugs and security risks, analysis and identification are carried out according to the scanning result obtained by detection, and real-time correction processing is carried out on the bugs obtained by detection through a known bug repairing scheme or an automatic repairing tool in the bug repairing tool, so that the testing correction result of the development platform is finally obtained.
Step S22: updating and configuring a development platform based on a BS architecture according to a development platform test and correction result to obtain an intelligent data processing platform;
according to the embodiment of the invention, the configuration requirement and parameter change of the repaired development platform are analyzed according to the test correction result of the development platform, and the development platform based on the BS framework is updated and configured according to the analysis result, wherein the updating configuration comprises but is not limited to configuration adjustment in aspects of a server, a network, a database, user authority and the like, so that the development platform can be ensured to smoothly perform intelligent data processing operation, and finally the intelligent data processing platform is obtained.
Step S23: accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user;
according to the embodiment of the invention, the user accesses the intelligent data processing platform by using the client and uploads the data which the user needs to process, analyze or perform other operations, and finally the data to be processed of the user is obtained.
Step S24: and carrying out noise reduction treatment on the user to-be-treated data by using a data noise reduction algorithm to obtain the user to-be-treated data after noise reduction.
According to the embodiment of the invention, a proper data noise reduction algorithm is constructed by combining the noise Gaussian filter distribution mean value, the noise Gaussian filter distribution standard deviation, the noise signal transfer function, the noise signal weight function and related parameters to carry out noise reduction on the data to be processed of the user, so that the influence of a noise source in the data to be processed of the user is eliminated, and finally the data to be processed of the user after noise reduction is obtained.
The invention carries out real-time correction processing on the abnormal result of the development platform in the test process by using the bug fixing tool, and the bug fixing tool can detect and fix the security bug and error in the development platform, including but not limited to code defect, configuration error, network bug and the like. After the correction processing is completed, the test result of the development platform is corrected, and the functions and performances of the development platform based on the BS architecture can be reflected more accurately, so that the quality and reliability of the development platform can be improved, and potential safety risks and errors can be reduced. And then, updating and configuring the development platform based on the BS framework according to the test and correction result of the development platform, and correspondingly updating and adjusting the configuration file, codes or other related components of the development platform according to the correction result so as to reflect the corrected result. The development platform can be ensured to be consistent with the corrected test result through updating configuration processing, so that the development platform has accurate functions and performances. Through the process, the original development platform is evolved into an intelligent data processing platform, and has higher reliability, stability and performance. Meanwhile, the user accesses the intelligent data processing platform through the client and uploads the data to be processed to the intelligent data processing platform, and the uploaded data can be data which the user needs to process, analyze or perform other operations. The uploading of data to the intelligent data processing platform is to further utilize the functions of the intelligent data processing platform to perform data processing so as to obtain more valuable results. The user can transmit the data to the intelligent data processing platform through a client interface or an API (application program interface) and the like, and the safety and the integrity of the data are ensured. Finally, the noise reduction processing is carried out on the data to be processed, which is uploaded to the intelligent data processing platform by a user, by using a proper data noise reduction algorithm, and noise, abnormal values or redundant information is removed from the original data by using a proper algorithm and technology, so that cleaner and more effective data are extracted. The noise reduction process helps to enhance the quality and accuracy of the data, reducing the effects of errors and anomalies, to provide more reliable and useful data results.
Preferably, step S24 comprises the steps of:
step S241: carrying out noise value calculation on data to be processed of a user by using a data noise reduction algorithm to obtain a user data noise value;
according to the embodiment of the invention, a proper data noise reduction algorithm is constructed by combining the noise Gaussian filter distribution mean value, the noise Gaussian filter distribution standard deviation, the noise signal transfer function, the noise signal weight function and related parameters to calculate the noise value of the data to be processed of the user, so that the influence of a noise source in the data to be processed of the user is eliminated, and finally the noise value of the user data is obtained.
The formula of the data noise reduction algorithm is as follows:
；
in the method, in the process of the invention,for user data noise value +.>For the amount of data to be processed by the user, +.>For the number of noise sources in the data to be processed for each user, and (2)>Is->Data to be processed by individual users->Is->Noise Gaussian filter distribution mean value of data to be processed of individual users, < >>Is->Noise gaussian filter distribution standard deviation of data to be processed by individual users,/->Is->Noise signal transfer function of data to be processed by individual users, < >>Is->The user is treated the data +.>Noise signal weight function of the individual noise sources, +.>Correction values for user data noise values;
The invention constructs a formula of a data denoising algorithm, which is used for calculating the noise value of the data to be processed by the user, and in order to eliminate the influence of a noise source in the data to be processed by the user on the subsequent data storage backup and data processing process, the data to be processed by the user needs to be subjected to denoising processing so as to obtain cleaner and more accurate data to be processed by the user, and the noise source and the interference data in the data to be processed by the user can be effectively removed by the data denoising algorithm, so that the accuracy and the reliability of the data to be processed by the user are improved. The algorithm function formula fully considers the noise value of the user dataThe amount of data to be processed by the user +.>Number of noise sources in data to be processed per user +.>First->Individual user pending data->First->Noise Gaussian filter distribution mean value of data to be processed of individual users>First->Noise Gaussian filter distribution standard deviation of data to be processed of individual users>First->Noise signal transfer function of data to be processed by individual users>First->The user is treated the data +.>Noise signal weight function of the individual noise sources +.>Correction value of user data noise value +.>And it is necessary to normalize it according to the noise value of user data +. >The interrelationship between the parameters constitutes a functional relationship: />
；
The algorithm formula can realize the noise value calculation process of the data to be processed by the user, and simultaneously, the correction value of the noise value of the user data is usedThe introduction of the data noise reduction algorithm can be adjusted according to actual conditions, so that the accuracy and the robustness of the data noise reduction algorithm are improved.
Step S242: judging the user data noise value according to a preset user data noise threshold, and removing the user data to be processed corresponding to the user data noise value when the user data noise value is greater than or equal to the preset user data noise threshold to obtain the user data to be processed after noise reduction;
according to the embodiment of the invention, whether the calculated user data noise value exceeds the preset user data noise threshold value is judged according to the preset user data noise threshold value, when the user data noise value is larger than or equal to the preset user data noise threshold value, the interference influence of a noise source in user data to be processed corresponding to the user data noise value is larger, the user data to be processed corresponding to the user data noise value is removed, and finally the user data to be processed after noise reduction is obtained.
Step S243: and judging the user data noise value according to a preset user data noise threshold, and directly defining the user data to be processed corresponding to the user data noise value as the user data to be processed after noise reduction when the user data noise value is smaller than the preset user data noise threshold.
According to the embodiment of the invention, whether the calculated user data noise value exceeds the preset user data noise threshold value is judged according to the preset user data noise threshold value, when the user data noise value is smaller than the preset user data noise threshold value, the interference influence of a noise source in user data to be processed corresponding to the user data noise value is smaller, and the user data to be processed corresponding to the user data noise value is directly defined as noise-reduced user data to be processed.
According to the invention, the noise value calculation is carried out on the user to-be-processed data obtained by uploading the user by using a proper data noise reduction algorithm, and the noise interference or abnormal noise source and other conditions possibly exist in the user to-be-processed data, so that the accuracy and the reliability of the subsequent data storage backup and data processing processes are adversely affected, so that the proper data noise reduction algorithm is required to be set for carrying out noise reduction processing on the user to-be-processed data, the noise source and the interference signal existing in the user to-be-processed data can be identified and measured, and the noise signal is removed from the source, thereby improving the accuracy and the reliability of the user to-be-processed data. The data noise reduction algorithm performs noise reduction treatment on a noise source in data to be treated by combining a noise Gaussian filter distribution mean value, a noise Gaussian filter distribution standard deviation, a noise signal transfer function, a noise signal weight function and related parameters, and adjusts and optimizes the noise reduction treatment process by a correction value so as to obtain an optimal noise reduction effect and a calculation result, thereby more accurately calculating the noise value of user data. Then, according to specific data noise reduction processing requirements and quality standards, the calculated user data noise value is judged by setting a proper user data noise threshold value, which user data to be processed need to be removed is judged, which user data to be processed can be reserved, the user data to be processed with larger user data noise value can be effectively removed, the influence of the user data to be processed with larger user data noise value on the whole data is avoided, the quality of the data is further improved, unnecessary interference and error are reduced, and therefore the accuracy and reliability of the user data to be processed are ensured. Finally, the user data noise value is judged by using a preset user data noise threshold value, the user data to be processed with smaller user data noise value is defined as the user data to be processed after noise reduction, more accurate and reliable user data to be processed can be obtained, the data are less interfered by noise, a more stable data base can be provided for the subsequent data storage backup and data processing process, and therefore the availability and the effectiveness of the user data to be processed after noise reduction are improved.
Preferably, step S3 comprises the steps of:
step S31: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface;
according to the embodiment of the invention, the user data to be processed after noise reduction is uploaded to the server side of the intelligent data processing platform in the form of a file by using the front-end interface.
Step S32: the server side of the intelligent data processing platform stores the received user data to be processed after noise reduction into a back-end database by utilizing a data storage technology to obtain the stored user data to be processed;
according to the embodiment of the invention, the user data to be processed after noise reduction is received through the server side of the intelligent data processing platform, and the received user data to be processed after noise reduction is stored in a back-end database such as a relational database, a NoSQL database, a MongoDB database and the like in a lasting manner by using a data storage technology.
Step S33: and backing up the stored user to-be-processed data by using a data backup technology to obtain the backed-up user to-be-processed data.
The embodiment of the invention performs backup processing on the stored user to-be-processed data by adopting a data backup technology and adopting backup strategies such as periodical backup, incremental backup and the like so as to prevent the data from being restored when the main data is damaged or lost and finally obtain the backup user to-be-processed data.
The invention uses the front-end interface to interact with the intelligent data processing platform, and selects and uploads the user data to be processed after noise reduction. Through interaction of the front-end interface, a user can conveniently transmit the user data to be processed after noise reduction to a service end of the intelligent data processing platform. The uploading of data may be performed using file uploading, API calls or other suitable means, which enable the user's data to be processed to be passed to a subsequent data processing stage. And then, the server side of the intelligent data processing platform stores the received user data to be processed after noise reduction into a back-end database by adopting a data storage technology. The backend database may be a relational database (e.g., mySQL, oracle), a document database (e.g., mongo db), or other suitable storage solution. By storing the data, the intelligent data processing platform can ensure the durability and accessibility of the data, and facilitate the subsequent data processing and analysis tasks. The resulting stored user-pending data may be used as input to a data processing flow for further data manipulation and analysis. Finally, by using data backup techniques to backup stored user-pending data, which is an important data management practice, data integrity may be restored in the event of data loss, system failure, or other unexpected situations. Through data backup, the intelligent data processing platform can ensure the safety and reliability of the data to be processed by the user. The data backup process may be performed periodically, using techniques such as redundant storage, offline storage, cloud storage, etc., to prevent data loss or corruption. The obtained backup user data to be processed can be used as the basis for data recovery so as to ensure the integrity and usability of the data when damage or error occurs.
Preferably, step S4 comprises the steps of:
step S41: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data;
according to the embodiment of the invention, a proper data processing algorithm is selected according to specific data processing requirements to process the backed-up user data to be processed, wherein the data processing algorithm comprises a feature extraction algorithm, a prediction algorithm, a clustering algorithm, a classification algorithm or other applicable algorithms, and the processing steps of the data processing algorithm are implemented on the backed-up user data to be processed, so that the processed user data is finally obtained.
Step S42: configuring query conditions of a data processing task through a front-end interface, and carrying out demand analysis on the query conditions to obtain data processing demand data;
according to the embodiment of the invention, the front-end interface of the intelligent data processing platform is used for configuring and inputting the query conditions of the data processing tasks including the data range, the time window, the data type, the feature selection and the like, the intelligent data processing platform performs the requirement analysis processing through the input query conditions, and the intelligent data processing platform finally obtains the data processing requirement data by verifying the validity of the query conditions, processing the association relation of the query conditions and the like.
Step S43: performing priority calculation on the processed user data by using a task priority algorithm according to the data processing requirement data to obtain a processing task priority value;
the embodiment of the invention carries out priority calculation on the processed user data by using data processing demand data and a proper task priority algorithm, wherein the task priority algorithm carries out calculation according to different indexes and weight coefficients, and the task priority algorithm comprises a time sensitivity degree, a resource demand degree, a task dependency degree and a demand risk degree, and finally obtains a processing task priority value.
Step S44: and sequencing the processed user data corresponding to the processing task priority values according to the sequence from large to small to obtain the user processing task priority data.
According to the embodiment of the invention, the processed user data corresponding to the calculated processing task priority values are sequenced according to the sequence from large to small, each processed user data is sequentially processed according to the sequencing result, and finally the user processing task priority data is obtained.
The invention processes the backup user data to be processed by using a data processing algorithm through the intelligent data processing platform so as to obtain the processed user data. The data processing algorithm may be a feature extraction algorithm, a prediction algorithm, a clustering algorithm, a classification algorithm, or other suitable algorithm. By applying a data processing algorithm to the backed-up user data to be processed, the intelligent data processing platform can extract useful information, correct data errors or generate new data results, and can convert the original data into processed data, thereby providing a more accurate and more effective data basis for subsequent data analysis and application. The user then configures query conditions for the data processing task via the front-end interface, which may include data range, time window, data type, feature selection, etc. And through carrying out demand analysis processing on the query conditions, the intelligent data processing platform can understand the data processing demands of the user, understand the expected results of the user on the data and the specific data processing demands so as to determine the targets and demands of the data processing tasks and provide guidance and basis for the subsequent data processing process. Next, according to the data processing requirement data, the intelligent data processing platform can use a proper task priority algorithm to perform priority calculation on the processed user data, the task priority algorithm can perform weight assignment and calculation according to factors such as importance, urgency, service requirement and the like of the processed user data, and through task priority calculation, the intelligent data processing platform can determine the processing priorities of different data processing tasks so as to reasonably allocate resources and determine processing sequences, can provide priority guidance for subsequent processing tasks, and ensures that high-priority tasks can be processed preferentially. Finally, the intelligent data processing platform sorts the processed user data corresponding to the processing task priority values according to the order from large to small, so that it can be determined which processing tasks should be processed first and which tasks can be deferred or processed in parallel. Through carrying out sequencing processing on the processed user data, the intelligent data processing platform can improve the processing efficiency, ensure that important tasks can be processed in time, and therefore the requirements of users are better met.
Preferably, the formula of the task priority algorithm in step S43 is specifically:
；
in the method, in the process of the invention,for processing task priority values, +.>For the number of processing tasks in the processed user data,/->For the +.>Individual processing tasks->Weight coefficient for processing time sensitivity in data processing requirement data, < >>Is a time sensitive factor, ++>Weight coefficient for the degree of resource demand in the data processing demand data, +.>Assessment factor for resource demand->Weight coefficient for processing task dependency in data processing requirement data, +.>For task dependent factors, ++>For the weight coefficient of the processing demand risk degree in the data processing demand data, +.>Is a risk factor>Correction values for the priority values of the processing tasks.
The invention constructs a formula of a task priority algorithm for carrying out priority calculation on the processed user data, wherein the task priority algorithm weights and sums the priorities of the processing tasks according to the characteristics of the processed user data and corresponding requirements in data processing requirement data, and adjusts according to actual conditions by setting corresponding weight coefficients and factors so as to adapt to specific application scenes. Wherein the gap between the deadline of the processing task and the current time is considered by setting a time sensitive factor according to the time limit of the processing task requirement. And according to the demand of the processing task on the computing resource, the storage resource or the network resource, the demand of the resource is evaluated, and a proper resource demand evaluation factor is set to consider the availability of the resource. Then, the processing tasks with more dependency relationships are evaluated by setting an appropriate task dependency factor in consideration of the dependency relationships between the processing tasks. The evaluation site is also provided by setting a suitable risk factor according to the possible risk and uncertainty of the processing task The degree of risk of managing the task. And the weight coefficient of the corresponding characteristic is given to be adjusted so as to meet specific data processing requirements, thereby providing priority guidance for subsequent processing tasks and ensuring that high-priority tasks can be processed preferentially. The algorithm function formula fully considers the priority value of the processing taskThe number of processing tasks in the processed user data +.>The treated user data is +.>Individual processing tasks->Weight coefficient of processing time sensitivity in data processing requirement data>Time sensitive factor->Weight coefficient of resource demand degree in data processing demand data +.>Resource demand assessment factor->Weight coefficient of processing task dependency in data processing requirement data>Task dependency factorWeight coefficient of processing demand risk degree in data processing demand data>Risk ofFactor->Correction value of processing task priority value +.>According to the priority value of the processing task->The interrelationship between the parameters constitutes a functional relationship:
；
the algorithm formula can realize the priority calculation process of the processed user data, and simultaneously, the correction value of the task priority value is processed The introduction of the task priority algorithm can be adjusted according to actual conditions, so that the accuracy and applicability of the task priority algorithm are improved.
Preferably, step S5 comprises the steps of:
step S51: monitoring computing resources on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition;
according to the embodiment of the invention, the load condition of the current computing resource is monitored by using the self-adaptive monitoring technology to monitor and analyze the indexes such as CPU utilization rate, memory occupation, network bandwidth, disk service condition and the like of the intelligent data processing platform service end in real time, and finally the load condition of the computing resource is obtained.
Step S52: the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained;
according to the embodiment of the invention, the distributed scheduling processing is carried out on the priority data of the user processing task through the server side of the intelligent data processing platform according to the monitored computing resource load condition, and the distributed scheduling processing process distributes the processing task to a proper computing node according to the computing resource load condition by using a load balancing algorithm to carry out operations such as task distribution, task scheduling, resource management and the like, so that efficient parallel processing is realized, and finally, the user data processing result is obtained.
Step S53: performing format conversion and output on the user data processing result to obtain a user data processing output result;
the embodiment of the invention converts the user data processing result into an acceptable format, such as text, CSV, JSON and the like, through converting the format of the user data processing result, and outputs the conversion result through the intelligent data processing platform, so as to finally obtain the user data processing output result.
Step S54: and displaying the user data processing output result to the front-end interface in a visual form by using a visual technology.
The embodiment of the invention displays the user data processing output result to the front end interface of the intelligent data processing platform in the visual forms of charts, images, reports and the like by using a visual technology.
The invention monitors and processes the computing resource of the service end of the intelligent data processing platform by using the self-adaptive monitoring technology, which comprises the real-time monitoring and analysis of the indexes such as CPU utilization rate, memory occupation, network bandwidth, disk use condition and the like. By monitoring the load condition of the computing resources, the intelligent data processing platform can know the running state of the current system, including the information of the resource utilization rate, busyness, availability and the like, so as to provide real-time monitoring of the computing resources of the server side, and can provide a foundation for subsequent task scheduling and resource management. Then, based on the load condition of computing resources, the intelligent data processing platform performs distributed scheduling processing on the priority data of the user processing tasks through the server, and according to the availability of computing resources and the priority of the processing tasks, the intelligent data processing platform can reasonably distribute the processing tasks to different computing nodes for parallel processing, and through the distributed scheduling processing, the intelligent data processing platform can utilize a plurality of computing resources to improve the efficiency and throughput of data processing, so that efficient task scheduling and resource utilization are realized, and the data processing tasks of the user can obtain quick and accurate processing results. Next, the user data processing results are format-converted and output through the intelligent data processing platform, which includes converting the user data processing results into a format required by the user, such as text, CSV, JSON, etc., so that the user can conveniently use and process. The intelligent data processing platform can also screen, filter or extract the user data processing results to meet the specific requirements of users. And the output data processing results may include data statistics reports, visualization charts, predictive models, and the like. The processed data result is subjected to format conversion and output according to the requirements of the user, so that a convenient data processing result can be provided for the user. And finally, displaying the user data processing output result to the front-end interface in a visual form by using a visual technology. By using visualization elements such as charts, graphs, thermodynamic diagrams, maps, and the like, the intelligent data processing platform can intuitively present data processing results to a user. So that the user can more easily understand and analyze the data processing results and find trends, patterns and anomalies. The visual display mode can be dynamically updated and customized according to the interactive operation of the user, and more visual and easy-to-understand data processing result display is provided, so that the insight and decision making capability of the user on the data are improved.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The data processing method of the development platform based on the BS architecture is characterized by comprising the following steps of:
step S1: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform; performing abnormality detection on the test result of the integrated configuration result of the development platform by using an abnormality detection algorithm to obtain a test abnormality result of the development platform;
Step S2: updating and configuring a development platform based on a BS architecture according to a development platform test abnormal result to obtain an intelligent data processing platform; accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user; carrying out noise reduction on the user data to be processed by using a data noise reduction algorithm to obtain the user data to be processed after noise reduction;
step S3: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface; storing and backing up the user to-be-processed data after noise reduction into a rear-end database of a server by using a data backup technology to obtain backed-up user to-be-processed data;
step S4: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data; the query condition of the data processing task is configured through the front-end interface, so that data processing requirement data is obtained; according to the data processing demand data and by utilizing a task priority algorithm, performing priority calculation on the processed user data to obtain user processing task priority data;
step S5: performing computing resource monitoring processing on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition; the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained; and displaying the user data processing result to the front-end interface in a visual form by utilizing a visual technology.
2. The data processing method of the BS-based architecture development platform according to claim 1, wherein the step S1 includes the steps of:
step S11: initializing and configuring a development platform based on a BS architecture through an integrated configuration technology to obtain an integrated configuration result of the development platform;
step S12: software testing is carried out on the integrated configuration result of the development platform through an automatic script and a cloud service technology, and the integrated configuration test result of the development platform is obtained;
step S13: real-time monitoring and deployment are carried out on the development platform integrated configuration test result by utilizing a reference test algorithm, and monitoring data acquisition is carried out, so that the development platform integrated configuration test real-time monitoring data are obtained;
the formula of the benchmark test algorithm is as follows:
；
in the method, in the process of the invention,configuring benchmark test scores of test results for development platform integration, +.>Configuring the number of test results for development platform integration, +.>Is->The weight of the test result is integrated and configured by the development platform, < + >>Is->Performance index of integrated configuration test result of individual development platform, < >>Configuring the average performance index of the test results for all development platform integration,/-for all development platform integration>Monitoring the time length for the benchmark test, < >>And- >Are all integral time variables, +.>Is->The development platform integrates collaborative resource load assessment functions related to configuration test results, and the development platform is->For at the moment +.>Lower->The development platforms integrate the acquisition indexes of configuration test results,for at the moment +.>Lower->Integrated configuration test results for individual development platformsAcquisition index of->Correction values for the benchmark test scores;
step S14: and carrying out anomaly detection on the real-time monitoring data of the integrated configuration test of the development platform by utilizing an anomaly detection algorithm to obtain a test anomaly result of the development platform.
3. The data processing method of the BS-based architecture development platform according to claim 2, wherein step S11 includes the steps of:
step S111: constructing an integrated configuration technology through a computer technology, wherein the integrated configuration technology comprises a server configuration technology, a client configuration technology and a menu configuration technology;
step S112: performing database connection and WCF service configuration on a development platform based on a BS architecture by using a server configuration technology to obtain an initial configuration result of a development platform server;
step S113: performing WCF information security compatible configuration on a development platform based on a BS architecture by using a client configuration technology to obtain an initial configuration result of a development platform client;
Step S114: carrying out demand menu configuration on a development platform based on a BS architecture by utilizing a menu configuration technology to obtain an initial configuration result of the development platform menu;
step S115: and carrying out integrated environment configuration on the initial configuration result of the development platform server, the initial configuration result of the development platform client and the initial configuration result of the development platform menu to obtain an integrated configuration result of the development platform.
4. The BS architecture-based development platform data processing method according to claim 2, wherein the formula of the anomaly detection algorithm in step S14 is specifically:
；
in the method, in the process of the invention,for abnormality detection algorithm function, ++>Feature data quantity of real-time monitoring data for integrated configuration test of development platform>Integrated configuration test for development platform in real time monitoring data>Personal characteristic data,/->Configuration test for development platform integration in real-time monitoring data>Weight coefficient corresponding to the individual characteristic data, < ->Configuration test for development platform integration in real-time monitoring data>Temporal variation of the individual characteristic data reconciles the smoothing parameters, < >>Configuration test for development platform integration in real-time monitoring data>Integral control factor of individual characteristic data, +.>Is an integral time variable +. >Configuration test for development platform integration in real-time monitoring data>Time-varying trend function of the individual characteristic data, +.>For the second order weight control coefficient, +.>Configuring test real-time monitoring data quantity for development platform integration,/-for>Is->The second derivative weight coefficient of the real-time monitoring data is tested by the integrated configuration of the development platform>For abnormality detection time variable, +.>Is->The development platform integrates an abnormality detection time control function for configuring test real-time monitoring data, and the detection time control function is +.>Correction values for the anomaly detection algorithm function.
5. The data processing method of the BS-based architecture development platform according to claim 1, wherein step S2 includes the steps of:
step S21: real-time correction is carried out on the test abnormal result of the development platform through a vulnerability restoration tool, so that a test correction result of the development platform is obtained;
step S22: updating and configuring a development platform based on a BS architecture according to a development platform test and correction result to obtain an intelligent data processing platform;
step S23: accessing an intelligent data processing platform through a client and uploading data to be processed to obtain data to be processed by a user;
step S24: and carrying out noise reduction treatment on the user to-be-treated data by using a data noise reduction algorithm to obtain the user to-be-treated data after noise reduction.
6. The BS architecture-based development platform data processing method of claim 5, wherein step S24 includes the steps of:
step S241: carrying out noise value calculation on data to be processed of a user by using a data noise reduction algorithm to obtain a user data noise value;
the formula of the data noise reduction algorithm is as follows:
；
in the method, in the process of the invention,for user data noise value +.>For the amount of data to be processed by the user, +.>For the number of noise sources in the data to be processed for each user, and (2)>Is->Data to be processed by individual users->Is->Noise Gaussian filter distribution mean value of data to be processed of individual users, < >>Is->Noise gaussian filter distribution standard deviation of data to be processed by individual users,/->Is->Noise signal transfer function of data to be processed by individual users, < >>Is->The user is treated the data +.>Noise signal weight function of the individual noise sources, +.>Correction values for user data noise values;
step S242: judging the user data noise value according to a preset user data noise threshold, and removing the user data to be processed corresponding to the user data noise value when the user data noise value is greater than or equal to the preset user data noise threshold to obtain the user data to be processed after noise reduction;
Step S243: and judging the user data noise value according to a preset user data noise threshold, and directly defining the user data to be processed corresponding to the user data noise value as the user data to be processed after noise reduction when the user data noise value is smaller than the preset user data noise threshold.
7. The BS architecture-based development platform data processing method according to claim 1, wherein step S3 comprises the steps of:
step S31: uploading the user data to be processed after noise reduction to a server side of the intelligent data processing platform through a front-end interface;
step S32: the server side of the intelligent data processing platform stores the received user data to be processed after noise reduction into a back-end database by utilizing a data storage technology to obtain the stored user data to be processed;
step S33: and backing up the stored user to-be-processed data by using a data backup technology to obtain the backed-up user to-be-processed data.
8. The data processing method of the BS-based architecture development platform according to claim 1, wherein step S4 includes the steps of:
step S41: processing the backup user data to be processed by using a data processing algorithm to obtain processed user data;
Step S42: configuring query conditions of a data processing task through a front-end interface, and carrying out demand analysis on the query conditions to obtain data processing demand data;
step S43: performing priority calculation on the processed user data by using a task priority algorithm according to the data processing requirement data to obtain a processing task priority value;
step S44: and sequencing the processed user data corresponding to the processing task priority values according to the sequence from large to small to obtain the user processing task priority data.
9. The BS architecture-based development platform data processing method according to claim 8, wherein the formula of the task priority algorithm in step S43 is specifically:
；
in the method, in the process of the invention,for processing task priority values, +.>For the number of processing tasks in the processed user data,/->For the +.>Individual processing tasks->Weight coefficient for processing time sensitivity in data processing requirement data, < >>Is a time sensitive factor, ++>Weight coefficient for the degree of resource demand in the data processing demand data, +.>Assessment factor for resource demand->Weight coefficient for processing task dependency in data processing requirement data, +. >For task dependent factors, ++>For the weight coefficient of the processing demand risk degree in the data processing demand data, +.>Is a risk factor>Correction values for the priority values of the processing tasks.
10. The data processing method of the BS-based architecture development platform according to claim 1, wherein step S5 includes the steps of:
step S51: monitoring computing resources on a server side of the intelligent data processing platform by utilizing a self-adaptive monitoring technology to obtain a computing resource load condition;
step S52: the method comprises the steps that distributed scheduling processing is conducted on user processing task priority data through a server based on computing resource load conditions, and user data processing results are obtained;
step S53: performing format conversion and output on the user data processing result to obtain a user data processing output result;
step S54: and displaying the user data processing output result to the front-end interface in a visual form by using a visual technology.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311200197.9A CN116932232B (en) | 2023-09-18 | 2023-09-18 | Data processing method of development platform based on BS architecture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311200197.9A CN116932232B (en) | 2023-09-18 | 2023-09-18 | Data processing method of development platform based on BS architecture |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116932232A true CN116932232A (en) | 2023-10-24 |
| CN116932232B CN116932232B (en) | 2024-01-05 |
Family
ID=88390060
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311200197.9A Active CN116932232B (en) | 2023-09-18 | 2023-09-18 | Data processing method of development platform based on BS architecture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116932232B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634932A (en) * | 2024-01-25 | 2024-03-01 | 深圳市微克科技股份有限公司 | Management system of platform for production test of intelligent watch |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9398042B1 (en) * | 2015-03-11 | 2016-07-19 | Symantec Corporation | Systems and methods for capturing input from users to prevent data loss |
| US10102691B1 (en) * | 2018-04-20 | 2018-10-16 | Smartdrive Systems, Inc. | Systems and methods for using on-board resources of individual vehicles in a fleet of vehicles as a distributed data center |
| CN109213656A (en) * | 2018-07-23 | 2019-01-15 | 武汉智领云科技有限公司 | A kind of interactive mode big data dysgnosis detection system and method |
| US20190129712A1 (en) * | 2017-10-27 | 2019-05-02 | Intuit Inc. | Methods, systems, and computer program products for an integrated platform for continuous deployment of software application delivery models |
| CN110837426A (en) * | 2019-11-06 | 2020-02-25 | 腾讯科技（深圳）有限公司 | Message processing method, device and system and storage medium |
| CN112379653A (en) * | 2020-12-01 | 2021-02-19 | 国能信控互联技术有限公司 | Smart power plant management and control system based on micro-service architecture |
| CN115550367A (en) * | 2022-11-30 | 2022-12-30 | 成都中星世通电子科技有限公司 | Radio monitoring method and system based on distributed task management and resource scheduling |
| CN116303391A (en) * | 2023-02-28 | 2023-06-23 | 上海电器科学研究所（集团）有限公司 | Processing method for remote transmission of electric energy time sequence data by intelligent ammeter |
| CN116662375A (en) * | 2023-08-02 | 2023-08-29 | 湖南远跃科技发展有限公司 | HIS-based prescription data verification method and system |
| CN116661330A (en) * | 2023-07-31 | 2023-08-29 | 深圳市云图数字科技有限公司 | Collaborative operation method of intelligent home system and intelligent home system |
| CN116700913A (en) * | 2022-09-13 | 2023-09-05 | 荣耀终端有限公司 | Scheduling method, equipment and storage medium of embedded file system |
-
2023
- 2023-09-18 CN CN202311200197.9A patent/CN116932232B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9398042B1 (en) * | 2015-03-11 | 2016-07-19 | Symantec Corporation | Systems and methods for capturing input from users to prevent data loss |
| US20190129712A1 (en) * | 2017-10-27 | 2019-05-02 | Intuit Inc. | Methods, systems, and computer program products for an integrated platform for continuous deployment of software application delivery models |
| US10102691B1 (en) * | 2018-04-20 | 2018-10-16 | Smartdrive Systems, Inc. | Systems and methods for using on-board resources of individual vehicles in a fleet of vehicles as a distributed data center |
| CN109213656A (en) * | 2018-07-23 | 2019-01-15 | 武汉智领云科技有限公司 | A kind of interactive mode big data dysgnosis detection system and method |
| CN110837426A (en) * | 2019-11-06 | 2020-02-25 | 腾讯科技（深圳）有限公司 | Message processing method, device and system and storage medium |
| CN112379653A (en) * | 2020-12-01 | 2021-02-19 | 国能信控互联技术有限公司 | Smart power plant management and control system based on micro-service architecture |
| CN116700913A (en) * | 2022-09-13 | 2023-09-05 | 荣耀终端有限公司 | Scheduling method, equipment and storage medium of embedded file system |
| CN115550367A (en) * | 2022-11-30 | 2022-12-30 | 成都中星世通电子科技有限公司 | Radio monitoring method and system based on distributed task management and resource scheduling |
| CN116303391A (en) * | 2023-02-28 | 2023-06-23 | 上海电器科学研究所（集团）有限公司 | Processing method for remote transmission of electric energy time sequence data by intelligent ammeter |
| CN116661330A (en) * | 2023-07-31 | 2023-08-29 | 深圳市云图数字科技有限公司 | Collaborative operation method of intelligent home system and intelligent home system |
| CN116662375A (en) * | 2023-08-02 | 2023-08-29 | 湖南远跃科技发展有限公司 | HIS-based prescription data verification method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634932A (en) * | 2024-01-25 | 2024-03-01 | 深圳市微克科技股份有限公司 | Management system of platform for production test of intelligent watch |
| CN117634932B (en) * | 2024-01-25 | 2024-04-30 | 深圳市微克科技股份有限公司 | Management system of platform for production test of intelligent watch |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116932232B (en) | 2024-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9940190B2 (en) | System for automated computer support | |
| Zhou et al. | Empirical analysis of object-oriented design metrics for predicting high and low severity faults | |
| JP4886512B2 (en) | System and method for automated computer support | |
| US8180710B2 (en) | System, method and computer program product for an interactive business services price determination and/or comparison model | |
| CN116932232B (en) | Data processing method of development platform based on BS architecture | |
| US9047396B2 (en) | Method, system and computer product for rescheduling processing of set of work items based on historical trend of execution time | |
| CN112702342B (en) | Network event processing method and device, electronic equipment and readable storage medium | |
| US20120116984A1 (en) | Automated evaluation of compliance data from heterogeneous it systems | |
| CN101321084A (en) | Method and apparatus for generating configuration rules for computing entities within a computing environment using association rule mining | |
| US20130111497A1 (en) | Staggering Execution of Scheduled Tasks Based on Behavioral Information | |
| US11403165B2 (en) | Cognitive disaster recovery workflow management | |
| US20130111479A1 (en) | Performance of Scheduled Tasks via Behavior Analysis and Dynamic Optimization | |
| CN110971464A (en) | Operation and maintenance automatic system suitable for disaster recovery center | |
| EP2113874A1 (en) | Method and system for monitoring computer-implemented processes | |
| CN111859047A (en) | Fault solving method and device | |
| CN111984442A (en) | Method and device for detecting abnormality of computer cluster system, and storage medium | |
| CN116594913B (en) | Intelligent software automatic test method | |
| CN116204385A (en) | Computer log monitoring method and system | |
| CN112733147A (en) | Equipment safety management method and system | |
| CN117056172B (en) | Data integration method and system for system integration middle station | |
| CN113626825A (en) | Security vulnerability management and control method, device, equipment and computer readable medium | |
| WO2019138891A1 (en) | Anomaly location identification device, and anomaly location identification method and program | |
| US20150248339A1 (en) | System and method for analyzing a storage system for performance problems using parametric data | |
| CN112348698A (en) | Nuclear power plant group pile management method, device and system | |
| ZA200601938B (en) | Systems and methods for creation and use of an adaptive reference model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |