Credit grading evaluation method and system for residential engineering detection institution

Abstract

The invention relates to the technical field of residential engineering detection institution credit evaluation, and discloses a residential engineering detection institution credit grading evaluation method and system, and the method comprises the steps: obtaining the basic qualification and historical business data of a detection institution, collecting the full-dimensional supervision data, cleaning and classifying, and building a multi-dimensional credit evaluation model. Forming an initial credit evaluation model; superposing the market environment parameters and the policy constraint conditions to generate a comprehensive credit evaluation model, and outputting dynamic credit evaluation indexes; a credit evaluation pre-training framework is constructed and optimized, a credit adjustment coefficient is extracted, and a credit state evolution graph is generated; and establishing a regulation and control model to optimize supervision strategy parameters, and realizing credit grading dynamic management and control through a grading decision model. The system comprises a data integration module, a model construction module, a credit analysis module, an evolution deduction module, a strategy optimization module and a grading execution module. The system can comprehensively and dynamically evaluate the credit of the detection mechanism, optimizes the supervision strategy, and guarantees the quality safety of residential engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of credit assessment of residential engineering inspection institutions, and in particular to a credit grading assessment method and system for residential engineering inspection institutions. Background Art

[0002] During residential construction, the credit standing of testing agencies is directly linked to project quality and safety. As residential construction continues to expand, the need for regulatory oversight of testing agencies is becoming increasingly urgent. However, existing credit assessment methods for residential project testing agencies have numerous shortcomings.

[0003] Traditional credit assessments often rely on a single data source or a simple indicator system, making it difficult to fully reflect a testing organization's true creditworthiness. For example, assessing a testing organization's creditworthiness solely based on its past testing volume or customer satisfaction ratings overlooks key factors such as operational compliance and changes in qualifications. This one-sided approach fails to effectively identify potential risks, allowing some testing organizations with irregular operations or flawed qualifications to continue accepting business in the market, thus creating hidden dangers for the quality of residential projects.

[0004] At the same time, existing assessment methods lack dynamic consideration of market and policy changes. The market environment is constantly changing. For example, intensified industry competition may lead some testing agencies to lower testing standards in pursuit of profit. Constant updates to policies and regulations also require testing agencies to promptly adjust their operational strategies to comply with new regulations. However, existing credit assessment systems are unable to promptly incorporate these changes, resulting in lagging assessment results and a failure to provide regulators and market participants with real-time, accurate credit information.

[0005] Furthermore, the current regulatory approach is largely a one-size-fits-all approach, lacking specificity and flexibility. Regulators apply the same frequency and intensity to testing institutions of varying creditworthiness, resulting in a waste of regulatory resources and a failure to effectively constrain high-risk institutions. For testing institutions with good creditworthiness, frequent inspections increase their operating costs; while for institutions with poor creditworthiness, existing regulatory measures are insufficient to motivate them to improve, making it difficult to achieve the goals of precise supervision and risk prevention.

[0006] With the rapid development of information technology, emerging technologies such as big data and artificial intelligence are gradually being applied to various fields. However, their application in the field of credit assessment of residential engineering inspection institutions is still in its infancy. How to make full use of these advanced technologies, integrate multi-source data, build a scientific and dynamic credit assessment system, and formulate personalized supervision strategies has become a pressing issue. The present invention aims to overcome the shortcomings of the existing technology and provide a comprehensive, dynamic, and accurate credit grading assessment method and system for residential engineering inspection institutions, so as to improve the overall credit level of the residential engineering inspection industry and ensure the quality and safety of residential projects. Summary of the Invention

[0007] The purpose of the present invention is to provide a credit grading and evaluation method and system for residential engineering inspection institutions to solve the problems raised in the above background technology.

[0008] To achieve the above-mentioned object, the present invention provides the following technical solution: a credit grading and assessment method for a residential engineering inspection agency, the method comprising: Obtain basic qualification information and historical business data of the testing agency; Collect all-dimensional regulatory data of testing institutions and clean and classify them, then establish a multi-dimensional credit evaluation model. Perform cluster analysis on the multi-dimensional credit evaluation model and compare it with industry benchmark parameters to form an initial credit evaluation model. Superimpose market environment parameters and policy constraints on the initial credit evaluation model to generate a comprehensive credit evaluation model, and combine it with industry benchmark parameters to output dynamic credit evaluation indicators; Build a credit assessment pre-training framework and optimize it through dynamic credit evaluation indicators to obtain a credit assessment model, and then extract the credit adjustment coefficient; Generate a credit status evolution graph based on credit adjustment coefficients, comprehensive credit evaluation models, and industry benchmark parameters; Based on the credit status evolution graph and regulatory parameter configuration, a regulatory model is established and the regulatory parameter configuration is iteratively optimized to obtain the optimal regulatory strategy parameters; The current cycle regulatory data is analyzed through a hierarchical decision-making model to output the current cycle credit rating results. By combining the current cycle optimal regulatory strategy parameters with the current cycle regulatory parameter configuration, the actual regulatory strategy of the current cycle is corrected to achieve dynamic management and control of credit grading.

[0009] Preferably, the dynamic credit evaluation indicators include at least a comprehensive scoring sequence, a behavioral compliance distribution, and a set of high-risk institution identifiers; the credit adjustment coefficient includes at least a qualification deviation coefficient, a business anomaly type, and problem distribution characteristics; and the credit status evolution map includes at least a risk diffusion path, associated impact indicators, and credit trend prediction results.

[0010] Preferably, the collecting of full-dimensional regulatory data of the testing agency and cleaning and classifying it, and then establishing a multi-dimensional credit evaluation model, performing cluster analysis on the multi-dimensional credit evaluation model and comparing it with industry benchmark parameters to form an initial credit evaluation model, includes the following steps: Extracting operational process data of testing institutions from multiple regulatory platforms to generate a multi-source heterogeneous regulatory data set; Cleaning and classifying a multi-source heterogeneous regulatory data set to obtain a structured regulatory data set, wherein the cleaning and classification includes one or more of missing value filling, data normalization, attribute alignment, redundant field removal, classification coding, and time series alignment; Based on the structured regulatory data set, a multi-dimensional credit evaluation model is constructed using a hierarchical modeling approach; Perform cluster analysis on the multidimensional credit evaluation model, divide it into groups with different credit characteristics, compare the group center values ​​with industry benchmark parameters, and form an initial credit evaluation model.

[0011] Preferably, the process of superimposing market environment parameters and policy constraints on the initial credit evaluation model to generate a comprehensive credit evaluation model, combining industry benchmark parameters, and outputting dynamic credit evaluation indicators comprises the following steps: Injecting market environment parameters into the initial credit evaluation model to adjust the evaluation weight distribution to obtain a first credit evaluation model; Adding policy constraints to the first credit evaluation model to generate a comprehensive credit evaluation model, where the policy constraints include compliance boundary conditions and industry reward and punishment rules; Based on the comprehensive credit evaluation model and industry benchmark parameters, dynamic credit evaluation indicators are output. The specific process includes: An evaluation index system is constructed, which includes at least qualification integrity indicators, business standardization indicators and complaint response indicators. Combined with the comprehensive credit evaluation model, the evaluation index system is layered and calculated through the weighted aggregation method to obtain a comprehensive score sequence, behavioral compliance distribution and a set of high-risk institution identifications.

[0012] Preferably, obtaining the high-risk institution identification set includes the following steps: Based on the behavioral compliance distribution, the compliance deviation of each testing organization is calculated; Identify the testing institutions whose compliance deviation in the comprehensive credit evaluation model is higher than the deviation threshold, and generate a set of high-risk institution identifications.

[0013] Preferably, the construction of a credit assessment pre-training framework and tuning through dynamic credit evaluation indicators to obtain a credit assessment model and then extracting a credit adjustment coefficient includes the following steps: Build a credit assessment pre-training framework based on a multi-task learning architecture; Use dynamic credit evaluation indicators to perform parameter tuning and cross-validation on the credit evaluation pre-training framework and output a credit evaluation model; Input the dynamic credit evaluation indicators of the institution to be evaluated into the credit evaluation model to predict the distribution of its potential credit problems; A credit adjustment coefficient is extracted based on the predicted credit problem distribution, wherein the credit adjustment coefficient at least includes a qualification deviation coefficient, a business anomaly type, and problem distribution characteristics.

[0014] Preferably, extracting the credit adjustment coefficient based on the predicted credit problem distribution includes the following steps: Locate the core cluster area of ​​the predicted credit problem distribution and determine the qualification deviation coefficient; Perform kernel density estimation on the spatial density of credit problem distribution to fit the problem distribution characteristics; Analyze the time evolution trend of credit issue distribution and extract classification labels of business anomaly types.

[0015] Preferably, generating a credit status evolution map based on the credit adjustment coefficient, the comprehensive credit evaluation model and the industry benchmark parameters comprises the following steps: Taking the qualification deviation coefficient as the starting point of evolution, the problem distribution characteristics are mapped into the comprehensive credit evaluation model, the abnormal propagation path weight of the model is adjusted, the evolution trigger conditions, risk transmission direction and impact diffusion step are defined, and a credit evolution analysis model is constructed; Based on the credit evolution analysis model, the Monte Carlo simulation method is used to conduct multi-scenario deductions. Based on the deduction results, a credit status evolution map is generated, which specifically includes: When the evolution trigger conditions are met, risk transmission is initiated, the risk transmission direction and impact diffusion step of the current node are updated, and the credit evolution analysis model is simultaneously revised; Re-run the deduction based on the revised credit evolution analysis model until the preset deduction rounds or convergence state are reached; Summarize the key path nodes and impact scope in each round of simulation results to generate risk diffusion paths and related impact indicators.

[0016] Preferably, the step of establishing a control model and iteratively optimizing the control parameter configuration based on the credit status evolution graph and the control parameter configuration to obtain the optimal control strategy parameters includes the following steps: Constructing a regulatory model, the model comprising a set of decision variables, an objective function group, and a constraint rule group. The regulatory parameter configuration, as a decision variable, includes at least an audit frequency threshold, an indicator weight configuration, and a data coverage range. The objective function group is constructed based on a credit stability index and a regulatory cost index. The constraint rule group includes resource constraints and compliance assurance conditions. Genetic algorithms are used to optimize the control model over multiple generations to obtain the initial optimization strategy parameters; The particle swarm algorithm is used to fine-tune the control model and output the optimal regulatory strategy parameters.

[0017] Preferably, the present invention further includes a residential engineering inspection agency credit rating and assessment system, the system comprising: Data integration module, used to obtain basic qualification information, historical business data and full-dimensional regulatory data of testing institutions; The model building module is used to clean and classify full-dimensional regulatory data, establish a multi-dimensional credit evaluation model, conduct cluster analysis and compare it with industry benchmark parameters to form an initial credit evaluation model, superimpose market environment parameters and policy constraints to generate a comprehensive credit evaluation model, and output dynamic credit evaluation indicators; The credit analysis module is used to build a credit assessment pre-training framework and optimize it through dynamic credit evaluation indicators to obtain a credit assessment model and extract credit adjustment coefficients; An evolutionary deduction module, which is used to generate a credit status evolution graph based on the credit adjustment coefficient, comprehensive credit evaluation model, and industry benchmark parameters; The strategy optimization module is used to establish a control model and iteratively optimize the regulatory parameter configuration based on the credit status evolution map and regulatory parameter configuration to obtain the optimal regulatory strategy parameters; The hierarchical execution module is used to analyze the current cycle regulatory data through a hierarchical decision-making model, output credit rating results, and modify the actual regulatory strategy based on the optimal regulatory strategy parameters to achieve dynamic hierarchical management and control.

[0018] Compared with the prior art, the present invention has the following beneficial effects: In terms of the comprehensiveness and accuracy of credit assessments, by obtaining the basic qualification information, historical business data, and full-dimensional regulatory data of the testing institutions, it is possible to comprehensively consider the testing institutions from multiple perspectives. The extraction, cleaning and classification of multi-source heterogeneous regulatory data sets ensures the quality and availability of the data. The hierarchical modeling method is used to construct a multidimensional credit evaluation model, and combined with cluster analysis and comparison with industry benchmark parameters, the resulting initial credit evaluation model is more objective and realistic. When subsequently generating a comprehensive credit evaluation model, market environment parameters and policy constraints are superimposed, fully considering the impact of external factors on the credit of the testing institutions, making the evaluation results more in line with the actual situation and accurately reflecting the credit status of the testing institutions.

[0019] The dynamic assessment mechanism is also a major advantage of this invention. With the constant evolution of market conditions and policies, traditional assessment methods struggle to timely update assessment results. This invention continuously collects new data, adjusts the model based on market environment parameters and policy constraints, and outputs dynamic credit evaluation indicators. Furthermore, a pre-trained credit assessment framework is constructed and optimized to extract credit adjustment coefficients based on the distribution of new credit issues, generate a credit status evolution map, and dynamically track and predict the credit of testing institutions. This provides regulators and relevant parties with timely and accurate credit information, facilitating scientific decision-making.

[0020] From the perspective of supervisory strategy optimization, a regulatory model is established based on the credit status evolution graph and supervisory parameter configuration, and iterative optimization is performed using genetic algorithms and particle swarm algorithms to obtain the optimal supervisory strategy parameters. This enables regulators to implement differentiated supervision for testing institutions with different credit ratings, increasing the frequency of reviews for high-risk institutions and appropriately relaxing supervision for institutions with good credit. This improves the efficiency of supervisory resource utilization, avoids the waste of resources and ineffective supervision caused by "one-size-fits-all" supervision, achieves precise supervision, and effectively reduces industry risks.

[0021] In terms of risk prevention and control, the risk diffusion paths, associated impact indicators, and credit trend forecasts in the credit status evolution map can help regulators identify potential risks in advance and take timely intervention measures to prevent the spread of risks. For example, if a testing agency is found to have a high qualification deviation coefficient and the distribution characteristics of the problems indicate the possibility of a chain reaction, regulators can intervene in advance and require rectification to avoid serious impacts on the quality of residential projects.

[0022] This invention is of great significance for promoting the healthy development of the residential engineering inspection industry. Accurate credit assessments and effective regulatory measures can encourage inspection agencies to improve their own creditworthiness, standardize market order, enhance trust among market players, attract more high-quality resources to the industry, and drive the entire industry towards a more standardized and efficient development direction, ultimately ensuring the quality and safety of residential projects and safeguarding the interests of consumers. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a diagram showing the working principle of the residential engineering inspection agency credit grading and assessment method described in the present invention.

[0024] Figure 2 Flowchart for comprehensive credit evaluation model generation and dynamic indicator output.

[0025] Figure 3 Flowchart for credit assessment model construction and credit adjustment coefficient extraction.

[0026] Figure 4Flowchart for credit adjustment factor extraction. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] See also Figure 1-4 The present invention provides a technical solution: a credit grading and assessment method for residential engineering inspection institutions, the method comprising: Obtain the testing agency's basic qualification information and historical business data. Basic qualification information includes the testing agency's business license and qualification certificate. This information clarifies key information such as the agency's legal operating status and the scope of its testing capabilities. Historical business data includes the number, type, completion time, and test results of residential project inspections undertaken in the past, reflecting the testing agency's actual business performance. By collecting this information, we provide basic data support for subsequent credit assessments.

[0029] Full-dimensional regulatory data from testing institutions is collected, cleaned, and classified. A multidimensional credit evaluation model is then constructed. Cluster analysis is performed on the multidimensional credit evaluation model and compared with industry benchmark parameters to form an initial credit evaluation model. Data generated by testing institutions during their operations, such as daily supervision and inspection records, violation penalty information, and customer feedback, is extracted from multiple regulatory platforms, including those of government regulatory agencies and industry associations. This data is then cleaned and classified, including filling missing values ​​to complete incomplete data; normalizing the data to unify its dimensions and scale; aligning attributes to ensure consistent meaning across data sources; removing redundant fields to eliminate duplicate and useless data; classifying and encoding the data for easier computer processing; and performing time series alignment to ensure accurate chronological order. This processing yields a structured regulatory data set, based on which a multidimensional credit evaluation model is constructed using a hierarchical modeling approach. Cluster analysis is then performed on this model to categorize testing institutions into groups with different credit characteristics. The group centers are then compared with industry benchmark parameters to form an initial credit evaluation model.

[0030] Market environment parameters and policy constraints are superimposed on the initial credit evaluation model to generate a comprehensive credit evaluation model. Combined with industry benchmark parameters, dynamic credit evaluation indicators are output. Market environment parameters, such as the level of competition in the local residential engineering testing market and market demand trends, are added to the initial credit evaluation model to adjust the evaluation weight distribution, resulting in a first credit evaluation model. Policy constraints are then added to the first credit evaluation model. These include compliance boundary conditions, such as the laws, regulations, and industry standards that testing organizations must follow, as well as industry reward and punishment rules. Compliance is rewarded with bonus points, while violations are penalized with deductions, thereby generating a comprehensive credit evaluation model. An evaluation indicator system is constructed, including qualification integrity indicators, business compliance indicators, and complaint response indicators. Combined with the comprehensive credit evaluation model, the evaluation indicator system is layered and calculated using a weighted aggregation method to obtain dynamic credit evaluation indicators such as a comprehensive score sequence, behavioral compliance distribution, and a set of high-risk institution identifiers.

[0031] A credit assessment pre-training framework is constructed and optimized using dynamic credit evaluation indicators to obtain a credit assessment model, which is then used to extract credit adjustment coefficients. The credit assessment pre-training framework is built based on a multi-task learning architecture. Dynamic credit evaluation indicators are used to adjust parameters and cross-validate the framework, outputting a credit assessment model. The dynamic credit evaluation indicators of the institution to be assessed are input into the credit assessment model to predict the distribution of its potential credit issues. Based on the predicted distribution of credit issues, core clusters are located to determine the qualification deviation coefficient. Kernel density estimation is performed on the spatial density of the credit issue distribution to fit the problem distribution characteristics. Temporal evolution trends are analyzed to extract classification labels for business anomaly types, thereby obtaining a credit adjustment coefficient that includes the qualification deviation coefficient, business anomaly type, and problem distribution characteristics.

[0032] A credit status evolution graph is generated based on the credit adjustment coefficient, a comprehensive credit assessment model, and industry benchmark parameters. Using the qualification deviation coefficient as the evolution starting point, the problem distribution characteristics are mapped into the comprehensive credit assessment model. The model's abnormal propagation path weights are adjusted, and evolution trigger conditions are defined, such as when a certain number of violations occur at the testing institution or when the comprehensive score falls below a certain threshold. The direction of risk transmission is determined, such as whether it is transmitted to other testing business areas or to partners. The impact diffusion step size, that is, the scope of each risk transmission, is set to construct a credit evolution analysis model. Based on this model, a Monte Carlo simulation method is used to conduct multiple scenario simulations. When the evolution trigger conditions are met, risk transmission is initiated, the risk transmission direction and impact diffusion step size of the current node are updated, and the credit evolution analysis model is simultaneously revised. Simultaneously, simulations are repeated based on the revised model until the preset simulation round or convergence is reached. Finally, the key path nodes and impact scopes of each simulation round are summarized to generate the risk diffusion path and associated impact indicators, thereby obtaining a credit status evolution graph.

[0033] Based on the credit status evolution graph and regulatory parameter configuration, a regulatory model is established and the regulatory parameter configuration is iteratively optimized to obtain the optimal regulatory strategy parameters. A regulatory model is constructed, which contains a set of decision variables, including regulatory parameter configuration as a decision variable, such as the audit frequency threshold, which determines the frequency of audits of testing institutions; indicator weight configuration, which determines the importance of different credit evaluation indicators in the comprehensive assessment; and data coverage, which specifies the aspects covered by the regulatory data used for the assessment. The objective function group is constructed based on the credit stability index and the regulatory cost index. The constraint rule group includes resource constraints, such as restrictions on regulatory manpower and material resources, as well as compliance assurance conditions. A genetic algorithm is used to perform multi-generation optimization on the regulatory model to obtain the initial optimized strategy parameters. The particle swarm algorithm is then used to fine-tune the regulatory model and output the optimal regulatory strategy parameters.

[0034] The hierarchical decision-making model analyzes the current cycle's regulatory data and outputs the current cycle's credit rating results. Combining the optimal regulatory strategy parameters for the current cycle with the current cycle's regulatory parameter configuration, the actual regulatory strategy for the current cycle is revised to achieve dynamic management and control of credit grading. The hierarchical decision-making model conducts an in-depth analysis of the regulatory data collected for the current cycle and outputs the current cycle's credit rating results based on the analysis results. For example, the testing agency is rated as excellent, good, qualified, or unqualified. Combining the optimal regulatory strategy parameters for the current cycle with the existing regulatory parameter configuration, the actual regulatory strategy implemented for the current cycle is adjusted and optimized, thereby achieving dynamic management and control of the credit grading of residential project testing agencies.

[0035] The present invention will be further described below in conjunction with Examples 1 to 6: Example 1

[0036] In this embodiment, the process of collecting full-dimensional regulatory data of testing institutions and cleaning and classifying them, then establishing a multidimensional credit evaluation model, performing cluster analysis on the multidimensional credit evaluation model and comparing it with industry benchmark parameters to form an initial credit evaluation model is described in detail.

[0037] Extract operational data from testing agencies from multiple regulatory platforms. For example, a region has a government construction project quality supervision and management platform, an industry association self-regulatory platform, and a consumer complaint feedback platform. The government construction project quality supervision and management platform collects records from testing agencies during their daily project quality inspections, including data on whether testing procedures are standardized and whether test reports are accurate. The industry association self-regulatory platform collects data on testing agencies' participation in industry activities, such as whether dues are paid on time and whether they actively participate in industry training. The consumer complaint feedback platform collects customer complaints against testing agencies, including the content of the complaints and the progress of their handling. By integrating these data from different platforms, we generate a multi-source, heterogeneous regulatory data set.

[0038] Clean and classify multi-source heterogeneous regulatory data sets. In terms of filling missing values, if it is found that the data on the number of testing items of a certain testing agency within a certain period of time is missing, the missing values ​​will be estimated and filled by consulting the agency's business contracts, financial statements and other information for the same period. During data normalization, the original value range of the testing agency's testing accuracy data is 0-100%. Through a specific normalization method, it is mapped to the interval of 0-1 to facilitate subsequent calculations and comparisons. During the attribute alignment process, different platforms may have different descriptions of the names of the testing agencies, some are full names, and some are abbreviations. By establishing a unified name comparison table, the agency names in all data are unified into full names. In terms of eliminating redundant fields, if it is found that some platforms have repeatedly recorded the business license number of the testing agency, only one valid record will be retained. When classifying and coding, the violations of the testing agency are coded according to different types, such as 1 for data falsification violations, 2 for uncalibrated testing equipment violations, etc. In terms of time alignment, ensure that the timestamps of all data are arranged in the order of actual occurrence. For data with inaccurate time records, corrections are made by cross-checking with relevant business records, and finally a structured regulatory data set is obtained.

[0039] Based on a structured regulatory data set, a multidimensional credit evaluation model is constructed using a hierarchical modeling approach. The regulatory data is first divided into multiple dimensions, including basic qualification, business operation, and customer feedback. In the basic qualification dimension, factors such as whether the testing agency's qualification certificates are complete and valid are considered; in the business operation dimension, attention is paid to whether the testing process complies with standards and specifications, and whether the test reports are issued in a timely and accurate manner; in the customer feedback dimension, the number of customer complaints and satisfaction with complaint handling are analyzed. Different weights are assigned to the data in each dimension based on their importance. For example, in the business operation dimension, the weight of compliance with standards and specifications for the testing process is set to 0.6, and the weight of timely and accurate test reports is set to 0.4. Through this hierarchical approach, a multidimensional credit evaluation model is constructed.

[0040] A cluster analysis was conducted on the multidimensional credit evaluation model. Using a clustering algorithm, the numerous testing institutions in the region were divided into different groups based on their credit characteristics. After analysis, it was found that they could be divided into an excellent credit group, a good credit group, a medium credit group, and a poor credit group. The central value of each group was calculated. For example, the central value of the excellent credit group represents the average performance of the testing institutions in that group in various credit indicators. These group central values ​​were compared with industry benchmark parameters, which were obtained through statistical analysis of the credit data of a large number of testing institutions across the country. If the central value of a group is higher than the industry benchmark parameter in terms of qualification completeness, is on par with the industry benchmark parameter in terms of business standardization, and is lower than the industry benchmark parameter in terms of complaint response, these comparison results are combined to form an initial credit evaluation model, which can preliminarily reflect the credit level position of the testing institutions in the region in the industry. Example 2

[0041] Market environment parameters are injected into the initial credit evaluation model to adjust the evaluation weight distribution and obtain the first credit evaluation model. Suppose that the residential engineering inspection market in a certain city has recently been highly competitive, the number of inspection institutions has increased, and market demand has grown slowly. In this market environment, the evaluation weight of the inspection institution's business development capabilities needs to be increased. Originally, in the initial credit evaluation model, the weight of the business development capability indicator was 0.2, which has now been adjusted to 0.3. At the same time, considering that market competition may cause some institutions to reduce service quality in order to obtain business, the weight of the service quality assurance indicator has also been adjusted from 0.3 to 0.4. Through such adjustments, market environment parameters are injected into the initial credit evaluation model to obtain the first credit evaluation model, making it more in line with the current market reality.

[0042] Policy constraints are applied to the first credit evaluation model to generate a comprehensive credit evaluation model. Policy constraints include compliance boundary conditions and industry reward and punishment rules. Regarding compliance boundary conditions, the state stipulates that testing institutions must operate in accordance with specific testing standards and procedures, and violations will result in penalties. For example, if a testing institution fails to process test samples according to standards during the testing process, it will violate compliance boundary conditions. Industry reward and punishment rules stipulate that testing institutions that receive provincial-level or higher-level awards for high-quality testing services will receive bonus points; those that are criticized for violations will receive penalty points deductions. For example, a testing institution that received a provincial-level award for high-quality testing services in the past year was awarded bonus points based on the first credit evaluation model according to industry reward and punishment rules. At the same time, if the institution violated regulations during a particular test, points were deducted according to the rules. Taking these situations into consideration, a comprehensive credit evaluation model is generated after policy constraints are applied.

[0043] Based on the comprehensive credit evaluation model and industry benchmark parameters, dynamic credit evaluation indicators are output. An evaluation indicator system is constructed, which includes qualification integrity indicators, business standardization indicators, and complaint response indicators. The qualification integrity indicator considers whether the various qualification certificates of the testing agency are complete, such as whether it has special residential engineering testing qualifications and whether the relevant testing personnel have the corresponding qualification certificates. The business standardization indicator focuses on whether the operations during the testing process are standardized, whether the content of the test report is complete and accurate, and whether the data records are true and reliable. The complaint response indicator analyzes the speed at which the testing agency handles customer complaints and the satisfaction with the handling results. Combined with the comprehensive credit evaluation model, the evaluation indicator system is layered and calculated using a weighted aggregation method. For example, the weight of the qualification integrity indicator is set to 0.3, the weight of the business standardization indicator is set to 0.4, and the weight of the complaint response indicator is set to 0.3. For a certain testing agency, its qualification integrity index score is 80 points, its business norms index score is 75 points, and its complaint response index score is 85 points. After weighted calculation: 80×0.3+75×0.4+85×0.3=24+30+25.5=79.5 points, the comprehensive score of the agency is obtained. According to the same method, all testing agencies are calculated to obtain a comprehensive score sequence. In terms of behavioral compliance distribution, the compliance status of each testing agency in various business operations is counted, and the proportion of compliant operations is calculated to form a behavioral compliance distribution. For the high-risk agency identification set, the compliance deviation of each testing agency is calculated based on the behavioral compliance distribution. If the proportion of compliant operations of a certain testing agency is far below the industry average, its compliance deviation is high. Set a deviation threshold to identify the testing agencies whose compliance deviation is higher than the deviation threshold in the comprehensive credit evaluation model, and generate a high-risk agency identification set. Example 3

[0044] This embodiment details the specific steps of constructing a credit assessment pre-training framework and optimizing it through dynamic credit evaluation indicators to obtain a credit assessment model and then extract a credit adjustment coefficient.

[0045] A credit assessment pre-training framework is built based on a multi-task learning architecture. This architecture can simultaneously learn multiple related tasks, improving the model's generalization capabilities. In this scenario, tasks such as the testing agency's qualification assessment, business risk assessment, and credit trend prediction are integrated into a single framework. The framework's bottom layer is the data input layer, which receives dynamic credit evaluation indicator data, such as comprehensive score sequences and behavioral compliance distributions. The middle layer is the feature extraction layer, which extracts features from the input data using techniques such as neural networks to mine the data for potential information. The top layer is the task output layer, which outputs qualification assessment results, business risk assessment results, and credit trend prediction results, respectively.

[0046] Dynamic credit evaluation indicators are used to perform parameter tuning and cross-validation on the credit evaluation pre-training framework, and then output a credit evaluation model. Taking the dynamic credit evaluation indicator data of a batch of testing institutions as an example, these data are divided into training sets and test sets. During the training process, the parameters in the framework are continuously adjusted using the gradient descent method. The formula for the gradient descent method is: ,in Indicates the parameters that need to be updated. is the learning rate, which controls the step size of each parameter update. is the loss function, which is used to measure the difference between the model's predicted results and the actual results. Represents the loss function for parameters The partial derivative of is obtained through multiple iterations of training, ensuring that the model's predictions on the training set are as close as possible to the actual situation. Once the model's performance on the training set stabilizes, the test set is used to validate the model. If the model's prediction accuracy on the test set reaches a certain standard, such as above 80%, the model training is considered successful and the credit assessment model is output.

[0047] The dynamic credit evaluation indicators of the institution to be assessed are input into the credit assessment model to predict the distribution of its potential credit issues. For example, consider a newly established testing institution entering the market. Its dynamic credit evaluation indicators, such as its comprehensive score sequence and behavioral compliance distribution, are input into the trained credit assessment model. The model analyzes this data to predict potential credit issues that the institution may face in the future. For example, the model predicts that the institution may face risks in maintaining its qualifications, given its low score for the qualification integrity indicator in its comprehensive score. It also predicts that data inaccuracies may exist in its business operations, based on the poor compliance of some of its testing operations in its behavioral compliance distribution.

[0048] Based on the predicted distribution of credit issues, extract the credit adjustment coefficient. Locate the core cluster of the predicted credit issue distribution and determine the qualification deviation coefficient. If the predicted credit issues are mainly concentrated in qualifications, such as qualification certificates that are about to expire and have not been renewed in time, or some key testing personnel who are underqualified, determine the qualification deviation coefficient by analyzing the severity of these issues and the impact on credit. Perform kernel density estimation on the spatial density of the credit issue distribution to fit the problem distribution characteristics. In kernel density estimation, the formula used is: ,in is the estimated probability density function, describing the distribution density of credit issues at point X; n is the sample size, i.e., the number of testing institutions; h is the bandwidth, controlling the smoothness of the kernel function; K is the kernel function, commonly including the Gaussian kernel function; and Xi is the observed value of the i-th sample, representing the credit issue data for the testing institution. Analysis revealed that credit issues for a particular testing institution occur more frequently in certain business areas, exhibiting a certain clustering pattern. Kernel density estimation was used to fit the distribution characteristics of these issues within these business areas. The temporal evolution of the credit issue distribution was analyzed to extract classification labels for business anomaly types. If a testing institution is found to have experienced a gradual increase in business operation violations over the past few months, with cyclical changes, a classification label for the business anomaly type, such as "business violation growth," was extracted based on this temporal evolution trend. Ultimately, a credit adjustment coefficient was derived, which incorporates the qualification deviation coefficient, business anomaly type, and issue distribution characteristics. Example 4

[0049] This embodiment describes in depth the specific implementation process of generating a credit status evolution map based on the credit adjustment coefficient, the comprehensive credit evaluation model, and industry benchmark parameters.

[0050] Using the qualification deviation coefficient as the starting point for evolution, the problem distribution characteristics are mapped into the comprehensive credit assessment model. The model's anomaly propagation path weights are adjusted, and the evolution trigger conditions, risk transmission direction, and impact diffusion step length are defined to construct a credit evolution analysis model. Assume that a testing agency has a high qualification deviation coefficient, indicating significant qualification issues. The agency's problem distribution characteristics, such as the predominance of business anomalies in concrete strength testing and the tendency for the problem distribution to spread from this business to other building materials testing businesses, are mapped into the comprehensive credit assessment model. In the model, the weight of the anomaly propagation path from the concrete strength testing business to other building materials testing businesses is increased. Evolution trigger conditions are defined, such as when the agency commits a serious violation in the concrete strength testing business, triggering evolution. The risk transmission direction is set from the concrete strength testing business to other building materials testing businesses and related projects. The impact diffusion step length is set so that each risk transmission affects two adjacent business areas or projects. With these settings, a credit evolution analysis model is constructed.

[0051] Based on the credit evolution analysis model, a Monte Carlo simulation method is used to conduct multiple scenario simulations. A credit status evolution graph is generated based on the simulation results. The Monte Carlo simulation method simulates different scenarios through multiple random samplings. In this example, 1000 simulations are performed. Risk transmission is initiated when the evolution trigger condition is met. For example, in the 100th simulation, the testing agency commits a serious violation in the concrete strength testing business. Risk transmission is initiated at this time. The risk transmission direction and impact diffusion step size of the current node are updated. Assume that the original risk transmission direction was from the concrete strength testing business to the rebar testing business. Due to the severity of this violation, the risk transmission direction is expanded to include more building materials testing businesses and related engineering projects. The impact diffusion step size was originally adjusted from affecting two adjacent business areas at a time to affecting three adjacent business areas. The credit evolution analysis model is then simultaneously revised, and relevant parameters within the model are adjusted based on the updated risk transmission direction and impact diffusion step size. Re-simulation is performed based on the revised credit evolution analysis model until the preset number of simulation rounds of 1000 or convergence is reached. During each simulation, key path nodes—business links or institutional departments that play a key role in the risk transmission process—are recorded, along with the scope of impact, including the business areas and engineering projects affected by the risk. The key path nodes and scope of impact from each round of simulation results are summarized to generate a risk diffusion path and associated impact indicators. For example, after 1,000 simulations, it was found that the risk diffusion path primarily spread from the concrete strength testing business to the rebar testing business, the cement testing business, and three related engineering projects. Associated impact indicators include the decline in testing accuracy for the affected businesses and the number of days of delay in the project schedule. Based on these results, a credit status evolution map is generated, visually demonstrating the evolution of the testing institution's credit risk. Example 5

[0052] This embodiment details the specific steps of establishing a control model and iteratively optimizing the regulatory parameter configuration based on the credit status evolution graph and regulatory parameter configuration to obtain the optimal regulatory strategy parameters.

[0053] A regulatory model is constructed, consisting of a set of decision variables, an objective function, and a set of constraint rules. The configuration of regulatory parameters, as decision variables, is crucial in practical operations. Regarding audit frequency thresholds, if a testing institution's credit risk is low, the audit frequency can be appropriately reduced to minimize waste of regulatory resources. If the credit risk is high, the audit frequency should be increased to strengthen regulatory oversight. For example, for testing institutions with good and long-term creditworthiness, the audit frequency could be adjusted from the typical annual to biennial. For institutions with significant credit issues, the audit frequency could be increased to quarterly. Regarding indicator weighting, the weightings of credit evaluation indicators such as qualification integrity, business compliance, and complaint responsiveness can be flexibly adjusted based on evolving regulatory priorities. In the current market environment, if the accuracy of testing data is a priority, the weighting of business compliance indicators can be appropriately increased. If there has been a recent surge in fraudulent qualification data, the weighting of qualification integrity indicators can be increased. Data coverage is also crucial, as it determines the source of regulatory data used for assessment. Whether to rely solely on data from government regulatory platforms or to incorporate data from industry associations and market feedback, such as customer and peer reviews, requires comprehensive consideration.

[0054] The objective function group is constructed based on the credit stability index and the regulatory cost index. The credit stability index measures the fluctuations in the credit status of a testing organization. A higher credit stability index indicates a more stable credit status and a more reliable market reputation and operational performance. The regulatory cost index comprehensively considers the human, material, and financial resources invested in the regulatory process. Regulatory agencies have limited manpower. Excessively increasing the frequency of audits or expanding the scope of supervision will inevitably lead to increased labor costs. Purchasing specialized testing equipment, conducting data collection and analysis, and other related activities will also incur corresponding material and financial costs. The constraint rule group includes resource constraints and compliance assurance conditions. Regarding resource constraints, the limited number of regulatory staff and testing equipment restricts the unlimited increase in audit frequency and limits the expansion of data coverage. Compliance assurance conditions require that all regulatory actions strictly comply with laws, regulations, and industry standards to ensure the legality, fairness, and transparency of the regulatory process.

[0055] A genetic algorithm is used to optimize the regulatory model over multiple generations to obtain the initial optimized strategy parameters. This algorithm simulates the process of biological evolution. It first randomly generates an initial set of regulatory parameter configurations, which are considered "individuals" within a "population." For each "individual," or each regulatory parameter configuration, its fitness is calculated under the objective function set. This fitness value comprehensively reflects the solution's performance in balancing credit stability and regulatory costs. Higher fitness values ​​indicate a more satisfactory solution. Based on these fitness values, individuals with higher fitness are selected for a crossover operation. This operation, similar to gene exchange in biological genetics, combines the advantageous parameters of different individuals to generate new individuals. Simultaneously, some individuals are mutated with a certain probability to introduce new parameter combinations and prevent the algorithm from falling into local optimal solutions. After multiple generations of evolutionary selection, individuals with higher fitness are gradually identified. The regulatory parameter configurations corresponding to these individuals serve as the initial optimized strategy parameters.

[0056] The particle swarm algorithm is used to fine-tune the control model and output the optimal control strategy parameters. The particle swarm algorithm simulates the foraging behavior of bird flocks and regards each initial optimization strategy parameter as a "particle" in the search space. Each "particle" has its own position and speed. The position represents the current control parameter configuration scheme, and the speed determines the direction and distance of movement of the "particle" in the search space. The "particle" adjusts its speed and position based on its own historical optimal position and the global optimal position in the group. During each iteration, the objective function value of the control strategy corresponding to each "particle" in the control model is calculated, and the global optimal position is continuously updated through comparison. After multiple iterations, when the position of the "particle" no longer changes significantly or the objective function value tends to be stable, the control parameter configuration represented by the corresponding "particle" position is the optimal control strategy parameter. For example, after optimization using the particle swarm algorithm, it was determined that for testing institutions with higher credit risks, the audit frequency threshold was adjusted to once a quarter, the weight of the qualification integrity index was increased to 0.4, and the data coverage was expanded to include industry association data and feedback data from some key customers. These parameters constitute the optimal regulatory strategy parameters, providing a scientific basis for subsequent regulatory decisions and making regulatory work more scientific and efficient. Example 6

[0057] In this embodiment, the process of analyzing the current cycle regulatory data through a hierarchical decision-making model, outputting the current cycle credit rating results, combining the current cycle optimal regulatory strategy parameters with the current cycle regulatory parameter configuration, and correcting the current cycle actual regulatory strategy to achieve dynamic credit grading management and control is specifically described.

[0058] A hierarchical decision-making model is used to conduct an in-depth analysis of regulatory data collected during the current cycle. For example, for a specific time period, regulatory data was collected from numerous residential project inspection agencies in the region, including information on inspection project completion, violation records, and customer complaint handling outcomes. The hierarchical decision-making model comprehensively evaluates this data based on pre-set rules and algorithms. For example, the model assumes that if an inspection agency has no violations during a cycle, its inspection report accuracy exceeds 95%, and its customer complaint handling satisfaction rate exceeds 90%, it will be initially rated as excellent. If there are a few minor violations, the inspection report accuracy rate is between 85% and 95%, and customer complaint handling satisfaction is between 75% and 90%, it will be initially rated as good. This continues, with different preliminary ratings assigned based on different data performance. The model then further adjusts and confirms these preliminary ratings based on factors such as the inspection agency's qualifications and market reputation, ultimately outputting a credit rating for the current cycle, categorizing the inspection agency into different levels: excellent, good, qualified, or unqualified.

[0059] Combining the optimal regulatory strategy parameters obtained for the current cycle with the existing regulatory parameter configuration, the actual regulatory strategy implemented for the current cycle is adjusted and optimized. Assume that in the current cycle, based on the optimal regulatory strategy parameters obtained in the previous step, the audit frequency for excellent-rated testing institutions can be appropriately reduced, and resource allocation can be tilted toward higher-risk institutions. For substandard testing institutions, the audit frequency should be significantly increased to strengthen regulatory oversight of their operations. However, in the existing regulatory parameter configuration, the audit frequency is a uniform standard. In this case, the actual regulatory strategy is revised based on the optimal regulatory strategy parameters and the existing configuration. For excellent-rated testing institutions, the audit frequency is adjusted from once a year to once every two years to reduce unnecessary waste of regulatory resources. For substandard testing institutions, the audit frequency is increased from once a year to once every six months. At the same time, the depth and breadth of inspections are increased, such as by conducting random re-inspections of all testing items and reviewing their internal quality control systems. This approach enables differentiated supervision of testing institutions with different credit ratings, dynamically adjusting regulatory strategies based on credit rating results, achieving the goal of dynamic credit grading and management, improving the effectiveness and relevance of supervision, and ensuring the healthy and orderly development of the residential engineering testing market.

[0060] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0061] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A credit rating and assessment method for residential engineering inspection institutions, characterized in that: The following steps are involved: Obtain basic qualification information and historical business data of the testing agency; Collect all-dimensional regulatory data of testing institutions and clean and classify them, then establish a multi-dimensional credit evaluation model. Perform cluster analysis on the multi-dimensional credit evaluation model and compare it with industry benchmark parameters to form an initial credit evaluation model. Superimpose market environment parameters and policy constraints on the initial credit evaluation model to generate a comprehensive credit evaluation model, and combine it with industry benchmark parameters to output dynamic credit evaluation indicators; Build a credit assessment pre-training framework and optimize it through dynamic credit evaluation indicators to obtain a credit assessment model, and then extract the credit adjustment coefficient; Generate a credit status evolution graph based on credit adjustment coefficients, comprehensive credit evaluation models, and industry benchmark parameters; Based on the credit status evolution graph and regulatory parameter configuration, a regulatory model is established and the regulatory parameter configuration is iteratively optimized to obtain the optimal regulatory strategy parameters; The current cycle regulatory data is analyzed through a hierarchical decision-making model to output the current cycle credit rating results. By combining the current cycle optimal regulatory strategy parameters with the current cycle regulatory parameter configuration, the actual regulatory strategy of the current cycle is corrected to achieve dynamic management and control of credit grading.

2. The residential engineering inspection agency credit rating assessment method according to claim 1 is characterized in that: The dynamic credit evaluation index at least includes a comprehensive scoring sequence, a behavioral compliance distribution, and a set of high-risk institution identifiers; the credit adjustment coefficient at least includes a qualification deviation coefficient, a business anomaly type, and problem distribution characteristics; The credit status evolution map at least includes risk diffusion paths, associated impact indicators and credit trend prediction results.

3. The residential engineering inspection agency credit rating assessment method according to claim 1 is characterized in that: The method of collecting full-dimensional regulatory data of the testing institutions and performing cleaning and classification, thereby establishing a multi-dimensional credit evaluation model, performing cluster analysis on the multi-dimensional credit evaluation model and comparing it with industry benchmark parameters to form an initial credit evaluation model, includes the following steps: Extracting operational process data of testing institutions from multiple regulatory platforms to generate a multi-source heterogeneous regulatory data set; Cleaning and classifying a multi-source heterogeneous regulatory data set to obtain a structured regulatory data set, wherein the cleaning and classification includes one or more of missing value filling, data normalization, attribute alignment, redundant field removal, classification coding, and time series alignment; Based on the structured regulatory data set, a multi-dimensional credit evaluation model is constructed using a hierarchical modeling approach; Perform cluster analysis on the multidimensional credit evaluation model, divide it into groups with different credit characteristics, compare the group center values ​​with industry benchmark parameters, and form an initial credit evaluation model.

4. The residential engineering inspection agency credit rating assessment method according to claim 1, characterized in that: The process of superimposing market environment parameters and policy constraints on the initial credit evaluation model to generate a comprehensive credit evaluation model, combining industry benchmark parameters, and outputting dynamic credit evaluation indicators includes the following steps: Injecting market environment parameters into the initial credit evaluation model to adjust the evaluation weight distribution to obtain a first credit evaluation model; Adding policy constraints to the first credit evaluation model to generate a comprehensive credit evaluation model, where the policy constraints include compliance boundary conditions and industry reward and punishment rules; Based on the comprehensive credit evaluation model and industry benchmark parameters, dynamic credit evaluation indicators are output. The specific process includes: An evaluation index system is constructed, which includes at least qualification integrity indicators, business standardization indicators and complaint response indicators. Combined with the comprehensive credit evaluation model, the evaluation index system is layered and calculated through the weighted aggregation method to obtain a comprehensive score sequence, behavioral compliance distribution and a set of high-risk institution identifications.

5. The residential engineering inspection agency credit rating assessment method according to claim 4 is characterized in that: Obtaining the high-risk institution identifier set includes the following steps: Based on the behavioral compliance distribution, the compliance deviation of each testing organization is calculated; Identify the testing institutions whose compliance deviation in the comprehensive credit evaluation model is higher than the deviation threshold, and generate a set of high-risk institution identifications.

6. The residential engineering inspection agency credit rating assessment method according to claim 1 is characterized in that: The construction of a credit assessment pre-training framework and optimization through dynamic credit evaluation indicators to obtain a credit assessment model and then extract a credit adjustment coefficient include the following steps: Build a credit assessment pre-training framework based on a multi-task learning architecture; Use dynamic credit evaluation indicators to perform parameter tuning and cross-validation on the credit evaluation pre-training framework and output a credit evaluation model; Input the dynamic credit evaluation indicators of the institution to be evaluated into the credit evaluation model to predict the distribution of its potential credit problems; A credit adjustment coefficient is extracted based on the predicted credit problem distribution, wherein the credit adjustment coefficient at least includes a qualification deviation coefficient, a business anomaly type, and problem distribution characteristics.

7. The residential engineering inspection agency credit rating assessment method according to claim 6 is characterized in that: The step of extracting the credit adjustment coefficient based on the predicted credit problem distribution includes the following steps: Locate the core cluster area of ​​the predicted credit problem distribution and determine the qualification deviation coefficient; Perform kernel density estimation on the spatial density of credit problem distribution to fit the problem distribution characteristics; Analyze the time evolution trend of credit issue distribution and extract classification labels of business anomaly types.

8. The residential engineering inspection agency credit rating and assessment method according to claim 1, characterized in that: The generation of a credit status evolution map based on the credit adjustment coefficient, the comprehensive credit evaluation model and the industry benchmark parameters includes the following steps: Taking the qualification deviation coefficient as the starting point of evolution, the problem distribution characteristics are mapped into the comprehensive credit evaluation model, the abnormal propagation path weight of the model is adjusted, the evolution trigger conditions, risk transmission direction and impact diffusion step are defined, and a credit evolution analysis model is constructed; Based on the credit evolution analysis model, the Monte Carlo simulation method is used to conduct multi-scenario deductions. Based on the deduction results, a credit status evolution map is generated, which specifically includes: When the evolution trigger conditions are met, risk transmission is initiated, the risk transmission direction and impact diffusion step of the current node are updated, and the credit evolution analysis model is simultaneously revised; Re-run the deduction based on the revised credit evolution analysis model until the preset deduction rounds or convergence state are reached; Summarize the key path nodes and impact scope in each round of simulation results to generate risk diffusion paths and related impact indicators.

9. The residential engineering inspection agency credit rating assessment method according to claim 1, characterized in that: The process of establishing a control model based on the credit status evolution graph and the regulatory parameter configuration and iteratively optimizing the regulatory parameter configuration to obtain the optimal regulatory strategy parameters includes the following steps: Constructing a regulatory model, the model comprising a set of decision variables, an objective function group, and a constraint rule group. The regulatory parameter configuration, as a decision variable, includes at least an audit frequency threshold, an indicator weight configuration, and a data coverage range. The objective function group is constructed based on a credit stability index and a regulatory cost index. The constraint rule group includes resource constraints and compliance assurance conditions. Genetic algorithms are used to optimize the control model over multiple generations to obtain the initial optimization strategy parameters; The particle swarm algorithm is used to fine-tune the control model and output the optimal regulatory strategy parameters.

10. A credit rating and assessment system for residential engineering inspection institutions, characterized in that: include: Data integration module, used to obtain basic qualification information, historical business data and full-dimensional regulatory data of testing institutions; The model building module is used to clean and classify full-dimensional regulatory data, establish a multi-dimensional credit evaluation model, conduct cluster analysis and compare it with industry benchmark parameters to form an initial credit evaluation model, superimpose market environment parameters and policy constraints to generate a comprehensive credit evaluation model, and output dynamic credit evaluation indicators; The credit analysis module is used to build a credit assessment pre-training framework and optimize it through dynamic credit evaluation indicators to obtain a credit assessment model and extract credit adjustment coefficients; An evolutionary deduction module, which is used to generate a credit status evolution graph based on the credit adjustment coefficient, comprehensive credit evaluation model, and industry benchmark parameters; The strategy optimization module is used to establish a control model and iteratively optimize the regulatory parameter configuration based on the credit status evolution map and regulatory parameter configuration to obtain the optimal regulatory strategy parameters; The hierarchical execution module is used to analyze the current cycle regulatory data through a hierarchical decision-making model, output credit rating results, and modify the actual regulatory strategy based on the optimal regulatory strategy parameters to achieve dynamic hierarchical management and control.

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