An intelligent analysis method for work order efficiency of equipment in buildings

Abstract

The present application provides a method for intelligent analysis of work order efficiency for equipment within a building, comprising: for each structured test point data, evaluating whether the key parameters of the test point meet the test specifications through a pre-established test specification condition judgment logic; if the key parameters of each test point meet the test specifications, the test quality of the test point is qualified; after obtaining the evaluation results of the test point coverage, key parameter completeness, test value range compliance, and test time adequacy indicators, setting the weight coefficient of each indicator, calculating the overall test step integrity score of the insulation resistance test and the insulation withstand voltage test, and obtaining a quantitative indicator for measuring the test standardization.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a work order efficiency intelligent analysis method applied to equipment in a building. Background Art

[0002] In the intelligent analysis of equipment work efficiency in buildings, the insulation resistance test and insulation withstand voltage test process of electrical equipment is a key link. It is necessary to identify the key parameter information of each test point, including the test voltage amplitude range, test voltage frequency range, test duration requirements, test current upper limit threshold, insulation resistance lower limit requirements, ambient temperature allowable range, ambient humidity allowable range and safety isolation distance requirements. After obtaining these parameter information, it is necessary to evaluate whether the parameters of the test point meet the test specification conditions and judge the quality of the test. At the same time, it is also necessary to calculate the test point coverage, key parameter integrity, test value range compliance and test time sufficiency indicators, and set weight coefficients to obtain the test step integrity score. Finally, combined with the actual test time and the test step integrity score, the time weight coefficient and the normative weight coefficient are set to calculate the comprehensive test efficiency index, and the overall efficiency level of the work order test is quantitatively evaluated. In this process, how to balance the relationship between test standardization and test efficiency, reasonably set the time weight coefficient and the normative weight coefficient, and organically combine the two to obtain an objective and accurate comprehensive test efficiency index is a technical issue worthy of in-depth study. Summary of the invention

[0003] The present invention provides a method for intelligent analysis of work efficiency of work orders for equipment in a building, which mainly includes:

[0004] According to the electrical industry standards and specifications, a standard test step library for insulation resistance test and insulation withstand voltage test is established based on the characteristics of equipment in the building. The test steps described in the work order are matched with the standard library to standardize the test steps. The key parameter information of each test point is identified from the standardized test step description to form structured test point key parameters. For each structured test point data, the key parameters of the test point are evaluated to see if they meet the test specifications through the pre-established test specification condition judgment logic. Based on the structured test point data with qualified test quality, the test point coverage, key parameter completeness, test value range compliance and test time adequacy index are calculated. Target evaluation results; set weight coefficients for each indicator, calculate the overall test step integrity score for insulation resistance test and insulation withstand voltage test, and obtain quantitative indicators to measure test standardization; extract test start and end time information from the work order, and calculate the actual test duration; use the test duration adequacy weight coefficient and the test value range compliance weight coefficient to weight the actual test duration and test step integrity score to obtain a comprehensive test efficiency index, and quantitatively evaluate the overall efficiency level of the work order test; compare the comprehensive test efficiency index with the preset efficiency threshold, analyze the test duration deviation and key test point standardization for insufficient work order efficiency, and generate corresponding optimization suggestions.

[0005] Furthermore, the establishment of a standard test step library for insulation resistance testing and insulation withstand voltage testing includes: receiving test request information carrying a unique identification number of the electrical equipment; obtaining standard test items and parameters corresponding to the electrical equipment from a pre-established standard library according to the test request information; performing natural language processing on the work order description in the test request information, extracting key information and generating structured data; vectorizing the structured data and the standard test items, and calculating the similarity between the vectors; if the similarity is greater than a preset threshold, determining the standard test steps corresponding to the similarity as the basis for normalized processing.

[0006] Furthermore, the identifying of key parameter information of each test point from the standardized test step description includes: obtaining the standardized test step description, and extracting numerical parameters from the standardized test step description using regular expressions; matching the extracted numerical parameters through a predefined JSON format parameter template, determining the category to which the numerical parameters belong and marking the upper and lower limit attributes; performing data cleaning on the marked numerical parameters, removing abnormal values ​​and erroneous data, and obtaining cleaned parameter data; and constructing a structured test point key parameter table based on the cleaned parameter data, wherein the test point key parameter table uses a nested dictionary structure to store various parameters and their upper and lower limit values.

[0007] Furthermore, the method of evaluating whether the key parameters of the test point meet the test specifications through the pre-established test specification condition judgment logic includes: obtaining the pre-established test specification condition judgment logic, the condition judgment logic is used to construct a binary tree structure condition judgment tree for different device types and test types; reading the structured test point data, and using the JSON parser to extract the key parameter value of each test point from the structured test point data; substituting the key parameter value into the condition judgment tree, starting from the root node, and comparing the current parameter value with the node threshold; and judging whether the key parameters of the test point meet the test specifications based on the preset threshold range.

[0008] Furthermore, a parameter specification judgment matrix is ​​constructed according to the preset threshold range; a three-dimensional array is generated according to the parameter specification judgment matrix, and the three-dimensional array is used to store structured test point data; the three-dimensional array is traversed to extract the actual values ​​of the key parameters of the test points, and the actual values ​​of the key parameters are compared with the thresholds in the parameter specification judgment matrix; the comparison result is encoded using a bit operation method, and the encoding result is a binary bit; the binary bit is compared with a preset mask through a bitwise AND operation, and if the comparison result is equal to the preset mask, the test quality of the test point is determined to be qualified; if the comparison result is not equal to the preset mask, the test quality of the test point is determined to be unqualified.

[0009] Furthermore, the evaluation results of test point coverage, key parameter completeness, test value range compliance and test time adequacy indicators are calculated based on the structured test point data with qualified test quality, including: obtaining qualified test point data and collected test point data, and constructing a hash table based on the qualified test point data and the collected test point data; performing an intersection operation on the key set of the hash table to obtain the number of common test points; calculating the test point coverage based on the number of common test points and the total number of qualified test points; reading the key parameter list, traversing each test point in the collected data, and counting the number of key parameters actually collected; and calculating the key parameter completeness score based on the number of key parameters actually collected and the total number of preset key parameters.

[0010] Furthermore, the overall test step integrity score of the insulation resistance test and the insulation withstand voltage test is calculated to obtain a quantitative indicator for measuring the test standardization, including: obtaining evaluation results of four indicators, namely, test point coverage, key parameter completeness, test value range compliance, and test time adequacy, from a database, and the evaluation results are used to construct an indicator data structure; reading a scoring grade classification standard in a configuration file according to the indicator data structure; obtaining a preset weight coefficient configuration; and using a weighted average method to calculate the overall test step integrity score of the insulation resistance test and the insulation withstand voltage test, wherein the score is obtained by weighting the four indicators, namely, test point coverage, key parameter completeness, test value range compliance, and test time adequacy;

[0011] It also includes: quantitative evaluation of the standard specifications of insulation resistance test and insulation withstand voltage test to obtain standard specification scores for insulation resistance test and insulation withstand voltage test, and combining the overall test step integrity score with the standard specification score for insulation resistance test and insulation withstand voltage test to form a quantitative indicator to measure the standardization of the test.

[0012] Furthermore, a quantitative evaluation is conducted on the standard specifications of the insulation resistance test and the insulation withstand voltage test to obtain the standard specification scores of the insulation resistance test and the insulation withstand voltage test, and a quantitative index for measuring the standardization of the test is formed by combining the overall test step integrity score with the standard specification scores of the insulation resistance test and the insulation withstand voltage test, including: obtaining the standard specification data of the insulation resistance test and the insulation withstand voltage test, and extracting key parameter values ​​from the standard specification data; the key parameter values ​​include the test voltage range, the test time requirement, the minimum insulation resistance value, and the maximum leakage current value; constructing a standard specification evaluation matrix according to the key parameter values; the standard specification evaluation matrix adopts a two-dimensional array structure, in which the rows represent different parameters, and the columns represent the standard values ​​and the allowable deviations; the membership function type and parameter value are set for each key parameter value; fuzzy evaluation is performed on the actual test data; an evaluation factor set is established, and an evaluation level set is determined, and the membership of each factor to each evaluation level is calculated according to the membership function type and the parameter value to form a fuzzy relationship matrix; and the standard specification scores of the insulation resistance test and the insulation withstand voltage test are obtained through fuzzy synthesis operations.

[0013] Furthermore, the method extracts the test start and end time information from the work order and calculates the actual test duration; uses the test duration sufficiency weight coefficient and the test value range compliance weight coefficient to weightedly calculate the actual test duration and the test step integrity score to obtain a comprehensive test efficiency index, including: obtaining the test start and end time information in the work order database; using a regular expression to match the time information to obtain a standard time format; calculating the actual test duration according to the standard time format; obtaining a preset standard test duration range from the specification database according to the actual test duration; determining whether the actual test duration falls within the standard test duration range to obtain a duration sufficiency score; obtaining the test step integrity score and the test value range compliance score; reading the duration sufficiency weight coefficient, the test value range compliance weight coefficient and the test step integrity weight coefficient from the configuration file; and using the weighted average method to calculate the comprehensive test efficiency index.

[0014] Furthermore, for work orders with insufficient work efficiency, the deviation of test time and the standardization of key test points are analyzed to generate corresponding optimization suggestions, including: for work orders whose comprehensive test efficiency index determines that the work efficiency is low, three indicators of test time deviation, key parameter integrity and test value range compliance are extracted; dynamic warning thresholds are read from configuration files; a decision tree structure is constructed, in which the root node is the work efficiency judgment result, the second-level nodes are the threshold judgments of the three indicators, and the leaf nodes are the optimization directions; the three indicators are analyzed using a decision tree; if the test time deviation exceeds the dynamic warning threshold, the time optimization branch is entered; if the key parameter integrity is lower than the dynamic warning threshold, the standard collection branch is entered; if the test value range compliance is lower than the dynamic warning threshold, the accuracy optimization branch is entered; according to the decision tree analysis results, corresponding suggestion content is selected from the optimization suggestion library to generate an optimization suggestion report.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0016] The present invention discloses an intelligent analysis method for the work efficiency of work orders for equipment in buildings. The method establishes a standard test step library, matches the work order test steps with the standard library in a standardized manner, and identifies and structures the key test parameters therefrom. Whether the parameters of each test point are qualified is determined by preset standard conditions. The test point coverage, parameter completeness, value range compliance and duration sufficiency are calculated, and the weight coefficient is set to obtain the overall test step integrity score. Combined with the standard specification score, a quantitative index of test standardization is formed. The test time information is extracted to calculate the actual duration, and the comprehensive efficiency index is obtained by weighting with the step integrity score. The efficiency index is compared with the threshold to determine the level of work efficiency, and the reasons for the inefficient work order are analyzed and optimization suggestions are generated. The present invention solves the problem of standardization and efficiency evaluation of insulation testing of building equipment, and realizes accurate quantitative evaluation of test quality and efficiency through standardized test steps, structured key parameters, multi-dimensional evaluation indicators and intelligent analysis and optimization, providing an effective method for improving test standardization and work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of a method for intelligently analyzing the work efficiency of work orders of equipment in a building. DETAILED DESCRIPTION

[0018] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0019] like Figure 1In this embodiment, a method for intelligent analysis of work order efficiency of equipment in a building may specifically include:

[0020] S101. According to the electrical industry standards and specifications, and targeting the characteristics of equipment in buildings, a standard test step library for insulation resistance testing and insulation withstand voltage testing is established, and the test steps described in the work order are matched with the standard library to standardize the test steps. Equipment in buildings includes air conditioning equipment, elevator systems, fire protection systems, and lighting controls.

[0021] Receive test request information carrying a unique identification number of the electrical equipment, wherein the test request information is issued when the electrical equipment is connected to the test system; obtain standard test items and parameters corresponding to the electrical equipment from a pre-established standard library according to the test request information; perform natural language processing on the work order description in the test request information, extract key information and generate structured data; use the TF-IDF algorithm to vectorize the structured data and the standard test items, and calculate the similarity between the vectors; if the similarity is greater than a preset threshold, determine the standard test steps corresponding to the similarity as the basis for normalization; according to the standard test steps, check and adjust the test parameters in the work order, and obtain specific test parameter values ​​through fuzzy reasoning; associate the normalized test steps with the electrical equipment information and the test results and store them in a graph database, wherein the graph database includes device nodes, test step nodes and their relationships; obtain multi-dimensional data from the graph database, and discover the equipment test rules through a graph traversal algorithm; update the standard test steps in the standard library according to the equipment test rules.

[0022] Specifically, we build a standard library for electrical equipment testing, classify air-conditioning equipment, elevator systems, fire protection systems and lighting control according to the electrical industry specifications, formulate standard test items and parameters for insulation resistance and insulation withstand voltage according to the characteristics of each type of equipment, including specific indicators such as the cooling capacity of air-conditioning equipment, the load capacity of elevator systems, the induction sensitivity of fire protection systems, and the brightness range of lighting control. We use decision trees to organize test items in a multi-level structure, and use keyword indexing to achieve fast retrieval. We perform natural language processing on the test steps described in the work order, extract key information and generate structured data, use TF-IDF vectorization to vectorize the work order description and standard library items, calculate the similarity between vectors, select the standard test steps with the highest similarity as the basis for normalization, store the matching results in the Neo4j graph database, and establish the association between the work order and the standard test steps. According to the matching standard test steps, the test parameters in the work order are verified and adjusted, and the input variables (equipment usage time, ambient temperature), output variables (test voltage value), and fuzzy rule sets (IF the usage time is long AND the ambient temperature is high THEN the test voltage drops slightly) are defined. The specific test parameter values ​​are obtained through fuzzy reasoning, and the test parameters are dynamically optimized according to the operating environment and usage conditions of different building equipment to generate standardized test steps that meet the actual situation. The standardized test steps are associated with equipment information and test results and stored in the Neo4j graph database. Equipment nodes, test step nodes and their relationships are constructed to achieve multi-dimensional data query and analysis. The equipment test rules are discovered through the traversal algorithm of the graph database, and the standard test step library is continuously optimized to provide data support for subsequent test step matching and parameter optimization. When constructing the electrical equipment test standard library, the test items for air-conditioning equipment include insulation resistance of not less than 20MΩ and insulation withstand voltage of 2000V for 1 minute without breakdown under rated cooling capacity of 10000BTU / h, insulation resistance of not less than 50MΩ and insulation withstand voltage of 3000V for 2 minutes without breakdown under elevator system load of 1000kg, insulation resistance of not less than 100MΩ and insulation withstand voltage of 1500V for 30 seconds without breakdown under fire protection system induction sensitivity of 0.1dB / m, insulation resistance of not less than 10MΩ and insulation withstand voltage of 1000V for 30 seconds without breakdown under lighting control brightness range of 100-1000l ux. These test items are organized by decision tree, with the root node as equipment type, the second layer as test type (insulation resistance or insulation withstand voltage), and leaf nodes as specific parameter values. When performing natural language processing on work orders, we use the jieba word segmenter to extract keywords, such as "air conditioning", "insulation resistance", "20MΩ", etc., and use the TF-IDF algorithm to calculate the weights of these keywords to form a vector representing the work order. The same method is used to process the test items in the standard library, calculate the cosine similarity between the work order vector and the standard test item vector, and select the one with the highest similarity as the matching result.The matching results are stored in the Neo4j graph database, and the work order nodes and standard test step nodes are created to establish a "matching" relationship. When fuzzy control is performed on the test parameters, the fuzzy set of the input variable "equipment usage time" is defined as {new, newer, older, old}, the fuzzy set of "ambient temperature" is defined as {low, medium, high}, and the fuzzy set of the output variable "test voltage adjustment coefficient" is defined as {slightly decreased, unchanged, slightly increased}. Fuzzy rules such as "IF usage time is old AND ambient temperature is high THEN test voltage is slightly decreased" are set up, and the fuzzification is performed through the center of gravity method to obtain the specific test voltage adjustment coefficient. The standardized test steps are stored in the Neo4j graph database, and the device nodes, test step nodes, and test result nodes are created to establish "execution" and "generation" relationships. Through the depth-first search algorithm of the graph database, the test parameter change rules of the same type of equipment in different environments are analyzed for continuous optimization of the standard test step library.

[0023] S102. Identify the key parameter information of the test voltage amplitude upper limit, test voltage amplitude lower limit, test voltage frequency upper and lower limits, test duration upper and lower limits, test current upper and lower limits, insulation resistance lower limit, ambient temperature upper and lower limits, ambient humidity upper and lower limits, and minimum safety isolation distance of each test point from the standardized test step description to form structured test point key parameters.

[0024] Obtain standardized test step descriptions, and use regular expressions to extract numeric parameters from the standardized test step descriptions; match the extracted numeric parameters through pre-defined JSON format parameter templates to determine the category to which the numeric parameters belong and mark the upper and lower limit attributes; perform data cleaning on the marked numeric parameters to remove outliers and erroneous data to obtain cleaned parameter data; construct a structured test point key parameter table based on the cleaned parameter data, and the test point key parameter table uses a nested dictionary structure to store various parameters and their upper and lower limit values; use a pre-built decision tree to classify and verify the structured test point key parameter table to determine whether the parameters are within a preset reasonable range; if the parameters exceed the preset threshold, mark the parameters as abnormal and record them in the abnormal parameter list.

[0025] Specifically, regular expressions are used to extract numerical parameters from the standardized test step descriptions, and parameters such as test voltage amplitude, test voltage frequency, test duration, test current, insulation resistance, ambient temperature, ambient humidity, and safe isolation distance are identified. The extracted values ​​are matched with parameter templates defined in JSON format, such as {"test voltage amplitude

[0026] ":{"regex":"(\d+)V","attr i butes":["Upper limit","Lower limit"]},"Safety isolation distance":{"regex":"(\d+)mm","attr i butes":["Minimum value"]}}, determine its category and mark the upper and lower limit attributes. Perform data cleaning on the extracted parameter values ​​to remove outliers and erroneous data. Select the filling method according to the parameter type. Numerical parameters are filled with the median, categorical parameters are filled with the mode, and time parameters are filled with the average. The standard deviation method is used to identify outliers and correct them to ensure the accuracy and completeness of the data. Construct a structured test point key parameter table, and use a nested dictionary structure to store various parameters and their upper and lower limits, such as {"Device type":{"Test type":{"Parameter name":{"Upper limit":value,"Lower limit":value}}}}. For different devices Establish parameter templates based on device type and test type, and fill the extracted parameters into corresponding fields through template matching to form a standardized test point parameter structure. Use the ID3 algorithm to build a decision tree with device type as the root node, test type as the second-level node, and parameter characteristics as leaf nodes to achieve automatic classification and verification of parameters. Use the constructed decision tree to classify and verify structured parameters, and execute decision rules based on device type, test type, and parameter characteristics to determine whether the parameters are within a reasonable range. Parameters that exceed the preset threshold are marked as abnormal and recorded in the abnormal parameter list. Generate an abnormal report, which contains detailed information on the abnormal parameters, the device type to which they belong, and the test type, for subsequent Provide a basis for parameter adjustment and optimization. In the standardized test step description, regular expressions are used to extract numerical parameters. For example, for the test voltage amplitude, use "(\d+)V" to match "500V-1000V" to extract the upper limit of 1000V and the lower limit of 500V. For the safety isolation distance, use "(\d+)mm" to match "minimum 100mm" to extract the minimum value of 100mm. For the ambient temperature, use "(\d+)℃" to match "25℃-40℃" to extract the upper limit of 40℃ and the lower limit of 25℃. The extracted data is cleaned. For the test voltage amplitude, it is assumed that multiple groups of data are extracted: 500V, 750V, 1000V, 1500V , where 1500V is obviously too high. By calculating the mean and standard deviation, setting the threshold as the mean plus or minus two times the standard deviation, 1500V is identified as an outlier and removed. For missing test current data, numerical parameters such as the upper limit of the test current are filled with the median 5mA, categorical parameters such as the test type are filled with the mode "insulation withstand voltage", and time parameters such as the test duration are filled with the average value 60 seconds. Construct a structured parameter table and use nested dictionary storage, such as {"air conditioning equipment":{"insulation withstand voltage":{"test voltage amplitude":{"upper limit":1000,"lower limit":500},"safety isolation distance":{"minimum value":100}}}}.The decision tree is constructed using the ID3 algorithm, with the air conditioning equipment as the root node, the insulation withstand voltage as the second-level node, and the test voltage amplitude as the leaf node. The decision tree classifies and verifies the parameters, such as determining whether the test voltage amplitude is within the range of 500V-1000V. If it is out of range, it is marked as abnormal and added to the abnormal parameter list. Finally, an abnormal report is generated, including detailed information on the abnormal parameters, such as "In the insulation withstand voltage test of the air conditioning equipment, the test voltage amplitude of 1200V exceeds the upper limit of 1000V", providing a basis for subsequent parameter adjustments.

[0027] S103. For each structured test point data, evaluate whether the key parameters of the test point meet the test specifications through the pre-established test specification condition judgment logic. If the key parameters of each test point meet the test specifications, the test quality of the test point is qualified.

[0028] Get the pre-established test specification conditional judgment logic, which is used to construct a binary tree structure conditional judgment tree for different equipment types and test types; each non-leaf node of the conditional judgment tree represents a parameter judgment, the left subtree indicates that the parameter meets the specification, the right subtree indicates that it does not meet the specification, and the leaf node represents the final judgment result. Read the structured test point data, and use the JSON parser to extract the key parameter values ​​of each test point from the structured test point data; the key parameter values ​​include the upper and lower limits of the test voltage amplitude, the upper and lower limits of the test voltage frequency, the upper and lower limits of the test duration, the upper and lower limits of the test current, the lower limit of the insulation resistance, the upper and lower limits of the ambient temperature, the upper and lower limits of the ambient humidity, and the minimum value of the safe isolation distance. Substitute the key parameter values ​​into the conditional judgment tree, starting from the root node, and compare the current parameter value with the node threshold; if the parameter value falls within the preset threshold range, it is judged to meet the specification, otherwise it is marked as non-compliant, and the specific non-compliant items and values ​​are recorded. Summarize the evaluation results of all key parameters of a single test point; if all key parameters meet the test specifications, the test quality of the test point is judged to be qualified, otherwise it is judged to be unqualified. Generate a test quality assessment report that includes the qualified status, non-compliant item list, and improvement suggestions.

[0029] Specifically, the pre-established test specification condition judgment logic is obtained from the database, and judgment rules are formulated based on industry standards and expert experience. A binary tree structured condition judgment tree is constructed for different equipment types and test types. Each non-leaf node represents a parameter judgment. The left subtree indicates that the parameter meets the specification, the right subtree indicates that it does not meet the specification, and the leaf node represents the final judgment result. The threshold range and judgment rules of the key parameters are set to form a complete test specification evaluation system. The structured test point data is read, and the key parameter values ​​of each test point are extracted from it using the JSON parser, including the upper and lower limits of the test voltage amplitude, the upper and lower limits of the test voltage frequency, the upper and lower limits of the test duration, the upper and lower limits of the test current, the lower limit of the insulation resistance, the upper and lower limits of the ambient temperature, the upper and lower limits of the ambient humidity, and the minimum value of the safe isolation distance. The extracted key parameter values ​​are substituted into the condition judgment tree, starting from the root node, and the current parameter value is compared with the node threshold. The left subtree or the right subtree is selected according to the comparison result until the leaf node is reached to obtain the judgment result. Each parameter is evaluated one by one to see if it meets the test specification. If the parameter value falls within the preset threshold range, it is judged to meet the specification, otherwise it is marked as non-compliant, and the specific non-compliant items and values ​​are recorded. According to the specific situation of the non-compliant items, select the corresponding improvement suggestions from the preset suggestion library. Summarize the evaluation results of all key parameters of a single test point. If all key parameters meet the test specifications, the test quality of the test point is judged to be qualified, otherwise it is judged to be unqualified. Generate a test quality evaluation report, including the qualified status, the list of non-compliant items and improvement suggestions. Store the evaluation results in the database for subsequent statistical analysis and test specification optimization. For the insulation withstand voltage test of air-conditioning equipment, obtain the pre-established test specification condition judgment logic from the database and construct a condition judgment tree with a binary tree structure. The root node of the tree is the test voltage amplitude judgment. The left subtree indicates compliance with the specification (500V-1000V), and the right subtree indicates non-compliance with the specification. The second-level node is the test duration judgment. The left subtree indicates compliance with the specification (50s-70s), and the right subtree indicates non-compliance with the specification. The leaf node represents the final judgment result. Use the JSON parser to extract key parameter values ​​from the structured test point data, such as {"test voltage amplitude":800,"test duration":60,"ambient temperature":28,"ambient humidity":65,"

[0030] Safety isolation distance": 150}. Substitute the extracted parameter values ​​into the conditional judgment tree and start the evaluation from the root node. The test voltage amplitude of 800V is within the range of 500V-1000V, which meets the specification and enters the left subtree; the test duration of 60s is within the range of 50s-70s, which meets the specification and enters the left subtree, and finally reaches the leaf node indicating "qualified". Similarly, evaluate other parameters: the ambient temperature of 28℃ is within the range of 25℃-35℃, the ambient humidity of 65% is within the range of 45%-75%, and the safety isolation distance of 150mm is greater than the minimum value of 100mm, all of which meet the specification. Summarize the evaluation results, all parameters meet the specification, and the quality of the test point is determined to be qualified. Generate a test quality evaluation report, including the "qualified" status and specific parameter values. If a parameter does not meet the specification, such as the test voltage amplitude of 1200V, which exceeds the upper limit of 1000V, select the corresponding suggestion from the preset suggestion library: "Please adjust the test voltage to the range of 500V-1000V". Store the evaluation results in the database for subsequent optimization of test specifications.

[0031] If the upper and lower limits of the test voltage amplitude do not meet the specifications, or the upper and lower limits of the test voltage frequency do not meet the specifications, or the upper and lower limits of the test duration do not meet the specifications, or the upper and lower limits of the test current do not meet the specifications, or the lower limit of the insulation resistance does not meet the specifications, or the upper and lower limits of the ambient temperature do not meet the specifications, or the upper and lower limits of the ambient humidity do not meet the specifications, or the minimum value of the safety isolation distance does not meet the specifications, the test quality of the test point is judged to be unqualified.

[0032] Obtain the preset test specification threshold from the database, and the test specification threshold is used to construct a parameter specification judgment matrix; generate a three-dimensional array according to the parameter specification judgment matrix, and the three-dimensional array is used to store structured test point data; traverse the three-dimensional array to extract the actual values ​​of the key parameters of the test points, and compare the actual values ​​of the key parameters with the thresholds in the parameter specification judgment matrix; use the bit operation method to encode the comparison result, and the encoding result is a binary bit; compare the binary bit with the preset mask through a bitwise AND operation, if the comparison result is equal to the preset mask, then the test quality of the test point is determined to be qualified; if the comparison result is not equal to the preset mask, then the test quality of the test point is determined to be unqualified; obtain the index of the unqualified parameter, and the index is obtained by performing a bitwise AND operation with the binary mask; determine the name, actual value, upper and lower limit values ​​of the specification and the cause of the unqualified parameter according to the index; record the detailed information of the unqualified parameter in the test quality assessment report.

[0033] Specifically, the preset test specification threshold is read from the database, and the parameter specification judgment matrix is ​​constructed. The two-dimensional array is used for storage, and the rows represent different parameters (test voltage amplitude, test voltage frequency, test duration, test current, insulation resistance, ambient temperature, ambient humidity and safe isolation distance), and the columns represent upper and lower limits or minimum values. Each element in the matrix contains the parameter name, unit, upper limit and lower limit. A three-dimensional array is used to store structured test point data, the first dimension represents the test point number, the second dimension represents the parameter type, and the third dimension stores the actual value and the specification value. The array is traversed to extract the actual value of the key parameter of each test point, and the actual value is compared with the threshold in the parameter specification judgment matrix. In the comparison process, the judgment result is directly encoded into a binary bit using the bit operation method. The test voltage amplitude, test voltage frequency, test duration, test current, insulation resistance, ambient temperature, ambient humidity and safe isolation distance correspond to 8 binary bits from low to high in turn. The code for compliance with the specification is 1, and the code for non-compliance with the specification is 0. By bitwise AND operation, the encoded judgment result is compared with the preset all-1 mask (binary 11111111, hexadecimal 0xFF). If the result is equal to the all-1 mask, the test quality of the test point is judged to be qualified, otherwise it is judged to be unqualified. For unqualified test points, use bitwise operation to obtain the index of the unqualified parameter. By performing bitwise AND operation with binary masks 10000000, 01000000, etc., determine which specific parameters are unqualified. Record the name, actual value, upper and lower limit values ​​of the specification, and the reason for failure (exceeding the upper limit or falling below the lower limit) of the unqualified parameter. Record the judgment results and detailed information of the unqualified parameters in the test quality assessment report for subsequent quality improvement and data analysis. For the insulation withstand voltage test of air-conditioning equipment, read the preset test specification threshold from the database to construct a parameter specification judgment matrix. The matrix stores the standard values ​​of test voltage amplitude 500V-1000V, test voltage frequency 45Hz-65Hz, test duration 50s-70s, test current 0-5mA, insulation resistance ≥20MΩ, ambient temperature 25℃-35℃, ambient humidity 45%-75% and safety isolation distance ≥100mm. A three-dimensional array is used to store test point data. For example, [1][0][0]=800 indicates that the actual value of the test voltage amplitude of the first test point is 800V. The array is traversed to compare the actual value with the standard value. For example, if the test voltage amplitude 800V is within the range of 500V-1000V, it is encoded as 1; if the test duration 75s exceeds the range of 50s-70s, it is encoded as 0. The eight parameters are encoded in sequence to obtain binary 10111111 (decimal 191). The bitwise AND operation of 191 and the all-1 mask 11111111 (255) is not equal to 255, and it is judged as unqualified. By performing the bitwise AND operation of 191 and 10000000, we get 0, and by performing the bitwise AND operation of 191 and 01000000, we get 64, which determines that the duration of the second bit test fails.The unqualified information recorded includes parameter name "test duration", actual value 75s, specification range 50s-70s, and unqualified reason "exceeded upper limit". The judgment result "unqualified" and detailed information are recorded in the test quality assessment report for subsequent analysis and improvement.

[0034] S104. Calculate the test point coverage by comparing the collected test point data with qualified structured test point data. Determine the completeness of the collection of key test parameters for each test point through key parameter integrity assessment. Determine whether the key parameters of each test point are within the specified qualified range using the test value range conformity. Determine whether the actual test duration of each test point meets the lower limit of the test duration based on the duration adequacy assessment standard.

[0035] Obtain qualified test point data and collected test point data, build a hash table based on the qualified test point data and collected test point data, the key of the hash table is the test point identifier, and the value of the hash table is the test point data; perform an intersection operation on the key set of the hash table to obtain the number of common test points; calculate the test point coverage based on the number of common test points and the total number of qualified test points. Read the key parameter list, which includes test voltage, test current, and insulation resistance; traverse each test point in the collected data and count the number of key parameters actually collected; calculate the key parameter completeness score based on the number of key parameters actually collected and the total number of preset key parameters. According to the equipment type and test type, query the predefined test value qualified range standard from the test specification database; traverse and compare the key parameters of each test point to determine whether the parameter value falls within the specified range; calculate the test value range compliance score based on the number of parameters that meet the specified range and the total number of key parameters. Obtain the preset lower limit of test duration from the test specification database; extract the actual test duration data of each test point; if the actual test duration is greater than or equal to the lower limit of test duration, determine that the duration adequacy of the test point meets the requirements; calculate the duration adequacy score based on the number of test points that meet the requirements and the total number of test points. According to the weight of each indicator, the test point coverage, key parameter completeness score, test value range compliance score and duration adequacy score are weighted averaged to obtain a comprehensive score.

[0036] Specifically, obtain the qualified structured test point data and the collected test point data from the database, and construct two hash tables, with the key being the test point identifier and the value being the test point data. Perform an intersection operation using the key set of the hash table to obtain the number of common test points, which is divided by the total number of qualified test points to calculate the test point coverage percentage. Store the coverage result in the evaluation result data structure. Read the key parameter list from the configuration file, including parameters such as test voltage, test current, and insulation resistance. Traverse each test point in the collected data, count the number of key parameters actually collected, divide it by the preset total number of key parameters, and calculate the key parameter completeness score. Add the completeness score to the evaluation result data structure. According to the device type and test type, query the predefined test value qualified range standard from the test specification database, such as the voltage range of 500V-1000V and the current range of 0-5mA. Traverse and compare the key parameters of each test point to determine whether the parameter value falls within the specified range, record the number of parameters that meet the range, and divide it by the total number of key parameters to obtain the test value range compliance score. Store the compliance score in the evaluation result data structure. Get the preset lower limit of test duration from the test specification database, which is determined according to the equipment type and test type, such as the lower limit of insulation withstand voltage test is 60 seconds. Extract the actual test duration data of each test point and compare it with the lower limit value. If the actual duration is greater than or equal to the lower limit value, the duration adequacy of the test point is determined to meet the requirements. Accumulate the number of test points that meet the requirements and divide it by the total number of test points to obtain the duration adequacy score. Add the adequacy score to the evaluation result data structure. Read the weights of each indicator from the configuration file, such as coverage 30%, completeness 25%, compliance 25%, and adequacy 20%. Perform weighted average calculation on the four scores in the evaluation result data structure to obtain a comprehensive score. The comprehensive score and each score form the final evaluation report and store it in the database for subsequent analysis and improvement. For the insulation withstand voltage test of air-conditioning equipment, obtain 100 structured test point data with qualified test quality and 90 collected test point data from the database. Construct two hash tables, with the key as the test point ID and the value as the test data. Through the intersection operation of the hash table key set, 85 common test points are obtained, and the calculation coverage is 85%. The key parameter list read from the configuration file includes test voltage, test current, insulation resistance, and test duration. After traversing 90 collected data points, it was found that 85 points contained all 4 parameters, 5 points lacked test duration, and the calculation completeness score was 94.4%. The test specification database was queried to obtain the voltage range of 500V-1000V, the current range of 0-5mA, and the insulation resistance ≥20MΩ. The parameters of the 90 test points were compared and it was found that all parameters of 82 points were within the range, and the insulation resistance of 8 points did not meet the standard. The calculation compliance score was 91.1%. The lower limit of the test duration was obtained as 60 seconds. After comparing 85 test points with duration data, 80 met the requirements, and the calculation adequacy score was 94.1%.The read weight configuration includes coverage 30%, completeness 25%, conformity 25%, and sufficiency 20%, and the calculated comprehensive score is 90.9%. The scores of each item and the comprehensive score are combined into an evaluation report and stored in the database for subsequent optimization of the test process and equipment parameter adjustment.

[0037] S105. After obtaining the evaluation results of the test point coverage, key parameter completeness, test value range compliance, and test time adequacy indicators, set the weight coefficient of each indicator, calculate the overall test step integrity score of the insulation resistance test and insulation withstand voltage test, and obtain quantitative indicators to measure the test standardization.

[0038] The evaluation results of the four indicators of test point coverage, key parameter completeness, test value range compliance, and test time adequacy are obtained from the database, and the evaluation results are used to construct the indicator data structure; the scoring grade classification standard in the configuration file is read according to the indicator data structure, and the scoring grade classification standard includes excellent, good, qualified, and unqualified score ranges; the preset weight coefficient configuration is obtained, and the weight coefficient configuration is used to set the weight values ​​of the four indicators of insulation resistance test and insulation withstand voltage test; the weighted average method is used to calculate the overall test step integrity score of insulation resistance test and insulation withstand voltage test, and the score is obtained by weighting the four indicators of test point coverage, key parameter completeness, test value range compliance, and test time adequacy; if the scoring result meets the preset scoring grade classification standard, the test standardization level is judged; a quantitative indicator report is generated according to the test standardization level, and the quantitative indicator report includes the total score, the score of each indicator, the rating result and the weight of each indicator.

[0039] Specifically, the evaluation results of the four indicators of test point coverage, key parameter completeness, test value range compliance, and test duration adequacy are read from the database, and an indicator data structure is constructed to store the values ​​of each indicator. At the same time, the scoring level division criteria are read from the configuration file, including the score intervals of excellent, good, qualified, and unqualified. Obtain the preset weight coefficient configuration, and set the weight values ​​of the four indicators for the insulation resistance test and the insulation withstand voltage test, such as test point coverage 0.3, key parameter completeness 0.25, test value range compliance 0.25, and test duration adequacy 0.2. At the same time, set the comprehensive scoring weights of the insulation resistance test and the insulation withstand voltage test, such as 0.5 each. Use the weighted average algorithm to calculate the overall test step integrity score of the insulation resistance test and the insulation withstand voltage test. The calculation formula is score = test point coverage 0.3 + key parameter completeness 0.25 + test value range compliance 0.25 + test duration adequacy 0.2. Then, the comprehensive scores of the two tests are calculated according to the set weights: comprehensive score = insulation resistance test score 0.5 + insulation withstand voltage test score 0.5. According to the calculated score results and the preset scoring grade classification standards, the test standardization level is judged. Generate the final quantitative indicator report, including the total score, the score of each indicator, the rating results and the weight of each indicator. Use data visualization tools to generate radar charts and bar charts of the scoring results to intuitively display the scores of each indicator and the comprehensive score. Save the quantitative indicator report and visualization charts to the database for subsequent test quality trend analysis and improvement. For the insulation test of a certain model of air conditioner, read the evaluation results of four indicators from the database: test point coverage 92%, key parameter completeness 95%, test value range compliance 88%, test time sufficiency 90%. At the same time, read the scoring grade standards from the configuration file: 90 points or more are excellent, 80-90 points are good, 70-80 points are qualified, and below 70 points are unqualified. Get the preset weight configuration: In the insulation resistance test, the coverage is 0.3, the completeness is 0.25, the compliance is 0.25, and the sufficiency is 0.2; the insulation withstand voltage test has the same weight. The comprehensive score weights of the two tests are 0.5 each. Use the weighted average algorithm to calculate the insulation resistance test score: 0.920.3+0.950.25+0.880.25+0.900.2=0.9125, with a score of 91.25. Similarly, the insulation withstand voltage test score is 89.75. The comprehensive score is 91.250.5+89.750.5=90.5. According to the scoring level standard, the test standardization is judged to be "excellent". Generate a quantitative indicator report, including a total score of 90.5, scores of each indicator, "excellent" rating and weight information. Use the matplotlib library to generate a radar chart to show the scores of the four indicators; use the seaborn library to generate a bar chart to compare the scores of the two tests.Save reports and charts to the "Test Quality Assessment" collection in the MongoDB database for subsequent quality trend analysis and equipment parameter optimization.

[0040] For the quantitative indicators to measure the standardization of tests, the standard specifications of insulation resistance test and insulation withstand voltage test are quantitatively evaluated to obtain the standard specification scores of insulation resistance test and insulation withstand voltage test. The overall test step integrity score is combined with the standard specification scores of insulation resistance test and insulation withstand voltage test to form the quantitative indicators to measure the standardization of tests.

[0041] Obtain standard specification data for insulation resistance test and insulation withstand voltage test, and extract key parameter values ​​from the standard specification data; key parameter values ​​include test voltage range, test time requirements, minimum insulation resistance, and maximum leakage current. Construct a standard specification evaluation matrix based on key parameter values; the standard specification evaluation matrix adopts a two-dimensional array structure, in which rows represent different parameters and columns represent standard values ​​and allowable deviations; set the membership function type and parameter value for each key parameter value. Implement fuzzy evaluation on actual test data; establish an evaluation factor set, including test voltage, test time, insulation resistance, leakage current and other factors; determine the evaluation grade set, including excellent, good, qualified, and unqualified grades; calculate the membership of each factor to each evaluation grade based on the membership function type and parameter value to form a fuzzy relationship matrix; obtain the standard specification score for insulation resistance test and insulation withstand voltage test through fuzzy synthesis operation. Obtain the overall test step integrity score; use the weighted average method to calculate the comprehensive score and generate a test standardization quantitative indicator report; the test standardization quantitative indicator report includes the total score, the score of each indicator, the rating results and the weight of each indicator; use data visualization tools to generate a radar chart of the scoring results; save the test standardization quantitative indicator report and radar chart to the database for subsequent test quality trend analysis.

[0042] Specifically, the standard specifications of insulation resistance test and insulation withstand voltage test are obtained from the database, and key parameters including test voltage range, test time requirements, minimum insulation resistance, maximum leakage current, etc. are extracted to construct a standard specification evaluation matrix. The matrix adopts a two-dimensional array structure, with rows representing different parameters and columns representing standard values ​​and allowable deviations. At the same time, the membership function parameters required for fuzzy evaluation are introduced, and the membership function type and parameter value are set for each key parameter. The fuzzy evaluation process is implemented for the actual test data. First, an evaluation factor set is established, including test voltage, test time, insulation resistance, leakage current and other factors. Then, an evaluation grade set is determined, such as excellent, good, qualified, and unqualified. According to the preset membership function, the membership of each factor to each evaluation grade is calculated to form a fuzzy relationship matrix. Through fuzzy synthesis operation, the standard specification scores of insulation resistance test and insulation withstand voltage test are obtained. The overall test step integrity score is obtained, and the comprehensive score is calculated in combination with the standard specification score. The standard specification scores of insulation resistance test and insulation withstand voltage test are integrated by weighted average method, and the weights are set to 0.5 respectively. Then use the weighted average method to calculate the final comprehensive score, with the weight distribution being 60% for the completeness of the overall test steps and 40% for the standard specification score. According to the comprehensive scoring results, set the scoring grade division standards, such as 90 points or above for excellent, 80-90 points for good, 70-80 points for qualified, and below 70 points for unqualified. Generate the final test normative quantitative indicator report, including the total score, the score of each indicator, the rating results and the weight of each indicator. Use data visualization tools to generate a radar chart of the scoring results to intuitively display the scores of each indicator. Save the quantitative indicator report and visualization charts to the database for subsequent test quality trend analysis and improvement. For the insulation test of a certain model of air conditioner, obtain the standard specifications from the database: the insulation resistance test voltage is 500V, the minimum insulation resistance value is 20MΩ, and the test time is 60s; the insulation withstand voltage test voltage is 1000V, the maximum leakage current is 1mA, and the test time is 60s. Construct a standard specification evaluation matrix, such as

[0043] [[500,50],[20,5],[60,10]] and [[1000,100],[1,0.2],[60,10]]. The membership function is set, and the Gaussian function is used. The parameter value is determined according to the allowable deviation. The actual test data is: insulation resistance test voltage 480V, insulation resistance value 25MΩ, test time 58s; insulation withstand voltage test voltage 1050V, leakage current 0.8mA, test time 62s. The membership of each factor is calculated by fuzzy evaluation, such as voltage membership [0.8,0.2,0,0], and the insulation resistance test score is 88 points and the insulation withstand voltage test score is 92 points. The overall test step integrity score is 90 points. The comprehensive calculation score is (880.5+920.5)0.4+900.6=90 points. According to the classification criteria, it is judged as "excellent". Use the matplotlib library to generate a radar chart to show the scores in three dimensions: voltage, resistance / leakage current, and time. Save the score report and chart to the "Test Quality Assessment" collection of the MongoDB database for subsequent analysis.

[0044] S106: Extract the test start and end time information from the work order and calculate the actual test duration. Use the test duration sufficiency weight coefficient and the test value range compliance weight coefficient to perform weighted calculation on the actual test duration and the test step integrity score to obtain a comprehensive test efficiency index to quantitatively evaluate the overall efficiency level of the work order test.

[0045] Get the test start and end time information from the work order database; use regular expressions to match the time information to get the standard time format; calculate the actual test time according to the standard time format. According to the actual test time, get the preset standard test time range from the specification database, which corresponds to the device type and test type; determine whether the actual test time falls within the standard test time range to get the time adequacy score. Get the test step integrity score and the test value range compliance score, which are extracted from the previous test result data; read the time adequacy weight coefficient, the test value range compliance weight coefficient, and the test step integrity weight coefficient from the configuration file. Use the weighted average method to calculate the comprehensive test efficiency index, and multiply the time adequacy score, the test value range compliance score, and the test step integrity score with the corresponding weight coefficients and sum them up through the weighted average method; determine the efficiency index evaluation level according to the comprehensive test efficiency index. Generate a test time distribution graph and an efficiency index trend graph, which are based on the comprehensive test efficiency index; adjust the test parameters and test time according to the test time distribution graph and the efficiency index trend graph to obtain an optimized workflow.

[0046] Specifically, extract the test start and end time information from the work order database, use the regular expression "^\d{4}-\d{2}-\d{2}\d{2}:\d{2}:\d{2}$" to match the timestamp in the "YYYY-MM-DDHH:mm:ss" format, convert the matched time string into the standard time format, calculate the difference between the two time points, and get the actual test duration. According to the device type and test type, query the preset standard test duration range from the specification database. Determine whether the actual test duration falls within a reasonable range, calculate the duration adequacy score, and set the scoring rules such as 100 points within the standard range, deduct 1 point for each minute exceeding or less than 1 minute, and the minimum score is 0. Store the calculated duration adequacy score in the work order database for comprehensive calculation in subsequent steps. Obtain the test step integrity score and extract the test value range compliance score from the previous test result data. Read the duration sufficiency weight coefficient and the test value range compliance weight coefficient from the configuration file, such as duration sufficiency weight 0.3, test value range compliance weight 0.3, and test step integrity weight 0.4. Use the weighted average algorithm to multiply the actual test duration sufficiency score, test value range compliance score, and test step integrity score with the corresponding weight coefficients and sum them to obtain the comprehensive test efficiency index. Set the efficiency index evaluation standard, such as 90 points or more for excellent, 80-90 points for good, 70-80 points for qualified, and 70 points or less for unqualified. Save the calculation results and evaluation levels to the work order database. Use the matplotlib library to generate the test duration distribution map and efficiency index trend map to intuitively display the test efficiency. Apply the comprehensive test efficiency index to workflow optimization, such as automatically adjusting test parameters and test time to improve the overall test quality and efficiency. For the insulation test work order of a certain model of air conditioner, extract the test time information from the database: start time "2023-09-1510:30:15", end time "2023-09-1511:05:42". Use the regular expression "^\d{4}-\d{2}-

[0047] \d{2}\d{2}:\d{2}:\d{2}$" matches and extracts the timestamp, and the actual test duration is calculated to be 35 minutes and 27 seconds. The standard test duration range of this model of air conditioner is 30-40 minutes. The actual duration is judged to be within the range, and the duration adequacy score is calculated to be 100 points. At the same time, the previously recorded test step completeness score of 95 points and the test value range compliance score of 92 points are obtained. The weight coefficients read from the configuration file include duration adequacy 0.3, test value range compliance 0.3, and test step completeness 0.4. The weighted average algorithm is used to calculate the comprehensive test efficiency index as 1000.3+920.3+95*0.4=95.8 points. According to the evaluation criteria, 95.8 points belongs to the "excellent" level. Use matp l ot l The ib library generates a bar chart to show the trend of efficiency indicators in the past 10 tests, and it is observed that the efficiency of this test has improved. The calculation results and evaluation levels are saved to the work order database for subsequent workflow optimization, such as automatically adjusting the test parameters of the next batch of air conditioners to maintain a high test efficiency.

[0048] S107. Compare the comprehensive test efficiency index with the preset efficiency threshold. If it is higher than the threshold, the work efficiency is high; if it is lower than the threshold, the work efficiency is low. For work orders with insufficient work efficiency, analyze the deviation of the test time and the standardization of key test points. If the deviation of the test time is large, generate optimization suggestions; if the completeness of key parameters is insufficient, generate optimization suggestions for standardizing the collection of key parameters; if the test value range conformity is low, generate optimization suggestions for the accuracy of the test value.

[0049] Obtain the comprehensive test efficiency index and preset efficiency threshold stored in the database to judge the work efficiency; if the comprehensive test efficiency index is higher than the preset efficiency threshold, the work efficiency is judged to be high, otherwise it is judged to be low; the judgment result is stored in the work order database. For the work order with low work efficiency, extract the three indicators of test time deviation, key parameter integrity and test value range compliance; read the dynamic warning threshold from the configuration file. Construct a decision tree structure, in which the root node is the work efficiency judgment result, the second-level node is the threshold judgment of the three indicators, and the leaf node is the optimization direction. Use the decision tree to analyze the three indicators; if the test time deviation exceeds the dynamic warning threshold, enter the time optimization branch; if the key parameter integrity is lower than the dynamic warning threshold, enter the specification collection branch; if the test value range compliance is lower than the dynamic warning threshold, enter the accuracy optimization branch. According to the decision tree analysis results, select the corresponding suggestion content from the optimization suggestion library; among them, the optimization suggestion library adopts a key-value pair structure, the key is the optimization direction, and the value is a list of specific suggestion contents. Generate an optimization suggestion report; store the optimization suggestion report in the work order database and send it to relevant personnel through the message push mechanism.

[0050] Specifically, the comprehensive test efficiency index and the preset efficiency threshold are obtained from the database, and the work efficiency is judged by the comparison algorithm. If the comprehensive test efficiency index is higher than the preset efficiency threshold, the work efficiency is judged to be high, otherwise it is judged to be low, and the judgment result is stored in the work order database. For the work orders with low work efficiency, three indicators, namely, test time deviation, key parameter integrity and test value range compliance, are extracted, and the dynamic warning threshold is read from the configuration file. The preliminary structure of the decision tree is constructed, the root node is the work efficiency judgment, the second-level node is the threshold judgment of the three indicators, and the leaf node is the optimization direction. The three indicators are analyzed using the constructed decision tree, starting from the root node, and whether each indicator exceeds the warning threshold is judged in turn. If the test time deviation exceeds the threshold, the time optimization branch is entered; if the key parameter integrity is lower than the threshold, the standard collection branch is entered; if the test value range compliance is lower than the threshold, the accuracy optimization branch is entered. The decision path and the corresponding leaf node results are recorded. According to the decision tree analysis results, the corresponding suggestion content is selected from the optimization suggestion library. The optimization suggestion library adopts a key-value pair structure, the key is the optimization direction, and the value is a list of specific suggestion contents. For example, the duration optimization suggestion is "adjust the preheating time of the test equipment", the standard collection suggestion is "check the calibration status of the data collection equipment", and the accuracy optimization suggestion is "optimize the temperature and humidity control of the test environment". Use natural language processing tools to optimize the language of the selected suggestions and generate a targeted optimization suggestion report. The report content is stored in the work order database and sent to relevant personnel through the message push mechanism. At the same time, a feedback mechanism for the execution effect of optimization suggestions is established to collect feedback data for continuous improvement of the decision tree structure and optimization suggestion library content. For the insulation test work order of a certain model of air conditioner, the comprehensive test efficiency index of 85 points is obtained from the database, and the preset efficiency threshold is 90 points. The comparison algorithm is used to judge that the work efficiency is low, and the results are stored in the work order database. Then, the test duration deviation of 15%, the key parameter completeness of 88%, and the test value range compliance of 92% are extracted, and the dynamic warning threshold is read from the configuration file: duration deviation of 20%, completeness of 90%, and compliance of 85%. Construct a decision tree, the root node is the work efficiency judgment, the second-level node is the three-index threshold judgment, and the leaf node is the optimization direction. Starting from the root node, we found that the completeness of key parameters was 88%, which was lower than the threshold of 90%, and entered the standard collection branch. The corresponding suggestion "Check the calibration status of data acquisition equipment to ensure that all key parameters are correctly recorded" was obtained from the optimization suggestion library. The natural language processing tool NLTK was used to optimize the language expression and generate a suggestion report: "It is recommended to check the calibration status of data acquisition equipment immediately, focusing on parameter items that have not been fully recorded. Please ensure that all key parameter collection devices are in normal working condition before the next test to improve data integrity." The report is stored in the work order database and pushed to the quality control department through the enterprise WeChat API. At the same time, a feedback table is created in the database to record the effect data after the execution of the suggestion, which is used for subsequent optimization of the decision tree structure and updating of the suggestion library content.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. 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 or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent analysis of work efficiency of work orders for equipment in a building, characterized in that: The method comprises the following steps: According to the electrical industry standards and specifications, and targeting the characteristics of the equipment in the building, a standard test step library for insulation resistance testing and insulation withstand voltage testing is established, and the test steps described in the work order are matched with the standard library to standardize the test steps; Identify the key parameter information of each test point from the standardized test step description to form structured test point key parameters; For each structured test point data, the pre-established test specification condition judgment logic is used to evaluate whether the key parameters of the test point meet the test specifications; Based on the structured test point data with qualified test quality, calculate the evaluation results of test point coverage, key parameter completeness, test value range compliance and test time adequacy indicators; Set the weight coefficient of each indicator, calculate the overall test step integrity score of the insulation resistance test and insulation withstand voltage test, and obtain the quantitative indicator to measure the standardization of the test; Extract the test start and end time information from the work order to calculate the actual test duration; use the test duration sufficiency weight coefficient and the test value range compliance weight coefficient to perform weighted calculation on the actual test duration and the test step integrity score to obtain a comprehensive test efficiency index to quantitatively evaluate the overall efficiency level of the work order test; Compare the comprehensive test efficiency index with the preset efficiency threshold, analyze the test time deviation and the standardization of key test points for insufficient work order efficiency, and generate corresponding optimization suggestions.

2. The method according to claim 1, characterized in that The method of establishing a standard test step library for insulation resistance test and insulation withstand voltage test includes: receiving test request information carrying a unique identification number of the electrical equipment; obtaining standard test items and parameters corresponding to the electrical equipment from a pre-established standard library according to the test request information; performing natural language processing on the work order description in the test request information, extracting key information and generating structured data; performing vectorization processing on the structured data and the standard test items, and calculating the similarity between the vectors; if the similarity is greater than a preset threshold, determining the standard test steps corresponding to the similarity as the basis for normalized processing.

3. The method according to claim 1, characterized in that The method for identifying key parameter information of each test point from the standardized test step description includes: obtaining the standardized test step description, and extracting numerical parameters from the standardized test step description by using regular expressions; matching the extracted numerical parameters by using a predefined JSON format parameter template, determining the category to which the numerical parameters belong, and marking the upper and lower limit attributes; performing data cleaning on the marked numerical parameters, removing abnormal values ​​and erroneous data, and obtaining cleaned parameter data; and constructing a structured test point key parameter table according to the cleaned parameter data, wherein the test point key parameter table uses a nested dictionary structure to store various parameters and their upper and lower limit values.

4. The method according to claim 1, characterized in that: The method of evaluating whether the key parameters of the test point meet the test specifications through the pre-established test specification condition judgment logic includes: obtaining the pre-established test specification condition judgment logic, the condition judgment logic is used to construct a binary tree structure condition judgment tree for different device types and test types; reading structured test point data, and using a JSON parser to extract the key parameter value of each test point from the structured test point data; substituting the key parameter value into the condition judgment tree, starting from the root node, and comparing the current parameter value with the node threshold; and judging whether the key parameters of the test point meet the test specifications based on a preset threshold range.

5. The method according to claim 4, characterized in that A parameter specification judgment matrix is ​​constructed according to the preset threshold range; a three-dimensional array is generated according to the parameter specification judgment matrix, and the three-dimensional array is used to store structured test point data; the three-dimensional array is traversed to extract the actual values ​​of the key parameters of the test points, and the actual values ​​of the key parameters are compared with the thresholds in the parameter specification judgment matrix; a bit operation method is used to encode the comparison result, and the encoding result is a binary bit; the binary bit is compared with a preset mask through a bitwise AND operation, and if the comparison result is equal to the preset mask, the test quality of the test point is determined to be qualified; if the comparison result is not equal to the preset mask, the test quality of the test point is determined to be unqualified.

6. The method according to claim 1, characterized in that The method calculates the evaluation results of the test point coverage, key parameter completeness, test value range compliance and test time adequacy indicators based on the structured test point data with qualified test quality, including: obtaining qualified test point data and collected test point data, and constructing a hash table based on the qualified test point data and the collected test point data; performing an intersection operation on the key set of the hash table to obtain the number of common test points; calculating the test point coverage based on the number of common test points and the total number of qualified test points; reading the key parameter list, traversing each test point in the collected data, and counting the number of key parameters actually collected; and calculating the key parameter completeness score based on the number of key parameters actually collected and the total number of preset key parameters.

7. The method according to claim 1, characterized in that The overall test step integrity score of the insulation resistance test and the insulation withstand voltage test is calculated to obtain a quantitative indicator for measuring the test standardization, including: obtaining evaluation results of four indicators, namely, test point coverage, key parameter completeness, test value range compliance, and test time adequacy, from a database, and the evaluation results are used to construct an indicator data structure; reading a scoring grade classification standard in a configuration file according to the indicator data structure; obtaining a preset weight coefficient configuration; and using a weighted average method to calculate the overall test step integrity score of the insulation resistance test and the insulation withstand voltage test, wherein the score is obtained by weighting the four indicators, namely, test point coverage, key parameter completeness, test value range compliance, and test time adequacy; It also includes: quantitative evaluation of the standard specifications of insulation resistance test and insulation withstand voltage test to obtain standard specification scores for insulation resistance test and insulation withstand voltage test, and combining the overall test step integrity score with the standard specification score for insulation resistance test and insulation withstand voltage test to form a quantitative indicator to measure the standardization of the test.

8. The method according to claim 7, characterized in that A quantitative evaluation is conducted on the standard specifications of the insulation resistance test and the insulation withstand voltage test to obtain the standard specification scores of the insulation resistance test and the insulation withstand voltage test. The overall test step integrity score is combined with the standard specification scores of the insulation resistance test and the insulation withstand voltage test to form a quantitative indicator for measuring the test standardization, including: obtaining the standard specification data of the insulation resistance test and the insulation withstand voltage test, and extracting key parameter values ​​from the standard specification data; the key parameter values ​​include the test voltage range, the test time requirement, the minimum insulation resistance value, and the maximum leakage current value; constructing a standard specification evaluation matrix based on the key parameter values; the standard specification evaluation matrix adopts a two-dimensional array structure, in which the rows represent different parameters and the columns represent the standard values ​​and the allowable deviations; the membership function type and parameter value are set for each key parameter value; fuzzy evaluation is performed on the actual test data; an evaluation factor set is established, and an evaluation level set is determined. According to the membership function type and parameter value, the membership of each factor to each evaluation level is calculated to form a fuzzy relationship matrix; and the standard specification scores of the insulation resistance test and the insulation withstand voltage test are obtained through fuzzy synthesis operations.

9. The method according to claim 1, characterized in that: The test start time and end time information are extracted from the work order to calculate the actual test duration; The test time sufficiency weight coefficient and the test value range compliance weight coefficient are used to perform weighted calculation on the actual test time and the test step integrity score to obtain a comprehensive test efficiency index, including: obtaining the test start and end time information in the work order database; using regular expressions to match the time information to obtain the standard time format; calculating the actual test time according to the standard time format; obtaining the preset standard test time range from the specification database according to the actual test time; judging whether the actual test time falls within the standard test time range to obtain the time sufficiency score; obtaining the test step integrity score and the test value range compliance score; reading the time sufficiency weight coefficient, the test value range compliance weight coefficient and the test step integrity weight coefficient from the configuration file; and using the weighted average method to calculate the comprehensive test efficiency index.

10. The method according to claim 1, characterized in that The method aims at analyzing the test time deviation and the standardization of key test points of work orders with insufficient work efficiency, and generating corresponding optimization suggestions, including: for work orders with low work efficiency as determined by the comprehensive test efficiency index, extracting three indicators, namely, test time deviation, key parameter integrity and test value range compliance; reading the dynamic warning threshold from the configuration file; constructing a decision tree structure, in which the root node is the work efficiency judgment result, the second-level node is the threshold judgment of the three indicators, and the leaf node is the optimization direction; using the decision tree to analyze the three indicators; if the test time deviation exceeds the dynamic warning threshold, entering the time optimization branch; if the key parameter integrity is lower than the dynamic warning threshold, entering the standard collection branch; if the test value range compliance is lower than the dynamic warning threshold, entering the accuracy optimization branch; according to the decision tree analysis results, selecting corresponding suggestion content from the optimization suggestion library, and generating an optimization suggestion report.

Images