CN117473257A - Monitoring data analysis method, system, electronic equipment and storage medium - Google Patents
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- CN117473257A CN117473257A CN202311420434.2A CN202311420434A CN117473257A CN 117473257 A CN117473257 A CN 117473257A CN 202311420434 A CN202311420434 A CN 202311420434A CN 117473257 A CN117473257 A CN 117473257A
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Abstract
The invention discloses a monitoring data analysis method, a monitoring data analysis system, electronic equipment and a storage medium, and belongs to the technical field of data processing. A method of monitoring data analysis, comprising: creating a workflow model; and calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data. The invention can customize the processing and analysis logic of the data, can complete the work of data analysis without processing and analyzing the data by other tools, saves manpower and reduces the capability requirement of the post.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and system for analyzing monitoring data, an electronic device, and a storage medium.
Background
In order to promote the stability and safety of the infrastructures on bridges, roads, slopes and other roads, related maintenance and operation work is done, and various sensors are installed during the construction of the infrastructures for monitoring the 'life health status' of the infrastructures. Typically, the result of data analysis is used to determine the health of the relevant infrastructure by data analysis of the sampled data of the sensor. When related staff performs data analysis, the related staff is faced with a plurality of states such as data acquisition of a large number of sensors, poor data quality, data deletion, data abnormality and the like. These data cannot be taken directly for data analysis. After the data are processed, abnormal information, noise interference and missing value interference of the data are removed, and then business modeling and data analysis are carried out, so that relevant data results are obtained and are used for judging the working state and supporting operation decision of infrastructure equipment.
In traditional bridge operation data analysis, a worker needs to take out bridge monitoring data, then builds a data model, and performs data operation through manual or matlab tools and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a monitoring data analysis method, a monitoring data analysis system, electronic equipment and a storage medium.
The aim of the invention is realized by the following technical scheme:
a first aspect of the present invention provides a method for analyzing monitoring data, comprising:
creating a workflow model;
and calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
Further, the monitoring data analysis method further includes:
and visually displaying the data analysis process and/or the data analysis result of each algorithm component.
Further, the visual display mode comprises one or more of a line graph, a scatter graph, a cluster graph, a box graph, a thermodynamic diagram and a wind speed and direction rose graph.
Further, creating a workflow model, comprising:
selecting a required algorithm component, wherein the algorithm component is obtained by data calculation algorithm encapsulation;
setting monitoring data to be analyzed;
configuring parameters of each algorithm component;
and sequencing and combining the algorithm components to generate a workflow model.
Further, creating a workflow model, further comprising:
and carrying out feasibility checking on the workflow model, and returning a reason of the failure when the feasibility checking fails.
Further, the monitoring data to be analyzed is from a sensor data acquisition system, user sample data stored by the system or a data file uploaded by a user.
Further, the monitoring data is monitoring data of bridges, roads or slopes.
A second aspect of the present invention provides a monitoring data analysis system comprising:
the business service module is used for creating a workflow model;
and the flow scheduling engine is used for calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
A third aspect of the present invention provides an electronic device comprising:
a memory storing execution instructions; and
and a processor executing the execution instructions stored in the memory, so that the processor executes the monitoring data analysis method according to the first aspect of the present invention.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein execution instructions which, when executed by a processor, are adapted to carry out the method of monitoring data analysis according to the first aspect of the present invention.
The beneficial effects of the invention are as follows:
(1) The invention is applied to the field of operation data analysis of infrastructures such as bridges, roads, slopes and the like, and can obtain data analysis results by automatically arranging a system algorithm model and executing the system algorithm model by a flow scheduling engine of the system, so that related personnel only need to know service requirements without consciously realizing and executing the algorithm, thereby reducing the professional degree, improving the working efficiency, reducing the production cost and rapidly and effectively completing the data analysis;
(2) The invention can customize the processing and analysis logic of the data, does not need to process and analyze the data by other tools, does not need to perform data analysis operation by itself, does not need to use tools such as matlab and the like, does not need to encode, can finish the work of data analysis, greatly saves manpower, and simultaneously effectively reduces the capability requirement of the post;
(3) In the invention, part of the service and the completed service of the user in the algorithm service design process can be stored in the strategy library of the system, and when the user needs to use the system subsequently, the algorithm service stored in the strategy library can be directly accessed into the current service, so that the repeated operation can be avoided, and the efficiency in data analysis can be effectively improved;
(4) The invention has complete data preprocessing, data secondary processing and data analysis standard flow, and supports the pre-processing requirements of data cleaning, missing data supplementation, data filtering noise reduction and the like.
Drawings
FIG. 1 is a flow chart of a method of analyzing monitoring data according to the present invention;
FIG. 2 is a block diagram of a system for analyzing monitored data according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
Referring to fig. 1 to 2, the present invention provides a method, a system, an electronic device and a storage medium for analyzing monitoring data:
a first aspect of the present embodiment provides a method for analyzing monitoring data, as shown in fig. 1, where the method for analyzing monitoring data includes steps S100 to S200.
And S100, creating a workflow model.
In some embodiments, creating the workflow model includes steps S110 through S140.
And S110, selecting a required algorithm component, wherein the algorithm component is obtained by packaging a data calculation algorithm.
Specifically, all algorithm components needed in the data analysis of the monitoring data are selected, wherein each algorithm component is obtained by packaging a data calculation algorithm.
For example, the data calculation algorithm includes a plurality of basic algorithms such as a mean value, a peak value, a variance, a covariance, a kurtosis coefficient, a skewness coefficient, a mode and the like, and also includes a plurality of advanced data algorithms such as a box graph, a 3 sigma algorithm, an isolated forest, a Lagrange interpolation method, a Fourier transform, a wavelet transform, a data fitting, a data decomposition, a modal decomposition and the like.
And S120, setting monitoring data to be analyzed.
Specifically, the input data, i.e. the monitoring data to be analyzed, is selected on the input component. In this embodiment, the monitoring data to be analyzed may be from a sensor data acquisition system, a user data sample stored in the system, or a data file uploaded by a user.
And S130, configuring parameters of each algorithm component.
In some embodiments, after the parameters of each algorithm component are configured, the basic rights of the algorithm components and the validity of the parameters and the like are checked.
And S140, sequencing and combining the algorithm components to generate a workflow model.
In some embodiments, creating the workflow model further comprises: and carrying out feasibility checking on the workflow model, and returning a reason of the failure when the feasibility checking fails.
For example, performing a feasibility check on the workflow model includes: checking whether the parameters of the algorithm components are reasonable, whether the ordering of the algorithm components is feasible, and the like.
And S200, invoking an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
Specifically, the corresponding algorithm components are sequentially called according to the logic sequence of each algorithm component in the workflow model to calculate the monitoring data, and the obtained data calculation result is the data analysis result.
And in the process of carrying out data analysis on the monitoring data, sequentially calling corresponding algorithm components to operate the monitoring data, returning to the reason of operation failure if the operation of one algorithm component fails, and returning to an operation result and an operation process if the operation of one algorithm component succeeds until all the corresponding algorithm components are operated or the operation of one algorithm component fails in the middle.
In this embodiment, the monitoring data is monitoring data of a bridge, a road or a slope.
In some embodiments, the monitoring data analysis method further comprises: and visually displaying the data analysis process and/or the data analysis result of each algorithm component.
In general, the visual display mode includes one or more of a line graph, a scatter graph, a cluster graph, a box graph, a thermodynamic diagram and a wind speed and direction rose graph.
A second aspect of the present embodiment provides a monitoring data analysis system, as shown in fig. 2, where the monitoring data analysis system includes a business service module and a flow scheduling engine.
And the business service module is used for creating a workflow model.
In some embodiments, when a user logs in a web client of the system through an account and edits a workflow model, the business service module presents an editing interface to the user through the web client, the user selects a required algorithm component in the editing interface and drags the required algorithm component to the editing interface, wherein each algorithm component is packaged by a data computing algorithm. The user connects the algorithm components using the connection lines, then inputs the data selected on the components, and then configures the parameters of each algorithm component to generate the workflow model.
And the flow scheduling engine is used for calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
In some embodiments, the workflow scheduling engine performs rules and feasibility checks on the workflow model, and after the checks pass, an automatic scheduling link is generated inside the workflow scheduling engine, and the workflow scheduling engine invokes a corresponding algorithm component to operate on the input monitoring data based on the automatic scheduling link. If the operation of a certain algorithm component fails, returning to the reason of the operation failure, and if the operation of a certain algorithm component succeeds, returning to the operation result and the operation process until all the corresponding algorithm components are operated or the operation of a certain algorithm component fails.
In some embodiments, the business service module is further configured to visually display the data analysis process and/or the data analysis result of each algorithm component.
Specifically, the service module judges the data returned by the flow scheduling engine, and judges whether the data is normal service data or error log data. If the error log data is wrong log data, sending error information to a web client; if the service data is normal service data, carrying out visual conversion on the service data, sending the operation process and the visual result of the operation result to the web client, and presenting the operation process and the visual result of the operation result to the user.
In general, the visual display mode includes one or more of a line graph, a scatter graph, a cluster graph, a box graph, a thermodynamic diagram and a wind speed and direction rose graph.
In some embodiments, the monitoring data analysis system further comprises an algorithm engine for packaging the data calculation algorithm into an algorithm component.
The monitoring data analysis system of the embodiment integrates a plurality of data calculation algorithms, and packages and optimizes the algorithms in an algorithm engine to form independent algorithm components.
A third aspect of the present embodiment discloses an electronic device comprising a memory and a processor. The memory stores execution instructions; the processor executes the execution instructions stored in the memory, so that the processor executes the monitoring data analysis method according to the first aspect of the present embodiment.
A fourth aspect of the present embodiment discloses a computer-readable storage medium having stored therein execution instructions which, when executed by a processor, are configured to implement the method for analyzing monitored data according to the first aspect of the present embodiment.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (10)
1. A method of monitoring data analysis, comprising:
creating a workflow model;
and calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
2. The method of claim 1, further comprising:
and visually displaying the data analysis process and/or the data analysis result of each algorithm component.
3. The method of claim 2, wherein the visual display includes one or more of a line graph, a scatter graph, a cluster graph, a bin graph, a thermodynamic diagram, and a wind speed and direction rose graph.
4. The method of claim 1, wherein creating a workflow model comprises:
selecting a required algorithm component, wherein the algorithm component is obtained by data calculation algorithm encapsulation;
setting monitoring data to be analyzed;
configuring parameters of each algorithm component;
and sequencing and combining the algorithm components to generate a workflow model.
5. The method of claim 4, wherein creating a workflow model, further comprises:
and carrying out feasibility checking on the workflow model, and returning a reason of the failure when the feasibility checking fails.
6. The method according to claim 4, wherein the monitoring data to be analyzed is from a sensor data collection system, user sample data stored in the system, or a data file uploaded by a user.
7. The method of claim 1, wherein the monitoring data is bridge, road or slope monitoring data.
8. A monitoring data analysis system, comprising:
the business service module is used for creating a workflow model;
and the flow scheduling engine is used for calling an algorithm component based on the workflow model to perform data analysis on the input monitoring data.
9. An electronic device, comprising:
a memory storing execution instructions; and
a processor executing the execution instructions stored in the memory, causing the processor to execute the monitoring data analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein execution instructions are stored in the computer-readable storage medium, which when executed by a processor, are for implementing the monitoring data analysis method of any one of claims 1 to 7.
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