Product quality sampling information processing method, system, device and computer readable medium

Abstract

The application discloses a product quality sampling information processing method, system, device and computer readable medium, and particularly relates to the technical field of product quality supervision information processing, and is used for solving the problem that the rules cannot be automatically adapted, the task allocation is static, and the information checking lags behind, resulting in that the data is not comparable in the existing cross-regional sampling work; by acquiring general standards, regional rules, resource information and task requirements, extracting clauses and establishing mapping relationships to generate adaptive rules, extracting and disassembling task complexity and resource capability characteristics into a hierarchical structure, calculating the adaptive evaluation value based on the hierarchical structure using the analytic hierarchy process and the fuzzy comprehensive evaluation method, generating a dynamic allocation scheme in combination with the adaptive rules and the resource information, performing consistency pre-checking on the collected information according to the allocation scheme and screening qualified information, and finally standardizing the qualified information according to the clause mapping relationship to form unified summary data, the accuracy and information comparability of the cross-regional sampling work are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of product quality supervision information processing technology, and more specifically, to product quality sampling information processing methods, systems, equipment, and computer-readable media. Background Technology

[0002] In fields such as product quality supervision, manufacturing quality control, and commercial circulation quality inspection, cross-regional product quality sampling has become commonplace. Examples include nationwide market supervision spot checks, full-chain sampling of large enterprises' cross-regional production bases, and quality verification of goods circulating across provinces. Currently, information processing for cross-regional product quality sampling relies heavily on a decentralized information management model. Nationally applicable sampling standards and regionally differentiated implementation rules are maintained by different entities. Sampling tasks are typically assigned based on static information such as product category and production batch, without considering the actual resource situation at the sampling execution end in each region. Furthermore, during the collection and transfer of cross-regional sampling information, existing technologies mostly only perform basic verification of information format standardization. Sampling information collected from each region needs to be aggregated before unified analysis can be performed, and the information processing stage lacks pre-emptive control over the consistency of sampling rule execution.

[0003] Existing technologies cannot automatically adapt and integrate nationally applicable sampling standards with regionally differentiated implementation rules, nor can they dynamically match sampling tasks based on regional resource status. Furthermore, the sampling information flowing across regions lacks a consistency verification mechanism at the rule level, which makes it easy for cross-regional sampling information to have collection bias due to misunderstandings of the rules. This bias will continue to amplify as information flows, ultimately resulting in a lack of horizontal comparability of sampling information analysis results from different regions, affecting the accuracy of cross-regional product quality sampling. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a product quality sampling information processing method, system, device and computer-readable medium to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Product quality sampling information processing methods include:

[0007] S1. Obtain nationally applicable sampling standards, regionally differentiated sampling implementation rules, regional sampling execution resource information, and cross-regional sampling task requirements.

[0008] S2. Extract the common and differentiated clauses of the national general sampling standard and the implementation details of differentiated sampling in various regions, establish the clause mapping relationship and generate sampling rules adapted to each region; at the same time, extract the complexity characteristics of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extract the resource professional capability characteristics from the resource information of the sampling execution end in each region, and decompose them into corresponding hierarchical structures respectively.

[0009] S3. Based on the hierarchical structure, the analytic hierarchy process and fuzzy comprehensive evaluation method are used to calculate the fit evaluation value;

[0010] S4. Based on the adaptation evaluation value, combined with the adaptation sampling rules of each region and the resource information of the sampling execution terminal of each region, conduct resource load assessment and generate allocation plan;

[0011] S5. Based on the allocation scheme, perform a consistency pre-verification on the collected cross-regional sampling information and select the cross-regional sampling information that has passed the verification.

[0012] S6. Standardize and organize the verified cross-regional sampling information according to the clause mapping relationship to form a unified summary data of cross-regional sampling information.

[0013] Furthermore, S1 includes:

[0014] Read the national general sampling standards and the regional differentiated sampling implementation rules from the central standards library;

[0015] The system reads the resource information of the sampling execution terminals in each region in real time and receives cross-regional sampling task requests submitted by the task initiator.

[0016] Furthermore, S2 includes:

[0017] By comparing the national general sampling standard with the regional differentiated sampling implementation rules, common clauses and differentiated clauses were extracted.

[0018] Establish a clause mapping relationship based on common and differentiated clauses, and generate regional adaptation sampling rules based on the clause mapping relationship;

[0019] The complexity features of cross-regional sampling tasks are extracted from the requirements of cross-regional sampling tasks, and the complexity features of cross-regional sampling tasks are decomposed into corresponding hierarchical structures.

[0020] Resource professional capability characteristics are extracted from the resource information of the sampling execution terminals in each region, and these characteristics are then decomposed into corresponding hierarchical structures.

[0021] Furthermore, S3 includes:

[0022] For each cross-regional sampling task, the hierarchical structure of the complexity characteristics of the decomposed cross-regional sampling task is logically correlated and matched with the hierarchical structure of the resource professional capability characteristics of each region.

[0023] Based on the results of the matching analysis, the analytic hierarchy process was used to construct judgment matrices of the complexity features of cross-regional sampling tasks relative to the resource professional capability features, and the weight allocation was calculated.

[0024] By incorporating weight allocation into the fuzzy comprehensive evaluation method, the degree of matching between the complexity characteristics of cross-regional sampling tasks and the characteristics of resource professional capabilities is comprehensively evaluated, generating a quantitative fit evaluation value.

[0025] Furthermore, S4 includes:

[0026] Based on the resource information of the sampling execution terminals in each region, assess the real-time resource load status of the sampling execution terminals in each region;

[0027] By combining the adaptation evaluation value with the real-time resource load status, adaptability sampling rules for each region that meet the load conditions are matched to the requirements of cross-regional sampling tasks.

[0028] Based on the matching results, an allocation scheme is generated that defines the matching relationship between each sampling task and the corresponding adaptive sampling rule.

[0029] Furthermore, S5 includes:

[0030] Based on the matching relationship defined by the allocation scheme, determine the appropriate sampling rules corresponding to the collected cross-regional sampling information;

[0031] Based on the requirements of the established adaptive sampling rules, a consistency pre-verification is performed on the collected cross-regional sampling information.

[0032] Based on the results of the consistency pre-verification, cross-regional sampling information that meets the requirements of the adaptive sampling rules is selected as qualified cross-regional sampling information.

[0033] Furthermore, S6 includes:

[0034] Based on the clause mapping relationship, the corresponding standardization transformation target is determined for the cross-regional sampling information that has passed the verification.

[0035] In accordance with the established standardization and transformation objectives, the verified cross-regional sampling information is standardized, organized, and transformed.

[0036] The standardized and transformed cross-regional sampling information is merged to form a unified summary of cross-regional sampling information data.

[0037] On the other hand, the present invention provides a product quality sampling information processing system, comprising:

[0038] The information acquisition module is used to acquire national general sampling standards, regional differentiated sampling implementation rules, regional sampling execution resource information, and cross-regional sampling task requirements.

[0039] The rule generation module is used to extract common and differentiated clauses from the national general sampling standard and the implementation details of differentiated sampling in various regions, establish clause mapping relationships and generate sampling rules adapted to each region; at the same time, it extracts the complexity characteristics of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extracts the resource professional capability characteristics from the resource information of sampling execution end in each region, and decomposes them into corresponding hierarchical structures.

[0040] The adaptation evaluation module is used to calculate the adaptation evaluation value based on the hierarchical structure using the analytic hierarchy process and the fuzzy comprehensive evaluation method.

[0041] The task allocation module is used to conduct resource load assessment based on the adaptation evaluation value, the adaptation sampling rules of each region, and the resource information of the sampling execution terminal of each region, and generate an allocation plan.

[0042] The information verification module is used to perform a pre-consistency verification on the collected cross-regional sampling information according to the allocation scheme, and to filter out the cross-regional sampling information that has passed the verification.

[0043] The data processing module is used to standardize and process the verified cross-regional sampling information according to the clause mapping relationship, forming a unified summary of cross-regional sampling information data.

[0044] On the other hand, the present invention provides an apparatus comprising: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement a product quality sampling information processing method.

[0045] On the other hand, the present invention provides a computer-readable medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, a method for processing product quality sampling information is implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. It has achieved efficient collaboration and precise control of rules, tasks, and data in cross-regional sampling, integrating previously scattered and static management processes into a dynamic, intelligent, and interconnected organic whole. This provides systematic support for cross-regional sampling at the information processing level, enabling automatic adaptation and rule fusion between national general standards and regionally differentiated details. It generates execution criteria that are both unified and flexible. Furthermore, by quantitatively analyzing the multi-dimensional characteristics of task requirements and execution resources, and using scientific decision-making methods for matching and evaluation, it can generate optimal task allocation schemes based on the real-time load status of each region. This transforms resource status from a static reference into a dynamic decision variable, improving the rationality and execution efficiency of sampling task assignment.

[0048] 2. By immediately performing consistency pre-verification based on the specific rules in the allocation scheme after information collection, the system ensures that each piece of raw data conforms to the rules and standards it should follow from the very beginning. This significantly advances the quality control process, effectively preventing collection errors caused by deviations in rule understanding or execution. Subsequently, based on the previously established clause mapping relationship, the system can automatically convert qualified information from different rules into a unified standard system, directly forming summary data that can be used for horizontal comparative analysis. This not only significantly reduces the complexity of subsequent data cleaning and processing but also ensures the comparability of cross-regional sampling information and the accuracy of analysis results, providing a reliable data foundation for high-quality inter-regional quality comparison and regulatory decision-making. Attached Figure Description

[0049] Figure 1 This is a flowchart of the product quality sampling information processing method of the present invention;

[0050] Figure 2 This is a schematic diagram of the product quality sampling information processing system of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Figure 1 The present invention provides a method for processing product quality sampling information, including:

[0053] S1. Obtain nationally applicable sampling standards, regionally differentiated sampling implementation rules, regional sampling execution resource information, and cross-regional sampling task requirements.

[0054] S2. Extract the common and differentiated clauses of the national general sampling standard and the implementation details of differentiated sampling in various regions, establish the clause mapping relationship and generate sampling rules adapted to each region; at the same time, extract the complexity characteristics of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extract the resource professional capability characteristics from the resource information of the sampling execution end in each region, and decompose them into corresponding hierarchical structures respectively.

[0055] S3. Based on the hierarchical structure, the analytic hierarchy process and fuzzy comprehensive evaluation method are used to calculate the fit evaluation value;

[0056] S4. Based on the adaptation evaluation value, combined with the adaptation sampling rules of each region and the resource information of the sampling execution terminal of each region, conduct resource load assessment and generate allocation plan;

[0057] S5. Based on the allocation scheme, perform a consistency pre-verification on the collected cross-regional sampling information and select the cross-regional sampling information that has passed the verification.

[0058] S6. Standardize and organize the verified cross-regional sampling information according to the clause mapping relationship to form a unified summary data of cross-regional sampling information.

[0059] In step S1, the specific implementation of obtaining the national general sampling standard, the detailed rules for differentiated sampling in each region, the resource information of the sampling execution terminal in each region, and the requirements for cross-regional sampling tasks is as follows:

[0060] The system retrieves nationally applicable sampling standards and regionally differentiated sampling implementation rules from the central standards repository. The central standards repository is a server storage device used to centrally store text and data files. Read operations are performed through a data access interface program configured with an access address and authentication protocol. This interface program sends a data query request to the central standards repository based on the input standard identifier. For example, the standard identifier can be a string containing the publication year and standard number, such as 2025-GB / T-12345. For regionally differentiated sampling implementation rules, the identifier includes the standard number and the regional administrative code, such as 440000-2025-GB / T-12345. This composite identifier is used to retrieve files from a specific region. The retrieved file content is parsed and loaded into memory, forming a structured data set containing clause numbers, clause text, and applicable region fields, such as tabular data organized in rows and columns.

[0061] Subsequently, resource information from each regional sampling execution terminal system is read in real time. Each regional sampling execution terminal system is a computing device equipped with resource management software, deployed in different geographical areas. Real-time reading refers to automatically performing a read operation at preset time intervals, such as every 5 minutes, or whenever the resource status changes. The reading process is achieved by sending network requests to a resource status query service running on each regional sampling execution terminal system. The resource information returned by this query service is a structured dataset, whose contents include: an integer representing the number of available sampling personnel, a decimal representing the average skill level of the personnel, a list of device identifiers, and the next available time in date and time format. This data is transmitted after being encapsulated in a specific data exchange format, such as text data in JSON format.

[0062] Simultaneously, the system receives cross-regional sampling task requests submitted by the task initiator. The task initiator is a user terminal device with task submission client software installed. The receiving process is completed through a task receiving service running on a server, which continuously listens on a specific network port. The cross-regional sampling task request is an electronic data packet assembled according to a predefined data structure, generated and sent by the client software of the task initiator. The data packet contains the following fields: a sample name in character type, a list of production batch numbers in character array type, a list of target region codes in character array type, a sampling time window consisting of the start and end dates, and a list of quality indicator names to be inspected in character array type. When receiving the data packet, the task receiving service checks the existence of the necessary fields in the data packet one by one and verifies the validity of the field content, such as checking whether the region code exists in the predefined region code master table. Only cross-regional sampling task request data that passes the verification and validation will be stored and processed by subsequent steps. Through the above specific implementation method, step S1 completes the acquisition and collection of all necessary input information, providing an accurate and timely data foundation for subsequent information processing.

[0063] In step S2, the extraction and disassembly operations are implemented as follows:

[0064] By comparing the national general sampling standard with the differentiated sampling implementation rules of various regions, common clauses and differentiated clauses are extracted. This comparison operation is performed on the structured data set that has been read and parsed in step S1, which includes the clause number, clause text, and applicable region field. During the comparison, the unique identifier composed of the clause number plus the core verb and noun combination in the clause text is used as the comparison benchmark. For the extraction of common clauses, the specific implementation method is as follows: each clause identifier in the national general sampling standard data set is compared one by one with all clause identifiers in the differentiated sampling implementation rules data set of a specific region. If the two clause identifiers are completely identical and the clause text content is also completely identical, then the clause is determined to be a common clause, and it is recorded in a common clause list and a mapping table recording the corresponding relationship. The extraction of differentiated clauses is implemented as follows: when clause identifiers are identical but the specific parameter values ​​in the clause text differ—for example, one clause specifies a sampling quantity of 10 while another specifies 15—then the clause is considered a differentiated clause. Alternatively, if a clause identifier exists in the regional implementation rules dataset but cannot be matched with any identifier in the general standard dataset, that clause is also considered a differentiated clause. All differentiated clauses are recorded in a differentiated clause list and mapping table.

[0065] A clause mapping relationship is established based on common and differentiated clauses, and regionally adapted sampling rules are generated according to this relationship. Establishing a clause mapping relationship involves constructing a database table or equivalent data structure. Each record in this structure is explicitly associated with three elements: a source clause identifier from a nationally applicable sampling standard, a target clause identifier from regional differentiated sampling implementation rules, and a relationship type field. The relationship type field identifies whether the association is an equi-mapping or a differentiated mapping. For differentiated mappings, the record additionally includes a difference description field, which records which parameter differs and the specific content of the difference. The specific steps for generating region-specific sampling rules based on this clause mapping relationship are as follows: For each region read in step S1, all records whose target clause identifiers belong to that region are selected from the above mapping relationship; then, using the data structure of the national general sampling standard as the skeleton, for each source clause in the skeleton, the corresponding target clause in the mapping relationship is searched; if the target clause relationship type found is an equivalent mapping, the clause content in the general standard is directly used as the final content of this clause in the region-specific sampling rule; if the target clause relationship type found is a differentiated mapping, the complete content of the target clause in the regional differentiated sampling implementation rules is used to replace the corresponding clause content in the general standard; finally, all clauses processed according to this rule are combined in sequence to form a complete document or data set of region-specific sampling rules for that region.

[0066] The complexity features of cross-regional sampling tasks are extracted from the requirements of these tasks and then decomposed into corresponding hierarchical structures. The extraction operation is performed on the cross-regional sampling task requirement data received and stored in step S1. This data includes fields such as sample name, production batch number list, target area code list, sampling time window, and list of quality indicator names to be inspected. The complexity features of cross-regional sampling tasks are scalar or categorical values ​​calculated based on the values ​​of these fields. Specifically, the extraction process is as follows: the number of different codes in the target area code list is calculated, and this number is used as a feature named "regional coverage quantity"; the number of different batch numbers in the production batch number list is calculated, and this number is used as another feature named "product batch quantity"; the number of different indicator names in the list of quality indicator names to be inspected is calculated, and this number is used as a feature named "detection indicator quantity"; the difference in the number of days between the end date and the start date in the sampling time window is calculated, and this difference in the number of days is used as a feature named "task time span". Decomposing these features into a hierarchical structure involves pre-defining a directed acyclic graph (DAG) structure. The root node represents the overall task complexity, and the first-level child nodes under the root node correspond to the specific features mentioned above, such as the region coverage quantity feature node, the product batch quantity feature node, the detection index quantity feature node, and the task time span feature node. This hierarchical structure can be represented in computer memory using a list of parent-child node relationships. Each item in the list stores a node identifier and its parent node identifier; the node without a parent node is the root node. When processing a specific cross-regional sampling task, the calculated feature values ​​are filled into the corresponding leaf nodes according to this predefined structure.

[0067] Resource professional capability characteristics are extracted from the resource information of the sampling execution terminals in each region, and these characteristics are then decomposed into corresponding hierarchical structures. The extraction operation is applied to the resource information of the sampling execution terminals in each region, which is read in real time during step S1. This information includes dynamic data such as a list of available sampling personnel skill levels, a list of equipment identifiers, and the current number of tasks. Resource professional capability characteristics are scalar values ​​calculated based on this dynamic data. Specifically, the extraction process is as follows: the arithmetic mean of all values ​​in the list of available sampling personnel skill levels is calculated, and this mean is used as a feature, named the average skill level of personnel; the number of devices in the equipment identifier list belonging to a preset high-precision equipment model list is divided by the total number of devices in the equipment identifier list, and the resulting ratio is used as a feature, named the proportion of high-precision equipment; the current number of tasks is divided by a pre-configured maximum parallel task capacity threshold for that region, and the resulting ratio is used as a feature, named the current task load rate. Decomposing these resource expertise features into a corresponding hierarchical structure involves pre-defining another directed acyclic graph (DAG) structure. The root node represents the overall resource capability of a region, and the first-level child nodes under the root node correspond to the specific features mentioned above, such as the average skill level of personnel, the proportion of high-precision equipment, and the current task load rate. This hierarchical structure is also implemented through a list of parent-child node relationships. In actual processing, a separate instance of this structure is instantiated for each region, and the feature values ​​calculated based on the resource information of that region are filled into the corresponding leaf nodes.

[0068] The maximum parallel task capacity threshold is set based on statistical analysis of historical task execution data from sampled execution terminals in a specific region. The specific method is as follows: collect the number of all sampled tasks successfully completed daily in the region within a preset period, such as 12 months, to form a historical task quantity sequence; calculate the statistical distribution of this sequence, such as its 85th percentile; round up this percentile value to obtain a benchmark value representing the robust execution capability of the region; then, taking into account the long-term development trend of the region's resources and security redundancy requirements, add a preset adjustment amount to this benchmark value, such as increasing the benchmark value by 10%, and the resulting integer value is set as the maximum parallel task capacity threshold for the region.

[0069] In step S3, the process of calculating the fit evaluation value based on the hierarchical structure using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method is specifically implemented as follows:

[0070] For each cross-regional sampling task, the hierarchical structure of its decomposed cross-regional sampling task complexity features is logically correlated and matched with the hierarchical structure of the decomposed resource professional capability features for each region. The hierarchical structures of the cross-regional sampling task complexity features and resource professional capability features here originate from the decomposition output of step S2. The specific operation of the logical correlation and matching analysis is as follows: each leaf node feature in the hierarchical structure of the cross-regional sampling task complexity features is paired with each leaf node feature in the hierarchical structure of the resource professional capability features for a specific region, forming multiple feature pair combinations. Then, for each feature pair, its logical correlation degree is evaluated according to a preset business rule base. The business rule base is a set of predefined if-if conditional statements, whose rules are derived from the inductive analysis of historical successful matching cases. For example, a rule could be: if the number of detection indicators in the task features is greater than a preset threshold for the number of complexity indicators, and the average skill level of personnel in the resource features is greater than a preset threshold for a high skill level, then the correlation degree of this feature pair is evaluated as high correlation. The degree of association is represented by discrete labels, such as high association, medium association, and low association. To facilitate subsequent numerical calculations, these labels are pre-mapped to numerical values; for example, high association is mapped to the value 3, medium association to the value 2, and low association to the value 1. This analysis process is executed independently for each task and each region to be evaluated, generating a set containing the association values ​​for all feature pairs for each task-region pair.

[0071] The specific implementation method for setting the threshold for the number of complex indicators is as follows: First, collect historical sampling task data, including the number of detection indicators and the actual execution time for each task. Then, calculate the average and standard deviation of the execution time of all historical tasks. Next, select tasks whose execution time exceeds the historical average execution time plus one standard deviation, defining them as tasks with significantly extended execution time. Finally, calculate the arithmetic mean of the number of detection indicators for these selected tasks, and round up the average value to the nearest integer, setting this as the threshold for the number of complex indicators.

[0072] The specific implementation method for setting the high skill level threshold is as follows: First, collect historical task completion quality data, including the quality evaluation score for each task and the skill level of the personnel performing the task. Set a high-quality evaluation score threshold, for example, defining an evaluation score higher than 80 out of 100 as a high evaluation result. Then, filter out all historical tasks that received high evaluation results. Next, calculate the arithmetic mean of the skill levels of the personnel performing these tasks. Finally, round this average up to one decimal place and set the resulting value as the high skill level threshold.

[0073] Based on the results of the matching analysis, the analytic hierarchy process (AHP) is used to construct judgment matrices for cross-regional sampling task complexity features relative to resource expertise features, and the weight allocation is calculated. In specific implementation, the AHP is applied to all leaf node features under the same parent node in the cross-regional sampling task complexity feature hierarchy structure corresponding to the current task. For example, for the first-level sub-features under the root node—namely, the number of regions covered, the number of product batches, the number of testing indicators, and the task time span—a 4x4 judgment matrix needs to be constructed. The value of the element in the i-th row and j-th column of the judgment matrix is ​​called the scale, representing the ratio of the importance of feature i to feature j in achieving the criterion of matching with the resource capabilities of the target region. This scale value is calculated based on the results of the matching analysis. The calculation method is as follows: First, for each task complexity feature, sum all the correlation scores obtained after matching analysis with all resource features of the target region to obtain the total correlation score of that feature. Then, calculate the ratio of the total correlation score of feature i to the total correlation score of feature j. Finally, the final scale value is determined according to a pre-defined mapping table between ratio ranges and scale values. This mapping table is set based on the standard scaling theory of the Analytic Hierarchy Process (AHP) and adjusted by experts' empirical judgments on the relative importance of task features. For example, the mapping table can stipulate: if the ratio is between 1.0 and 1.4, the scale value is 1, indicating that the two features are equally important; if the ratio is between 1.5 and 2.4, the scale value is 3, indicating that feature i is slightly more important than feature j; if the ratio is between 2.5 and 4.4, the scale value is 5, indicating significant importance; if the ratio is between 4.5 and 6.4, the scale value is 7, indicating strong importance; if the ratio is greater than or equal to 6.5, the scale value is 9, indicating absolute importance; if the ratio is less than 1, the reciprocal of the corresponding scale value is taken. After the judgment matrix is ​​constructed, the largest eigenvalue of the matrix is ​​calculated, and the normalized eigenvector corresponding to the largest eigenvalue is solved. Each component of this feature vector is a weighted value for the complexity feature of the corresponding cross-regional sampling task, and the sum of all weighted values ​​is 1.

[0074] The weight allocation method is introduced into the fuzzy comprehensive evaluation method to comprehensively evaluate the matching degree between the complexity characteristics of cross-regional sampling tasks and the characteristics of resource professional capabilities, generating a quantitative fit evaluation value. The implementation of the fuzzy comprehensive evaluation method includes the following sub-steps: The first step is to determine the set of evaluation factors and the set of evaluation levels. The set of evaluation factors is the set of complexity characteristics of the current cross-regional sampling task, such as the number of regional coverage areas, the number of product batches, the number of testing indicators, and the task time span. The set of evaluation levels is a predefined set of linguistic terms used to describe the final matching degree, for example, set as high matching degree, medium matching degree, and low matching degree. The second step is to construct a membership function for each task complexity characteristic. The membership function defines the degree to which the specific value of the characteristic belongs to each evaluation level, and its value ranges from 0 to 1. The shape and parameters of the membership function are pre-defined based on historical data analysis and business knowledge. Specifically, by analyzing historical sampled task data, the probability distribution of successful task-resource matching when a task complexity feature falls within different numerical ranges is observed, thereby determining the typical degree of membership of that feature to each evaluation level. For example, for the quantity feature of detection indicators, historical data analysis may show that when the quantity is less than or equal to 3, the probability of successful task matching is very high, so the degree of membership to high matching is set to 0.8; when the quantity is between 4 and 6, the success rate is moderate, so the degree of membership to moderate matching is set to 0.6; when the quantity is greater than or equal to 7, the success rate is low, so the degree of membership to low matching is set to 0.7. These specific degree values ​​constitute the parameters of the membership function. The third step is to construct a fuzzy relation matrix. The specific values ​​of each complexity feature of the current task are input into its corresponding membership function to calculate the membership degree of each feature relative to each evaluation level. The membership degree calculation results of all features are arranged into a matrix, where the rows of the matrix correspond to the task complexity features and the columns correspond to the evaluation levels. The fourth step is to perform fuzzy synthesis operations. The weighted row vector of cross-regional sampling task complexity features calculated in the previous step is multiplied with the fuzzy relation matrix. Specifically, for each evaluation level, the weighted value of each feature is multiplied by the feature's membership degree to that evaluation level. Then, the products of all features are summed to obtain the comprehensive membership degree for that evaluation level. The result is a comprehensive membership degree vector, where each element represents the degree to which the overall evaluation belongs to the corresponding evaluation level. The fifth step is defuzzification to generate quantified fit evaluation values. Each evaluation level is pre-assigned a score representing its level; for example, 90 points for high matching degree, 70 points for medium matching degree, and 50 points for low matching degree. These scores are benchmark values ​​set based on the value assessment of different matching degrees in business operations. Then, the weighted sum of each comprehensive membership degree in the comprehensive membership degree vector and its corresponding level score is calculated.The calculation formula is: Fit Evaluation Value = (First element of the comprehensive membership vector × Score for high matching degree) + (Second element × Score for medium matching degree) + (Third element × Score for low matching degree). The final calculated value is the fit evaluation value between the task and the resources of the region.

[0075] In step S4, the process of assessing resource load and generating an allocation plan based on the adaptation evaluation value, combined with the adaptation sampling rules for each region and the resource information of the sampling execution terminals in each region, is implemented as follows: Based on the resource information of the sampling execution terminals in each region, the real-time resource load status of the sampling execution terminals in each region is assessed. The resource information of the sampling execution terminals in each region here comes from the dynamic data read in real time in step S1, including the number of available sampling personnel, the number of available devices, and the number of sampling tasks currently being executed. The specific method for assessing the real-time resource load status is to calculate a quantified real-time resource load status assessment value. For each region, the calculation process requires three input data: the first is the number of sampling tasks currently being executed in that region; the second is a preset maximum parallel task capacity threshold for that region, which is a fixed value set after assessing the historical task execution capacity of that region, and the setting method has been described above; the third is the number of available sampling personnel and the total number of registered sampling personnel in that region; and the fourth is the number of available devices and the total number of registered devices in that region. The calculation formula for the real-time resource load status assessment value is executed in the following order. First, calculate the base load ratio, which equals the number of currently executing sampling tasks divided by the maximum concurrent task capacity threshold. Next, calculate the personnel availability rate, which equals the number of available sampling personnel divided by the total number of registered sampling personnel. Then, calculate the equipment availability rate, which equals the number of available devices divided by the total number of registered devices. Finally, sum the above three ratios using a weighted average, expressed by the formula: Real-time resource load status assessment value = (Base load ratio × weight W1) + (Personnel availability rate × weight W2) + (Equipment availability rate × weight W3). Here, weights W1, W2, and W3 are pre-defined constants that satisfy W1 + W2 + W3 = 1. For example, weight W1 can be set to 0.6, weight W2 to 0.25, and weight W3 to 0.15. The calculated real-time resource load status assessment value is a value between 0 and 1. The closer the value is to 1, the heavier the resource load in the area; the closer it is to 0, the less idle the area. This calculation process is executed automatically at fixed time intervals, such as once every 60 seconds, to ensure the timeliness of status information.

[0076] Combining the adaptation evaluation value and real-time resource load status, adaptive sampling rules that meet the load conditions are matched for cross-regional sampling task requirements. The adaptation evaluation value here comes from the calculation result of step S3 and is a quantitative score calculated for the degree of resource matching between each cross-regional sampling task and each region. The real-time resource load status is the real-time resource load status evaluation value of each region calculated in the previous step. The specific implementation logic of the matching process is as follows: The first step is to set the load conditions for screening, which includes two numerical thresholds. The first is the resource load status threshold, for example, set to 0.85. The setting of this resource load status threshold of 0.85 is based on the analysis of a large amount of historical task execution data. Specifically, historical task execution records are collected, which contain the regional resource load status evaluation value before task execution and the status indicator of whether the task actually experienced a delay. Through statistical analysis, the critical point of the resource load status evaluation value that causes the probability of task delay to begin to increase significantly is found. For example, statistics show that when the evaluation value exceeds 0.85, the delay probability jumps from 5% to 20%, therefore 0.85 is determined as the resource load status threshold. The second item is the adaptation evaluation value threshold, for example, set to 70. This threshold of 70 is based on the correlation analysis between historical adaptation evaluation values ​​and task execution results. Specifically, the method involves collecting the adaptation evaluation values ​​and final execution effect scores of historical tasks; analyzing the average execution effect score of tasks with adaptation evaluation values ​​in different score ranges to determine the minimum adaptation evaluation value boundary that guarantees basic execution effect requirements. For example, statistics show that tasks with adaptation evaluation values ​​above 70 points generally achieve an average execution effect score above the preset passing standard of 80 points; therefore, 70 is determined as the adaptation evaluation value threshold. The second step is to iterate through all regions for each cross-regional sampling task to be assigned, filtering out candidate regions that simultaneously meet two conditions: Condition 1: The real-time resource load status assessment value of the region is less than or equal to the resource load status threshold of 0.85. Condition 2: The adaptation evaluation value between the task and the region is greater than or equal to the adaptation evaluation value threshold of 70. The third step is to determine the corresponding regional adaptation sampling rules for each selected candidate region. Each region's adaptive sampling rule originates from the generation result of step S2. It has a unique binding relationship with the region, and the unique rule identifier can be indexed through the region code.

[0077] Based on the matching results, an allocation scheme is generated that defines the matching relationships between each sampling task and its corresponding adaptive sampling rule. The matching results are the list output from the previous step, which records the matching relationship between each cross-regional sampling task and one or more candidate regions and their corresponding rules. The specific implementation of generating the allocation scheme involves constructing an electronic spreadsheet containing several rows of data, each defining a matching relationship. Each row contains the following four fields: the first field, named Task Identifier, is used to fill in the unique identifier required by the cross-regional sampling task; the second field, named Matching Region Code, is used to fill in the code of the successfully matched region; the third field, named Adaptation Rule Identifier, is used to fill in the unique identifier of the adaptive sampling rule corresponding to that region; and the fourth field, named Matching Adaptation Evaluation Value, is used to fill in the specific adaptation evaluation value between the task and the region calculated in step S3. If a cross-regional sampling task matches multiple regions, multiple rows of data are created for that task in the spreadsheet, each row corresponding to a different matching region. Furthermore, the allocation scheme embeds a priority logic for handling resource conflicts. The specific implementation of this logic is as follows: The system monitors all matching relationships. When it detects that multiple different tasks have matching region code fields pointing to the same region code, it determines that a potential resource conflict exists. The system estimates the increase in the real-time resource load status assessment value of the region after each new task is assigned to it. The estimation method is based on the average resource consumption data of similar tasks in history. Then, the system sorts these task matching relationship rows pointing to the same region in descending order according to the value of the matching adaptation evaluation value field. The system attempts to assign tasks to the region in this sorted order, accumulating the estimated load increment after each task assignment and recalculating the estimated load value of the region. When the estimated load value exceeds the resource load status threshold of 0.85, the assignment stops, and subsequent task matching relationship rows that are ranked lower are marked as pending or removed from the matching candidates of the region. The final output electronic data sheet is the allocation scheme, which clearly specifies which tasks should be assigned to which regions and which specific adaptation sampling rules should be followed, providing a complete and operable set of instructions for the actual task dispatch.

[0078] In step S5, the process of performing a consistency pre-verification on the collected cross-regional sampling information according to the allocation scheme and selecting the cross-regional sampling information that has passed the verification is specifically implemented as follows:

[0079] Based on the matching relationships defined by the allocation scheme, appropriate sampling rules are determined to correspond to the collected cross-regional sampling information. The allocation scheme here originates from the results generated in step S4, and its data structure is an electronic data table containing fields such as task identifier, matching region code, and appropriate rule identifier. The collected cross-regional sampling information is an electronic data record reported after information collection is completed at the actual sampling site. Each record contains a task identifier field for associating with the sampling task and a region code field for identifying the sampling execution area. The specific steps for determining the corresponding adaptive sampling rule are as follows: When a piece of cross-regional sampling information is received, the task identifier and region code fields in the information are parsed; then, a query is performed in the electronic data table of the allocation scheme to check if there is a row of data whose task identifier field value is completely consistent with the parsed task identifier, and whose matching region code field value is completely consistent with the parsed region code; if a matching row is found, the value of the adaptive rule identifier field is read from that row of data; finally, based on the adaptive rule identifier, the complete rule content corresponding to the identifier is retrieved from the regional adaptive sampling rule library generated and persistently stored in step S2. This content is the adaptive sampling rule corresponding to the currently collected cross-regional sampling information.

[0080] Based on the requirements of the established adaptive sampling rules, a pre-consistency check is performed on the collected cross-regional sampling information. The adaptive sampling rules are a structured dataset where each clause explicitly specifies a particular technical requirement for sampling. For example, clause A stipulates that the sampling location must be upstream of the production line, clause B stipulates that the sample weight of a single sample must be between 50 and 100 grams, and clause C stipulates that the resolution of the sample photograph must not be lower than 1920×1080 pixels. The specific implementation process of the pre-consistency check involves performing the following operations for each applicable clause in the rules: Locating the data field corresponding to the clause requirement from the data records of the collected cross-regional sampling information. For example, for clause B, which specifies the sample weight, locating the numeric field recording the sample weight in the collected information. Then, comparing the actual value in the field with the numerical range or specific value specified in the clause requirement. The comparison logic varies depending on the data type of the clause requirement, mainly including numerical range comparison, string equality comparison, and logical inclusion relationship comparison. For example, for Clause B, check if the actual sample weight falls within the closed range of 50 to 100; for Clause A, check if the sampling location description text in the data collection information contains the keyword "upstream of the production line"; for Clause C, check if the width and height values ​​of the photo resolution field in the data collection information are greater than or equal to 1920 and 1080, respectively. Each completed clause comparison generates a verification result, either passed or failed. After all clauses have been verified, an overall verification conclusion is generated, which can be either all clauses passed or at least one clause failed.

[0081] Based on the results of the consistency pre-verification, cross-regional sampling information that meets the requirements of the adaptive sampling rules is selected as qualified cross-regional sampling information. The specific implementation logic of the selection process is as follows: A verification status identifier is established for each received cross-regional sampling information in the processing flow. When the overall verification conclusion of the information is that all clauses pass, the processing flow sets the verification status identifier of the information to qualified. When the overall verification conclusion is that at least one clause fails, the processing flow sets the verification status identifier of the information to unqualified, and optionally records the clause number and specific reason for the failure in the information's auxiliary log. The selection operation occurs when the information needs to be summarized or used for subsequent analysis. The processing flow provides a selection interface that, according to the calling instruction, extracts data records with qualified verification status identifiers from all verified cross-regional sampling information. The set of these extracted data records is defined as the qualified cross-regional sampling information. For information with an unqualified verification status, the processing flow can perform preset follow-up processing, such as routing it to a queue for correction and triggering a notification process to inform relevant parties that the information has been blocked because it does not meet the rule requirements and needs to be verified and re-collected. Through the above specific and coherent implementation methods, step S5, after information collection and before summary analysis, completes rule determination based on exact matching, automated verification based on clause comparison, and data filtering based on verification status, thereby ensuring the consistency of sampled information entering subsequent stages at the rule level.

[0082] In step S6, the process of standardizing and organizing the verified cross-regional sampling information according to the clause mapping relationship and forming unified cross-regional sampling information summary data is specifically implemented as follows:

[0083] Based on the clause mapping relationship, the corresponding standardization conversion target is determined for the verified cross-regional sampling information. The clause mapping relationship here originates from the data structure established in step S2, which records the association between nationally applicable sampling standard clauses and the clauses of regionally differentiated sampling implementation rules. The verified cross-regional sampling information originates from the output of step S5, with each piece of information associated with a specific adaptive sampling rule identifier. The specific operation for determining the standardization conversion target is as follows: For a verified cross-regional sampling information, firstly, through its associated adaptive sampling rule identifier, trace back to the identifier of the specific regionally differentiated sampling implementation rule clause on which this rule was based when it was generated; then, query the clause mapping relationship table established in step S2, and based on the identifier of this regionally differentiated sampling implementation rule clause, find its corresponding standard clause identifier in the nationally applicable sampling standard; the specific clause content in the nationally applicable sampling standard pointed to by this standard clause identifier is the target of this standardization conversion. For example, a piece of information from a specific region has a sample size of 15 items, based on a rule clause that specifies a sample size of 15 items for regional differentiation. By querying the clause mapping relationship, the corresponding clause in the national standard maps to a sample size of 10 items. Therefore, the specific requirement of the national standard clause that specifies a sample size of 10 items becomes the target for standardization transformation of the sample size field in this piece of information.

[0084] According to the established standardization and transformation goals, the verified cross-regional sampling information is standardized and transformed. Standardization and transformation involves performing corresponding data transformation operations on each data field requiring transformation, based on the requirements of the transformation goals. The transformation operations primarily rely on a predefined transformation rule base. This rule base is a set of data processing logics manually summarized and set after analyzing the differences between the national general sampling standard and the implementation details of differentiated sampling in various regions. The transformation rule base defines standardized processing methods for different types of differences. Common transformation types include numerical conversion, unit conversion, code mapping, and text normalization. For example, in the example above, when the sampling quantity is transformed from the regional requirement of 15 items to the national standard requirement of 10 items, the corresponding numerical conversion rule in the transformation rule base is triggered. The core logic of this rule is to calculate a transformation coefficient, which is equal to the value required by the national standard divided by the value actually implemented in the region; that is, the transformation coefficient is 10 / 15, approximately equal to 0.6667. Then, all derived values ​​related to the sampling quantity in the original information, such as the total sample weight and estimated testing cost, are multiplied by this conversion factor to adjust them to equivalent values ​​under the national standard of a sampling base of 10 items. For unit conversion, for example, if the regional information uses jin (a traditional Chinese unit of weight) as the weight unit, while the national standard requires the use of kilograms, the calculation is performed according to a predefined conversion rate, such as 1 jin equals 0.5 kilograms. For code mapping, for example, if the internal product classification code used in the region is A01, while the unified product classification code used in the national standard is GB-1001, A01 is replaced with GB-1001 by querying a pre-defined code mapping table. Text normalization converts texts with different expressions but the same meaning into standard terms, such as unifying "upstream of the production line" and "front end of the production line" into the standard term "starting segment of the production line." Each conversion operation generates a conversion log, recording the original value, the target conversion value, the applied conversion rule, and the conversion result value. For conversion types not defined in the conversion rule base, the system will mark the information as awaiting manual processing.

[0085] The standardized and verified cross-regional sampling information is merged to form a unified summary of cross-regional sampling information. The merging process involves creating a new, uniformly structured data storage table. The field architecture of this table is designed entirely according to the data items specified in the nationally accepted sampling standard. The merging process handles each verified cross-regional sampling information record that has undergone standardization and transformation. For each record, the following operations are performed: all data fields, including transformed numerical fields, replaced code fields, and normalized text fields, are filled into the corresponding columns of the new data storage table according to predefined field correspondences. Simultaneously, the unique identifier of the original information and the source region code are filled in as metadata fields for traceability. Once all information is filled in, the new data storage table contains data records from different regions, all converted to the unified national standard. This collection of data records constitutes the unified summary of cross-regional sampling information. To ensure the integrity of the aggregated data, a consistency check is performed during the merging process. For example, it checks whether the transformed core indicator values ​​of sampling information from different regions for the same batch of products are within a reasonable statistical fluctuation range, such as ±20% of the average. This aggregated data exists in structured spreadsheet or database table format and can be directly used for cross-regional comparative analysis, trend statistics, and comprehensive quality assessment. Through the above specific and coherent implementation methods, step S6 utilizes pre-established clause mapping relationships and a transformation rule base to organize and merge compliance information from different rule systems into a unified data set through a series of well-defined transformation operations.

[0086] Example 2: Figure 2 A schematic diagram of the product quality sampling information processing system of the present invention is provided. The product quality sampling information processing system includes:

[0087] The information acquisition module is used to acquire national general sampling standards, regional differentiated sampling implementation rules, regional sampling execution resource information, and cross-regional sampling task requirements.

[0088] The rule generation module is used to extract common and differentiated clauses from the national general sampling standard and the implementation details of differentiated sampling in various regions, establish clause mapping relationships and generate sampling rules adapted to each region; at the same time, it extracts the complexity characteristics of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extracts the resource professional capability characteristics from the resource information of sampling execution end in each region, and decomposes them into corresponding hierarchical structures.

[0089] The adaptation evaluation module is used to calculate the adaptation evaluation value based on the hierarchical structure using the analytic hierarchy process and the fuzzy comprehensive evaluation method.

[0090] The task allocation module is used to conduct resource load assessment based on the adaptation evaluation value, the adaptation sampling rules of each region, and the resource information of the sampling execution terminal of each region, and generate an allocation plan.

[0091] The information verification module is used to perform a pre-consistency verification on the collected cross-regional sampling information according to the allocation scheme, and to filter out the cross-regional sampling information that has passed the verification.

[0092] The data processing module is used to standardize and process the verified cross-regional sampling information according to the clause mapping relationship, forming a unified summary of cross-regional sampling information data.

[0093] Example 3: The present invention provides a device, the device including: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein when the program or instructions are executed by the processor, a product quality sampling information processing method is implemented.

[0094] Example 4: The present invention provides a computer-readable medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, a method for processing product quality sampling information is implemented.

[0095] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0096] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0097] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0098] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0100] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for processing product quality sampling information, characterized in that, include: S1. Obtain nationally applicable sampling standards, regionally differentiated sampling implementation rules, resource information for sampling execution in each region, and cross-regional sampling task requirements. S2. Extract common and differentiated clauses from the national general sampling standard and the implementation details of differentiated sampling in various regions, establish clause mapping relationships, and generate sampling rules adapted to each region; at the same time, extract the complexity features of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extract resource professional capability features from the resource information of sampling execution terminals in each region, and decompose them into corresponding hierarchical structures; in the hierarchical structure of task complexity features, the first-level sub-nodes under the root node correspond to the regional coverage quantity feature node, the product batch quantity feature node, the testing indicator quantity feature node, and the task time span feature node; in the hierarchical structure of resource professional capability features, the first-level sub-nodes under the root node correspond to the average skill level feature node, the proportion of high-precision equipment feature node, and the current task load rate feature node. S3. Based on the hierarchical structure, the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation method are used to calculate the fit evaluation value, including: For each cross-regional sampling task, the features of each leaf node in the task complexity feature hierarchy are paired with the features of each leaf node in the resource professional capability feature hierarchy of a specific region to form multiple feature pair combinations. For each feature pair, its logical correlation is evaluated according to a preset business rule base. One of the rules is: if the feature value of the number of detection indicators is greater than a preset threshold for the number of complexity indicators, and the feature value of the average skill level of personnel is greater than a preset threshold for the high skill level, then this feature pair is evaluated as having a high correlation. For the first-level child nodes under the root node in the hierarchical structure of task complexity features, a judgment matrix is ​​constructed. The value of the element in the i-th row and j-th column of the judgment matrix is ​​called the scale, which represents the ratio of the importance of feature i to feature j in achieving the criterion of matching with the resource capabilities of the target region. For each task complexity feature, all correlation scores obtained after matching analysis with all resource features in the target region are summed to obtain the total correlation score of the feature. The ratio of the total correlation score of feature i to the total correlation score of feature j is calculated, and the value of the scale is determined according to a preset mapping table between the ratio interval and the scale value. The maximum eigenvalue of the judgment matrix is ​​calculated, and the normalized eigenvector corresponding to the maximum eigenvalue is solved. Each component of the eigenvector is the weight allocation value of the corresponding cross-regional sampling task complexity feature. Determine the set of evaluation factors and the set of evaluation levels; construct a membership function for each task complexity feature; construct a fuzzy relation matrix: input the specific values ​​of each complexity feature of the current task into its corresponding membership function, and calculate the membership degree of each feature relative to each evaluation level; arrange the membership degree calculation results of all features into a matrix, where the rows of the matrix correspond to the task complexity features and the columns correspond to the evaluation levels; perform fuzzy synthesis operation: perform matrix multiplication operation on the feature vector and the fuzzy relation matrix; defuzzify to generate quantified adaptive evaluation values; S4. Based on the adaptation evaluation value, combined with the adaptation sampling rules of each region and the resource information of the sampling execution terminal of each region, conduct resource load assessment and generate allocation plan; S5. Based on the allocation scheme, perform a consistency pre-verification on the collected cross-regional sampling information and select the cross-regional sampling information that has passed the verification. S6. Standardize and organize the qualified cross-regional sampling information according to the clause mapping relationship to form a unified summary data of cross-regional sampling information.

2. The product quality sampling information processing method according to claim 1, characterized in that, S1 includes: Read the national general sampling standards and the regional differentiated sampling implementation rules from the central standards library; The system reads the resource information of the sampling execution terminals in each region in real time and receives cross-regional sampling task requests submitted by the task initiator.

3. The product quality sampling information processing method according to claim 2, characterized in that, S2 include: By comparing the national general sampling standard with the regional differentiated sampling implementation rules, common clauses and differentiated clauses were extracted. Establish a clause mapping relationship based on common and differentiated clauses, and generate regional adaptation sampling rules based on the clause mapping relationship; The complexity features of cross-regional sampling tasks are extracted from the requirements of cross-regional sampling tasks, and the complexity features of cross-regional sampling tasks are decomposed into corresponding hierarchical structures. Resource professional capability characteristics are extracted from the resource information of the sampling execution terminals in each region, and these characteristics are then decomposed into corresponding hierarchical structures.

4. The product quality sampling information processing method according to claim 3, characterized in that, S5 include: Based on the matching relationship defined by the allocation scheme, determine the appropriate sampling rules corresponding to the collected cross-regional sampling information; Based on the requirements of the established adaptive sampling rules, a consistency pre-verification is performed on the collected cross-regional sampling information. Based on the results of the consistency pre-verification, cross-regional sampling information that meets the requirements of the adaptive sampling rules is selected as qualified cross-regional sampling information.

5. The product quality sampling information processing method according to claim 4, characterized in that, S6 include: Based on the clause mapping relationship, the corresponding standardization transformation target is determined for the cross-regional sampling information that has passed the verification. In accordance with the established standardization and transformation objectives, the verified cross-regional sampling information is standardized, organized, and transformed. The standardized and transformed cross-regional sampling information is merged to form a unified summary of cross-regional sampling information data.

6. A product quality sampling information processing system, used to implement the product quality sampling information processing method according to any one of claims 1-5, characterized in that, include: The information acquisition module is used to acquire national general sampling standards, regional differentiated sampling implementation rules, regional sampling execution resource information, and cross-regional sampling task requirements. The rule generation module is used to extract common and differentiated clauses from the national general sampling standard and the implementation details of differentiated sampling in various regions, establish clause mapping relationships and generate sampling rules adapted to each region; at the same time, it extracts the complexity characteristics of cross-regional sampling tasks from the requirements of cross-regional sampling tasks, extracts the resource professional capability characteristics from the resource information of sampling execution end in each region, and decomposes them into corresponding hierarchical structures. The adaptation evaluation module is used to calculate the adaptation evaluation value based on the hierarchical structure using the analytic hierarchy process and the fuzzy comprehensive evaluation method. The task allocation module is used to conduct resource load assessment based on the adaptation evaluation value, the adaptation sampling rules of each region, and the resource information of the sampling execution terminal of each region, and generate an allocation plan. The information verification module is used to perform a pre-consistency verification on the collected cross-regional sampling information according to the allocation scheme, and to filter out the cross-regional sampling information that has passed the verification. The data processing module is used to standardize and process the verified cross-regional sampling information according to the clause mapping relationship, forming a unified summary of cross-regional sampling information data.

7. A computer-readable medium, characterized in that, A program or instructions are stored on a computer-readable medium, which, when executed by a processor, implement the product quality sampling information processing method as described in any one of claims 1-5.

Images