Product testing method, device and equipment for workshop production and medium

Abstract

The invention relates to the technical field of test management, and relates to a product test method and device for workshop production, equipment and a medium, and the method comprises the steps: matching a product production log and a product demand document according to a product serial number of a to-be-tested product; performing demand keyword matching and demand semantic analysis on the product demand document to obtain a test demand set; performing log analysis and production process extraction on the product production log to obtain a key production process, and generating a product test scheme according to the test demand set and the key production process; performing function test on the to-be-tested product according to the product test scheme to obtain a primary test result; performing abnormal data detection on the primary test result to obtain abnormal test data, and performing test case adjustment on the primary test scheme according to the abnormal test data to obtain a standard test scheme; and performing function testing and quality labeling on the to-be-tested product according to the standard test scheme, and generating a test report. According to the invention, the product testing efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of test management, and in particular to a product testing method, device, equipment and medium for workshop production. Background Art

[0002] A workshop refers to a special place within an enterprise used for product manufacturing, processing, assembly or testing. It is the core area of ​​production activities. In order to ensure product quality and optimize the production process, it is necessary to test the products produced in the workshop.

[0003] Most of the existing product testing methods are based on manual testing. Trained personnel actually read the product requirements and conduct various functional tests on the product based on the product requirements. In actual applications, testing methods based on manual testing cannot quickly respond to changing production needs, lack flexibility and intelligence, and it is difficult to complete testing in a timely manner and meet customers' delivery time requirements, which may lead to low product testing efficiency. Summary of the invention

[0004] The present invention provides an artificial intelligence product testing method, device, equipment and medium for workshop production to solve the technical problems that the testing method cannot quickly respond to changing production needs, lacks flexibility and intelligence, is difficult to complete the test in time and meet the customer's delivery time requirements, and thus makes the product testing efficiency low.

[0005] In a first aspect, a product testing method for workshop production is provided, comprising:

[0006] Obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number;

[0007] Performing requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set;

[0008] Perform log analysis and production process extraction on the product production log according to the test requirement set to obtain key production processes, and generate a product test plan according to the test requirement set and the key production processes;

[0009] Performing a functional test on the product to be tested according to the product test plan to obtain a primary test result;

[0010] Performing abnormal data detection on the primary test results to obtain abnormal test data, and adjusting the test cases of the primary test plan according to the abnormal test data to obtain a standard test plan;

[0011] Perform functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

[0012] In a second aspect, a product testing device for workshop production is provided, comprising:

[0013] The log matching module is used to obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number;

[0014] A requirement analysis module is used to perform requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set;

[0015] A solution generation module, used to perform log analysis and production process extraction on the product production log according to the test requirement set, obtain key production processes, and generate a product test solution according to the test requirement set and the key production processes;

[0016] A primary test module, used to perform a functional test on the product to be tested according to the product test plan to obtain a primary test result;

[0017] A scheme adjustment module, used to perform abnormal data detection on the primary test results to obtain abnormal test data, and adjust the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme;

[0018] The functional testing module is used to perform functional testing and quality marking on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

[0019] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned product testing method for workshop production when executing the computer program.

[0020] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned product testing method for workshop production are implemented.

[0021] In the scheme implemented by the above-mentioned workshop production product testing method, device, computer equipment and storage medium, the product production log and product requirement document can be matched according to the product serial number, and the product data of the entire process of the product to be tested in the production process can be matched according to the unique product serial number, thereby realizing the full traceability management of product testing and improving product testing efficiency. The existing system cannot adapt to small batch and diversified production needs, and cannot respond quickly to changing production needs, resulting in the testing link becoming the bottleneck of the entire production process. By performing demand keyword matching and demand semantic analysis, the corresponding test demand semantics can be automatically matched, thereby improving the flexibility of product testing. By performing log analysis and production process extraction on the product production log according to the test requirement set, the process related to production testing can be extracted, and the product testing plan can be generated through the key production process. The test plan can be flexibly generated according to the type of each product and the production process, thereby improving the efficiency of test plan generation.

[0022] By performing functional testing on the product to be tested according to the product testing scheme, flexible product testing can be achieved. By detecting abnormal test data and adjusting the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme, real-time feedback and monitoring during product testing can be achieved, potential problems can be discovered in the early stages of production, and the efficiency of product testing can be improved. By performing functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test scheme, the intuitiveness of the test results of the product test can be improved, and the efficiency of product testing can be improved. Therefore, the product testing method, device, computer equipment and storage medium for workshop production proposed in the present invention can solve the problem of low efficiency in product testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0024] Figure 1 It is a flow chart of a product testing method for workshop production in one embodiment of the present invention;

[0025] Figure 2 yes Figure 1 A schematic flow chart of a specific implementation of step S20;

[0026] Figure 3 yes Figure 1 A schematic flow chart of a specific implementation of step S30;

[0027] Figure 4 It is a structural schematic diagram of a product testing device for workshop production in one embodiment of the present invention;

[0028] Figure 5 is a schematic diagram of a structure of a computer device in one embodiment of the present invention; DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, Figure 1 A flow chart of a product testing method for workshop production provided by an embodiment of the present invention includes the following steps:

[0031] S10: Obtain the product serial number of the product to be tested, and match the product production log and the product requirement document according to the product serial number.

[0032] In detail, the product to be tested refers to the product produced in the production workshop that needs to be quality tested. The product to be tested can be a complete computer, an all-in-one computer, a laptop computer and other devices. The product serial number refers to a unique identifier used to track various activities of the product to be tested during the production and quality inspection process.

[0033] Specifically, the product production log refers to a document or database entry that records the entire process information of the product from the start to the completion of production. The product production log includes production plans, process flows, operator information, equipment status and environmental parameters, etc. The product requirement document is a document that describes production goals, technical requirements, resource requirements and delivery requirements. It is the basic guide for production. The product requirement document is a detailed conversion of customer requirements and the standardization of internal technical language, including product specifications, process requirements, resource requirements and delivery requirements, etc.

[0034] In detail, the product serial number can be matched by reading the SN barcode or MAC address information of the product to be tested, and the product production log and product requirement document can be matched by performing a database search in the Manufacturing Execution System (MES).

[0035] In an embodiment of the present invention, by matching the product production log and the product requirement document according to the product serial number, the product data of the entire process of the product to be tested during the production process can be matched through the unique product serial number, thereby realizing full traceability management of product testing and improving product testing efficiency.

[0036] S20: Perform requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set.

[0037] In detail, each test requirement in the test requirement set includes functional items that need to be tested on the product to be tested and corresponding test parameters and operation methods, wherein the functional items include BIOS detection, CPU detection, memory detection, hard disk detection, HW (Hardware) diagnosis, network adapter diagnosis, wireless network card test and Bluetooth test, etc., and the test parameters include test time, test audio size and write data size, etc.

[0038] In the embodiment of the present invention, referring to Figure 2 As shown, the product requirement document is subjected to requirement keyword matching and requirement semantic analysis to obtain a test requirement set, including:

[0039] S21, performing demand keyword matching on the product demand document to obtain a demand keyword set;

[0040] S22, matching a set of requirement statements from the product requirement document according to the set of requirement keywords;

[0041] S23, performing text segmentation and text vectorization operations on the demand statement set to obtain a demand statement feature set;

[0042] S24, using a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set;

[0043] S25, performing demand matching on the matching demand feature set to obtain a matching demand set;

[0044] S26, performing semantic analysis on the non-matching demand feature set to obtain a non-matching demand set;

[0045] S27. Generate a test requirement set according to the matching requirement set and the non-matching requirement set.

[0046] In detail, the demand keyword matching refers to matching keywords related to the demand from the product demand document using keyword matching methods such as regular expressions, TF-IDF, RAKE or TextRank methods. The demand keywords may be "WiFi support", "memory size" and "CPU frequency" etc.

[0047] Specifically, the bidirectional maximum matching method can be used for text segmentation, and BERT, GPT or Sentence-BERT can be used for text vectorization operations, that is, each demand statement in the demand statement set after text segmentation is converted into corresponding demand statement features to obtain a demand statement feature set.

[0048] In detail, the requirement semantic feature library is a pre-set database that records various possible test requirement semantic texts and corresponding semantic features.

[0049] Specifically, performing demand matching on the matching demand feature set to obtain a matching demand set means taking the demand semantic features corresponding to the extreme similarities of each matching demand feature in the matching demand feature set as test demand features, and aggregating the test demands corresponding to the test demand features as matching demands into a matching demand set.

[0050] In detail, the semantic analysis of the non-matching demand feature set to obtain the non-matching demand set refers to using a pre-trained BERT, GPT or LSTM model to generate demand texts corresponding to each non-matching demand feature in the non-matching demand feature set, and aggregating the demand texts as non-matching demands into a non-matching demand set.

[0051] Specifically, the use of a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set includes:

[0052] Select the demand statement features in the demand statement feature set one by one as the target demand statement feature, calculate the feature similarity between the target demand statement feature and each demand semantic feature in the preset demand semantic feature library, and obtain a target feature similarity set;

[0053] Filter out extreme similarities from the target feature similarity set;

[0054] Determine whether the extreme value similarity is greater than a preset similarity threshold;

[0055] If yes, the target requirement statement feature is used as a matching requirement feature, and all matching requirement features are aggregated into a matching requirement feature set;

[0056] If not, the target requirement statement feature is used as a non-matching requirement feature, and all non-matching requirement features are aggregated into a non-matching requirement feature set.

[0057] In detail, the feature similarity between the target requirement statement feature and each requirement semantic feature in the preset requirement semantic feature library may be calculated using a cosine similarity algorithm or a Euclidean distance algorithm.

[0058] Specifically, the filtering out of the test requirement feature set from the requirement semantic feature library according to all feature similarities refers to filtering out the requirement semantic features with the closest feature similarity corresponding to each requirement statement feature in the requirement statement feature set from the requirement semantic feature library and aggregating them into a test requirement feature set, and the semantic matching refers to aggregating the requirement semantics corresponding to each test requirement feature in the test requirement feature set in the requirement semantic feature library as test requirements into a test requirement set.

[0059] In the embodiments of the present invention, the existing system cannot adapt to small batch and diversified production needs, and cannot quickly respond to changing production needs, causing the testing link to become the bottleneck of the entire production process. By performing demand keyword matching and demand semantic analysis, the corresponding test demand semantics can be automatically matched, thereby improving the flexibility of product testing.

[0060] S30: performing log analysis on the product production log and extracting production processes according to the test requirement set to obtain key production processes, and generating a product test plan according to the test requirement set and the key production processes.

[0061] In detail, the key production process refers to the production process of the product to be tested recorded in the product production log, including the process parameters of each process of product production and the equipment operation status, etc.

[0062] In the embodiment of the present invention, refer to Figure 3 As shown, the product production log is analyzed and the production process is extracted according to the test requirement set to obtain the key production process, including:

[0063] S31, parsing the product production log to obtain product production information;

[0064] S32, sorting the product production information on a time axis to obtain a production information sequence;

[0065] S33, extracting events from the production information sequence to obtain a production event sequence;

[0066] S34, constructing a process chain for the production event sequence to obtain a product production process;

[0067] S35, extracting a production process node sequence from the product production process;

[0068] S36, performing a correlation analysis on the production process node sequence according to the test requirement set to obtain a node correlation coefficient sequence;

[0069] S37, screening the production process node sequence for key nodes according to the node association coefficient sequence to obtain a key node sequence;

[0070] S38. Screen the product production process according to the key node sequence to obtain the key production process.

[0071] Specifically, the log parsing refers to parsing the product production log into an easy-to-understand document format according to the log format of the product production log. The log format can be CSV, JSON and XML. Log analysis tools such as Pandas or Logstash can be used for log parsing. The timeline sorting refers to sorting in chronological order according to the timestamps of each log record in the product production information.

[0072] In detail, the event extraction refers to extracting the production events of each production information in the production information sequence, such as "welding completed" and "temperature control exceeded the limit", etc., by using methods such as keyword matching.

[0073] Specifically, the correlation analysis is a method for calculating the potential correlation relationship between the test requirement set and the production process node sequence. The correlation analysis can be performed using algorithms such as the Apriori algorithm, the FP-Growth algorithm, the GSP algorithm, and the Eclat algorithm. Each node correlation coefficient in the node correlation coefficient sequence corresponds to the correlation coefficient between each production process node in the production process node sequence and the test requirement set.

[0074] In detail, the key node screening of the production process node sequence according to the node correlation coefficient sequence to obtain the key node sequence means aggregating the production process nodes in the node correlation coefficient sequence whose node correlation coefficients are greater than a preset correlation threshold into a key node sequence as key nodes, and the process screening of the product production process according to the key node sequence to obtain the key production process means aggregating the process nodes corresponding to the key node sequence in the product production process into a key production process.

[0075] Specifically, constructing a process chain for the production event sequence to obtain a product production process includes:

[0076] Performing continuity detection on the production event sequence and supplementing missing processes to obtain a primary production process;

[0077] Unifying the process steps of the primary production process to obtain a secondary production process;

[0078] Marking node parameters of the secondary production process to obtain a standard production process;

[0079] The standard production process is annotated with process dependencies to obtain the product production process.

[0080] In detail, the longest common subsequence algorithm or the rule engine such as Drools can be used for continuity detection, and the method for supplementing the missing process can be process mapping or the process sequence analysis method using the LSTM model trained based on historical data to supplement the missing process.

[0081] Specifically, the process step unification refers to unifying the names of each process step, for example, unifying all "welding" into "spot welding", and the node parameter labeling refers to labeling node parameters such as input, output and process parameters for each process node of the secondary production process. The process dependency of the standard production process can be obtained by using graph structure modeling, and the process dependency labeling can be performed in combination with the edge dependency strength labeling method.

[0082] In detail, generating a product test plan according to the test requirement set and the key production process includes:

[0083] Selecting process nodes in the key production process one by one as target process nodes, and taking the test requirements corresponding to the target process nodes in the test requirement set as target test requirements;

[0084] Performing test case mapping on the target test requirement to obtain a target requirement test case;

[0085] Extracting target process parameters from the target process node;

[0086] Parameter mapping is performed on the target requirement test case according to the target process process parameters to obtain a target process test case;

[0087] Gathering all of the target process test cases into a process test case set;

[0088] The process dependencies are extracted from the key production process, and the process test case set is dependency sorted according to the process dependencies to obtain a product test plan.

[0089] In detail, the test case mapping refers to screening out the test cases corresponding to the target test requirements from a preset test case template library. For example, when the target test requirement is to perform a wireless network card test, the path of the test case is WifiTestManager and the executable file is WifiTestManager.exe.

[0090] Specifically, the target process parameter refers to the process parameter corresponding to the target process node. For example, when the target process node is a network card configuration, the target process parameter is AdapterCount=1|WifiSignalStrength_24G=90%|WifiSignalStrength_5G=90%|ConnectToWifi=True, wherein AdapterCount=1 indicates that the number of network adapters currently detected by the device is 1; WifiSignalStrength_24G=90% indicates that the signal strength of the 2.4GHz Wi-Fi band is 90%; WifiSignalStrength_5G=90% indicates that the signal strength of the 5GHz Wi-Fi band is 90%; ConnectToWifi=True indicates that the device is currently connected to the Wi-Fi network, and the parameter mapping refers to screening out corresponding parameters according to the target process parameter and the target test requirement corresponding requirement parameter range, and using the parameters as test parameters.

[0091] In detail, the process dependency is the same as the process dependency of the standard production process obtained by graph structure modeling in the above steps, and is used to describe the dependency relationship between the various processes in the key production process. The process test case set can be dependency sorted by topological sorting.

[0092] In an embodiment of the present invention, by performing log analysis and production process extraction on the product production log according to the test requirement set, the processes related to production testing can be extracted, and a product test plan can be generated through the key production process. The test plan can be flexibly generated according to the type of each product and the production process, thereby improving the efficiency of test plan generation.

[0093] S40: Performing a functional test on the product to be tested according to the product testing plan to obtain a primary test result.

[0094] In detail, the primary test result refers to a list of various results obtained by conducting product testing according to the product testing plan.

[0095] In an embodiment of the present invention, the product to be tested is functionally tested according to the product testing plan to obtain primary test results, including selecting process test cases in the product testing plan one by one as the target process testing plan, performing functional testing on the product to be tested according to the target process testing plan to obtain target process testing results, and aggregating all the target process testing results into primary test results.

[0096] In detail, functional testing can be performed using tools such as Selenium, Appium, LabVIEW, and TestStand, or through manually triggered automation scripts or manually completed semi-automated testing methods.

[0097] In the embodiment of the present invention, flexible product testing can be achieved by performing functional testing on the product to be tested according to the product testing scheme.

[0098] S50: performing abnormal data detection on the primary test results to obtain abnormal test data, and adjusting the test cases of the primary test solution according to the abnormal test data to obtain a standard test solution.

[0099] In detail, the abnormal test data refers to the test results that are beyond the expected range or do not meet the expected results in the functional test. The abnormal test data may be caused by parameter mismatch of the test case configuration, product functional defects or failures, test environment noise or equipment errors, etc.

[0100] In the embodiment of the present invention, the step of performing abnormal data detection on the primary test result to obtain abnormal test data includes:

[0101] Selecting process test results in the primary test results one by one as target process test results, and extracting target process functions and target result parameters from the target process test results;

[0102] Obtaining a historical process test parameter set of the target process function;

[0103] Calculating an outlier coefficient between the historical process test parameter set and the target result parameter;

[0104] Determining whether the outlier coefficient is greater than a preset outlier threshold;

[0105] If not, returning to the step of selecting the process test results in the primary test results one by one as the target process test results;

[0106] If so, an abnormal process result is generated according to the outlier coefficient and the target process test result, and abnormal test data is generated according to all abnormal process results.

[0107] In detail, the process test result corresponds to the target process test result in the above step S4, the target process function refers to the function of the production process corresponding to the target process test result, and the target result parameter is the final test numerical parameter of the target process test result.

[0108] Specifically, the historical process test parameter set is a set of result parameters obtained from functional testing of the target process function recorded in the past. The outlier coefficient can be calculated using the Z-Score and four-way method, and the outlier threshold is a threshold set based on historical experience.

[0109] In detail, the adjusting the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme includes:

[0110] Selecting abnormal process results in the abnormal test data one by one as target abnormal process results, and taking process test cases corresponding to the target abnormal process results as target process test cases;

[0111] Extracting an outlier coefficient from the target abnormal process result;

[0112] Extracting target test parameters from the target process test case;

[0113] Performing gradient descent optimization on the target test parameter according to the outlier coefficient to obtain a primary optimization parameter;

[0114] Perform cross-mutation on the primary optimization parameters according to the target test parameters to obtain standard optimization parameters;

[0115] The target process test case is updated according to the standard optimization parameters to obtain a target standard test case;

[0116] The primary test plan is updated according to all the target standard test cases to obtain a standard test plan.

[0117] In detail, the gradient descent optimization refers to optimizing and updating the target test parameters by generating an optimization target based on the outlier coefficient using the gradient descent algorithm. The crossover mutation is an operation for generating new solutions or new individuals. Through crossover and mutation operations, a larger search space can be explored to find the optimal solution to the problem.

[0118] In an embodiment of the present invention, by detecting abnormal test data and adjusting the test cases of the primary test plan according to the abnormal test data, a standard test plan is obtained, which can achieve real-time feedback and monitoring during product testing, discover potential problems in the early stages of production, and improve the efficiency of product testing.

[0119] S60: Performing functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test solution, and generating a test report.

[0120] In detail, in order to improve the intuitiveness of the test results, it is necessary to indicate the product test results through a test report, wherein the test report includes the marking results of whether each function is qualified or unqualified.

[0121] In an embodiment of the present invention, the step of performing functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test scheme, and generating a test report, includes:

[0122] Screening out standard test cases from the standard test schemes one by one, and taking the test requirements corresponding to the standard test cases in the test requirement set as target test requirements;

[0123] Extracting a target requirement parameter interval from the target test requirement;

[0124] Perform functional testing on the product to be tested according to the standard test case to obtain a target product test result;

[0125] Determine whether the result parameter of the target product test result is within the target requirement parameter range;

[0126] If yes, the product to be tested is marked as qualified in function according to the standard test case to obtain the primary marking quality, until the standard test case is the last standard test case in the standard test plan, and a test report is generated according to all the primary marking qualities;

[0127] If not, the product to be tested is marked as unqualified in function according to the standard test case to obtain the primary marking quality, until the standard test case is the last standard test case in the standard test plan, and a test report is generated according to all the primary marking qualities.

[0128] Specifically, the target requirement parameter interval is a parameter interval where test parameters within a qualified range required by the target test requirement are located, and the target requirement parameter interval can be extracted by a keyword matching method.

[0129] In detail, the function qualification marking refers to marking that the product under test meets the qualification standards in the function corresponding to the standard test case, and the function failure marking refers to marking that the product under test does not meet the qualification standards in the function corresponding to the standard test case.

[0130] Specifically, by performing functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test solution, the intuitiveness of the test results of the product test can be improved and the efficiency of the product test can be improved.

[0131] It can be seen that in the above scheme, by matching the product production log and the product requirement document according to the product serial number, the product data of the entire process of the product to be tested during the production process can be matched through the unique product serial number, thereby realizing the full traceability management of product testing and improving product testing efficiency. The existing system cannot adapt to small batches and diversified production needs, and cannot respond quickly to changing production needs, resulting in the testing link becoming a bottleneck in the entire production process. By performing demand keyword matching and demand semantic analysis, the corresponding test demand semantics can be automatically matched, thereby improving the flexibility of product testing. By performing log analysis and production process extraction on the product production log according to the test requirement set, the processes related to production testing can be extracted, and the product testing plan can be generated through the key production process. The test plan can be flexibly generated according to the type of each product and the production process, thereby improving the efficiency of test plan generation.

[0132] By performing functional testing on the product to be tested according to the product testing scheme, flexible product testing can be achieved. By detecting abnormal test data and adjusting the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme, real-time feedback and monitoring during product testing can be achieved, potential problems can be discovered in the early stages of production, and the efficiency of product testing can be improved. By performing functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test scheme, the intuitiveness of the test results of the product test can be improved, and the efficiency of product testing can be improved. Therefore, the product testing method for workshop production proposed by the present invention can solve the problem of low efficiency in product testing.

[0133] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0134] In one embodiment, a product testing device for workshop production is provided, and the product testing device for workshop production corresponds one-to-one to the product testing method for workshop production in the above embodiment. Figure 4 As shown, the product testing device for workshop production includes a log matching module 101, a demand analysis module 102, a solution generation module 103, a primary test module 104, a solution adjustment module 105 and a functional test module 106. The detailed description of each functional module is as follows:

[0135] The log matching module 101 is used to obtain the product serial number of the product to be tested, and match the product production log and the product requirement document according to the product serial number;

[0136] The requirement analysis module 102 is used to perform requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set;

[0137] A solution generation module 103 is used to perform log analysis and production process extraction on the product production log according to the test requirement set to obtain the key production process, and generate a product test solution according to the test requirement set and the key production process;

[0138] A primary test module 104 is used to perform a functional test on the product to be tested according to the product test plan to obtain a primary test result;

[0139] A scheme adjustment module 105 is used to perform abnormal data detection on the primary test results to obtain abnormal test data, and adjust the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme;

[0140] The functional testing module 106 is used to perform functional testing and quality marking on the product to be tested according to the test requirement set and the standard test solution, and generate a test report.

[0141] In one embodiment, when the requirement analysis module 102 performs requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set, it is used to:

[0142] Performing demand keyword matching on the product demand document to obtain a demand keyword set;

[0143] Matching a set of requirement statements from the product requirement document according to the set of requirement keywords;

[0144] Performing text segmentation and text vectorization operations on the demand statement set to obtain a demand statement feature set;

[0145] Using a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set;

[0146] Performing demand matching on the matching demand feature set to obtain a matching demand set;

[0147] Performing semantic analysis on the non-matching requirement feature set to obtain a non-matching requirement set;

[0148] A test requirement set is generated according to the matching requirement set and the non-matching requirement set.

[0149] In one embodiment, when the demand analysis module 102 uses a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set, it is used to:

[0150] Select the demand statement features in the demand statement feature set one by one as the target demand statement feature, calculate the feature similarity between the target demand statement feature and each demand semantic feature in the preset demand semantic feature library, and obtain a target feature similarity set;

[0151] Filter out extreme similarities from the target feature similarity set;

[0152] Determine whether the extreme value similarity is greater than a preset similarity threshold;

[0153] If yes, the target requirement statement feature is used as a matching requirement feature, and all matching requirement features are aggregated into a matching requirement feature set;

[0154] If not, the target requirement statement feature is used as a non-matching requirement feature, and all non-matching requirement features are aggregated into a non-matching requirement feature set.

[0155] In one embodiment, when performing log analysis on the product production log and extracting the production process according to the test requirement set to obtain the key production process, the solution generation module 103 is used to:

[0156] Parsing the product production log to obtain product production information;

[0157] Sorting the product production information on a time axis to obtain a production information sequence;

[0158] Performing event extraction on the production information sequence to obtain a production event sequence;

[0159] Constructing a process chain for the production event sequence to obtain a product production process;

[0160] Extracting a production process node sequence from the product production process;

[0161] Performing a correlation analysis on the production process node sequence according to the test requirement set to obtain a node correlation coefficient sequence;

[0162] Screening the production process node sequence for key nodes according to the node association coefficient sequence to obtain a key node sequence;

[0163] The product production process is screened according to the key node sequence to obtain the key production process.

[0164] In one embodiment, when the solution generation module 103 constructs a process chain for the production event sequence to obtain a product production process, it is used to:

[0165] Performing continuity detection on the production event sequence and supplementing missing processes to obtain a primary production process;

[0166] Unifying the process steps of the primary production process to obtain a secondary production process;

[0167] Marking node parameters of the secondary production process to obtain a standard production process;

[0168] The standard production process is annotated with process dependencies to obtain the product production process.

[0169] In one embodiment, when the solution generation module 103 generates a product test solution according to the test requirement set and the key production process, it is used to:

[0170] Selecting process nodes in the key production process one by one as target process nodes, and taking the test requirements corresponding to the target process nodes in the test requirement set as target test requirements;

[0171] Performing test case mapping on the target test requirement to obtain a target requirement test case;

[0172] Extracting target process parameters from the target process node;

[0173] Parameter mapping is performed on the target requirement test case according to the target process process parameters to obtain a target process test case;

[0174] Gathering all of the target process test cases into a process test case set;

[0175] The process dependencies are extracted from the key production process, and the process test case set is dependency sorted according to the process dependencies to obtain a product test plan.

[0176] In one embodiment, when performing the abnormal data detection on the primary test results to obtain abnormal test data, the scheme adjustment module 105 is used to:

[0177] Selecting process test results in the primary test results one by one as target process test results, and extracting target process functions and target result parameters from the target process test results;

[0178] Obtaining a historical process test parameter set of the target process function;

[0179] Calculating an outlier coefficient between the historical process test parameter set and the target result parameter;

[0180] Determining whether the outlier coefficient is greater than a preset outlier threshold;

[0181] If not, returning to the step of selecting the process test results in the primary test results one by one as the target process test results;

[0182] If so, an abnormal process result is generated according to the outlier coefficient and the target process test result, and abnormal test data is generated according to all abnormal process results.

[0183] In one embodiment, when the solution adjustment module 105 adjusts the test cases of the primary test solution according to the abnormal test data to obtain a standard test solution, it is used to:

[0184] Selecting abnormal process results in the abnormal test data one by one as target abnormal process results, and taking process test cases corresponding to the target abnormal process results as target process test cases;

[0185] Extracting an outlier coefficient from the target abnormal process result;

[0186] Extracting target test parameters from the target process test case;

[0187] Performing gradient descent optimization on the target test parameter according to the outlier coefficient to obtain a primary optimization parameter;

[0188] Perform cross-mutation on the primary optimization parameters according to the target test parameters to obtain standard optimization parameters;

[0189] The target process test case is updated according to the standard optimization parameters to obtain a target standard test case;

[0190] The primary test plan is updated according to all the target standard test cases to obtain a standard test plan.

[0191] In one embodiment, when the functional testing module 106 performs functional testing and quality marking on the product to be tested according to the test requirement set and the standard test scheme and generates a test report, it is used to:

[0192] Screening out standard test cases from the standard test schemes one by one, and taking the test requirements corresponding to the standard test cases in the test requirement set as target test requirements;

[0193] Extracting a target requirement parameter interval from the target test requirement;

[0194] Perform functional testing on the product to be tested according to the standard test case to obtain a target product test result;

[0195] Determine whether the result parameter of the target product test result is within the target requirement parameter range;

[0196] If yes, the product to be tested is marked as qualified in function according to the standard test case to obtain the primary marking quality, until the standard test case is the last standard test case in the standard test plan, and a test report is generated according to all the primary marking qualities;

[0197] If not, the product to be tested is marked as unqualified in function according to the standard test case to obtain the primary marking quality, until the standard test case is the last standard test case in the standard test plan, and a test report is generated according to all the primary marking qualities.

[0198] For the specific definition of the product testing device for workshop production, please refer to the definition of the product testing method for workshop production above, which will not be repeated here. Each module in the above-mentioned product testing device for workshop production can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0199] In one embodiment, a computer device is provided, wherein the internal structure diagram of the computer device can be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a product testing method for workshop production

[0200] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0201] Obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number;

[0202] Performing requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set;

[0203] Perform log analysis and production process extraction on the product production log according to the test requirement set to obtain key production processes, and generate a product test plan according to the test requirement set and the key production processes;

[0204] Performing a functional test on the product to be tested according to the product test plan to obtain a primary test result;

[0205] Performing abnormal data detection on the primary test results to obtain abnormal test data, and adjusting the test cases of the primary test plan according to the abnormal test data to obtain a standard test plan;

[0206] Perform functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

[0207] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0208] Obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number;

[0209] Performing requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set;

[0210] Perform log analysis and production process extraction on the product production log according to the test requirement set to obtain key production processes, and generate a product test plan according to the test requirement set and the key production processes;

[0211] Performing a functional test on the product to be tested according to the product test plan to obtain a primary test result;

[0212] Performing abnormal data detection on the primary test results to obtain abnormal test data, and adjusting the test cases of the primary test plan according to the abnormal test data to obtain a standard test plan;

[0213] Perform functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

[0214] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description in the aforementioned method embodiment. In order to avoid repetition, they will not be described one by one here.

[0215] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0216] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0217] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in the protection scope of the present invention. It should be noted that if software tools or components other than those of the company appear in the embodiments of this application, they are only used for example introduction and do not represent actual use.

Claims

1. A product testing method for workshop production, characterized in that: The method comprises: Obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number; Performing requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set; Perform log analysis and production process extraction on the product production log according to the test requirement set to obtain key production processes, and generate a product test plan according to the test requirement set and the key production processes; Performing a functional test on the product to be tested according to the product test plan to obtain a primary test result; Performing abnormal data detection on the primary test results to obtain abnormal test data, and adjusting the test cases of the primary test plan according to the abnormal test data to obtain a standard test plan; Perform functional testing and quality labeling on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

2. The product testing method for workshop production as claimed in claim 1, characterized in that: The step of performing requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set includes: Performing demand keyword matching on the product demand document to obtain a demand keyword set; Matching a set of requirement statements from the product requirement document according to the set of requirement keywords; Performing text segmentation and text vectorization operations on the demand statement set to obtain a demand statement feature set; Using a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set; Performing demand matching on the matching demand feature set to obtain a matching demand set; Performing semantic analysis on the non-matching demand feature set to obtain a non-matching demand set; A test requirement set is generated according to the matching requirement set and the non-matching requirement set.

3. The product testing method for workshop production as claimed in claim 2, characterized in that: The method of using a preset demand semantic feature library to perform semantic similarity splitting on the demand statement feature set to obtain a matching demand feature set and a non-matching demand feature set includes: Select the demand statement features in the demand statement feature set one by one as the target demand statement feature, calculate the feature similarity between the target demand statement feature and each demand semantic feature in the preset demand semantic feature library, and obtain a target feature similarity set; Filter out extreme similarities from the target feature similarity set; Determine whether the extreme value similarity is greater than a preset similarity threshold; If yes, the target requirement statement feature is used as a matching requirement feature, and all matching requirement features are aggregated into a matching requirement feature set; If not, the target requirement statement feature is used as a non-matching requirement feature, and all non-matching requirement features are aggregated into a non-matching requirement feature set.

4. The product testing method for workshop production according to claim 1, characterized in that: The log analysis and production process extraction of the product production log according to the test requirement set to obtain the key production process includes: Parsing the product production log to obtain product production information; Sorting the product production information on a time axis to obtain a production information sequence; Performing event extraction on the production information sequence to obtain a production event sequence; Constructing a process chain for the production event sequence to obtain a product production process; Extracting a production process node sequence from the product production process; Performing a correlation analysis on the production process node sequence according to the test requirement set to obtain a node correlation coefficient sequence; Screening the production process node sequence for key nodes according to the node association coefficient sequence to obtain a key node sequence; The product production process is screened according to the key node sequence to obtain the key production process.

5. The product testing method for workshop production as claimed in claim 4, characterized in that: The process chain is constructed for the production event sequence to obtain the product production process, including: Performing continuity detection on the production event sequence and supplementing missing processes to obtain a primary production process; Unifying the process steps of the primary production process to obtain a secondary production process; Marking node parameters of the secondary production process to obtain a standard production process; The standard production process is annotated with process dependencies to obtain the product production process.

6. The product testing method for workshop production as claimed in claim 1, characterized in that: Generating a product test plan according to the test requirement set and the key production process includes: Selecting process nodes in the key production process one by one as target process nodes, and taking the test requirements corresponding to the target process nodes in the test requirement set as target test requirements; Performing test case mapping on the target test requirement to obtain a target requirement test case; Extracting target process parameters from the target process node; Parameter mapping is performed on the target requirement test case according to the target process process parameters to obtain a target process test case; Gathering all of the target process test cases into a process test case set; The process dependencies are extracted from the key production process, and the process test case set is dependency sorted according to the process dependencies to obtain a product test plan.

7. The product testing method for workshop production as claimed in claim 1, characterized in that: The performing abnormal data detection on the primary test result to obtain abnormal test data includes: Selecting process test results in the primary test results one by one as target process test results, and extracting target process functions and target result parameters from the target process test results; Obtaining a historical process test parameter set of the target process function; Calculating an outlier coefficient between the historical process test parameter set and the target result parameter; Determining whether the outlier coefficient is greater than a preset outlier threshold; If not, returning to the step of selecting the process test results in the primary test results one by one as the target process test results; If so, an abnormal process result is generated according to the outlier coefficient and the target process test result, and abnormal test data is generated according to all abnormal process results.

8. A product testing device for workshop production, characterized in that: include: The log matching module is used to obtain the product serial number of the product to be tested, and match the product production log and product requirement document according to the product serial number; A requirement analysis module is used to perform requirement keyword matching and requirement semantic analysis on the product requirement document to obtain a test requirement set; A solution generation module, used to perform log analysis and production process extraction on the product production log according to the test requirement set, obtain key production processes, and generate a product test solution according to the test requirement set and the key production processes; A primary test module, used to perform a functional test on the product to be tested according to the product test plan to obtain a primary test result; A scheme adjustment module, used to perform abnormal data detection on the primary test results to obtain abnormal test data, and adjust the test cases of the primary test scheme according to the abnormal test data to obtain a standard test scheme; The functional testing module is used to perform functional testing and quality marking on the product to be tested according to the test requirement set and the standard test plan, and generate a test report.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the product testing method for workshop production as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the product testing method for workshop production as claimed in any one of claims 1 to 7 is implemented.

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