Industrial pilot plant test platform tool remote monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of equipment monitoring, and discloses an industrial pilot plant test platform tool remote monitoring method and system based on the Internet of Things, and the method comprises the steps: selecting remote monitoring points in a test platform tool, and calculating the distribution uniformity of the remote monitoring points in the test platform tool; adjusting the remote monitoring points by using the distribution uniformity to obtain adjusted monitoring points; analyzing the information flow direction of the test platformization tool from the remote monitoring data, and reducing the remote monitoring tool in the test platformization tool to obtain a reduced monitoring tool; calculating a data weight value of the trial platformization data, and performing data variation on the trial platformization data according to the data weight value to obtain variation data; and extracting a remote monitoring index of the test platformization tool from the execution information flow, calculating an index weight value of the remote monitoring index, and analyzing a remote monitoring score of the test platformization tool by using the remote monitoring index and the index weight value. According to the invention, the number of monitoring devices can be reduced, and monitoring vulnerabilities of test tools can be comprehensively monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to a remote monitoring method and system for an industrial pilot platform tool based on the Internet of Things. Background Art

[0002] Industrial pilot platform tools refer to product testing tools that are used to detect product risks and vulnerabilities. However, the test results of the testing tools may not fully reflect the risks and vulnerabilities of the products. This is because the test environment set up by the testing tools is an artificially set environment. Such a testing environment is prone to frequently testing the vulnerabilities of a certain product while ignoring the vulnerabilities of other products.

[0003] At present, it is a common consensus to install monitoring devices in the monitored software to monitor the monitored software. However, in the process of installing the monitoring devices, the number of devices to be installed and the installation location need to be solved. With the exponential growth of data, if monitoring devices are installed on each basic block in the monitored software, the number of installed monitoring devices will increase. Secondly, when debugging the test tools using the artificially set test environment, the problem of frequently testing the vulnerabilities of a certain product while ignoring the vulnerabilities of other products will occur during the process of testing the products through the test tools. Therefore, a solution is urgently needed to reduce the number of monitoring devices and the monitoring vulnerabilities of the comprehensive monitoring test tools. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an industrial pilot platform tool remote monitoring method and system based on the Internet of Things, which can reduce the number of monitoring devices and the monitoring loopholes of comprehensive monitoring test tools.

[0005] In a first aspect, the present invention provides an industrial pilot platform tool remote monitoring method based on the Internet of Things, comprising:

[0006] Constructing a remote monitoring system of the test platform tool, selecting remote monitoring points in the test platform tool, and calculating the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform;

[0007] The remote monitoring point is adjusted by using the distribution uniformity to obtain an adjusted monitoring point, the remote monitoring tool is set on the adjusted monitoring point, and the remote monitoring tool is used to collect remote monitoring data in the test platform tool;

[0008] The remote monitoring data is transmitted to the data analysis platform, and on the data analysis platform, the information flow of the test platform tool is analyzed from the remote monitoring data, and based on the information flow, the remote monitoring tool is reduced in the test platform tool to obtain a reduced monitoring tool;

[0009] Using the reduction monitoring tool to collect the test platform data in the test platform tool, calculating the data weight value of the test platform data, performing data mutation on the test platform data according to the data weight value to obtain mutated data, and executing the mutated data in the test platform tool to obtain an execution information flow;

[0010] The remote monitoring indicators of the test platform tool are extracted from the execution information flow, the indicator weight values ​​of the remote monitoring indicators are calculated, the remote monitoring scores of the test platform tool are analyzed using the remote monitoring indicators and the indicator weight values, the remote monitoring scores are used to complete the remote monitoring of the test platform tool, and the remote monitoring results of the test platform tool are obtained.

[0011] In a possible implementation manner of the first aspect, the calculating the distribution uniformity of the remote monitoring points in the test platform tool includes:

[0012] Analyze the superior-subordinate relationship and the parallel-level relationship between each code block in the code block of the test platform tool;

[0013] Generate a code tree structure of the code block by using the superior-subordinate relationship and the parallel level relationship;

[0014] After marking the remote monitoring point in the code tree structure, identifying a covering code block of the remote monitoring point in the code tree structure;

[0015] Calculating the ratio of the number of the covered code blocks to the number of the code blocks;

[0016] The number ratio is taken as the distribution uniformity.

[0017] In a possible implementation manner of the first aspect, adjusting the remote monitoring point by using the distribution uniformity to obtain the adjusted monitoring point includes:

[0018] Obtaining a code tree structure corresponding to the test platform tool;

[0019] Identifying non-covered code blocks in the code tree structure;

[0020] Querying a target monitoring point among the remote monitoring points that is closest to the uncovered code block;

[0021] Obtaining a first target code block corresponding to the target monitoring point;

[0022] Querying a second target code block in the first target code block that is closest to the non-covered code block;

[0023] Determining whether the target monitoring point is located on the second target code block;

[0024] When the target monitoring point is not located on the second target code block, transferring the target monitoring point to the second target code block;

[0025] Determine whether the non-covered code block belongs to the coverage range of the second target code block;

[0026] When the non-covered code block does not belong to the coverage range of the second target code block and the target monitoring point is on the second target code block, obtaining a covered code block in the code tree structure;

[0027] Obtain all code blocks of the remote monitoring point;

[0028] Determine whether all the code blocks belong to other monitoring points of the remote monitoring point;

[0029] When all the code blocks belong to other monitoring points of the remote monitoring point, the remote monitoring point is transferred to the non-covered code block to obtain an adjusted monitoring point.

[0030] In a possible implementation of the first aspect, analyzing the information flow of the test platform tool from the remote monitoring data includes:

[0031] Obtaining assignment statements, function call statements, control statements and return statements in the remote monitoring data;

[0032] Extracting a first information flow from an assigning party to an assigned party in the assignment statement;

[0033] Extracting a second information flow from a caller to a callee in the function call statement;

[0034] Extracting a third information flow from an input condition to an output result in the control statement;

[0035] Extracting a fourth information flow direction from the returned party to the returning party in the return statement;

[0036] The first information flow direction, the second information flow direction, the third information flow direction, and the fourth information flow direction are used as the information flow direction.

[0037] In a possible implementation manner of the first aspect, reducing the remote monitoring tool in the test platform tool based on the information flow to obtain a reduced monitoring tool includes:

[0038] Obtaining an information flow starting point and an information flow destination in the information flow direction;

[0039] The remote monitoring tool is respectively set at the information flow starting point and the information flow destination to obtain a reduced monitoring tool.

[0040] In a possible implementation manner of the first aspect, calculating the data weight value of the test platform data includes:

[0041] Distinguishing between test platform conditions and test platform results in the test platform data;

[0042] Obtaining a code block in the test platformization tool;

[0043] Based on the test platform result and the code block, the data weight value of the test platform condition is calculated using the following formula:

[0044]

[0045] Among them, α i represents the data weight value of the ith test platform condition, x j Indicates the total execution result of the jth code block, y ij It represents the test platforming result of the j-th code block under the i-th test platforming condition.

[0046] In a possible implementation manner of the first aspect, performing data mutation on the test platform data according to the data weight value to obtain mutated data includes:

[0047] Selecting a target weight value that is smaller than a preset weight value among the data weight values;

[0048] Obtaining a target condition corresponding to the target weight value from the test platform data;

[0049] Randomly select a unit character in the target condition;

[0050] Reverse the unit character to obtain a reversed character;

[0051] The inverted character is inserted into the character position of the unit character in the target condition to obtain variant data.

[0052] In a possible implementation manner of the first aspect, extracting the remote monitoring indicator of the test platform tool from the execution information flow includes:

[0053] Obtaining variation data corresponding to the execution information flow;

[0054] Determining a mutation result of the mutation data;

[0055] Calculating result similarity between the execution result in the execution information flow and the variation result;

[0056] Using the result similarity, selecting the number of similar results and the number of dissimilar results from between the execution result and the variation result;

[0057] Obtaining test platform conditions and test platform results in test platform data;

[0058] Calculating the risk similarity between the number of risks in the test platform result and the number of risks in the execution result based on the correlation between the variation data and the test platform condition;

[0059] Using the risk similarity, selecting the number of risk similarities and the number of risk dissimilarities from the number of risks in the test platformization result and the number of risks in the execution result;

[0060] The result similarity, the number of similar results, the number of dissimilar results, the risk similarity, the number of similar risks and the number of dissimilar risks are used as the remote monitoring indicators.

[0061] In a possible implementation manner of the first aspect, calculating the indicator weight value of the remote monitoring indicator includes:

[0062] Standardizing the remote monitoring indicators to obtain standardized indicators;

[0063] The indicator weight value of the standardized indicator is calculated using a preset entropy weight method.

[0064] In a second aspect, the present invention provides an industrial pilot platform tool remote monitoring system based on the Internet of Things, the system comprising:

[0065] A uniform calculation module is used to construct a remote monitoring system of the test platform tool, select remote monitoring points in the test platform tool, and calculate the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform;

[0066] A data acquisition module, used for adjusting the remote monitoring point by using the distribution uniformity to obtain an adjusted monitoring point, setting the remote monitoring tool on the adjusted monitoring point, and using the remote monitoring tool to collect remote monitoring data in the test platform tool;

[0067] A tool reduction module, used for transmitting the remote monitoring data to the data analysis platform, analyzing the information flow of the test platform tool from the remote monitoring data on the data analysis platform, and reducing the remote monitoring tool in the test platform tool based on the information flow to obtain a reduced monitoring tool;

[0068] a data execution module, for collecting the test platform data in the test platform tool by using the reduction monitoring tool, calculating the data weight value of the test platform data, performing data mutation on the test platform data according to the data weight value to obtain mutated data, and executing the mutated data in the test platform tool to obtain an execution information flow;

[0069] A tool monitoring module is used to extract the remote monitoring indicators of the test platform tool from the execution information flow, calculate the indicator weight value of the remote monitoring indicator, use the remote monitoring indicator and the indicator weight value to analyze the remote monitoring score of the test platform tool, use the remote monitoring score to complete the remote monitoring of the test platform tool, and obtain the remote monitoring result of the test platform tool.

[0070] Compared with the prior art, the technical principle and beneficial effects of this solution are:

[0071] The embodiment of the present invention selects remote monitoring points in the test platform tool so that the monitoring device can be evenly distributed in the code data of the test platform tool, thereby ensuring that the complete information flow of the test platform tool can be collected later. Further, the embodiment of the present invention reduces the remote monitoring tools in the test platform tool based on the information flow direction, so as to reduce the original large number of evenly distributed remote monitoring tools to only set remote monitoring tools at the starting point and the destination of the information flow, thereby greatly reducing the number of remote monitoring devices. Further, the embodiment of the present invention calculates the data weight value of the test platform data to screen out the test platform conditions with infrequent use times, and focuses on monitoring the test results under these infrequently used test platform conditions, thereby comprehensively monitoring the monitoring vulnerabilities of the test tool. Further, the embodiment of the present invention performs data mutation on the test platform data according to the data weight value to increase the number of different manifestations of the same type of test platform conditions. Therefore, the remote monitoring method and system for industrial pilot platform tools based on the Internet of Things proposed in the embodiment of the present invention can reduce the number of monitoring devices and comprehensively monitor the monitoring vulnerabilities of the test tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0074] Figure 1 A flowchart of a remote monitoring method for an industrial pilot platform tool based on the Internet of Things provided by an embodiment of the present invention;

[0075] Figure 2 A module diagram of an industrial pilot platform tool remote monitoring system based on the Internet of Things provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0077] The embodiment of the present invention provides a remote monitoring method for an industrial pilot platform tool based on the Internet of Things. The execution subject of the remote monitoring method for an industrial pilot platform tool based on the Internet of Things includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the remote monitoring method for an industrial pilot platform tool based on the Internet of Things can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0078] See also Figure 1 FIG. 1 is a flow chart of a remote monitoring method for an industrial pilot platform tool based on the Internet of Things provided by an embodiment of the present invention. Figure 1 The remote monitoring method of the industrial pilot platform tool based on the Internet of Things described in the article includes:

[0079] S1. Construct a remote monitoring system for the test platform tool, select remote monitoring points in the test platform tool, and calculate the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform.

[0080] In the embodiment of the present invention, the test platform tool refers to a software testing tool.

[0081] Furthermore, the embodiment of the present invention selects remote monitoring points in the test platform tool so that monitoring devices can be evenly distributed in the code data of the test platform tool, thereby ensuring that the complete information flow of the test platform tool can be collected subsequently.

[0082] The remote monitoring point refers to a location in the test platform tool waiting to be monitored.

[0083] Optionally, the process of selecting the remote monitoring point in the test platform tool is a random selection process.

[0084] In one embodiment of the present invention, the calculation of the distribution uniformity of the remote monitoring points in the test platform tool includes: analyzing the superior-subordinate relationship and the parallel relationship between each code block in the code block in the test platform tool; generating a code tree structure of the code block using the superior-subordinate relationship and the parallel relationship; after marking the remote monitoring point in the code tree structure, identifying the coverage code block of the remote monitoring point in the code tree structure; calculating the number ratio between the coverage code block and the code block; and using the number ratio as the distribution uniformity.

[0085] Among them, the code block refers to the basic block of code, which refers to the sequence of statements executed sequentially in the program of the test platform tool, the superior-subordinate relationship refers to the relationship of inclusion and being included between codes, the parallel-level relationship refers to the equal-level relationship, and the coverage code block refers to the code block that each remote monitoring point is responsible for monitoring.

[0086] S2. Using the distribution uniformity to adjust the remote monitoring point to obtain an adjusted monitoring point, setting the remote monitoring tool on the adjusted monitoring point, and using the remote monitoring tool to collect remote monitoring data in the test platform tool.

[0087] In one embodiment of the present invention, the method of adjusting the remote monitoring point by using the distribution uniformity to obtain the adjusted monitoring point includes: obtaining a code tree structure corresponding to the test platform tool; identifying a non-covered code block in the code tree structure; querying a target monitoring point in the remote monitoring point that is closest to the non-covered code block; obtaining a first target code block corresponding to the target monitoring point; querying a second target code block in the first target code block that is closest to the non-covered code block; judging whether the target monitoring point is on the second target code block; when the target monitoring point is not on the second target code block, transferring the target monitoring point to the second target code block; judging whether the non-covered code block belongs to the coverage range of the second target code block; when the non-covered code block does not belong to the coverage range of the second target code block and the target monitoring point is on the second target code block, obtaining a covered code block in the code tree structure; obtaining all code blocks of the remote monitoring point; judging whether all code blocks belong to other monitoring points of the remote monitoring point; when all code blocks belong to other monitoring points of the remote monitoring point, transferring the remote monitoring point to the non-covered code block to obtain the adjusted monitoring point.

[0088] The said all code blocks refer to all responsible code blocks of a certain remote monitoring point.

[0089] Exemplarily, the process of determining whether all the code blocks belong to other monitoring points of the remote monitoring point refers to determining whether all three code blocks of the a-th remote monitoring point are also the first code block that the b-th other monitoring point is responsible for, and the second and third code blocks that the c-th other monitoring point is responsible for. Furthermore, when all the code blocks belong to other monitoring points of the remote monitoring point, it indicates that the density of the remote monitoring points is too high, and one remote monitoring point is assigned to a non-covered code block.

[0090] Furthermore, in an embodiment of the present invention, the remote monitoring tool refers to a data recording tool for monitoring the test data of the test platform tool, and is mainly used to record changes in code data of the test platform tool, and the remote monitoring data refers to change data of the code block of the test platform tool.

[0091] S3. The remote monitoring data is transmitted to the data analysis platform. On the data analysis platform, the information flow of the test platform tool is analyzed from the remote monitoring data. Based on the information flow, the remote monitoring tool is reduced in the test platform tool to obtain a reduced monitoring tool.

[0092] In the embodiment of the present invention, the information flow refers to the flow direction of information flow and data flow in the code block of the test platform tool.

[0093] In one embodiment of the present invention, analyzing the information flow of the test platform tool from the remote monitoring data includes: obtaining assignment statements, function call statements, control statements and return statements in the remote monitoring data; extracting the first information flow from the assigner to the assigned party in the assignment statement; extracting the second information flow from the caller to the callee in the function call statement; extracting the third information flow from the input condition to the output result in the control statement; extracting the fourth information flow from the returned party to the return party in the return statement; and using the first information flow, the second information flow, the third information flow and the fourth information flow as the information flow.

[0094] Furthermore, the embodiment of the present invention reduces the remote monitoring tools in the test platform tool based on the information flow direction, so as to reduce the originally large number of evenly distributed remote monitoring tools to only the remote monitoring tools set at the starting point and the destination of the information flow, thereby greatly reducing the number of remote monitoring devices.

[0095] The reduced monitoring tool refers to a remote monitoring tool with a reduced number of installations.

[0096] In one embodiment of the present invention, based on the information flow direction, the remote monitoring tool is reduced in the test platform tool to obtain a reduced monitoring tool, including: obtaining the information flow starting point and the information flow destination in the information flow direction; and setting the remote monitoring tool at the information flow starting point and the information flow destination respectively to obtain the reduced monitoring tool.

[0097] S4. Use the reduction monitoring tool to collect the test platform data in the test platform tool, calculate the data weight value of the test platform data, perform data mutation on the test platform data according to the data weight value to obtain mutated data, execute the mutated data in the test platform tool to obtain the execution information flow.

[0098] In the embodiment of the present invention, the test platform data includes not only the test scenario or test code data in the debugged test platform tool, but also the test result data obtained by the test platform tool from the tested software.

[0099] Furthermore, the embodiment of the present invention calculates the data weight value of the test platform data to screen out the test platform conditions that are not used frequently, and focuses on monitoring the test results under these test platform conditions that are not used frequently, thereby comprehensively monitoring the monitoring vulnerabilities of the test tool.

[0100] In one embodiment of the present invention, the calculation of the data weight value of the test platform data includes: distinguishing the test platform conditions and the test platform results in the test platform data; obtaining the code block in the test platform tool; and calculating the data weight value of the test platform condition based on the test platform result and the code block using the following formula:

[0101]

[0102] Among them, α i represents the data weight value of the ith test platform condition, x j Indicates the total execution result of the jth code block, y ij It represents the test platforming result of the j-th code block under the i-th test platforming condition.

[0103] The test platform conditions refer to the test scenarios set for the software under test, such as test cases.

[0104] Furthermore, the embodiment of the present invention performs data variation on the test platform data according to the data weight value, so as to increase the number of different expressions of the same type of test platform conditions.

[0105] In one embodiment of the present invention, the test platform data is mutated according to the data weight value to obtain mutated data, including: selecting a target weight value that is less than a preset weight value from the data weight value; obtaining a target condition corresponding to the target weight value from the test platform data; randomly selecting a unit character in the target condition; inverting the unit character to obtain an inverted character; and inserting the inverted character into the character position of the unit character in the target condition to obtain mutated data.

[0106] Among them, the preset weight value is intended to screen out trial platform conditions that are not used frequently, the target condition refers to the trial platform condition corresponding to the target weight value, and the unit character refers to a 1-bit character. In computer language, each program code or character is represented by 0 and 1, so the unit character is inverted, that is, 0 is changed to 1, and 1 is changed to 0.

[0107] Furthermore, in the embodiment of the present invention, the execution information flow refers to a data flow composed of the test results of the variant data, that is, the execution results.

[0108] S5. Extract the remote monitoring indicators of the test platform tool from the execution information flow, calculate the indicator weight value of the remote monitoring indicator, use the remote monitoring indicator and the indicator weight value to analyze the remote monitoring score of the test platform tool, use the remote monitoring score to complete the remote monitoring of the test platform tool, and obtain the remote monitoring result of the test platform tool.

[0109] In one embodiment of the present invention, the remote monitoring indicator of the test platform tool is extracted from the execution information flow, including: obtaining variant data corresponding to the execution information flow; determining the variant result of the variant data; calculating the result similarity between the execution result in the execution information flow and the variant result; selecting the number of result similarities and the number of result dissimilarities between the execution result and the variant result using the result similarity; obtaining the test platform conditions and the test platform results in the test platform data; calculating the risk similarity between the number of risks in the test platform result and the number of risks in the execution result based on the correlation between the variant data and the test platform conditions; selecting the number of risk similarities and the number of risk dissimilarities between the number of risks in the test platform result and the number of risks in the execution result using the risk similarity; and using the result similarity, the number of result similarities, the number of result dissimilarities, the risk similarity, the number of risk similarities and the number of risk dissimilarities as the remote monitoring indicator.

[0110] Among them, the mutation result refers to the test result that the mutation data estimated by human experts should correspond to. The result similarity, the correlation and the risk similarity are all obtained by calculating the code similarity. The similarity calculation formula can adopt Euclidean, Manhattan and cosine similarity algorithms, etc.

[0111] Optionally, the process of calculating the risk similarity between the number of risks in the test platform results and the number of risks in the execution results based on the correlation between the variant data and the test platform conditions refers to calculating the risk similarity between the number of risks in the test platform results and the number of risks in the execution results when the correlation between the variant data and the test platform conditions is high, wherein the risks are data such as software vulnerabilities and software failures in the software being tested by the test platform tool.

[0112] In one embodiment of the present invention, the calculation of the indicator weight value of the remote monitoring indicator includes: standardizing the remote monitoring indicator to obtain a standardized indicator; and calculating the indicator weight value of the standardized indicator using a preset entropy weight method.

[0113] Furthermore, in an embodiment of the present invention, the remote monitoring score refers to the weighted sum of the remote monitoring indicator and the indicator weight value. The remote monitoring score can be used to obtain the difference between the vulnerabilities obtained by the mutant data test and the vulnerabilities obtained by the actual non-mutated test platform condition test. If the difference is large, it indicates that the testing tool has poor testing capabilities for vulnerabilities that are not frequently tested.

[0114] It can be seen that the embodiment of the present invention selects the remote monitoring points in the test platform tool so that the monitoring devices can be evenly distributed in the code data of the test platform tool, thereby ensuring that the complete information flow of the test platform tool can be collected later. Further, the embodiment of the present invention reduces the remote monitoring tools in the test platform tool based on the information flow direction, so as to reduce the original large number of evenly distributed remote monitoring tools to only set remote monitoring tools at the starting point and the destination of the information flow, thereby greatly reducing the number of remote monitoring devices. Further, the embodiment of the present invention calculates the data weight value of the test platform data to screen out the test platform conditions with infrequent use times, and focuses on monitoring the test results under these infrequently used test platform conditions, thereby comprehensively monitoring the monitoring vulnerabilities of the test tool. Further, the embodiment of the present invention performs data mutation on the test platform data according to the data weight value to increase the number of different manifestations of the same type of test platform conditions. Therefore, the remote monitoring method and system for industrial pilot platform tools based on the Internet of Things proposed in the embodiment of the present invention can reduce the number of monitoring devices and comprehensively monitor the monitoring vulnerabilities of the test tool.

[0115] like Figure 2 The figure shows a functional module diagram of the remote monitoring system of the industrial pilot platform tool based on the Internet of Things of the present invention.

[0116] The industrial pilot platform tool remote monitoring system 200 based on the Internet of Things of the present invention can be installed in an electronic device. According to the functions implemented, the industrial pilot platform tool remote monitoring system based on the Internet of Things can include a uniform calculation module 201, a data acquisition module 202, a tool reduction module 203, a data execution module 204 and a tool monitoring module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0117] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0118] The uniform calculation module 201 is used to construct a remote monitoring system of the test platform tool, select remote monitoring points in the test platform tool, and calculate the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform;

[0119] The data acquisition module 202 is used to adjust the remote monitoring point by using the distribution uniformity to obtain an adjusted monitoring point, set the remote monitoring tool on the adjusted monitoring point, and use the remote monitoring tool to collect remote monitoring data in the test platform tool;

[0120] The tool reduction module 202 is used to transmit the remote monitoring data to the data analysis platform, and on the data analysis platform, analyze the information flow of the test platform tool from the remote monitoring data, and based on the information flow, reduce the remote monitoring tool in the test platform tool to obtain a reduced monitoring tool;

[0121] The data execution module 204 is used to collect the test platform data in the test platform tool by using the reduction monitoring tool, calculate the data weight value of the test platform data, perform data mutation on the test platform data according to the data weight value to obtain mutated data, and execute the mutated data in the test platform tool to obtain an execution information flow;

[0122] The tool monitoring module 205 is used to extract the remote monitoring indicators of the test platform tool from the execution information flow, calculate the indicator weight value of the remote monitoring indicator, use the remote monitoring indicator and the indicator weight value to analyze the remote monitoring score of the test platform tool, use the remote monitoring score to complete the remote monitoring of the test platform tool, and obtain the remote monitoring result of the test platform tool.

[0123] In detail, the modules in the industrial pilot platform tool remote monitoring system 200 based on the Internet of Things in the embodiment of the present invention are used in the same manner as above. Figure 1 The remote monitoring method of the industrial pilot platform tool based on the Internet of Things described in the article has the same technical means and can produce the same technical effects, so I will not go into details here.

[0124] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0125] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0126] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0127] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0128] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A remote monitoring method for industrial pilot platform tools based on the Internet of Things, characterized in that: The method comprises: Constructing a remote monitoring system of the test platform tool, selecting remote monitoring points in the test platform tool, and calculating the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform; The remote monitoring point is adjusted by using the distribution uniformity to obtain an adjusted monitoring point, the remote monitoring tool is set on the adjusted monitoring point, and the remote monitoring tool is used to collect remote monitoring data in the test platform tool; The remote monitoring data is transmitted to the data analysis platform, and on the data analysis platform, the information flow of the test platform tool is analyzed from the remote monitoring data, and based on the information flow, the remote monitoring tool is reduced in the test platform tool to obtain a reduced monitoring tool; Using the reduction monitoring tool to collect the test platform data in the test platform tool, calculating the data weight value of the test platform data, performing data mutation on the test platform data according to the data weight value to obtain mutated data, and executing the mutated data in the test platform tool to obtain an execution information flow; The remote monitoring indicators of the test platform tool are extracted from the execution information flow, the indicator weight values ​​of the remote monitoring indicators are calculated, the remote monitoring scores of the test platform tool are analyzed using the remote monitoring indicators and the indicator weight values, the remote monitoring scores are used to complete the remote monitoring of the test platform tool, and the remote monitoring results of the test platform tool are obtained.

2. The method according to claim 1, characterized in that The calculating the distribution uniformity of the remote monitoring points in the test platform tool includes: Analyze the superior-subordinate relationship and the parallel-level relationship between each code block in the code block of the test platform tool; Generate a code tree structure of the code block by using the superior-subordinate relationship and the parallel level relationship; After marking the remote monitoring point in the code tree structure, identifying a covering code block of the remote monitoring point in the code tree structure; Calculating the ratio of the number of the covered code blocks to the number of the code blocks; The number ratio is taken as the distribution uniformity.

3. The method according to claim 1, characterized in that The step of adjusting the remote monitoring point by using the distribution uniformity to obtain the adjusted monitoring point includes: Obtaining a code tree structure corresponding to the test platform tool; Identifying non-covered code blocks in the code tree structure; Querying a target monitoring point among the remote monitoring points that is closest to the uncovered code block; Obtaining a first target code block corresponding to the target monitoring point; Querying a second target code block in the first target code block that is closest to the non-covered code block; Determining whether the target monitoring point is located on the second target code block; When the target monitoring point is not located on the second target code block, transferring the target monitoring point to the second target code block; Determine whether the non-covered code block belongs to the coverage range of the second target code block; When the non-covered code block does not belong to the coverage range of the second target code block and the target monitoring point is on the second target code block, obtaining a covered code block in the code tree structure; Obtain all code blocks of the remote monitoring point; Determine whether all the code blocks belong to other monitoring points of the remote monitoring point; When all the code blocks belong to other monitoring points of the remote monitoring point, the remote monitoring point is transferred to the non-covered code block to obtain an adjusted monitoring point.

4. The method according to claim 1, characterized in that: The analyzing the information flow of the test platform tool from the remote monitoring data includes: Obtaining assignment statements, function call statements, control statements and return statements in the remote monitoring data; Extracting a first information flow from an assigning party to an assigned party in the assignment statement; Extracting a second information flow from a caller to a callee in the function call statement; Extracting a third information flow from an input condition to an output result in the control statement; Extracting a fourth information flow direction from the returned party to the returning party in the return statement; The first information flow direction, the second information flow direction, the third information flow direction, and the fourth information flow direction are used as the information flow direction.

5. The method according to claim 1, characterized in that: Based on the information flow, reducing the remote monitoring tool in the test platform tool to obtain a reduced monitoring tool includes: Obtaining an information flow starting point and an information flow destination in the information flow direction; The remote monitoring tool is respectively set at the information flow starting point and the information flow destination to obtain a reduced monitoring tool.

6. The method according to claim 1, characterized in that The calculating the data weight value of the test platform data includes: Distinguishing between test platform conditions and test platform results in the test platform data; Obtaining a code block in the test platformization tool; Based on the test platform result and the code block, the data weight value of the test platform condition is calculated using the following formula: Among them, α i represents the data weight value of the ith test platform condition, x j Indicates the total execution result of the jth code block, y ij It represents the test platforming result of the j-th code block under the i-th test platforming condition.

7. The method according to claim 1, characterized in that The step of performing data mutation on the test platform data according to the data weight value to obtain mutated data includes: Selecting a target weight value that is smaller than a preset weight value among the data weight values; Obtaining a target condition corresponding to the target weight value from the test platform data; Randomly select a unit character in the target condition; Reverse the unit character to obtain a reversed character; The inverted character is inserted into the character position of the unit character in the target condition to obtain variant data.

8. The method according to claim 1, characterized in that: The extracting the remote monitoring indicator of the test platform tool from the execution information flow includes: Obtaining variation data corresponding to the execution information flow; Determining a mutation result of the mutation data; Calculating result similarity between the execution result in the execution information flow and the variation result; Using the result similarity, selecting the number of similar results and the number of dissimilar results from between the execution result and the variation result; Obtaining test platform conditions and test platform results in test platform data; Calculating the risk similarity between the number of risks in the test platform result and the number of risks in the execution result based on the correlation between the variation data and the test platform condition; Using the risk similarity, selecting the number of risk similarities and the number of risk dissimilarities from the number of risks in the test platformization result and the number of risks in the execution result; The result similarity, the number of similar results, the number of dissimilar results, the risk similarity, the number of similar risks and the number of dissimilar risks are used as the remote monitoring indicators.

9. The method according to claim 1, characterized in that: The calculating the indicator weight value of the remote monitoring indicator includes: Standardizing the remote monitoring indicators to obtain standardized indicators; The indicator weight value of the standardized indicator is calculated using a preset entropy weight method.

10. An industrial pilot platform tool remote monitoring system based on the Internet of Things, characterized in that: The system comprises: A uniform calculation module is used to construct a remote monitoring system of the test platform tool, select remote monitoring points in the test platform tool, and calculate the distribution uniformity of the remote monitoring points in the test platform tool, wherein the remote monitoring system includes a remote monitoring tool and a data analysis platform; A data acquisition module, used for adjusting the remote monitoring point by using the distribution uniformity to obtain an adjusted monitoring point, setting the remote monitoring tool on the adjusted monitoring point, and using the remote monitoring tool to collect remote monitoring data in the test platform tool; A tool reduction module, used for transmitting the remote monitoring data to the data analysis platform, analyzing the information flow of the test platform tool from the remote monitoring data on the data analysis platform, and reducing the remote monitoring tool in the test platform tool based on the information flow to obtain a reduced monitoring tool; a data execution module, for collecting the test platform data in the test platform tool by using the reduction monitoring tool, calculating the data weight value of the test platform data, performing data mutation on the test platform data according to the data weight value to obtain mutated data, and executing the mutated data in the test platform tool to obtain an execution information flow; A tool monitoring module is used to extract the remote monitoring indicators of the test platform tool from the execution information flow, calculate the indicator weight value of the remote monitoring indicator, use the remote monitoring indicator and the indicator weight value to analyze the remote monitoring score of the test platform tool, use the remote monitoring score to complete the remote monitoring of the test platform tool, and obtain the remote monitoring result of the test platform tool.

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