CN111897540B - Data collection method and device based on private cloud, server and storage medium - Google Patents
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Abstract
A private cloud-based data collection method, the method comprising: deploying the application program APP integrated with the collection tool to a plurality of user terminals; starting APP on the plurality of user terminals through private cloud service to start the collecting tool to scan problems according to a preset test case; after the scanning is finished, starting an independent thread through the private cloud service; collecting current problem data collected by the collecting tool through the independent thread, and writing the current problem data into a temporary file of the APP of the user terminal; and calling a data collection service, and storing the current problem data in the temporary file of the APP of each user terminal into a local database. The invention further provides a data collection device, a server and a storage medium based on the private cloud. The present invention also relates to blockchain techniques that can upload current problem data to the blockchain. The invention can improve the efficiency of problem collection.
Description
Technical Field
The present invention relates to the field of private cloud technologies, and in particular, to a private cloud-based data collection method, device, server, and storage medium.
Background
Currently, before an APP is released, it is often necessary to debug on a single device, and manually trigger to get to the problem that occurs when the APP is running.
However, the debugging on the single device cannot adapt to problem collection of different operating systems and different APP versions, and meanwhile, some problems are easily missed by manual triggering, so that the efficiency of problem collection is low and the coverage is small.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a data collection method, apparatus, server, and storage medium based on private cloud, which can improve efficiency of problem collection.
The first aspect of the invention provides a private cloud-based data collection method, which comprises the following steps:
deploying the application program APP integrated with the collection tool to a plurality of user terminals;
starting APP on the plurality of user terminals through private cloud service to start the collecting tool to scan problems according to a preset test case;
after the scanning is finished, starting an independent thread through the private cloud service;
collecting current problem data collected by the collecting tool through the independent thread, and writing the current problem data into a temporary file of the APP of the user terminal;
and calling a data collection service, and storing the current problem data in the temporary file of the APP of each user terminal into a local database.
In one possible implementation manner, after the collecting, by the independent thread, the current problem data collected by the collecting tool, the private cloud-based data collecting method further includes:
collecting associated data related to the current problem data through the independent thread, wherein the dimension of the associated data is used for representing the dimension of the hardware aspect of the user terminal and the dimension of the APP version aspect;
and writing the associated data into a temporary file of the APP of the user terminal.
In one possible implementation, the invoking the data collection service, storing the current problem data in the temporary file of the APP of each of the user terminals into a local database includes:
creating a timing task;
monitoring the data state of the current problem data, and dynamically adjusting the timing period of the timing task according to the data state;
invoking a data collection service through the adjusted timing task;
triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
In one possible implementation, the invoking the data collection service, storing the current problem data in the temporary file of the APP of each of the user terminals into a local database includes:
creating a timing task;
monitoring the storage state of the temporary file, and dynamically adjusting the timing period of the timing task according to the storage state;
invoking a data collection service through the adjusted timing task;
triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
In one possible implementation manner, after the invoking the data collection service and storing the current problem data in the temporary file of the APP of each of the user terminals into a local database, the private cloud-based data collection method further includes:
acquiring historical problem data;
comparing the current problem data with the historical problem data;
if first problem data exists in the historical problem data and the first problem data does not exist in the current problem data, updating the state of the first problem data in the local database to be repaired; or (b)
And if second problem data exists in the current problem data and the second problem data does not exist in the historical problem data, updating the state of the second problem data in the local database to be repaired.
In one possible implementation manner, after the invoking the data collection service and storing the current problem data in the temporary file of the APP of each of the user terminals into a local database, the private cloud-based data collection method further includes:
acquiring the problem type of the current problem data;
determining the version of the APP corresponding to the current problem data of the same problem type;
and setting the risk level of the current question data of the same question type according to the number of versions of the APP and the occurrence times of the current question data of the same question type.
In one possible implementation manner, the private cloud-based data collection method further includes:
according to each question type of the current question data, carrying out association processing on the current question data and the association data to obtain processing data for representing association relation;
generating a visual interface according to the processing data;
and outputting the visual interface.
A second aspect of the present invention provides a private cloud-based data collection apparatus, comprising:
the deployment module is used for deploying the application program APP integrated with the collection tool to a plurality of user terminals;
the starting module is used for starting the APP on the plurality of user terminals through private cloud service so as to start the collecting tool to scan the problems according to a preset test case;
the starting module is also used for starting an independent thread through the private cloud service;
the collection module is used for collecting current problem data collected by the collection tool through the independent thread and writing the current problem data into a temporary file of the APP of the user terminal;
and the storage module is used for calling a data collection service and storing the current problem data in the temporary file of the APP of each user terminal into a local database.
A third aspect of the present invention provides a server comprising a processor and a memory, the processor being adapted to implement the private cloud based data collection method when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the private cloud based data collection method.
According to the technical scheme, the application program APP integrated with the collecting tool can be deployed to the user terminals through the private cloud service, the collecting tool on each user terminal can conduct problem scanning according to the same test case, and the problems found by the user terminals when the APP is operated can be automatically collected through independent threads.
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Fig. 1 is a flow chart of a preferred embodiment of a private cloud-based data collection method of the present disclosure.
Fig. 2 is a functional block diagram of a preferred embodiment of a private cloud-based data collection device according to the present disclosure.
Fig. 3 is a schematic structural diagram of a server according to a preferred embodiment of the present invention for implementing a private cloud-based data collection method.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first and second in the description and claims of this application and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, and should not be understood to indicate or imply relative importance or to implicitly indicate the number of technical features indicated. It is to be understood that the data so used may be interchanged where appropriate, such that the embodiments described herein may be implemented in additional orders other than those illustrated or described herein, and that a feature defining "a first" or "a second" may be explicitly or implicitly included in at least one such feature.
Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
A server may refer to a computer system that is capable of providing services to other devices in a network, such as a user terminal.
The user terminal is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware of the user terminal includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like. May include, but is not limited to, web servers, personal computers, tablet computers, smart phones, personal digital assistants PDAs, and the like.
Fig. 1 is a flow chart of a preferred embodiment of a private cloud-based data collection method of the present disclosure. The sequence of steps in the flowchart may be changed and some steps may be omitted according to different needs.
S11, deploying the application program APP integrated with the collection tool to a plurality of user terminals.
Wherein the collection tool may be a StrictMode performance tool that may be used to discover some non-canonical problems in code, such as: disk I/O, network access violations, etc. that occur in the application main thread. Before releasing the APP new version, the StrictMode tool needs to be used for scanning to make a white box test every time so as to better control the quality of products.
Specifically, the stractmode may be embedded into the APP through jenkins (integration tool) for packaging, and then, the installation package of the APP integrated with the stractmode is installed to a plurality of user terminals through a private cloud service, so as to complete the deployment of the performance tool.
S12, starting APP on the plurality of user terminals through private cloud service to start the collection tool to scan the problems according to a preset test case.
Among other things, private cloud services may provide the most effective control over data, security, and quality of service. In the embodiment of the invention, the private cloud service is adopted to start the APP on the plurality of user terminals so as to start the collection tool to perform the problem scanning test according to the preset test case, thereby ensuring the safety of data collection and the efficiency of data collection.
The private cloud platform is maintained with a test case, and the test case is the flow step of version test of the APP. When APP on a plurality of user terminals operates, collection tools integrated in the APP are started, and the collection tools on each user terminal can mobilize the page of the APP according to a preset test case to carry out scanning test so as to find problems occurring in the APP operation, and meanwhile, the StrictMode collection tools can collect corresponding problem data. The method comprises the steps that test cases can be preset, each user terminal adopts the same test case and cannot be limited by different user terminals or different operating systems or different APP versions, so that collected problems can be guaranteed to be more comprehensive and rich later, meanwhile, private cloud is adopted to uniformly schedule APP tests of a plurality of user terminals, the test cases do not need to be split, and the test efficiency can be improved.
In the embodiment of the invention, a plurality of user terminals can be scheduled through the private cloud service, and the APP on the user terminal is automatically tested according to the designed test case, so that manual intervention can be avoided, and automation is convenient to realize.
And S13, after the scanning is finished, starting an independent thread through the private cloud service.
The independent thread is mainly used for collecting problems found by collecting tools when APP run on a plurality of user terminals. Independent threads are started through private cloud service, so that unified collection of problems of APP on a plurality of user terminals can be realized, collection is not needed by one user terminal, and therefore problem collection efficiency can be improved.
S14, collecting the current problem data collected by the collecting tool through the independent thread, and writing the current problem data into a temporary file of the APP of the user terminal.
The current problem data, i.e. the problem found by the collection tool scanning the APP while it is currently running, may include, but is not limited to: time-consuming calling, disk reading and writing, network operation, activity leakage, unclosed closed-able object leakage, leaked Sqlite objects, detection instance number and the like.
After the collection tool stractmode is triggered, the collected stractmode information (i.e. problem data) is written into the locator (i.e. log space of the user terminal), and the independent thread can regularly pull the stractmode information in the locator.
Optionally, after the collecting, by the independent thread, the current problem data collected by the collecting tool, the method further includes:
collecting associated data related to the current problem data through the independent thread, wherein the dimension of the associated data is used for representing the dimension of the hardware aspect of the user terminal and the dimension of the APP version aspect;
and writing the associated data into a temporary file of the APP of the user terminal.
Wherein the dimensions of the associated data are different from the dimensions of the current issue data, the dimensions of the associated data are used to characterize the dimensions of the hardware aspect of the user terminal and the dimensions of the APP version aspect, such as may include, but not limited to, device identification, operating system version, APP version, etc. The current problem data is typically some problem that arises from dimensions such as the APP running aspect.
The independent thread can collect the problem information found by the structmode, and can acquire information (i.e. associated data) of the system and the APP through a command, such as collecting device identification (e.g. device model of a user terminal), operating system version and APP version.
The current problem data collected by the StrictMode includes the problem type and the problem discovery time, and needs to be analyzed from multiple dimensions when the problem is located later, for example: the XX model XX operating system version XXAPP version frequency of occurrence of problems in a certain time period can be quickly found by a developer according to the information. Thus, in addition to the problem data collected by the StrictMode, some correlation data needs to be collected to expand the dimension of the analysis problem.
By the mode, the collected data is richer in types and wide in coverage, meanwhile, comprehensive data is provided, the comprehensive analysis of the problems is facilitated, and reliable basis is provided for developing and positioning the problems and repairing the priorities of the problems.
S15, invoking a data collection service, and storing the current problem data in the temporary file of the APP of each user terminal into a local database.
The local database, i.e. a database arranged at the server side, can be used to store the collected problem data. Temporary files of the user terminal, i.e. log spaces of the user terminal, such as locators, are typically used for temporarily storing some log data.
Optionally, the current problem data can also be uploaded to the blockchain, thereby ensuring the privacy and security of the data.
As an optional implementation manner, step S15 of invoking a data collection service, storing the current problem data in the temporary file of the APP of each of the user terminals into a local database includes:
11 Creating a timed task;
12 Monitoring the data state of the current problem data, and dynamically adjusting the timing period of the timing task according to the data state;
13 Calling a data collection service through the adjusted timing task;
14 Triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
Wherein a timing period, which may be user-set or default to the system, may be set on jenkins and a timing task (job) created. The timing task can call a data collection service, the data collection service is triggered to extract the problem data in the temporary files of the APP of each user terminal according to the adjusted timing period, the problem data are analyzed and stored in a local database, namely the problem data fall into the database to leave marks, and then the data are analyzed and classified.
In this alternative embodiment, the data state of the current problem data, that is, the data size, may be monitored, the timing period may be dynamically adjusted according to the data state of the current problem data after the timing task is created, and the current problem data may be subsequently extracted according to the adjusted timing period.
Step 12) the specific implementation manner of dynamically adjusting the timing period of the timing task according to the data state of the current problem data may be:
monitoring the data volume of the current problem data; if the data quantity of the current problem data is larger than a preset quantity threshold value, shortening the timing period of the timing task; or if the data quantity of the current problem data is smaller than a preset quantity threshold value, increasing the timing period of the timing task.
Wherein, some adjustment rules may be preset, if the data amount of the current problem data is large, the timing period is shortened according to the adjustment rules, such as 20%, etc., and if the data amount of the current problem data is small, the timing period is increased according to the adjustment rules, such as 30%, etc. Alternatively, the adjustment rule may be dynamically changed according to the data size of the current problem data, for example, a functional relation between the data size and the timing period may be preset, and then the adjustment rule is changed in real time according to the functional relation.
According to the alternative implementation mode, the timing period of the timing task can be dynamically adjusted according to the data size of the current problem data, so that the data collection service can collect the current problem data in time, data extrusion is avoided, and meanwhile waste of resources caused by frequent data collection of the data collection service can be avoided.
As another alternative embodiment, step S15 of invoking a data collection service, storing the current problem data in the temporary file of the APP of each of the user terminals in a local database includes:
21 Creating a timed task;
22 Monitoring the storage state of the temporary file and dynamically adjusting the timing period of the timing task according to the storage state;
23 Calling a data collection service through the adjusted timing task;
24 Triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
In this alternative embodiment, the storage state of the temporary file, that is, the state of the storage space of the temporary file may be monitored, after the timing task is created, the timing period may be dynamically adjusted according to the storage state of the temporary file, and then the current problem data may be extracted according to the adjusted timing period.
Step 22) monitoring a storage state of the temporary file, and dynamically adjusting a timing period of the timing task according to the storage state may be:
monitoring the size of the remaining storage space of the temporary file; if the size of the residual storage space is smaller than the preset storage space, shortening the timing period of the timing task; or if the size of the residual storage space is larger than the preset storage space, maintaining the timing period of the timing task unchanged.
Wherein, some adjustment rules can be preset, if the size of the remaining storage space of the temporary file is insufficient, the timing period can be shortened according to the adjustment rules, such as 20%, and if the size of the remaining storage space of the temporary file is sufficient, the timing period of the timing task can be maintained unchanged. Optionally, a functional relation between the size of the remaining storage space and the timing period may be preset, and then the adjustment rule may be changed in real time according to the functional relation.
According to the alternative implementation mode, the timing period of the timing task can be dynamically adjusted according to the size of the remaining storage space of the temporary file, so that the data collection service can collect current problem data timely, enough storage space is reserved for the temporary file, normal storage of the temporary file is ensured, meanwhile, frequent adjustment of the timing period can be avoided, and system resources are saved.
As an optional implementation manner, after invoking a data collection service and storing the current problem data in the temporary file of the APP of each of the user terminals in a local database, step S15 further includes:
acquiring historical problem data;
comparing the current problem data with the historical problem data;
if first problem data exists in the historical problem data and the first problem data does not exist in the current problem data, updating the state of the first problem data in the local database to be repaired; or (b)
And if second problem data exists in the current problem data and the second problem data does not exist in the historical problem data, updating the state of the second problem data in the local database to be repaired.
After the same test case is executed, if the first problem data exists in the historical problem data and the first problem data does not exist in the current problem data, the fact that the problem data appearing in the history does not appear at present indicates that the problem data is repaired can be considered, and the state of the problem data needs to be updated to be repaired in a database. If the second problem data exists in the problem data and the second problem data does not exist in the history problem data, the problem data which is closed is indicated, the scanning is found, and the state of the problem data can be updated to be repaired.
In this alternative embodiment, the status of the problem can be discovered in time by continuously automatically updating the status of the problem data.
As another alternative embodiment, step S15 invokes a data collection service, and after storing the current problem data in the temporary file of the APP of each of the user terminals in a local database, the method further includes:
acquiring the problem type of the current problem data;
determining the version of the APP corresponding to the current problem data of the same problem type;
and setting the risk level of the current question data of the same question type according to the number of versions of the APP and the occurrence times of the current question data of the same question type.
Wherein, if the current problem data of the same problem type appears in a plurality of APP versions, the current problem data is indicated to belong to a common problem in different APP versions, and meanwhile, if the number of occurrences of the current problem data of the same problem type is relatively large, the frequency of occurrence of the current problem data can be indicated to be relatively high. Based on the number and occurrence times of the APP cases, the risk level of the current problem data can be set. Specifically, a first threshold value for measuring the number of APP versions belonging to a common problem may be preset, a second threshold value for measuring the number of occurrences of problem data having a high frequency of occurrence may be set, and a risk level may be set according to the first threshold value, the second threshold value, the number of APP versions, and the number of occurrences of problem data of the same problem type, and may be classified into a normal level and a severity level.
As an optional implementation manner, after invoking a data collection service and storing the current problem data in the temporary file of the APP of each of the user terminals in a local database, step S15 further includes:
according to each question type of the current question data, carrying out association processing on the current question data and the association data to obtain processing data for representing association relation;
generating a visual interface according to the processing data;
and outputting the visual interface.
Wherein, the current question data and the associated data may be associated with each other based on the question type of the current question data, so as to obtain processing data for representing an association relationship, for example: problem type 1-current problem data 1-model 1-operating system version 1-APP version 1, again such as: question type 2-current question data 2-model 2-operating system version 2-APP version 2.
The visual page is provided for displaying the processing data, so that all people (including non-professional people) can analyze the performance data conveniently, and reasonable optimization suggestions are provided. The visual interface supports screening and sorting of APP versions, operating system versions, problem occurrence time and problem occurrence times, and provides a problem detail viewing page.
In the method flow described in fig. 1, a plurality of user terminals can be scheduled through private cloud service, and problems found by the plurality of user terminals when running APP can be automatically collected, so that the problem collection efficiency can be improved without manual intervention in the whole process, meanwhile, the collected problems can be richer and more comprehensive, the problem omission caused by artificial objective reasons can be avoided, in addition, the collected problems remain in a local database, and the problem state can be maintained.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Fig. 2 is a functional block diagram of a preferred embodiment of a private cloud-based data collection device according to the present disclosure.
In some embodiments, the private cloud-based data collection device is run in a server. The private cloud-based data collection device may include a plurality of functional modules comprised of program code segments. Program code of each program segment in the private cloud-based data collection apparatus may be stored in a memory and executed by at least one processor to perform part or all of the steps in the private cloud-based data collection method described in fig. 1, and specific reference may be made to the related description in fig. 1, which is not repeated herein.
In this embodiment, the private cloud-based data collection device may be divided into a plurality of functional modules according to the functions performed by the private cloud-based data collection device. The functional module may include: a deployment module 201, a start-up module 202, a collection module 203 and a storage module 204. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory.
A deployment module 201, configured to deploy an application APP integrated with a collection tool to a plurality of user terminals;
the starting module 202 is configured to start the APPs on the plurality of user terminals through a private cloud service, so as to start the collecting tool to perform problem scanning according to a preset test case;
the starting module 202 is further configured to start an independent thread through the private cloud service after the scanning is finished;
the collecting module 203 is configured to collect, by using the independent thread, current problem data collected by the collecting tool, and write the current problem data into a temporary file of the APP of the user terminal, where the current problem data is used to characterize a problem occurring when the APP runs;
and the storage module 204 is used for calling a data collection service and storing the current problem data in the temporary file of the APP of each user terminal into a local database.
In the private cloud-based data collection device described in fig. 2, a plurality of user terminals can be scheduled through private cloud services, an application program APP integrated with collection tools is deployed to the plurality of user terminals, the collection tools on each user terminal can perform problem scanning according to the same test use cases, and problems found by the plurality of user terminals when the APP is operated can be automatically collected by using independent threads.
Fig. 3 is a schematic structural diagram of a server according to a preferred embodiment of the present invention for implementing a private cloud-based data collection method. The server 3 comprises a memory 31, at least one processor 32, a computer program 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.
It will be appreciated by those skilled in the art that the schematic diagram shown in fig. 3 is merely an example of the server 3 and is not limiting of the server 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g. the server 3 may also include input and output devices, network access devices, etc.
The at least one processor 32 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 32 may be a microprocessor or the processor 32 may be any conventional processor or the like, the processor 32 being a control center of the server 3, the various interfaces and lines being used to connect the various parts of the entire server 3.
The memory 31 may be used to store the computer program 33 and/or modules/units, and the processor 32 may implement the various functions of the server 3 by running or executing the computer program and/or modules/units stored in the memory 31 and invoking data stored in the memory 31. The memory 31 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the server 3, and the like. In addition, the memory 31 may include a nonvolatile memory such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other nonvolatile solid state storage device.
In connection with fig. 1, the memory 31 in the server 3 stores a plurality of instructions to implement a private cloud based data collection method, the processor 32 being executable to implement:
deploying the application program APP integrated with the collection tool to a plurality of user terminals;
starting APP on the plurality of user terminals through private cloud service to start the collecting tool to scan problems according to a preset test case;
after the scanning is finished, starting an independent thread through the private cloud service;
collecting current problem data collected by the collecting tool through the independent thread, and writing the current problem data into a temporary file of the APP of the user terminal;
and calling a data collection service, and storing the current problem data in the temporary file of the APP of each user terminal into a local database.
Specifically, the specific implementation method of the above instructions by the processor 32 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
In the server 3 described in fig. 3, a plurality of user terminals can be scheduled through a private cloud service, an application program APP integrated with a collection tool is deployed to the plurality of user terminals, the collection tool on each user terminal can perform problem scanning according to the same test case, and the problems found by the plurality of user terminals when running the APP can be automatically collected by using independent threads, so that the whole process can realize unified testing of the APP running on the plurality of user terminals without manual intervention or limitation of different user terminals or different operating systems or different APP versions, the problem collection efficiency is improved, meanwhile, problem omission caused by objective reasons can be avoided, the collected problems are richer and more comprehensive, and in addition, the collected problems remain in a local database, and the problem state can be maintained.
The modules/units integrated by the server 3 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device, recording medium, USB flash disk, removable hard disk, magnetic disk, optical disk, computer Memory, and Read-Only Memory capable of carrying the computer program code.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. 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. Any reference signs in the claims shall not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. The data collection method based on the private cloud is characterized by comprising the following steps of:
deploying the application program APP integrated with the collection tool to a plurality of user terminals;
starting APP on the plurality of user terminals through private cloud service to start the collecting tool to call a page of the APP to perform problem scanning according to a preset test case, wherein the preset test case in each user terminal is identical;
after the scanning is finished, starting an independent thread through the private cloud service;
collecting current problem data collected by the collecting tool through the independent thread, and collecting associated data related to the current problem data through the independent thread, wherein the dimension of the associated data is used for representing the dimension of the hardware aspect of the user terminal and the dimension of the APP version aspect; writing the current problem data into a temporary file of the APP of the user terminal, wherein the current problem data is used for representing problems occurring in the APP operation;
invoking a data collection service, storing current problem data in temporary files of the APP of each user terminal into a local database, wherein the method comprises the following steps of:
creating a timing task; monitoring the data state of the current problem data, and dynamically adjusting the timing period of the timing task according to the data state, wherein the data state is the data size of the current problem data; invoking a data collection service through the adjusted timing task; triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database; or alternatively
Creating a timing task; monitoring the storage state of the temporary file, and dynamically adjusting the timing period of the timing task according to the storage state, wherein the storage state is the state of the storage space of the temporary file; invoking a data collection service through the adjusted timing task; triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
2. The private cloud-based data collection method according to claim 1, wherein after the invoking the data collection service to store the current problem data in the temporary file of the APP of each of the user terminals into a local database, the private cloud-based data collection method further comprises:
acquiring historical problem data;
comparing the current problem data with the historical problem data;
if first problem data exists in the historical problem data and the first problem data does not exist in the current problem data, updating the state of the first problem data in the local database to be repaired; or (b)
And if second problem data exists in the current problem data and the second problem data does not exist in the historical problem data, updating the state of the second problem data in the local database to be repaired.
3. The private cloud-based data collection method according to claim 1, wherein after the invoking the data collection service to store the current problem data in the temporary file of the APP of each of the user terminals into a local database, the private cloud-based data collection method further comprises:
acquiring the problem type of the current problem data;
determining the version of the APP corresponding to the current problem data of the same problem type;
and setting the risk level of the current question data of the same question type according to the number of versions of the APP and the occurrence times of the current question data of the same question type.
4. The private cloud-based data collection method according to claim 1, wherein after the invoking the data collection service to store the current problem data in the temporary file of the APP of each of the user terminals into a local database, the private cloud-based data collection method further comprises:
according to each question type of the current question data, carrying out association processing on the current question data and the association data to obtain processing data for representing association relation;
generating a visual interface according to the processing data;
and outputting the visual interface.
5. A private cloud-based data collection apparatus, characterized in that the apparatus comprises means for implementing the private cloud-based data collection method according to any one of claims 1 to 4, the private cloud-based data collection apparatus comprising:
the deployment module is used for deploying the application program APP integrated with the collection tool to a plurality of user terminals;
the starting module is used for starting the APP on the plurality of user terminals through private cloud service so as to start the collecting tool to scan the problems according to a preset test case;
the starting module is also used for starting an independent thread through the private cloud service;
the collection module is used for collecting current problem data collected by the collection tool through the independent thread, and collecting associated data related to the current problem data through the independent thread, wherein the dimension of the associated data is used for representing the dimension of the hardware aspect of the user terminal and the dimension of the APP version aspect; writing the current problem data into a temporary file of the APP of the user terminal;
the storage module is used for calling a data collection service and storing current problem data in the temporary file of the APP of each user terminal into a local database, and comprises the following steps:
creating a timing task; monitoring the data state of the current problem data, and dynamically adjusting the timing period of the timing task according to the data state, wherein the data state is the data size of the current problem data; invoking a data collection service through the adjusted timing task; triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database; or alternatively
Creating a timing task; monitoring the storage state of the temporary file, and dynamically adjusting the timing period of the timing task according to the storage state, wherein the storage state is the state of the storage space of the temporary file; invoking a data collection service through the adjusted timing task; triggering the data collection service to extract current problem data in the temporary files of the APP of each user terminal according to the adjusted timing period and storing the current problem data in a local database.
6. A server comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the private cloud-based data collection method of any of claims 1 to 4.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the private cloud-based data collection method according to any one of claims 1 to 4.
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