An air conditioning hose product performance test data management system

Abstract

The application discloses a kind of air conditioner hose product performance test data management system, belong to air conditioner hose product performance test data management technical field, including data statistics module and data analysis module;Data statistics module is used to connect the database of air conditioner hose product performance test data storage, identifies each performance test data, carries out classification statistics to each performance test data, forms the test statistical graph of air conditioner hose product;Data analysis module is used to carry out the performance analysis of air conditioner hose product based on test statistical graph, obtains batch analysis data from test statistical graph according to the product number table obtained;Batch test value set is set according to batch analysis data;Batch analysis value is calculated according to batch test value set;According to batch test value and test batch number, corresponding batch curve is generated;Vertex in batch curve is identified in real time, and the curve difference value of each test batch is calculated in real time based on vertex;When curve difference value is greater than threshold X1, production optimization data is generated.

Description

Technical Field

[0001] The present invention belongs to the technical field of performance test data management of air-conditioning hose products, and specifically relates to a performance test data management system for air-conditioning hose products. Background Art

[0002] An air-conditioning hose is an important component in an air conditioner, and it needs to have certain performance indicators during use, such as pressure resistance, heat resistance, corrosion resistance, etc.; in order to ensure that the quality and performance of the product meet the requirements, comprehensive performance tests and evaluations are required; the performance tests of air-conditioning hose products usually generate a large amount of data, including test parameters, test results, etc. Traditional data management methods may have problems such as manual recording and paper files, which are not only inefficient but also prone to data loss or errors. Therefore, in the current testing of air-conditioning hoses, a corresponding data management system is basically introduced, but the existing data management systems mainly focus on the test results of air-conditioning hoses, that is, whether the test is qualified, and the utilization rate of other data is relatively low, and the role of test data cannot be fully exerted, especially for small, medium and micro production enterprises; based on this, the present invention provides a performance test data management system for air-conditioning hose products. Summary of the Invention

[0003] In order to solve the problems existing in the above solutions, the present invention provides a performance test data management system for air-conditioning hose products.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A performance test data management system for air-conditioning hose products includes a data statistics module, a data management module, and a data analysis module;

[0006] The data statistics module is used to connect to a database storing performance test data of air-conditioning hose products, identify each performance test data, classify and statistically analyze each performance test data, and form a test statistical chart for each air-conditioning hose product.

[0007] Further, the method for generating the test statistical chart includes:

[0008] Identify each performance test data, and convert each performance test data into a corresponding performance coordinate; input each performance coordinate into the corresponding coordinate space;

[0009] Classify according to the distribution of each performance coordinate in the coordinate space to obtain several performance classifications; generate a corresponding spatial distribution chart according to the performance classifications; supplement a classification detailed list for each classification area in the spatial distribution chart;

[0010] The coordinate record features corresponding to the classification area are identified in real time, and the coordinate record features are recorded in the classification details table.

[0011] Furthermore, methods for classifying performance test data include:

[0012] Step SA1: Obtain historical performance test data and establish a corresponding judgment model based on the historical performance test data;

[0013] The expression for the judgment model is: In the formula: c represents the combined performance coordinate;

[0014] Step SA2: Determine initial coordinates in the coordinate space, identify performance coordinates adjacent to the initial coordinates, and mark them as associated coordinates; combine the initial coordinates and the associated coordinates to form a combined performance coordinate.

[0015] Step SA3: Analyze the combined performance coordinates using the judgment model to determine whether they meet the merging requirements;

[0016] When the merging requirements are met, the associated coordinates and the initial coordinates are marked in the same category.

[0017] If the merging requirements are not met, the associated coordinate markers will be removed.

[0018] Step SA4: Mark the performance coordinates adjacent to the associated coordinates as associated coordinates, and form a new combined performance coordinate system from the initial coordinates and the associated coordinates; return to step SA3;

[0019] If there are no combined performance coordinates, proceed to step SA5;

[0020] Step SA5: Group the performance coordinates with the same type of label into one category and label it as a performance category;

[0021] Step SA6: Repeat steps SA2 to SA5 until all performance coordinates in the coordinate space are classified, and obtain several performance classifications.

[0022] Furthermore, the methods for setting up spatial distribution maps include:

[0023] Based on the performance coordinates corresponding to each performance category, identify the region boundaries corresponding to each performance category, form corresponding category regions in the coordinate space based on the region boundaries, and mark the corresponding initial coordinates in each category region.

[0024] A corresponding spatial distribution map is formed by mapping each of the classification regions in the coordinate space.

[0025] Furthermore, the test statistics chart is updated accordingly based on the updates to the performance test data stored in the database;

[0026] Update methods include:

[0027] Identify the updated and stored performance test data in the database, and convert the performance test data into performance coordinates and input them into the coordinate space;

[0028] When the performance coordinate is located within the corresponding classification area, the performance classification corresponding to the classification area is identified, and the corresponding classification details table is matched in the test statistics chart according to the performance classification; the coordinate recording characteristics of the performance coordinate are identified, and the coordinate recording characteristics are recorded in the classification details table;

[0029] When the performance coordinates are not located within any classification region, the distance between the performance coordinates and the boundaries of each adjacent classification region is identified, and the performance coordinates are evaluated sequentially in ascending order of distance to determine whether the initial coordinates corresponding to the classification regions meet the merging requirements. If the merging requirements are met, the performance coordinates are merged into the corresponding classification region, and the classification region is adjusted. The region classification and the coordinate recording characteristics of the performance coordinates are recorded in the corresponding classification details table. If the merging requirements are not met, the next classification region is evaluated, and so on. When there is no classification region that meets the merging requirements, a new performance classification and classification region are generated based on the performance coordinates, and the test statistics chart is adjusted.

[0030] Furthermore, the data management module is used to manage the stored performance test data, identify the user-preset storage data management scheme, obtain the test statistics chart, identify the initial coordinates corresponding to each category area in the test statistics chart, and store and mark the performance test data corresponding to the initial coordinates.

[0031] The performance test data stored in the database is managed according to the aforementioned data management scheme and the preset storage tagging processing scheme.

[0032] The data analysis module is used to perform performance analysis on air conditioning hose products based on test statistics charts, obtain a product number table for each production batch of air conditioning hoses; obtain test statistics charts, and retrieve corresponding batch analysis data from the test statistics charts according to the product number table; and set corresponding batch test value sets according to the batch analysis data.

[0033] according to Calculate the corresponding batch analysis values;

[0034] Where: FPZ is the batch analysis value; PCGi represents the corresponding batch test value in the set of batch test values, where i = 1, 2, ……, n, and n is a positive integer;

[0035] Generate corresponding batch curves according to the batch test values and the corresponding test batch numbers;

[0036] Real-time identify the vertices in the batch curve, and calculate the curve differences of each test batch in real time based on the vertices; when the curve difference is greater than the threshold X1, generate corresponding production optimization data; when the curve difference is not greater than the threshold X1, no corresponding operation is performed.

[0037] Further, the method for obtaining batch analysis data includes:

[0038] Identify the performance classifications to which each air-conditioning hose product belongs in the test statistical chart according to the product number table, count the number of products in each performance classification, calculate the classification proportion of each performance classification according to the number of each product; identify the performance test data corresponding to the initial coordinates of each performance classification, and mark the corresponding performance classification label, product quantity label and classification proportion label on the performance test data; integrate each performance test data into batch analysis data.

[0039] Further, the method for setting the set of batch test values includes:

[0040] Identify each performance test data in the batch analysis data, convert the performance test data into performance coordinates, calculate the corresponding positioning value based on the performance coordinates, and mark it as DP

[0041] Identify the classification proportion corresponding to each performance test data, and mark it as μ;

[0042] According to the formula Calculate the corresponding batch test value; in the formula: PCG is the batch test value; D1 and D2 are the corresponding boundary positioning values respectively, and D1 < D2; the value range of the batch test value is [0, 10];

[0043] Integrate the batch test values of each performance test data into a set of batch test values.

[0044] Further, the method for calculating the positioning value includes:

[0045] Obtain the performance test data corresponding to the evaluation qualified standard, convert it into the corresponding performance coordinates, mark it as the standard coordinates, and mark each element value in the standard coordinates as YSt, where t = 1, 2, ……, v, v is a positive integer, and t represents the performance test item;

[0046] Label each element value in the performance coordinate as YXt, where t = 1, 2, ..., v, v is a positive integer, and t represents the performance test item;

[0047] The weight coefficients for each performance test item are preset and denoted as σt;

[0048] Then according to the formula Calculate the corresponding positioning value, where: DW is the positioning value;

[0049] For two boundary location values, the calculation can be performed by converting the boundary coordinates corresponding to boundary 0 and 10.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] Through the collaboration between the data statistics module and the data management module, intelligent management of large amounts of performance test data stored in the database can be achieved. When necessary, only the performance test data corresponding to the initial coordinate of each performance category needs to be retained, and other performance test data in the performance category can be deleted. Moreover, after deleting performance test data, by combining it with the test statistics chart, the same analysis results can be achieved as if the performance test data had not been deleted. This maximizes the use of storage space while ensuring the needs of subsequent data analysis. Furthermore, even if the user deletes all performance test data in a performance category with management permissions, the corresponding data analysis can still be performed when any performance test data in that performance category is stored later. It has great fault tolerance and there is no need to worry about accidentally deleting performance test data. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0054] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0055] like Figure 1As shown, an air conditioning hose product performance test data management system includes a data statistics module, a data management module, and a data analysis module.

[0056] The data statistics module is used to connect to the database storing performance test data for various air conditioning hose products, identify the stored performance test data for each air conditioning hose product, classify and statistically analyze the performance test data, and generate a test statistics chart for the air conditioning hose product; the specific method is as follows:

[0057] Identify the performance test data for each air conditioning hose product. Performance test data can include pressure resistance, temperature resistance, corrosion resistance, abrasion resistance, sealing performance, bending performance, and durability performance, depending on actual testing requirements. Set performance coordinates for each performance test data point for the air conditioning hose product. These coordinates are generated by transforming and integrating the performance test data for each performance test item. Each performance test item corresponds to an element in the performance coordinates; simply fill in the corresponding element position in the performance coordinates with the values ​​corresponding to the performance test item data.

[0058] Because different testing methods may result in differences in the units of data for performance test items, it is necessary to set the appropriate conversion method according to the testing method used by the user application. Common units of data for several performance test items are: pressure resistance, measured in pressure (e.g., megapascals / hcPa) or pressure differential (e.g., bar / pascal); temperature resistance, measured in temperature (e.g., degrees Celsius / Fahrenheit); sealing performance, typically expressed as leakage rate (e.g., millimeters per minute) or pressure loss (e.g., pascals); bending performance, measured in angle (e.g., degrees) or bending radius (e.g., millimeters per inch); and durability performance, typically expressed as service life (e.g., ...). For example, performance test data can be expressed as hours / cycles or failure rate (e.g., percentage). For non-numerical performance test data, a corresponding conversion method can be preset according to existing numerical conversion methods. The data can be converted according to the preset conversion method. For example, the possible test data range of the performance test data can be obtained, and the two range boundaries can be marked as B1 and B2 respectively. If B1>B2, the converted value is SZ=[(100-60)÷(B1-B2)]×(Bz-B2), where SZ is the corresponding converted value and Bz is the detected data. The difference is calculated according to the performance level. Other existing methods can also be used for numerical conversion.

[0059] Each performance coordinate of an air conditioning hose product is input into its corresponding coordinate space. An air conditioning hose product may have one or two coordinate spaces. If the converted performance coordinates of qualified and unqualified performance test data are in the same coordinate space, then an air conditioning hose product has one coordinate space; otherwise, it has two coordinate spaces. Performance coordinates are categorized based on their distribution in the coordinate space, resulting in several performance categories. Based on the position of each performance category in the coordinate space, corresponding category regions are generated, using the performance coordinates at the boundaries of each category as region boundaries. Initial coordinates within each category region are then marked. Subsequently, performance test data can be quickly categorized based on these regions. For cases not directly located within a category region, the initial coordinates are evaluated and matched with those in nearby category regions to determine the corresponding category region, and the category region is then updated accordingly.

[0060] Based on the coordinate space, a corresponding spatial distribution map is generated for each category region. Specifically, the spatial map is modified according to the shape and size of each category region in the coordinate space to form the spatial distribution map. A classification detail table for each category region is added to the spatial distribution map, including data columns such as performance coordinate number, recording time, and sequence number. The classification detail table can be collapsed. The coordinate recording characteristics, such as the performance coordinate number and recording time, in the corresponding category region of the coordinate space are identified in real time, and the data is recorded in the corresponding classification detail table based on these characteristics.

[0061] Mark the current spatial distribution map as a test statistical map.

[0062] Among them, the classification method based on the distribution of performance coordinates in coordinate space includes:

[0063] Step SA1: Acquire a large amount of historical performance test data, convert it into corresponding historical performance coordinates, and set the equivalent deviation for each performance test item. This means that for each performance test item, the numerical deviation can be considered the same, resulting in the same effect on subsequent data analysis. This can be set in conjunction with the corresponding allowable deviation. For example, for temperature resistance of 60℃, there are equivalent deviations of 3℃ for 60℃. Therefore, 60℃, 60.1℃, and 62.5℃ produce the same analytical effect and can be considered to meet the equivalent deviation requirement. Specific settings need to be determined by professionals based on historical performance test data and actual conditions. Next, simulate and mark other historical performance coordinates to see if they meet the equivalent deviation requirement, meaning all performance test items meet it. Integrate the marked data into a training set to build a judgment model. The expression of the judgment model is: In the formula: c represents the combined performance coordinates, that is, the two performance coordinates that need to be evaluated to see if they meet the equivalent deviation requirements; if c is an anomaly, it means that the combined performance coordinates do not meet the equivalent deviation requirements.

[0064] Step SA2: Determine the initial coordinates in the coordinate space, identify the associated coordinates adjacent to the initial coordinates, excluding adjacent performance coordinates with different test results, that is, tests and test failures cannot be used as associated coordinates and are directly disregarded; combine the initial coordinates with each associated coordinate to form a combined performance coordinate.

[0065] In one embodiment, a performance coordinate in the coordinate space can be arbitrarily chosen as the initial coordinate.

[0066] In another embodiment, existing methods for evaluating performance test data can be used to obtain evaluation values, which can be in the form of priority values, stored values, etc., in other existing technologies; for example, the priority deletion order of each stored data can be evaluated, and the last data to be filtered out can be selected as the initial coordinate; or corresponding intelligent models can be built based on neural networks such as CNN networks and DNN networks for intelligent evaluation.

[0067] Step SA3: Analyze the combined performance coordinates using the judgment model to determine whether they meet the requirements for equal deviation, i.e., the merging requirements;

[0068] When the merging requirements are met, the corresponding associated coordinates are marked as the same as the initial coordinates.

[0069] If the merging requirements are not met, the associated coordinate marker is removed; that is, the performance coordinate is not an associated coordinate of the initial coordinate.

[0070] Step SA4: Treat the performance coordinates adjacent to the associated coordinates with the same type of label as new associated coordinates, and form a new combined performance coordinate by the initial coordinates and each associated coordinate. Return to step SA3 for analysis.

[0071] Continue until no combined performance coordinates are found, then proceed to step SA5;

[0072] Step SA5: Group all performance coordinates with the same type of label into one category and label it as performance category;

[0073] Step SA6: Repeat steps SA2 to SA5 until all performance coordinates in the coordinate space have been classified, and obtain several performance classifications.

[0074] The data management module is used to manage the stored performance test data. In actual production testing, a large amount of performance test data will be generated. The conventional management method is to set a deadline and delete the corresponding performance test data when the deadline is reached, resulting in poor utilization and low retention rates. Therefore, this invention manages the data by combining it with test statistics charts in the data statistics module. Even if subsequent performance test data is deleted, corresponding data analysis can still be performed through the test statistics charts. Because the analysis effect of performance test data for each performance category is basically equivalent, analysis can be performed based on the corresponding quantity and proportion.

[0075] The method is as follows:

[0076] Obtain the user-defined data storage management scheme, such as the user's settings for how long to save data before deleting it, and which data is marked as non-deletable.

[0077] Obtain the test statistics chart, identify the initial coordinates corresponding to each category area in the test statistics chart, and store and mark the performance test data corresponding to the initial coordinates. Data with storage marks is not to be deleted and can only be deleted with the user's application administrator privileges.

[0078] Identify the corresponding storage data management scheme, and manage the performance test data stored in the database according to the storage data management scheme and the preset storage tagging processing scheme.

[0079] Through the collaboration between the data statistics module and the data management module, intelligent management of large amounts of performance test data stored in the database can be achieved. When necessary, only the performance test data corresponding to the initial coordinate of each performance category needs to be retained, and other performance test data in the performance category can be deleted. Moreover, after deleting performance test data, by combining it with the test statistics chart, the same analysis results can be achieved as if the performance test data had not been deleted. This maximizes the use of storage space while ensuring the needs of subsequent data analysis. Furthermore, even if the user deletes all performance test data in a performance category with management permissions, the corresponding data analysis can still be performed when any performance test data in that performance category is stored later. It has great fault tolerance and there is no need to worry about accidentally deleting performance test data.

[0080] The data analysis module is used to perform performance analysis of air conditioning hose products based on test statistics charts and obtain a product number table for each production batch of air conditioning hoses. The product number table includes the product numbers of the air conditioning hoses in the production test batch.

[0081] Obtain test statistics charts, and retrieve corresponding batch analysis data from the test statistics charts based on the product number table; evaluate the corresponding batch test value set based on the obtained batch analysis data.

[0082] The corresponding batch analysis value is calculated based on the set of batch test values, using the following formula:

[0083]

[0084] In the formula: FPZ is the batch analysis value, PCGi represents the corresponding batch test value in the batch test value set, i = 1, 2, ..., n, where n is a positive integer;

[0085] The obtained batch analysis values ​​and the corresponding number of test batches are integrated into a coordinate system, with the horizontal axis representing the number of test batches and the vertical axis representing the batch analysis values; the corresponding batch curves are then formed by connecting the coordinates in the coordinate system.

[0086] The system identifies the vertex in the batch curve, i.e., the coordinates corresponding to the largest batch analysis value; it also identifies the difference between the batch analysis values ​​corresponding to other curves and the batch analysis value corresponding to the vertex in real time, marking it as the curve difference, which is the difference between the batch analysis value of the vertex and the batch analysis values ​​corresponding to other curves; when the curve difference is greater than the threshold X1, it marks the corresponding coordinates and generates production optimization data based on the marked coordinates; that is, it identifies the production data of the batch of air conditioning hose products and the production data of the air conditioning hose products corresponding to the vertex, as well as their respective performance test data based on the marked coordinates, to help users move production parameters and management methods closer to the vertex; thus facilitating production optimization for users.

[0087] Methods for obtaining batch analysis data include:

[0088] Identify the performance category of each air conditioning hose product in the test statistics chart based on the product number table, count the number of products in each performance category, and calculate the category weight (i.e., quantity share) of each performance category based on the quantity of each product; identify the performance test data corresponding to the initial coordinates of each performance category, and mark the corresponding performance category label, product quantity label, and category weight label on the obtained performance test data; integrate all performance test data into batch analysis data.

[0089] Evaluation methods for batch test value sets include:

[0090] The system identifies each performance test data in the batch analysis data, evaluates the corresponding performance assessment value of each performance test data, and the performance assessment value ranges from [0, 10]. 10 and 0 represent the best and worst state that the performance test data can reach at the user's location. It can combine historical performance test data to determine the various performance test data, obtain the performance test data corresponding to 10 and 0, convert them into corresponding performance coordinates, and mark them as boundary coordinates. The system presets the weight coefficient of each performance test item, which can be set by the user, or set according to the proportion of the overall evaluation of whether each performance test item is qualified, or according to the proportion of economic loss caused by the failure of each performance test item. The specific settings can be adjusted according to the actual situation.

[0091] Obtain the performance test data corresponding to the evaluation pass standard, convert it into corresponding performance coordinates, and mark it as standard coordinates. Subtract the corresponding element values ​​in the standard coordinates from the boundary coordinates; then multiply by the corresponding weighting coefficient to obtain the corresponding individual values. Sum these individual values ​​to obtain the boundary positioning values ​​corresponding to performance evaluation values ​​of 0 and 10, respectively, and mark them as D1 and D2. <D2;

[0092] Obtain the performance test data that needs to be evaluated, convert it into the corresponding performance coordinates, mark it as the evaluation coordinates, calculate the positioning value corresponding to the evaluation coordinates, and mark it as DP;

[0093] The performance evaluation value of the performance test data to be evaluated is XN = [10 ÷ (D2 - D1)] × DP, where XN is the performance evaluation value;

[0094] Identify the category weight corresponding to each performance test data and label it as μ;

[0095] Then the batch test value PCG = μ × XN; where: PCG is the batch test value.

[0096] The batch test values ​​of each performance test data are integrated into a batch test value set.

[0097] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0098] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A data management system for performance testing of air conditioning hoses, characterized in that, It includes a data statistics module and a data analysis module; The data statistics module is used to connect to the database storing performance test data of air conditioning hose products, identify each performance test data, classify and statistically analyze each performance test data, and form a test statistics chart of each air conditioning hose product. The data analysis module is used to perform performance analysis on air conditioning hose products based on test statistics charts, obtain a product number table for each production batch of air conditioning hoses; obtain test statistics charts, and retrieve corresponding batch analysis data from the test statistics charts according to the product number table; and set corresponding batch test value sets according to the batch analysis data. according to Calculate the corresponding batch analysis values; In the formula: FPZ is the batch analysis value; PCGi represents the corresponding batch test value in the batch test value set, i=1, 2, ..., n, where n is a positive integer; Generate a corresponding batch curve based on the batch test values ​​and the corresponding number of test batches; The system identifies vertices in the batch curves in real time and calculates the curve difference between each test batch based on these vertices. When the curve difference is greater than a threshold X1, corresponding production optimization data is generated. When the difference between the curves is not greater than the threshold X1, no corresponding operation is performed; Methods for generating test statistical charts include: Identify each of the performance test data, convert each of the performance test data into corresponding performance coordinates, and input each of the performance coordinates into the corresponding coordinate space; Based on the distribution of each performance coordinate in the coordinate space, several performance categories are obtained; a corresponding spatial distribution map is generated based on the performance categories; and a classification detail table for each category region is added to the spatial distribution map. The coordinate record features corresponding to the classification area are identified in real time, and the coordinate record features are recorded in the classification details table; Methods for classifying performance test data include: Step SA1: Obtain historical performance test data and establish a corresponding judgment model based on the historical performance test data; The expression for the judgment model is: In the formula: c represents the combined performance coordinate; Step SA2: Determine initial coordinates in the coordinate space, identify performance coordinates adjacent to the initial coordinates, and mark them as associated coordinates; combine the initial coordinates and the associated coordinates to form a combined performance coordinate. Step SA3: Analyze the combined performance coordinates using the judgment model to determine whether they meet the merging requirements; When the merging requirements are met, the associated coordinates and the initial coordinates are marked in the same category. If the merging requirements are not met, the associated coordinate markers will be removed. Step SA4: Mark the performance coordinates adjacent to the associated coordinates as associated coordinates, and form a new combined performance coordinate system from the initial coordinates and the associated coordinates; return to step SA3; If there are no combined performance coordinates, proceed to step SA5; Step SA5: Group the performance coordinates with the same type of label into one category and label it as a performance category; Step SA6: Repeat steps SA2 to SA5 until all performance coordinates in the coordinate space are classified, and obtain several performance classifications; Methods for setting up batch test value sets include: Identify each performance test data point in the batch analysis data, convert the performance test data into performance coordinates, calculate the corresponding positioning value based on the performance coordinates, and label it as DP. Identify the category weight corresponding to each of the aforementioned performance test data and label it as μ; According to the formula calculate the corresponding batch test value; where: PCG is the batch test value; D1 and D2 are the corresponding boundary positioning values respectively, and D1 < D2; the value range of the batch test value is [0, 10]; The batch test values ​​of each of the aforementioned performance test data are integrated into a batch test value set.

2. The air conditioning hose product performance test data management system according to claim 1, characterized in that, Methods for setting up spatial distribution maps include: Based on the performance coordinates corresponding to each performance category, identify the region boundaries corresponding to each performance category, form corresponding category regions in the coordinate space based on the region boundaries, and mark the corresponding initial coordinates in each category region. A corresponding spatial distribution map is formed by mapping each of the classification regions in the coordinate space.

3. The air conditioning hose product performance test data management system according to claim 2, characterized in that, The test statistics chart is updated accordingly based on the updates to the performance test data stored in the database; Update methods include: Identify the updated and stored performance test data in the database, and convert the performance test data into performance coordinates and input them into the coordinate space; When the performance coordinate is located within the corresponding classification area, the performance classification corresponding to the classification area is identified, and the corresponding classification details table is matched in the test statistics chart according to the performance classification; the coordinate recording characteristics of the performance coordinate are identified, and the coordinate recording characteristics are recorded in the classification details table; When the performance coordinates are not located within any classification region, the distance between the performance coordinates and the boundaries of each adjacent classification region is identified, and the performance coordinates are evaluated sequentially in ascending order of distance to determine whether the initial coordinates corresponding to the classification regions meet the merging requirements. If the merging requirements are met, the performance coordinates are merged into the corresponding classification region, and the classification region is adjusted. The region classification and the coordinate recording characteristics of the performance coordinates are recorded in the corresponding classification details table. If the merging requirements are not met, the next classification region is evaluated, and so on. When there is no classification region that meets the merging requirements, a new performance classification and classification region are generated based on the performance coordinates, and the test statistics chart is adjusted.

4. The air conditioning hose product performance test data management system according to claim 1, characterized in that, Methods for obtaining batch analysis data include: Based on the product number table, identify the performance category of each air conditioning hose product in the test statistics chart, count the number of products in each performance category, and calculate the category weight of each performance category based on the number of products. Identify the performance test data corresponding to the initial coordinates of each performance category, and mark the corresponding performance category label, product quantity label, and category weight label on the performance test data. Integrate all the performance test data into batch analysis data.

5. The air conditioning hose product performance test data management system according to claim 1, characterized in that, It also includes a data management module, which is used to manage the stored performance test data, identify the user-preset storage data management scheme, obtain test statistics charts, identify the initial coordinates corresponding to each category area in the test statistics charts, and store and mark the performance test data corresponding to the initial coordinates. The performance test data stored in the database is managed according to the aforementioned data management scheme and the preset storage tagging processing scheme.

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