Inspection institution-oriented review dispatch scheme generation method and device, and medium

Abstract

The embodiment of the invention provides an examination institution-oriented review dispatch scheme generation method and device and a medium. The method comprises the following steps: reading attribute data of a plurality of key judgment elements and all candidate reviews, and screening the attribute data based on a preset fairness avoidance condition to obtain a to-be-selected reviewer set; based on an analytic hierarchy process, constructing a hierarchical data structure for optimizing a courier dispatching scheme; based on a preset discrimination matrix, calculating the weight of each quantitative characteristic parameter in the plurality of quantitative characteristic parameters and performing consistency verification to obtain an effective weight set; generating a plurality of candidate review group schemes according to the to-be-selected reviewer set, and for each candidate review group scheme, calculating a comprehensive evaluation value according to the effective weight set and the corresponding attribute data; and based on the comprehensive evaluation value, determining an optimal dispatch decision in the plurality of candidate review group schemes. By using the method provided by the embodiment of the invention, the efficiency and the accuracy of dispatch decision making can be obviously improved.

Description

Technical Field

[0001] This disclosure relates to the field of information technology management and application technology, specifically to a method, apparatus and medium for generating review and dispatch schemes for inspection agencies. Background Technology

[0002] With the rapid development of the testing and inspection industry, the demand for accreditation assessments continues to grow. Currently, the industry generally adopts the traditional manual assignment model, which relies on staff experience and manually selects assessors based on basic criteria (such as professional field and region). However, this method has significant limitations: firstly, over-reliance on subjective experience leads to poor consistency in assignment results; secondly, it lacks quantifiable means for implicit criteria such as assessor performance history, impartiality avoidance, and training needs, making it difficult to achieve multi-dimensional and accurate matching; and thirdly, it lacks a feedback mechanism for assignment effectiveness, failing to form a closed-loop decision-making process for continuous optimization.

[0003] While decision-making methods such as neural networks and fuzzy comprehensive evaluation exist, none are well-suited to this scenario. Neural networks require a large number of training samples, but the specific nature of accreditation review projects leads to insufficient samples; fuzzy comprehensive evaluation suffers from strong subjectivity in weight determination and lacks a correction mechanism. Therefore, existing technologies cannot meet the needs of accreditation review personnel for quantitative decision-making and adaptability to small samples, necessitating an innovative solution that can systematically integrate multi-dimensional criteria and achieve scientific quantitative decision-making. Summary of the Invention

[0004] To address the aforementioned technical problems, the present disclosure provides a solution. Embodiments of this disclosure offer a method, apparatus, and medium for generating review dispatch plans for inspection agencies.

[0005] According to a first aspect of the present disclosure, a method for generating a review personnel dispatch plan for inspection agencies is provided, wherein the method includes: Read multiple key judgment elements and attribute data of all candidate reviewers, wherein the key judgment elements come from a pre-stored rule base and the attribute data come from a pre-stored database; The attribute data is filtered based on preset impartiality avoidance conditions to obtain a set of candidate reviewers; A hierarchical data structure for optimizing personnel dispatching schemes is constructed based on the analytic hierarchy process (AHP). The hierarchical data structure includes an optimization target configuration, a parameter calculation layer, and a scheme layer. The parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements. The scheme layer is used to accommodate candidate review group schemes. Based on the preset discrimination matrix, with the optimization target configuration as the optimization target, the weight of each of the multiple quantization feature parameters is calculated and consistency verification is performed to obtain an effective weight set; In response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, for each candidate review group scheme, a comprehensive evaluation value of the candidate review group scheme is calculated based on the effective weight set and the corresponding attribute data. Based on the comprehensive evaluation value, the optimal dispatch plan is determined from the multiple candidate review group plans included in the plan layer.

[0006] According to a second aspect of the present disclosure, an apparatus for generating review dispatch schemes for inspection agencies is provided, wherein the apparatus includes: The data acquisition unit is configured to read multiple key judgment elements and attribute data of all candidate reviewers, wherein the key judgment elements come from a pre-stored rule base and the attribute data come from a pre-stored database; The data filtering unit is configured to filter the attribute data based on preset impartiality avoidance conditions to obtain a set of candidate reviewers; The modeling unit is configured to: construct a hierarchical data structure for optimizing dispatching schemes based on the analytic hierarchy process; wherein the hierarchical data structure includes optimization target configuration, parameter calculation layer and scheme layer, the parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements, and the scheme layer is used to accommodate candidate review group schemes; The weight calculation unit is configured to: calculate the weight of each of the multiple quantized feature parameters based on a preset discrimination matrix and with the optimization target configuration as the optimization target, and perform consistency verification to obtain an effective weight set; The evaluation value determination unit is configured to: in response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, calculate the comprehensive evaluation value of each candidate review group scheme based on the set of effective weights and the corresponding attribute data; The decision-making unit is configured to: determine the optimal dispatch plan from multiple candidate review group plans included in the plan layer based on the comprehensive evaluation value.

[0007] According to a third aspect of the present disclosure, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the review dispatch scheme generation method for inspection agencies described in the present disclosure.

[0008] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, the storage medium storing a computer program for executing the review dispatch scheme generation method for inspection agencies described in this disclosure.

[0009] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, it implements the method for generating review dispatch schemes for inspection agencies as described in the present disclosure.

[0010] As described above, the method for generating assessment dispatch plans for inspection agencies provided in this disclosure effectively overcomes the shortcomings of traditional manual dispatch models, such as strong subjectivity and poor consistency, by automatically reading standardized data from a pre-stored rule base and database and conducting preliminary screening based on clear impartiality avoidance conditions. This lays a reliable data foundation for subsequent scientific decision-making. Its core lies in systematically applying the Analytic Hierarchy Process (AHP) to dispatch decision-making. By constructing a structured "objective-criteria-plan" model, complex dispatch criteria are transformed into a quantifiable evaluation system. Objective weight allocations are calculated using a discriminant matrix and a strict consistency verification mechanism, thereby transforming qualitative judgments based on personal experience into data-driven decisions based on unified standards. Ultimately, through automatic plan generation, quantitative evaluation, and optimized selection, the efficiency and accuracy of dispatch decisions are significantly improved, ensuring the impartiality and authority of accreditation activities. Furthermore, an iteratively optimized closed-loop process is formed, providing practical technical support for the intelligent management of accreditation agencies. Attached Figure Description

[0011] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 This is a flowchart illustrating a method for generating review dispatch schemes for inspection agencies, provided in an exemplary embodiment of this disclosure. Figure 2 This is a public announcement Figure 1 One of the exemplary flowcharts of the method for generating review dispatch schemes for inspection agencies provided in the embodiment; Figure 3 This is a public announcement Figure 1 The second exemplary flowchart of the method for generating review personnel dispatch schemes for inspection agencies provided in the embodiment; Figure 4 This is a public announcement Figure 1 The third exemplary flowchart of the method for generating review personnel dispatch schemes for inspection agencies provided in the embodiment; Figure 5 This is a public announcement Figure 1The fourth exemplary flowchart of the method for generating review dispatch schemes for inspection agencies provided in the embodiment; Figure 6 This is a public announcement Figure 1 The fifth exemplary flowchart of the method for generating review personnel dispatch schemes for inspection agencies provided in the embodiment; Figure 7 This is a public announcement Figure 1 The sixth exemplary flowchart of the method for generating review personnel dispatch schemes for inspection agencies provided in the embodiment; Figure 8 This is a schematic diagram of the structure of an inspection agency review dispatch scheme generation device provided in an exemplary embodiment of the present disclosure; Figure 9 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed herein. Detailed Implementation

[0013] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present disclosure, and not all embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0014] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0015] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0016] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0017] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0018] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0019] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0020] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0021] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0022] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0024] Overview of the inventive concept The core inventive concept of this disclosed technical solution lies in addressing the pain points of the traditional manual dispatch model in the accreditation review of the inspection and testing industry, which suffers from strong subjectivity and poor consistency. It creatively applies the Analytic Hierarchy Process (AHP) to this specific scenario, systematically organizing international and domestic standards to form multi-dimensional quantitative characteristic parameters, and constructing a "target..." Guidelines The hierarchical structure model of the "solution" uses a discrimination matrix to calculate the criterion weights and ensures the scientific nature of the decision through consistency verification. Finally, it outputs the optimal dispatch decision by automatically calculating the comprehensive evaluation value of the candidate review group's schemes, realizing the transformation from an experience-dependent to a data-driven intelligent dispatch model.

[0025] Based on the above-mentioned inventive concept, this disclosure proposes a method, apparatus and medium for generating review dispatch schemes for inspection agencies as described in the following embodiments.

[0026] Example 1 Figure 1 This is a schematic flowchart of a method for generating review dispatch schemes for inspection agencies, provided by an exemplary embodiment of this disclosure. The method can be executed on a server (e.g., a cloud service platform or a locally deployed server).

[0027] Specifically, refer to Figure 1 The method for generating review and dispatch plans for inspection agencies includes: S110: Read multiple key judgment elements and attribute data of all candidate reviewers.

[0028] The key decision-making elements come from a pre-stored rule base, and the attribute data comes from a pre-stored database.

[0029] The system (i.e., the system deployed on the server side executing the "Method for Generating Review and Dispatch Schemes for Inspection Agencies" provided in this disclosure; hereinafter the same) accesses a pre-stored rule base through a predefined data interface and automatically reads multiple key decision elements. This rule base is a digital knowledge base constructed through structured parsing of international standard ISO / IEC 17011:2017, national standard GB / T 27011-2019, and CNAS normative documents. Based on a built-in natural language processing module and rule extraction algorithm, the system automatically identifies and extracts core requirements related to dispatch decisions from standard texts, performs automatic classification and deduplication through a classifier, and finally generates 20 structured key decision elements. These elements include, but are not limited to: the reviewer's qualification status, the type of database the reviewer is in, the professional coverage of the review team members, the professional coverage of the review team leader, the requirement for simultaneous review of qualification certification, the type of employment, the requirement for joint review, the internal calibration requirement, the limit on the number of reviews, the requirement for free time during designated periods, the reviewer's past performance, the distance from the review location, the restriction on reviewing the same institution twice consecutively, the ratio of trainee reviewers to technical experts, the limit on the total number of people in one review, the supervision arrangement for the chief reviewer, the requirement for maintaining the reviewer's qualification, the requirement for promotion from trainee to technical level, and the requirement for promotion from trainee team leader to chief level.

[0030] Simultaneously, the system also accesses the pre-stored reviewer information database through a database query engine to batch read the attribute data of all candidate reviewers. The system employs a structured data table design, with each field corresponding to a specific attribute dimension, including: reviewer unique identifier, qualification status identifier, qualification level code, database type code, professional field code set, historical review frequency statistics, schedule status identifier, review performance score data, geographical location coordinates, conflict of interest declaration identifier, consultation service record list, social relationship identifier, employment type code, and training need status identifier, etc.

[0031] The system utilizes automated data pipeline technology to synchronously load key decision-making element definitions from the rule base and attribute records from the database into the in-memory data processing module, converting them into a unified internal data format. The entire process is triggered by scheduled tasks or API calls, achieving fully automated data collection and standardized processing without manual intervention, providing a complete and accurate data foundation for subsequent intelligent dispatch decisions.

[0032] S120. Based on preset impartiality avoidance conditions, the attribute data is filtered to obtain a set of candidate reviewers.

[0033] Specifically, the pre-defined conditions for recusal based on impartiality may include: the reviewer having a conflict of interest with the reviewed institution, having provided consulting services to the institution within the past three years, or having a specific relationship that could affect impartiality (such as a kinship or mentorship relationship). The system automatically scans attribute data to determine whether candidate reviewers violate at least one of the above rules; if so, they are excluded, thereby generating a set of candidate reviewers.

[0034] S130. Construct a hierarchical data structure for optimizing dispatching schemes based on the analytic hierarchy process.

[0035] The hierarchical data structure includes an optimization target configuration, a parameter calculation layer, and a scheme layer. The parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements, and the scheme layer is used to accommodate candidate review group schemes.

[0036] Specifically, the Analytic Hierarchy Process (AHP) is used to construct a three-tiered hierarchical data structure: the optimization objective is configured as "optimal decision-making for intelligent personnel dispatch in accreditation review"; the parameter operation layer contains multiple quantitative characteristic parameters, which are grouped based on key judgment elements, including four primary parameters: professional competence, review experience, review performance, and training needs, with each primary parameter corresponding to several secondary indicators (a total of 20 key judgment elements); the solution layer is used to accommodate candidate review group solutions. The data structure establishes a dominance relationship between the layers, with the optimization objective dominating the parameter operation layer, and the parameter operation layer dominating the solution layer.

[0037] S140. Based on the preset discrimination matrix, with the optimization target configuration as the optimization target, calculate the weight of each of the multiple quantization feature parameters and perform consistency verification to obtain an effective weight set.

[0038] Specifically, the preset discrimination matrix is ​​constructed based on the Satie 1-9 scaling method and determined by averaging expert scores. The system uses the aforementioned optimization target configuration as the optimization objective, employs eigenvalue calculation to solve for the maximum eigenvalue (λ_max) and eigenvector of the matrix, and normalizes the eigenvector to obtain the initial weights of each quantized feature parameter. Then, it calculates the consistency index CI = (λ_max - n) / (n-1) (where n is the matrix order), queries the random consistency index RI, and calculates the consistency ratio CR = CI / RI. When CR < 0.1, the consistency check is passed, the initial weights are valid, and a valid weight set is formed; otherwise, the matrix is ​​adjusted and recalculated.

[0039] S150. In response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, for each candidate review group scheme, calculate the comprehensive evaluation value of the candidate review group scheme based on the effective weight set and the corresponding attribute data.

[0040] Specifically, based on the current review task requirements (such as institution size and professional field), the system automatically generates multiple candidate review group schemes from the set of candidate reviewers (such as different combinations of group leader and members in each group). For each scheme, based on the effective weight set and the attribute data of each reviewer (such as professional matching degree and historical scores), each quantitative feature parameter is quantitatively scored, and then the comprehensive evaluation value of the scheme is calculated by weighted summation.

[0041] S160. Based on the comprehensive evaluation value, determine the optimal dispatch plan from the multiple candidate review group plans included in the plan layer.

[0042] Specifically, the system compares the comprehensive evaluation values ​​of all candidate review group proposals and selects the proposal with the highest comprehensive evaluation value as the optimal personnel dispatch decision. If multiple proposals have the same and highest comprehensive evaluation value, the system further compares the review costs (such as travel expenses and review person-days) and selects the proposal with the lowest cost as the final decision. The result output is a specific list of review groups.

[0043] As described above, the method for generating assessment dispatch plans for inspection agencies provided in this disclosure automatically reads standardized data from a pre-stored rule base and database, and performs preliminary screening based on clear impartiality avoidance conditions. This effectively overcomes the shortcomings of traditional manual dispatch models, such as strong subjectivity and poor consistency, laying a reliable data foundation for subsequent scientific decision-making. Its core lies in systematically applying the Analytic Hierarchy Process (AHP) to dispatch decision-making. By constructing a structured hierarchical data structure of "objective-criteria-plan," complex dispatch criteria are transformed into a quantifiable evaluation system. An objective weight allocation is calculated using a discriminant matrix and a strict consistency verification mechanism, thereby transforming qualitative judgments based on personal experience into data-driven decisions based on unified standards. Ultimately, through automatic plan generation, quantitative evaluation, and optimized selection, not only is the efficiency and accuracy of dispatch decision-making significantly improved, ensuring the impartiality and authority of accreditation activities, but also an iteratively optimized closed-loop process is formed, providing practical technical support for the intelligent management of accreditation agencies.

[0044] Example 2 Based on the above embodiment 1, referring to Figure 2 As an optional implementation, the step S120, "screening the attribute data based on preset impartiality avoidance conditions to obtain a set of candidate reviewers," can be achieved in the following way: S1210. Read the preset impartiality avoidance conditions. These conditions include conflict of interest detection conditions, service time window conditions, and specific relationship identification conditions.

[0045] As an optional example, the system reads preset impartiality avoidance conditions from a pre-stored rule base. These conditions are hard rules that are digitally defined based on the specific requirements of international standards, national standards, and CNAS normative documents.

[0046] Specifically: The conflict of interest detection condition is used to detect whether there is a conflict of interest between the candidate reviewer and the reviewed organization that may affect impartiality. Specific rules include, but are not limited to, determining whether the candidate reviewer's "work unit," "shareholding information," or "part-time organization" attributes overlap or are related to the reviewed organization or its affiliates (such as parent companies or subsidiaries). The service time window condition is used to detect whether the candidate reviewer has recently provided services to the reviewed organization. Specific rules involve setting a preset time window (e.g., industry practice, setting it to the past 3 years) and determining whether the candidate reviewer's "historical service record" attribute data contains records of providing consulting, technical support, or other services to the current reviewed organization within that time window. The specific relationship identification condition is used to detect whether there is a specific social relationship between the candidate reviewer and key personnel of the reviewed organization that may affect impartiality. Specific rules include, but are not limited to, comparing the reviewed organization's "key personnel information" (such as top manager, technical head) with the candidate reviewer's "social relationship" attribute data to determine whether there are direct relatives, collateral relatives within three generations, close relatives by marriage, or a long-term, stable mentor-apprentice relationship.

[0047] S1220. Match the attribute data of the candidate reviewers with the impartiality avoidance conditions, and identify the candidate reviewer identifiers that meet any one of the impartiality avoidance conditions.

[0048] As an optional example, the system iterates through the attribute data of all candidate reviewers and automatically compares and matches it with the various impartiality avoidance conditions read in S1210. This matching process is based on logical judgments according to predefined rules: for the conflict of interest detection condition, the system performs string matching, organization code comparison, or relationship graph query. For the service time window condition, the system performs timestamp numerical comparison to check whether the service record time falls within the preset time window. For the specific relationship identification condition, the system performs precise or fuzzy matching of the name field, ID number field, or relationship identifier field. Once the attribute data of a candidate reviewer meets the rules defined by any condition, the system records its unique identifier (such as reviewer ID) in the "List of Reviewers to be Excluded". This process is completely automated by the server and requires no manual intervention.

[0049] S1230. Remove all data records corresponding to the identified candidate reviewer identifiers from the initial candidate reviewer set to generate the candidate reviewer set.

[0050] As an optional example, the system obtains the "List of Reviewers to be Excluded" generated in step S1220. Subsequently, the system operates on the initial set of candidate reviewers stored in memory or a database, checking the unique identifier of each data record. If a record's identifier exists in the "List of Reviewers to be Excluded," that record is removed from the currently processed dataset. This "removal" operation can logically be performed by setting a status flag (e.g., setting the is_eligible field to false), or it can directly generate a new dataset that does not contain these records. Finally, the system outputs a clean set of candidate reviewers that has passed the initial impartiality screening, which will serve as input for subsequent personnel assignment decisions. Simultaneously, the system can record an operation log for this screening, including the identifiers of the excluded reviewers and the specific avoidance conditions triggered, for audit traceability.

[0051] Based on the above embodiment 1, referring to Figure 3 As an optional implementation, the "hierarchical data structure for optimizing dispatching schemes based on the analytic hierarchy process" in S130 can be implemented in the following way: S1310. Establish the dominance relationship between the optimization target configuration, the parameter calculation layer, and the scheme layer. Wherein, the optimization target configuration dominates the parameter calculation layer, and the parameter calculation layer dominates the scheme layer.

[0052] As an optional example, this step completes the logical structure construction of the hierarchical data structure within the system. The establishment of the dominance relationship is achieved through data modeling: First, in the system data model, "Optimal Decision for Smart Dispatch of Personnel for Approval Review" is set as the root node (optimization target configuration). This root node is directly linked to a set of nodes representing various quantitative characteristic parameters (parameter operation layer) through predefined logical associations. This means that the final evaluation of the dispatch decision will be entirely determined by the various criteria contained in the parameter operation layer, and the degree to which the optimization target configuration is achieved depends on the satisfaction of the parameter operation layer.

[0053] Secondly, each quantified feature parameter node in the parameter calculation layer is further associated with each candidate review group scheme node in the scheme layer. This association means that the merits of each candidate review group scheme need to be measured and evaluated by its degree of compliance with each quantified feature parameter. The scheme layer consists of specific, alternative operational schemes, and its value is determined by its contribution to the upper-level criteria.

[0054] By using the above methods, a directed hierarchical relationship from top-level goals to bottom-level solutions is constructed in the system's data model or knowledge graph, ensuring the hierarchical and structured nature of the decision-making logic.

[0055] S1320. The plurality of quantization feature parameters are grouped according to their attributes to form a hierarchical structure of the parameter operation layer.

[0056] As an optional example, this step refines and organizes the internal structure of the parameter operation layer, and its specific implementation is as follows: First, the system calls multiple key decision elements (e.g., 20 elements) read from the rule base.

[0057] Secondly, based on the predefined classification logic (which is set based on the interpretation of international standard ISO / IEC 17011, national standard GB / T 27011 and CNAS normative documents), the system classifies key judgment elements with the same or similar attributes into different groups, forming a higher-level quantitative characteristic parameter (i.e., first-level criteria).

[0058] The primary criteria for group formation include at least the following four categories: **Professional Competency:** This category specifically includes elements related to the professional and technical competence of the reviewers and review teams, such as reviewer qualification status, the type of database the reviewers belong to, the professional coverage of review team members, the professional coverage of the review team leader, simultaneous (two-in-one) qualification accreditation review, employment type, implementation of joint review, and internal calibration. **Review Experience:** This category specifically includes elements related to the reviewers' practical experience, such as the number of reviews allowed, and available time slots (no review assignments / no leave). **Review Performance:** This category specifically includes elements related to the reviewers' past work performance and the operational requirements of this review, such as considering the reviewers' past performance, distance from the review location, reviewing the same institution twice consecutively, the proportion of trainee reviewers / technical experts in the group, and the total number of people in a single review. (Note: While impartiality avoidance requirements are important criteria, they are already a prerequisite screening condition and are generally not included in the weighting calculation model here.) **Training Needs:** This category... Specifically, it includes elements related to the long-term development and talent cultivation of the reviewer team, such as the supervision arrangement of chief reviewers, the maintenance of reviewer qualifications, the requirements for technical promotion from internship (whether it is arranged), and the promotion from internship group leader to chief (whether it is arranged).

[0059] Finally, through the above grouping, a two-level structure is formed within the parameter calculation layer: primary criteria (the four categories mentioned above) and secondary indicators (the original key judgment elements). The primary criteria, as an intermediate layer, are used to summarize and organize the evaluation dimensions; the secondary indicators, as the most basic evaluation indicators, are directly used to quantify and score the schemes. This hierarchical structure makes the complex evaluation system clear and orderly, facilitating subsequent weight allocation and comprehensive evaluation.

[0060] Through the implementation of S1310 and S1320, a complete, clear, and AHP-compliant hierarchical data structure is constructed in the system.

[0061] Based on the above embodiment 1, referring to Figure 4 As an optional implementation, the step S140, "based on a preset discrimination matrix, using the optimization target configuration as the optimization target, calculating the weight of each of the multiple quantization feature parameters and performing consistency verification to obtain an effective weight set," can be achieved in the following way: S1410. Based on the optimized target configuration, solve for the maximum eigenvalue of the preset discrimination matrix and its corresponding eigenvector.

[0062] The preset discrimination matrix is ​​pre-constructed based on the Satie scaling method.

[0063] As an optional example, the system invokes a pre-stored discrimination matrix constructed based on the Saaty 1-9 scale. This matrix is ​​constructed according to the optimization objective configuration (i.e., achieving the optimal dispatch decision), and its element values ​​are formed through the collective judgment of multiple accreditation experts (typically 5 to 7 senior reviewers and accreditation process managers). Specifically, based on their understanding of the importance of achieving the optimization objective, the experts perform pairwise comparisons of the quantitative feature parameters belonging to the same level in the parameter calculation layer, and quantify them using the Saaty scale (where 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, 9 indicates extremely important, 2, 4, 6, and 8 are median values, and the reciprocal is the inverse comparison). The system summarizes all the valid scores from the experts and calculates the arithmetic mean of the comparison scores for each pair of elements, ultimately forming the pre-set discrimination matrix. This matrix is ​​a positive reciprocal matrix, and its order n is consistent with the number of quantitative feature parameters currently participating in the comparison. Subsequently, the system employs a numerical calculation algorithm to solve for the maximum eigenvalue (λ_max) and its corresponding eigenvector of the discrimination matrix. The numerical calculation algorithm can utilize approximate methods such as the power method or the sum-product method. Taking the sum-product method as an example, the system first normalizes each column element of the discrimination matrix (i.e., divides each column element by the sum of its elements), then sums the elements of each row of the normalized matrix, and then normalizes the resulting row sum vector. The final vector is the approximate eigenvector. Finally, the system determines the maximum eigenvalue λ_max by multiplying the original discrimination matrix by this eigenvector and then calculating the average of the ratios of each component of the resulting vector to the corresponding component of the original eigenvector. The obtained eigenvector reflects the relative weight importance of each quantified feature parameter relative to its superior element (or overall objective), laying the foundation for subsequent weight calculations.

[0064] S1420. Normalize the feature vector to obtain the initial weights of each quantized feature parameter.

[0065] As an optional example, the sum of the components of the feature vector calculated in the previous step S1410 is usually not 1. The system normalizes this feature vector by calculating the sum of all its components and then dividing each component by the sum. After this process, the sum of the components of the resulting new vector is 1, and these components are the initial weights of the corresponding quantized feature parameters. This step transforms the relative comparison results into absolute weights that can be weighted and summed.

[0066] S1430. Calculate the consistency index CI according to the consistency index formula CI=(λ_max-n) / (n-1).

[0067] Wherein, λ_max is the maximum eigenvalue, and n is the order of the preset discrimination matrix.

[0068] As an optional example, the system substitutes the maximum eigenvalue λ_max calculated in S1410 and the order n of the preset discrimination matrix (i.e., the number of quantized feature parameters involved in the comparison) into the given formula CI=(λ_max - n) / (n - 1) to directly calculate the value of the consistency index CI. CI is used to measure the degree to which the judgment matrix deviates from consistency.

[0069] S1440. Query the average random consistency index RI value corresponding to the order n.

[0070] As an optional example, the system queries a pre-stored average random consistency index (RI) value lookup table based on the order n. This table stores the average RI value obtained by generating a large number of positive reciprocal matrices using a random method and calculating their CI values ​​for different orders n (typically 1 to 15). For example, the RI correction value table may contain: n=1, RI=0.00; n=2, RI=0.00; n=3, RI=0.58; n=4, RI=0.90; n=5, RI=1.12, etc. The system obtains the RI value for the corresponding order n by looking up the table.

[0071] S1450. Calculate the consistency ratio CR according to the consistency ratio formula CR=CI / RI.

[0072] As an optional example, the system substitutes the consistency index CI calculated in S1430 and the average random consistency index RI queried in S1440 into the formula CR = CI / RI to calculate the consistency ratio CR. CR is the final indicator used to determine whether matrix consistency is acceptable.

[0073] S1460. In response to the consistency ratio CR being less than a preset consistency ratio threshold, the consistency check is determined to be passed, and the initial weights are determined as a set of valid weights.

[0074] As an optional example, the system compares the calculated CR value with a preset consistency ratio threshold. According to AHP theory, this preset threshold is usually set to 0.1. The judgment logic is as follows: if CR < 0.1, the discriminant matrix is ​​considered to have satisfactory consistency, and the experts' judgment logic is basically consistent. At this time, the system determines that the consistency check has passed, and formally determines the initial weights obtained in S1420 as the effective weight set for subsequent calculations. If CR ≥ 0.1, the check has failed, and the system can trigger an alarm or prompt message to notify relevant personnel (such as model administrators or expert groups) that the scaling assignments in the discriminant matrix need to be reviewed and adjusted, and then the calculation process restarts from S1410 until an effective weight set that satisfies the condition CR < 0.1 is obtained. The effective weight set contains the final weight values ​​of all quantized feature parameters of the parameter operation layer.

[0075] Example 3 Based on the above embodiments 1 and 2, and referring to... Figure 5 As an optional implementation, the step S150, "generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers," can be achieved in the following way: S1510. Based on the resource requirements of the current review task, generate an initial review group combination that meets the preset role constraints from the set of candidate reviewers.

[0076] The resource requirements configuration includes the number of review team members, role qualification requirements, and professional field coverage requirements.

[0077] As an optional example, the system first automatically parses the resource requirements configuration for the current review task. This configuration includes the total number of review team members required, the qualification requirements for each role (such as team leader, team members, technical experts, and trainee reviewers) (e.g., the team leader must possess the qualification of a chief reviewer), and the overall professional capabilities of the review team must cover all technical fields for which the reviewed organization is applying for accreditation. Subsequently, based on this configuration, the system automatically generates an initial review team combination that meets the preset role constraints from the set of candidate reviewers. Specifically, the system selects qualified reviewers based on role qualification requirements and attempts to combine reviewers from different professional fields according to the professional field coverage requirements, ensuring that the combined review team's professional capabilities cover the business scope of the reviewed organization. The generation of the initial combination must meet the basic hard constraints of personnel number and role configuration.

[0078] S1520. Apply time and space availability constraints and cost constraints to the initial review group combination for filtering and screening.

[0079] The time and space availability constraints include no task conflicts during the review period and travel distance within a preset threshold. The cost constraints are set based on the estimated travel expenses and review workload. The review workload is measured in reviewer days, which is the product of the number of review team members and the estimated number of review days.

[0080] As an optional example, the system applies temporal and spatial availability constraints and cost constraints sequentially to filter the initial review group combinations generated in S1510. The temporal and spatial availability constraint requires that all reviewers within the group have no other assigned tasks during the preset review period (i.e., "free time"), and that the straight-line or actual travel distance between each reviewer's permanent residence and the review location must be within a preset threshold range to control travel time and costs. The cost constraint is calculated based on estimated travel expenses (calculated according to distance, mode of transportation, etc.) and review workload, where the review workload is measured in review person-days, which is the product of the total number of reviewers in the review group and the estimated number of review days. The system compares the calculated total cost with a preset cost ceiling, eliminating combinations that exceed the limits.

[0081] S1530. The filtered review group combination is optimized using a combinatorial optimization algorithm to generate multiple candidate review group schemes, which are then loaded into the scheme generation layer.

[0082] As an optional example, the system uses combinatorial optimization algorithms (such as greedy algorithms and genetic algorithms) to optimize the set of review group combinations filtered by the above constraints. This optimization aims to improve the overall suitability of the solutions, comprehensively considering the performance of each combination in multiple dimensions, including the completeness of professional coverage, the matching degree of member experience, and total cost control. Through iterative evaluation and selection, the algorithm ultimately generates a batch of candidate review group solutions with differentiated advantages in personnel configuration, professional composition, and cost-effectiveness. These optimized solutions are then loaded into the solution generation layer, serving as the data foundation for subsequent quantitative evaluation and optimal solution selection.

[0083] Based on the above embodiments 1 and 2, and referring to... Figure 6 As an optional implementation, the step S150, "calculating the comprehensive evaluation value of each candidate review group scheme based on the effective weight set and the corresponding attribute data," can be achieved in the following way: S1510' Based on the attribute data, determine the quantitative score of each reviewer in the candidate review group scheme on each quantitative characteristic parameter.

[0084] As an optional example, the system reads the corresponding attribute data (such as professional qualifications, review experience, historical performance records, etc.) for each reviewer in the candidate review group scheme. Then, according to predefined scoring rules, the system converts each reviewer's attribute data into quantitative scores under various quantitative characteristic parameters. These quantitative characteristic parameters correspond to indicators determined based on key judgment elements (e.g., secondary indicators such as "reviewer qualification status" and "professional coverage" under the primary criterion of "professional competence"). Scoring rules are typically in the form of piecewise functions or lookup tables. For example, for the "number of reviews" indicator under the "review experience" criterion, a score of 85 points can be set for the number of reviews in the [10, 20) interval, 90 points for the [20, 30) interval, and so on; for historical evaluations under the "review performance" criterion, qualitative evaluations such as "excellent" and "good" can be directly mapped to specific percentage scores (e.g., 95 points, 80 points). This process ensures that each reviewer receives an objective numerical score for each quantitative characteristic parameter.

[0085] S1520' Based on the weights of each quantitative feature parameter in the effective weight set, the quantitative score is weighted and calculated to obtain the individual evaluation value for each reviewer.

[0086] As an optional example, the system obtains a valid set of weights generated in step S140 and verified for consistency. This set contains the final weights for each quantified feature parameter (e.g., weight W1 for criterion C1, weight W2 for criterion C2, ..., weight Wn for criterion Cn). For each reviewer in the candidate review group, the system performs a weighted summation of their quantified scores (e.g., Score_C1, Score_C2, ..., Score_Cn) on each quantified feature parameter obtained in S1510' with the corresponding criterion weights (W1, W2, ..., Wn). The formula for calculating the individual score (Individual_Score) can be expressed as: Individual_Score = (Score_C1 * W1 + Score_C2 * W2 + … + Score_Cn *Wn) Or, normalization may be performed as needed. This calculation process is performed independently for each reviewer within the scheme, ultimately generating a comprehensive score for each reviewer that reflects their performance under each criterion, i.e., an individual evaluation value.

[0087] S1530': Calculate the overall evaluation value of the candidate review group scheme by combining the individual evaluation values ​​of all reviewers in the candidate review group scheme.

[0088] As an optional example, after obtaining the individual evaluation values ​​of all reviewers in the candidate review group's proposal, the system aggregates these individual evaluation values ​​to obtain a comprehensive evaluation value (Overall_Score) representing the overall merits of the proposal. Depending on the optimization objective (pursuing overall optimization), this aggregation typically employs a summation or weighted average method. For example, the comprehensive evaluation value can be directly calculated as the arithmetic sum of all reviewers' individual evaluation values: Overall_Score = Σ(Individual_Score_i), where i iterates through all reviewers in the scheme.

[0089] Alternatively, considering the differences in roles within the review panel (e.g., the panel leader has higher weight), individual evaluation values ​​can be weighted by role before summing. The final calculated comprehensive evaluation value serves as a scalar, used to quantitatively characterize the overall level of the candidate review panel's proposals, providing a direct basis for subsequent proposal comparisons and optimal decision-making.

[0090] Based on the above embodiments 1 and 2, and referring to... Figure 7 As an optional implementation, the step of "determining the optimal dispatching scheme from multiple candidate review group schemes included in the scheme layer based on the comprehensive evaluation value" in S160 can be achieved in the following way: S1610. Select candidate schemes whose comprehensive evaluation value meets the preset evaluation threshold from the multiple candidate review group schemes.

[0091] As an optional example, the system obtains the comprehensive evaluation value of all candidate review group proposals. The preset evaluation threshold can be set based on historical data, expert experience, or specific business requirements. For example, it can be set as the highest comprehensive evaluation value of all proposals, or a certain percentage of the highest score (such as the top 95% score range). The system compares the comprehensive evaluation value of each proposal with this threshold, and filters out all candidate proposals whose comprehensive evaluation value reaches or exceeds the threshold, forming a set of preferred proposals. The purpose of this step is to initially eliminate proposals with significantly lower overall evaluations, narrowing down the scope of the final decision.

[0092] S1620. If there is only one candidate solution selected, then that candidate solution shall be determined as the optimal dispatch decision.

[0093] As an optional example, the system determines the number of options in the set of preferred options obtained after step S1610. If the set contains only one candidate option, that option is directly determined as the optimal personnel dispatch decision for this round of decision-making. The system outputs the specific information of the option (such as the list of review team members, review schedule, etc.) as the final result, and the personnel dispatch decision process ends.

[0094] S1630. When there are multiple candidate solutions selected, based on the preset review cost calculation rules, the candidate review group solution with the lowest review cost is selected as the optimal dispatch decision from the multiple candidate solutions.

[0095] As an optional example, if, after screening in step S1610, the preferred solution set contains two or more candidate solutions (i.e., multiple solutions have the same or similar comprehensive evaluation values ​​and all meet the threshold requirements), then the system initiates a cost comparison mechanism. The preset review cost calculation rules define how to quantify and calculate the total cost of a review group's solutions. Cost calculation mainly considers the following factors: First, travel expenses. Specifically, transportation and travel allowances are estimated based on the distance between the locations of the review team members and the review venue (corresponding to key decision element C12).

[0096] Second, review person-days. Specifically, the total number of workdays estimated based on the complexity of the review task and the level of the members, and the corresponding labor cost.

[0097] According to the above rules, the system calculates the total review cost for each candidate solution. Finally, the system compares the total costs of these candidate solutions and selects the one with the lowest review cost as the optimal dispatch decision and outputs it. This step ensures that, when the overall performance of the solutions is comparable, the most economically efficient solution is prioritized, achieving the dual objectives of "highest dispatch matching degree" and "lowest review cost." If costs are also the same, the final solution can be determined according to preset secondary rules (such as random selection or according to the order in which the solutions are generated). The decision result is also output for execution.

[0098] As described above, the assessment dispatch scheme generation method for inspection agencies provided in Embodiments 1-3 of this disclosure systematically integrates multi-dimensional criteria such as professional competence, assessment experience, assessment performance, and training needs by transforming international standards and industry norms into quantifiable decision-making models. Furthermore, it utilizes the scientific computing framework of the Analytic Hierarchy Process (AHP) to transform qualitative judgments into objective weights, ensuring the scientific rigor and logical soundness of the decision-making process. Its automated decision-making process achieves full digitalization from data screening and scheme generation to quantitative evaluation and selection, significantly improving dispatch efficiency and accuracy. Simultaneously, the method's built-in consistency verification and cost optimization mechanisms guarantee the rationality and economy of the decision results, while the feedback-based closed-loop design lays the foundation for continuous iterative optimization of the model. Ultimately, this method provides accreditation agencies with a practical and traceable intelligent dispatch tool, effectively supporting the fairness, authority, and efficiency of accreditation assessment activities.

[0099] Example 4 It should be understood that the method for generating review dispatch schemes for inspection agencies described in the foregoing embodiments herein can also be similarly applied to the following apparatus for generating review dispatch schemes for inspection agencies for similar extensions. For simplicity, it is not described in detail.

[0100] Figure 8 This is a schematic diagram of a review dispatch scheme generation device for inspection agencies provided in an exemplary embodiment of this disclosure. (Refer to...) Figure 8 The device includes: The data acquisition unit 110 is configured to read multiple key judgment elements and attribute data of all candidate reviewers, wherein the key judgment elements come from a pre-stored rule base and the attribute data come from a pre-stored database.

[0101] The data filtering unit 120 is configured to filter the attribute data based on preset impartiality avoidance conditions to obtain a set of candidate reviewers.

[0102] Modeling unit 130 is configured to: construct a hierarchical data structure for optimizing dispatch schemes based on the analytic hierarchy process (AHP); wherein the hierarchical data structure includes optimization target configuration, parameter calculation layer and scheme layer, the parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements; the scheme layer is used to accommodate candidate review group schemes.

[0103] The weight calculation unit 140 is configured to: calculate the weight of each of the multiple quantized feature parameters based on a preset discrimination matrix and with the optimization target configuration as the optimization target, and perform consistency verification to obtain an effective weight set.

[0104] The evaluation value determination unit 150 is configured to: in response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, calculate the comprehensive evaluation value of the candidate review group scheme for each candidate review group scheme based on the set of effective weights and the corresponding attribute data; The decision-making unit 160 is configured to: determine the optimal dispatching scheme from multiple candidate review group schemes included in the scheme layer based on the comprehensive evaluation value.

[0105] As described above, the assessment dispatch scheme generation device for inspection agencies provided in this disclosure systematically integrates multi-dimensional criteria such as professional competence, assessment experience, assessment performance, and training needs by transforming international standards and industry norms into quantifiable decision-making models. It also utilizes the scientific computing framework of the Analytic Hierarchy Process (AHP) to transform qualitative judgments into objective weights, ensuring the scientific rigor and logical soundness of the decision-making process. Its automated decision-making process achieves full digitalization from data screening and scheme generation to quantitative evaluation and selection, significantly improving dispatch efficiency and accuracy. Simultaneously, the device's built-in consistency verification and cost optimization mechanisms ensure the rationality and economy of the decision results, while the feedback-based closed-loop design lays the foundation for continuous iterative optimization of the model. Ultimately, this device provides accreditation agencies with a practical and traceable intelligent dispatch tool, effectively supporting the fairness, authority, and efficiency of accreditation assessment activities.

[0106] Example 5 In addition, this disclosure also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, wherein when the computer program is executed, it implements the method for generating review dispatch schemes for inspection agencies as described in any of the above embodiments of this disclosure.

[0107] Figure 9 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed herein. Below, reference is made to… Figure 9 This describes an electronic device according to embodiments of the present disclosure. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0108] like Figure 9 As shown, the electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the assessment dispatch scheme generation method for inspection agencies described in the various embodiments of this disclosure above, and / or other desired functions.

[0109] In one example, the electronic device may further include input and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). Furthermore, the input device may include, for example, a keyboard, a mouse, etc. The output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0110] Of course, for the sake of simplicity, Figure 9 Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0111] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for generating a review dispatch scheme for inspection agencies according to various embodiments of this disclosure as described in the foregoing portions of this specification.

[0112] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0113] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for generating a review dispatch scheme for inspection agencies according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0114] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0115] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0116] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0117] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0118] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0119] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0120] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0121] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0122] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for generating review and dispatch plans for inspection agencies, characterized in that, The method includes: Read multiple key judgment elements and attribute data of all candidate reviewers, wherein the key judgment elements come from a pre-stored rule base and the attribute data come from a pre-stored database; The attribute data is filtered based on preset impartiality avoidance conditions to obtain a set of candidate reviewers; A hierarchical data structure for optimizing personnel dispatching schemes is constructed based on the analytic hierarchy process (AHP). The hierarchical data structure includes an optimization target configuration, a parameter calculation layer, and a scheme layer. The parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements. The scheme layer is used to accommodate candidate review group schemes. Based on the preset discrimination matrix, with the optimization target configuration as the optimization target, the weight of each of the multiple quantization feature parameters is calculated and consistency verification is performed to obtain an effective weight set; In response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, for each candidate review group scheme, a comprehensive evaluation value of the candidate review group scheme is calculated based on the effective weight set and the corresponding attribute data. Based on the comprehensive evaluation value, the optimal dispatch plan is determined from the multiple candidate review group plans included in the plan layer.

2. The method according to claim 1, characterized in that, The attribute data is filtered based on preset impartiality avoidance conditions to obtain a set of candidate reviewers, including: Read the preset impartiality avoidance conditions; wherein, the impartiality avoidance conditions include interest detection conditions, service time window conditions, and specific relationship identification conditions; The attribute data of the candidate reviewers are matched with the impartiality avoidance conditions to identify the candidate reviewer identifiers that meet any one of the impartiality avoidance conditions. Remove all data records corresponding to the identified candidate reviewer identifiers from the initial candidate reviewer set to generate the candidate reviewer set.

3. The method according to claim 1, characterized in that, The hierarchical data structure for optimizing dispatching plans, constructed based on the analytic hierarchy process (AHP), includes: Establish a dominance relationship between the optimization target configuration, the parameter calculation layer, and the scheme layer, wherein the optimization target configuration dominates the parameter calculation layer, and the parameter calculation layer dominates the scheme layer; The multiple quantization feature parameters are grouped according to their attributes to form a hierarchical structure of the parameter operation layer.

4. The method according to claim 1, characterized in that, The step involves calculating the weight of each of the multiple quantized feature parameters based on a preset discrimination matrix, using the optimization target configuration as the optimization objective, and performing consistency verification to obtain an effective weight set, including: Based on the optimization target configuration, the maximum eigenvalue of the preset discrimination matrix and its corresponding eigenvector are solved; wherein, the preset discrimination matrix is ​​pre-constructed based on the Satie scaling method; The feature vector is normalized to obtain the initial weights of each quantized feature parameter; The consistency index CI is calculated according to the consistency index formula CI=(λ_max-n) / (n-1), where λ_max is the maximum eigenvalue and n is the order of the preset discrimination matrix. Query the average random consistency index (RI) value corresponding to the order n; The consistency ratio CR is calculated using the consistency ratio formula CR=CI / RI; If the consistency ratio CR is less than a preset consistency ratio threshold, the consistency check is passed, and the initial weights are determined as a valid weight set.

5. The method according to claim 1, characterized in that, The step of generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers includes: Based on the resource requirements of the current review task, an initial review group combination that meets the preset role constraints is generated from the set of candidate reviewers; wherein, the resource requirements configuration includes the number of review group members, role qualification requirements, and professional field coverage requirements; The initial review team is filtered by applying time and space availability constraints and cost constraints. The time and space availability constraints include no task conflicts during the review period and travel distance within a preset threshold. The cost constraints are set based on the estimated travel expenses and review workload. The review workload is measured in reviewer days, which is the product of the number of review team members and the estimated number of review days. The filtered review group combinations are optimized using a combinatorial optimization algorithm to generate multiple candidate review group schemes, which are then loaded into the scheme generation layer.

6. The method according to claim 1, characterized in that, For each candidate review group scheme, the comprehensive evaluation value of the candidate review group scheme is calculated based on the effective weight set and the corresponding attribute data, including: Based on the attribute data, determine the quantitative score of each reviewer in the candidate review group scheme on each quantitative characteristic parameter; Based on the weights of each quantitative feature parameter in the effective weight set, the quantitative score is weighted and calculated to obtain the individual evaluation value for each reviewer. The overall evaluation value of the candidate review group scheme is calculated by combining the individual evaluation values ​​of all reviewers in the candidate review group scheme.

7. The method according to claim 1, characterized in that, The step of determining the optimal dispatch plan from multiple candidate review group plans included in the plan layer based on the comprehensive evaluation value includes: From the multiple candidate review group schemes, candidate schemes whose comprehensive evaluation values ​​meet the preset evaluation threshold are selected; If there is only one candidate solution selected, then that candidate solution is determined as the optimal dispatch decision. When there are multiple candidate solutions selected, the candidate review group solution with the lowest relative review cost is selected as the optimal dispatch decision based on the preset review cost calculation rules.

8. A device for generating review and dispatch plans for inspection agencies, characterized in that, The device includes: The data acquisition unit is configured to read multiple key judgment elements and attribute data of all candidate reviewers, wherein the key judgment elements come from a pre-stored rule base and the attribute data come from a pre-stored database; The data filtering unit is configured to filter the attribute data based on preset impartiality avoidance conditions to obtain a set of candidate reviewers; The modeling unit is configured to: construct a hierarchical data structure for optimizing dispatching schemes based on the analytic hierarchy process; wherein the hierarchical data structure includes optimization target configuration, parameter calculation layer and scheme layer, the parameter calculation layer contains multiple quantitative feature parameters determined based on the multiple key judgment elements, and the scheme layer is used to accommodate candidate review group schemes; The weight calculation unit is configured to: calculate the weight of each of the multiple quantized feature parameters based on a preset discrimination matrix and with the optimization target configuration as the optimization target, and perform consistency verification to obtain an effective weight set; The evaluation value determination unit is configured to: in response to generating multiple candidate review group schemes in the scheme layer based on the set of candidate reviewers, calculate the comprehensive evaluation value of each candidate review group scheme based on the set of effective weights and the corresponding attribute data; The decision-making unit is configured to: determine the optimal dispatch plan from multiple candidate review group plans included in the plan layer based on the comprehensive evaluation value.

9. A computer-readable storage medium storing a computer program for executing the review dispatch scheme generation method for inspection agencies as described in claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the review dispatch scheme generation method for inspection agencies as described in claims 1 to 7.

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