Information optimization method for cooperative supervision of functional departments

Abstract

The invention relates to the technical field of information, in particular to an information optimization method for cooperative supervision of functional departments, which comprises the following steps: for a structured supervision data set, classifying permission allocation modes by adopting an analysis algorithm, and determining permission feature distribution; according to the permission feature distribution, constructing a correlation model of permission configuration and supervision efficiency, and obtaining a contribution index of the permission configuration to the efficiency through a quantitative analysis technology; adjusting a permission allocation scheme according to the contribution degree index, and generating a dynamic permission configuration scheme; aiming at a collaborative path optimization result, acquiring information sharing data by adopting a data stream processing technology, and generating an information sharing efficiency index; according to the information sharing efficiency index and the contribution degree index, performing grading evaluation on the supervision efficiency through a classification algorithm to obtain an efficiency evaluation result; adjusting a cooperation mechanism model according to an efficiency evaluation result, and generating an optimized cooperation mechanism scheme; and the supervision efficiency and the information sharing level are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an information optimization method for collaborative supervision by functional departments. Background Art

[0002] In modern society, coordinated oversight among functional departments is a crucial cornerstone for ensuring the efficient operation of public services. Especially in the context of rapidly growing information data, optimizing the processing of regulatory information to enhance collaborative efficiency has become a crucial research area. This area not only concerns information sharing and collaboration between departments but also directly impacts the effectiveness of policy implementation and the rational allocation of social resources. Its research value and practical significance are self-evident.

[0003] However, the existing methods for collaborative regulatory information processing among functional departments still have many shortcomings. From a management perspective, many solutions lack the ability to dynamically adjust authority allocation and are unable to respond flexibly according to actual needs, resulting in waste of resources or regulatory loopholes. From a technical perspective, data analysis methods are relatively extensive, making it difficult to accurately identify the specific impact of authority configuration on regulatory efficiency, which in turn affects the scientific nature of decision-making. From an application perspective, inter-departmental coordination mechanisms often lack data-driven support, and the effectiveness evaluation of regulatory activities often relies on subjective judgment and lacks objective quantitative basis. These limitations make it difficult for regulatory information processing to adapt to complex and changing environments. This has led to problems with low regulatory efficiency and information sharing in the existing collaborative regulatory information processing among functional departments. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an information optimization method for collaborative supervision of functional departments, which realizes the dynamic optimization configuration of supervision authority and the continuous improvement of the inter-departmental collaboration mechanism, thereby improving supervision efficiency and information sharing level.

[0005] To achieve the above-mentioned purpose, the specific scheme of the present invention is as follows: A method for optimizing information for collaborative supervision by functional departments may include the following steps:

[0006] Obtain historical regulatory data and authority allocation records from functional departments, cleanse and standardize the historical regulatory data and authority allocation records to generate a structured regulatory data set; use an analytical algorithm to classify authority allocation patterns for the structured regulatory data set and determine the authority feature distribution; construct a correlation model between authority configuration and regulatory efficiency based on the authority feature distribution, and obtain the contribution index of authority configuration to efficiency through quantitative analysis technology; adjust the authority allocation plan based on the contribution index to generate a dynamic authority configuration plan; extract authority allocation rules from the dynamic authority configuration plan, and build a collaboration mechanism model based on departmental collaboration needs through network analysis technology to obtain collaboration path optimization results; based on the collaboration path optimization results, use data stream processing technology to obtain information sharing data and generate information sharing efficiency indicators; based on the information sharing efficiency indicators and contribution indicators, conduct a hierarchical assessment of regulatory effectiveness through a classification algorithm to obtain effectiveness evaluation results; adjust the collaboration mechanism model based on the effectiveness evaluation results to generate an optimized collaboration mechanism plan; extract information sharing rules from the optimized collaboration mechanism plan, update the inter-departmental data sharing agreement through distributed storage technology, and build a regulatory information processing system.

[0007] Furthermore, the historical regulatory data and authority allocation records are obtained from the functional departments, and the historical regulatory data and authority allocation records are cleaned and standardized by data processing technology to generate a structured regulatory data set, including: extracting the historical regulatory data and authority allocation records from the functional departments through a preset interface protocol to obtain the original data set; for the original data set, a deduplication processing method is used to detect duplicate records through a hash algorithm to generate a deduplication data set; according to format standardization rules, field mapping and type conversion are performed on the deduplication data set to obtain a format-unified data set; if the field consistency of the format-unified data set is lower than a preset threshold, the missing values ​​are corrected by a field completion algorithm to obtain a field-consistent data set; and a data storage structure design is used to store the field-consistent data set according to a predefined pattern to generate the structured regulatory data set.

[0008] Furthermore, for the structured regulatory data set, an analysis algorithm is used to classify the authority allocation patterns and determine the distribution of authority characteristics, including: extracting core fields related to authority allocation from the structured regulatory data set through preset field screening rules to obtain an authority allocation subset; based on the authority allocation subset, a clustering analysis method is used to group the allocation patterns to obtain preliminary classification results of authority concentration and authority dispersion; if the distribution characteristics of the preliminary classification results do not match the preset threshold, the boundaries of authority concentration and authority dispersion are adjusted through a field comparison tool to obtain an adjusted classification set; based on the adjusted classification set, statistical indicators of distribution characteristics are obtained to judge the concentration or dispersion tendency of the authority allocation pattern; through a data mapping method, the identification results of the authority allocation pattern are associated with other fields to obtain an associated feature set; and a data analysis tool is used to deeply mine the associated feature set to determine the final authority pattern distribution law.

[0009] Furthermore, based on the authority feature distribution, a correlation model between authority configuration and regulatory efficiency is constructed, and a contribution index of authority configuration to efficiency is obtained through quantitative analysis technology, including: standardizing the authority configuration parameters in the authority feature distribution through a data preprocessing tool to obtain a standardized authority configuration data set; based on the standardized authority configuration data set, using an efficiency evaluation module to measure the regulatory efficiency under each authority configuration scenario to obtain efficiency evaluation result data; identifying the correlation strength between the authority configuration parameters and regulatory efficiency through an association analysis method, and determining a set of significantly correlated configuration factors; based on the set of configuration factors, constructing a multivariate linear regression model to establish a mathematical correlation relationship between authority configuration and regulatory efficiency; if the fit of the regression model is lower than a preset threshold, adjusting the regression parameters and variable selection strategy through a model optimization tool to obtain an optimized correlation model; based on the optimized correlation model, calculating the efficiency contribution value of each configuration factor to generate a set of authority configuration efficiency contribution indicators.

[0010] Furthermore, the permission allocation scheme is adjusted according to the contribution index to generate a dynamic permission configuration scheme, including: detecting configuration items in the contribution index that are lower than a preset threshold through a threshold comparator to obtain a list of permission configurations to be optimized; based on the list of permission configurations to be optimized, using a permission resource analysis module to count the resource occupancy of each permission type to determine resource allocation constraints; if there is a conflict in the resource allocation constraints, adjusting the constraint parameter range through a constraint relaxation processor to obtain a set of feasible constraints; for the set of feasible constraints, constructing a fitness function of a genetic algorithm and establishing a mathematical target model for permission configuration optimization; based on the mathematical target model, using a genetic algorithm to generate an initial permission configuration population and obtain a coding sequence of configuration parameters; evaluating the configuration effect of the coding sequence through a fitness calculator to judge the pros and cons of the configuration scheme; generating a new generation of permission configuration population through selection, crossover and mutation operations to obtain an adaptive permission configuration strategy.

[0011] Furthermore, the authority allocation rules are extracted from the dynamic authority configuration scheme, combined with the department collaboration needs, and a collaboration mechanism model is constructed through network analysis technology to obtain the collaboration path optimization results, including: obtaining the specific content of the authority allocation rules from the dynamic authority configuration scheme, extracting data for department collaboration terms, and obtaining a list of inter-department collaboration needs; based on the collaboration needs list, using graph network technology to construct a topological structure of department collaboration, and determining a preliminary framework of the collaboration mechanism through relationship mapping of nodes and edges; for the preliminary framework, analyzing the connection strength and path length between nodes to obtain the key area distribution of path optimization; based on the key area distribution, adjusting the edge weight parameters in the graph network, if the connection strength of a path is lower than a preset threshold, recalculating the path priority through the information processing module to obtain an optimized collaboration path set; for the optimized collaboration path set, analyzing the resource allocation ratio, if the allocation ratio is unbalanced, adjusting the allocation rules through the resource balancing tool to determine the final collaboration mechanism model.

[0012] Furthermore, the data stream processing technology is used to obtain information sharing data and generate information sharing efficiency indicators based on the collaborative path optimization results, including: based on the collaborative path optimization results, real-time data stream processing technology is used to obtain information sharing related data from regulatory activities, pre-processing is performed through a data cleaning module, and a preliminary organized information sharing data set is obtained; based on the preliminary organized information sharing data set, a classification tool is used to perform layered processing on the data, and the priority distribution of different categories of data is determined to generate a classified shared data set; the classified shared data set is screened by a preset threshold, and if the relevance of a certain type of data is lower than the preset threshold, the data weight is adjusted through the information processing module to obtain a screened shared data subset; for the screened shared data subset, an information integration tool is used to perform structured processing to generate a data framework that meets the requirements of the sharing mechanism; based on the data framework, a regression analysis model is used to quantify key fields to determine a preliminary evaluation value of information sharing efficiency; the preliminary evaluation value is adjusted through real-time feedback to obtain a final information sharing efficiency indicator.

[0013] Furthermore, the regulatory effectiveness is graded and evaluated through a classification algorithm based on the information sharing efficiency index and contribution index to obtain an effectiveness evaluation result, including: using a data integration tool to obtain regulatory-related data sets from multiple sources to generate a preliminary shared information library; based on the preliminary shared information library, pre-processing is performed through a data cleaning module to obtain a sorted information set; if there are missing or abnormal fields in the sorted information set, repairing is performed through a data completion tool to obtain a repaired information data set; based on the repaired information data set, a decision tree algorithm is used to classify the data to obtain a classified effectiveness level distribution; if the classified effectiveness level distribution is unbalanced, adjustment is performed through a data balancing module to obtain an adjusted level distribution result; and the adjusted level distribution result is associated with the sharing mechanism through a data mapping tool to generate a final effectiveness evaluation data set.

[0014] Furthermore, the collaborative mechanism model is adjusted according to the performance evaluation result to generate an optimized collaborative mechanism solution, including: if the efficiency index of a collaborative path in the performance evaluation result is lower than a preset threshold, then the path-related data records are obtained through a feedback loop mechanism to determine the deviation range of the path efficiency; according to the deviation range, the path performance information is extracted from the historical collaborative records using a data integration tool to obtain the fluctuation characteristics of the path efficiency; if the fluctuation characteristics show that certain links are continuously inefficient, the configuration parameters of the collaborative path are corrected through an adjustment module to obtain a corrected parameter set; a logical comparison tool is used to analyze the degree of matching between the corrected parameter set and the historical optimization solution to determine the update direction of the collaborative mechanism; the corrected parameter set is associated with the actual scenario through a data mapping module to obtain an updated mechanism model framework; a decision tree algorithm is used to predict and analyze the efficiency indicators in the updated mechanism model framework to determine whether the prediction results reach the preset threshold range and generate a final optimization solution.

[0015] Furthermore, the information sharing rules are extracted from the optimized collaboration mechanism scheme, the inter-departmental data sharing agreement is updated through distributed storage technology, and a regulatory information processing system is constructed, including: obtaining the specific content of the information sharing rules from the optimized collaboration mechanism scheme, using data analysis tools for structured processing, and obtaining a structured sharing rule set; storing and classifying the sharing rule set through distributed database technology, and determining the priority configuration of inter-departmental data interaction; according to the priority configuration, using a mapping tool to associate the configuration with the data protocol update requirements, and obtaining an updated data protocol framework; if the interaction permissions in the updated data protocol framework do not match the department collaboration requirements, adjusting the permission allocation through a permission verification tool to obtain an adjusted permission set; using a logical comparison tool to analyze the adaptability of the adjusted permission set to the data flow record, and determining the final permission allocation scheme; combining the final permission allocation scheme with the regulatory system construction requirements through a data integration module to construct a final regulatory information processing system.

[0016] The beneficial effects of the present invention are as follows: The present invention obtains historical regulatory data and authority allocation records, cleans and standardizes the data, and generates a structured regulatory data set. Then, an analytical algorithm is used to classify the authority allocation pattern, determine the authority feature distribution, construct a correlation model between authority configuration and regulatory efficiency, and obtain the contribution index of authority configuration to efficiency. Based on the contribution index, the authority allocation plan is adjusted to generate a dynamic authority configuration plan. Combined with the departmental collaboration needs, a collaboration mechanism model is constructed and the collaboration path is optimized. Information sharing data is obtained through data stream processing technology to generate information sharing efficiency indicators. A graded assessment of regulatory effectiveness is performed based on the efficiency index, and the collaboration mechanism model is adjusted accordingly. Finally, information sharing rules are extracted, the inter-departmental data sharing agreement is updated, and a regulatory information processing system is constructed. The present invention realizes the dynamic optimization configuration of regulatory authority and the continuous improvement of the inter-departmental collaboration mechanism, thereby improving regulatory efficiency and information sharing levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of an information optimization method for collaborative supervision by functional departments provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the scope of implementation of the present invention is not limited thereto.

[0019] like Figure 1 As shown, this embodiment provides an information optimization method for collaborative supervision by functional departments, which may specifically include the following steps:

[0020] Step 100: Obtain historical supervision data and authority allocation records from functional departments, clean and standardize the historical supervision data and authority allocation records through data processing technology, and generate a structured supervision data set. Specifically, step S100 may include the following steps:

[0021] Step S101 extracts historical regulatory data and authority allocation records from the regulatory department using a pre-defined interface protocol to obtain a raw data set. For example, a request is sent from the regulatory department's historical database using a RESTful interface protocol. The protocol defines data fields such as timestamps, authority IDs, and regulatory event descriptions. This allows the extraction of a raw data set containing thousands of records, ensuring real-time data integrity.

[0022] In step S102, a deduplication method is applied to the original dataset, detecting duplicate records using a hash algorithm to generate a deduplication dataset. Specifically, a hash value is calculated for each record in the original dataset, such as using the MD5 algorithm to generate a unique identifier. If two records have the same hash value, they are considered duplicates and one is removed to generate a deduplication dataset. This method can effectively reduce data redundancy and improve the efficiency of subsequent processing.

[0023] In one embodiment, the deduplication processing of step S102 can be applied to large-scale regulatory data scenarios. For example, when the original data set contains 100,000 permission allocation records, the hash algorithm quickly scans and eliminates approximately 5% of duplicate items, ensuring that the size of the deduplication data set is reduced while maintaining information integrity, which is beneficial to reducing storage costs and improving query speed.

[0024] Step S103: Field mapping and type conversion are performed on the deduplicated dataset according to format standardization rules to obtain a unified format dataset. For example, the date field in the deduplicated dataset is converted from a string format to a unified timestamp format, and the permission type field is mapped to a standard coding system to obtain a unified format dataset.

[0025] In step S104, if the field consistency of the unified format dataset falls below a preset threshold, a field completion algorithm is used to correct missing values ​​and obtain a field-consistent dataset. Specifically, field consistency is assessed by calculating the completeness rate of each field, for example, completeness rate = (number of non-missing records / total number of records) × 100. If the average completeness rate falls below a preset threshold of 80%, a mean-based completion algorithm is applied. Missing numeric fields are filled with the field's average value, and missing categorical fields are filled with the most frequent value, thereby correcting missing values ​​and obtaining a field-consistent dataset. This algorithm ensures data integrity and avoids analytical bias.

[0026] In one embodiment, step S104 can be expanded to address scenarios where permission allocation records are missing. For example, in a uniformly formatted dataset, if the permission level field is missing at 15%, the completion algorithm first calculates the distribution of existing permission levels, e.g., high-level permissions account for 40%, intermediate permissions account for 50%, and low-level permissions account for 10%. The intermediate level is then used as the fill-in value for the missing value to generate a consistent field dataset. This process maintains the statistical characteristics of the dataset and improves the accuracy of subsequent permission analysis.

[0027] For example, in another scenario, when the threshold is set to 90%, if the consistency is only 85%, the algorithm can combine the similarity of adjacent records to complete the task. For example, for a missing regulatory date in a record, the average of the dates of the two previous and next records can be used to fill it in, ensuring the temporal continuity of the data set with consistent fields, thereby improving the traceability of regulatory data.

[0028] In step S105, a data storage structure is designed to store the consistent field dataset according to a predefined schema, generating the structured regulatory dataset. Specifically, the predefined schema includes a relational database schema. For example, the consistent field dataset is mapped to a table structure, with permission allocation records stored in a master table and historical regulatory data linked to a subtable. This structured regulatory dataset is generated through optimized queries using indexes. This design facilitates efficient retrieval.

[0029] In one embodiment, the storage in step S105 can cover data of different sizes. For example, for small and medium-sized regulatory data sets, the SQLite storage mode is used to partition and store field-consistent data sets by time and department ID to generate a structured regulatory data set, which is beneficial for fast access and backup.

[0030] Step S106: Integrate the structured regulatory data set using storage rules to obtain a data foundation for subsequent analysis. For example, apply merge rules to integrate the scattered sub-table data into a unified view to obtain a data foundation containing complete authority and regulatory information.

[0031] Step 200: For the structured supervision data set, use an analysis algorithm to classify the authority allocation pattern and determine the authority feature distribution. Specifically, step S200 may include the following steps:

[0032] Step 201 extracts core fields related to permission allocation from the structured regulatory dataset using pre-defined field filtering rules to obtain a permission allocation subset. Specifically, pre-defined field filtering rules are defined. These rules select relevant fields from the structured regulatory dataset, such as permission ID, assigned object, and permission level, based on criteria such as permission type, allocation time, and department ID. This ensures that the extracted data is directly related to permission allocation behavior.

[0033] Step S202: Based on the authority allocation subset, cluster analysis is used to group allocation patterns to obtain preliminary classification results for authority concentration and authority dispersion. Specifically, the data points in the authority allocation subset are grouped using the K-means clustering algorithm. This algorithm calculates the Euclidean distance between data points to group similar allocation patterns into the same cluster. Authority concentration is manifested as a small number of departments holding the majority of authority, while authority dispersion is manifested as an even distribution of authority across multiple departments. The preliminary classification is marked based on the clustering results. For example, if the intra-cluster variance is less than a certain value, it is considered authority concentration.

[0034] For example, in regulatory data processing, cluster analysis helps to quickly identify permission patterns and avoid the inefficiency of manual classification. This can improve the accuracy and speed of data processing and benefit subsequent regulatory optimization.

[0035] Step S203: If the distribution characteristics of the preliminary classification results do not match the preset threshold, the boundaries between the centralized and decentralized permissions are adjusted through the field comparison tool to obtain an adjusted classification set. Specifically, the distribution characteristics of the preliminary classification results, such as the density and discreteness of the cluster, are calculated and compared with the preset threshold, such as a density standard with a threshold of 0.8. If the density is lower than this value, it is considered a mismatch; the field comparison tool is used, which identifies records with blurred boundaries by comparing the numerical differences in the permission allocation subset field by field, such as comparing the ratio of the number of permissions to the number of departments; the boundaries are adjusted according to the comparison results, for example, the boundary value is adjusted from the original permission concentration threshold of 0.7 to 0.75 to reallocate boundary records to appropriate clusters; and the adjusted classification set is generated to ensure that all records are correctly classified.

[0036] For example, in one scenario, for historical regulatory data, if the preliminary clustering shows that the distribution density of certain permission records is 0.6, which is lower than the preset threshold of 0.8, the field comparison tool will compare the permission level fields of these records. For example, if the number of permissions of a record is 50, the number of departments is 5, and the ratio is 10, it will be moved into the centralized cluster after exceeding the adjustment threshold. For example, in another scenario, for the boundary adjustment of decentralized permissions, if the ratio is lower than 2, the recently allocated records will be moved out of the decentralized cluster by comparing the allocation time field to optimize the classification accuracy. For example, the adjustment process of the field comparison tool involves the specific calculation of the difference in field values ​​​​pair by pair. First, the difference in the number of permissions between the two records is calculated. If the difference is less than 5, they are considered similar, and then the boundary is adjusted. This can accurately correct the classification deviation and improve the reliability of the permission distribution analysis.

[0037] Specifically, in regulatory scenarios, this adjustment is beneficial in dealing with data noise, ensuring that classification results reflect the true authority pattern, and avoiding regulatory loopholes caused by inefficient allocation.

[0038] It is understandable that through multiple iterative adjustments, the boundary stability can be verified from different data scales. For example, after adjustment, the classification accuracy is improved by 15% on a small data set and by 10% on a large data set, which jointly supports the robustness of the method.

[0039] Step S204: Obtain statistical indicators of distribution characteristics based on the adjusted classification set to determine whether the permission allocation pattern is centralized or decentralized. Specifically, statistical indicators such as average permission density and standard deviation are calculated, and the tendency is determined based on these indicators. For example, if the density is higher than a threshold, it indicates a centralized tendency.

[0040] Step S205: Using a data mapping method, the recognition result of the permission allocation pattern is associated with other fields to obtain an associated feature set. Specifically, the data mapping method is used to associate the pattern labels in the adjusted classification set with other fields of the structured supervision dataset, such as the supervision event frequency, to form an associated feature set.

[0041] In step S206, data analysis tools are used to deeply mine the associated feature set to determine the final distribution pattern of the authority model. Specifically, principal component analysis tools are used to reduce the dimensionality of the associated feature set and extract the main feature vectors. Association rule mining algorithms such as the Apriori algorithm are used to discover frequent item sets between features, such as the association between authority concentration and high regulatory efficiency. Based on the mining results, the distribution pattern is determined, such as the fact that the distribution is more stable when authority is concentrated in core departments.

[0042] For example, in rights management, principal component analysis first calculates the covariance matrix of the associated feature set, and then extracts the first three principal components, which capture 80% of the variance and are helpful in simplifying complex data.

[0043] Specifically, the Apriori algorithm scans the associated feature set and generates item sets with support greater than 0.5, such as the combination of concentrated authority and low event frequency. This can reveal hidden patterns and improve the accuracy of regulatory decisions.

[0044] It is understandable that under different data set sizes, for example, 3 rules were mined in small regulatory data and 5 rules were mined in large data, which supported the applicability of the method and enhanced the depth of the overall distribution analysis.

[0045] Step 300: Based on the permission feature distribution, a correlation model between permission configuration and supervision efficiency is constructed, and a contribution index of permission configuration to efficiency is obtained through quantitative analysis technology. Specifically, step S300 may include the following steps:

[0046] Step S301: Use a data preprocessing tool to standardize the permission configuration parameters in the permission feature distribution to obtain a standardized permission configuration dataset. Specifically, the permission feature distribution data is imported into the preprocessing tool. The tool first identifies the numerical range of parameters such as permission concentration or dispersion. Then, it applies a min-max normalization method to map each parameter value to a range of 0 to 1. For example, the original concentration value from 50 to 200 is mapped to 0.2 to 0.8, thereby eliminating dimensional differences and obtaining a standardized permission configuration dataset. This dataset contains parameters on a uniform scale, which facilitates subsequent analysis.

[0047] For example, suppose the authority characteristic distribution shows that the authority concentration of a department is 150, which becomes 0.6 after standardization. This helps to unify the comparison of different authority types and avoid high-value parameters dominating the analysis.

[0048] In step S302, based on the standardized permission configuration dataset, an efficiency evaluation module is used to measure the regulatory efficiency under various permission configuration scenarios, obtaining efficiency evaluation results. Specifically, the standardized dataset is input into the efficiency evaluation module, which simulates scenarios based on historical regulatory data. For example, it calculates regulatory response time and accuracy under a centralized permission scenario, obtaining efficiency evaluation results. For example, efficiency is quantified using metrics such as average response time and error rate.

[0049] For example, in the decentralized authority scenario, the module measured an average response time of 5 seconds and an error rate of 2%. These result data are tabulated for correlation analysis, which helps reveal the potential loss of efficiency caused by decentralized configuration.

[0050] Step S303 uses correlation analysis to identify the strength of the correlation between authority configuration parameters and regulatory efficiency, and determines a set of significantly correlated configuration factors. Specifically, the Pearson correlation coefficient method is used to calculate the correlation coefficient between the standardized parameters and the efficiency result data. The Pearson correlation coefficient is calculated by dividing the covariance of two variables by the product of their respective standard deviations and ranges from -1 to 1. A coefficient with an absolute value greater than 0.5 is considered significantly correlated. This allows factors such as the authority concentration factor to be screened out to form a set of configuration factors.

[0051] For example, in the regulatory scenario, the correlation coefficient between authority concentration and response time is -0.7, indicating a negative correlation. That is, the higher the concentration, the faster the response. This identifies a significant factor and is beneficial to the accuracy of model construction.

[0052] In one possible implementation, the correlation analysis of step S303 can be extended to multivariate scenarios. For example, the correlation coefficient between the calculated authority dispersion and the error rate is 0.6, indicating that dispersion increases errors, which supplements the limitations of a single factor and supports the formation of a comprehensive factor set.

[0053] Step S304: Based on the set of configuration factors, a multivariate linear regression model is constructed to establish a mathematical relationship between authority configuration and regulatory efficiency. Specifically, significant factors such as concentration and dispersion are used as independent variables, and regulatory efficiency, such as response time, is used as the dependent variable. The model Y = β0 + β1 * X1 + β2 * X2 + ε is constructed, where Y is efficiency, X1 is concentration, X2 is dispersion, and β0, β1, and β2 are coefficients. The coefficients are estimated using the least squares method, which minimizes the sum of squared residuals to find the best fit line, thereby establishing the relationship.

[0054] For example, in the authority concentration scenario, the model estimates β1 to be -0.3, indicating that for every 1 unit increase in concentration, the response time decreases by 0.3 seconds. This establishes a mathematical relationship that is helpful in quantifying the impact of authority on efficiency.

[0055] In one possible implementation, step S304 can be applied to different regulatory departments. For example, a compliance factor X3 is added to financial supervision, and the model is expanded to Y=β0+β1*X1+β2*X2+β3*X3+ε. The coefficient estimation shows that β3 is 0.4, indicating compliance enhancement efficiency, which enriches the applicability of the model in authority business.

[0056] In step S305, if the goodness of fit of the regression model is below a preset threshold, the model optimization tool adjusts the regression parameters and variable selection strategy to obtain an optimized association model. Specifically, the R-squared value is first calculated as the goodness of fit. If it is below 0.7, the tool applies a stepwise regression strategy. Stepwise regression is a method that selects the best subset by adding or removing variables one by one based on the F-test. The F-test compares the model variance and re-estimates the coefficients after adjustment to obtain the optimized model.

[0057] For example, the initial model R-squared is 0.6, which is lower than 0.7. By removing low-significance variables such as minor dispersion, the R-squared increases to 0.8 after optimization, which improves the accuracy of the model and is beneficial for reliable contribution calculation.

[0058] In one possible implementation, the optimization of step S305 may be combined with ridge regression to handle multicollinearity. Ridge regression adds a penalty term λ to the least squares, and the optimization objective may be expressed as: ;

[0059] Adjusting the λ value to 0.1 can alleviate coefficient inflation, which is effective in scenarios with high collinearity in permission data and improves model stability.

[0060] In one embodiment, step S305 can also be optimized through cross-validation, dividing the data set into training and validation sets, and verifying the R-squared after adjusting the variables. If it improves, it is retained, which ensures the generalization ability of the model under different permission configurations.

[0061] In step S306, the efficiency contribution of each configuration factor is calculated based on the optimized correlation model to generate a set of permission configuration efficiency contribution indicators. Specifically, the regression coefficient β is extracted from the model as the contribution. For example, a β1 value of -0.3 indicates a high concentration contribution. This is then normalized to a percentage, such as a 20% contribution, to form the indicator set.

[0062] For example, in the regulatory efficiency model, the contribution of concentration is 25% and that of dispersion is 15%. These indicators together guide the adjustment of authority, which is beneficial to improving the overall regulatory effectiveness.

[0063] Step 400: According to the contribution index, adjust the authority allocation plan and generate a dynamic authority configuration plan. Specifically, step S400 may include the following steps:

[0064] In step 401, a threshold comparator detects configuration items in the contribution index that are below a preset threshold, and obtains a list of permission configurations to be optimized. Specifically, the value of each configuration item in the contribution index is compared one by one with a preset threshold. The preset threshold is set to 0.5 based on historical regulatory efficiency data, and items below this threshold are marked as inefficient. The marked items are compiled into a list, ensuring that the list includes the item ID, current contribution, and deviation value, forming the list of permission configurations to be optimized.

[0065] In step S402, based on the list of permissions to be optimized, a permission resource analysis module is used to analyze resource usage by permission type and determine resource allocation constraints. Specifically, the permission types in the list, such as data access rights and approval rights, are categorized and analyzed, and the CPU usage and memory usage of each type are calculated. For example, if access rights occupy 30% of resources and approval rights occupy 20%, constraints are set accordingly, such as total resource usage not exceeding 80%.

[0066] In step S403, if there are conflicts in the resource allocation constraints, the constraint relaxation processor adjusts the constraint parameter range to obtain a feasible constraint set. Specifically, the constraints are checked for conflicts. For example, if the access right resource demand exceeds the total limit, the conflict point is identified by comparing the demand value with the available value. The constraint relaxation processor is then applied to gradually expand the parameter range, for example, adjusting the resource upper limit from 80% to 85%, until the conflict is resolved and a feasible set is generated. This adjustment ensures flexibility in permission allocation, helps avoid system resource deadlock, and improves the continuity of the supervision process.

[0067] For example, in a regulatory data sharing scenario, if the resource requirements of approval rights and access rights conflict, the relaxed feasible set allows the approval rights to be adjusted to 25%, thereby maintaining the efficiency of data flow between departments.

[0068] Step S404: Construct a fitness function for the genetic algorithm based on the set of feasible constraints, and establish a mathematical target model for optimizing permission configuration. Specifically, constraint parameters such as resource upper limits and permission type weights are extracted from the feasible set to construct a fitness function. This function is defined as: fitness = efficiency contribution * (1-resource overrun rate), where efficiency contribution is derived from the previous indicator and resource overrun rate is the ratio of actual occupancy divided by the upper limit, which is used to quantify the pros and cons of the configuration solution. The fitness function is integrated into the target model, which aims to maximize overall fitness while minimizing conflicts, ensuring that the model iteratively solves the optimal solution for permission allocation.

[0069] In one possible implementation, for regulatory authority optimization, the fitness function takes into account the impact of authority concentration. For example, in a scenario where authority is decentralized, the function penalizes highly decentralized configurations to improve centralized efficiency.

[0070] It should be noted that this construction method is derived from the standard principles of genetic algorithms, in which the fitness function evaluates the ability of individuals to adapt to the environment, where the environment is resource constraints. This model can effectively identify efficient authority schemes, which is beneficial to improving regulatory effectiveness.

[0071] For example, in a departmental collaborative supervision scenario, if the constraint set limits the total resources to 100 units, the fitness function calculates the fitness of a certain configuration to be 0.8, indicating that it contributes high efficiency without overspending; in another embodiment, for high-load supervision activities, the function adjusts the weight to emphasize low resource overspending, causing the model to prioritize streamlined authority types, reduce redundant allocations, and thus optimize overall performance.

[0072] In step S405, a genetic algorithm is used to generate an initial population of permission configurations based on the mathematical target model, and a coding sequence of the configuration parameters is obtained. Specifically, 10 initial configurations are randomly generated from the model, each of which is encoded using a binary code to represent the permission allocation, such as 001 indicating access is disabled, and these are aggregated into the coding sequence of the population.

[0073] Step S406: The fitness calculator evaluates the configuration effect of the coding sequence and determines the quality of the configuration scheme. Specifically, the fitness function is input into each coding sequence to calculate the value. Values ​​above 0.7 are considered good, and values ​​below 0.5 are considered bad, forming a ranked list.

[0074] Step S407 generates a new generation of permission configuration populations through selection, crossover, and mutation operations to obtain an adaptive permission configuration strategy. Specifically, a selection operation is performed, using a roulette wheel method to select high-quality individuals based on fitness probability. For example, individuals with a fitness of 0.8 have a higher probability of being selected. A crossover operation is performed to swap the coding sequences of two individuals at random points, such as crossing 001 and 110 into 011 and 100, to generate a new configuration. A mutation operation is applied to randomly flip the coding bits, such as changing 0 to 1, to introduce diversity and avoid local optimality. This operation is repeated until the average fitness of the population stabilizes, and the configuration with the highest fitness is output as the adaptive permission configuration strategy.

[0075] In one possible implementation, selection operations ensure inheritance of excellent genes, crossover enhances combinatorial diversity, and mutation prevents premature convergence. These operations together form an iterative optimization chain that is beneficial for dynamic adaptation to regulatory changes.

[0076] For example, in the adjustment of regulatory authority, the initial population has inefficient configurations, and the efficient ones are retained through selection. New allocations are cross-generated, such as merging access and approval rights, and individual permissions are mutated and fine-tuned. The final strategy improves efficiency by 20%. In another embodiment, to address the problem of decentralized authority, the strategy is centralized after 10 generations of operation iterations, reducing resource waste and ensuring the robustness of the generated dynamic authority configuration scheme.

[0077] Step 500: Extract the authority allocation rules from the dynamic authority configuration scheme, combine the department collaboration needs, build a collaboration mechanism model through network analysis technology, and obtain the collaboration path optimization results. Specifically, step S500 may include the following steps:

[0078] Step S501: Obtain the specific content of the permission allocation rules from the dynamic permission configuration scheme, extract data for department collaboration clauses, and obtain a list of inter-department collaboration requirements. Specifically, by parsing the file structure of the dynamic permission configuration scheme, extract the department identifier and collaboration clause fields in the rules to form a list.

[0079] In step S502, based on the collaboration requirements list, a graph network technology is used to construct a topological structure for departmental collaboration. By mapping the relationships between nodes and edges, a preliminary framework for the collaboration mechanism is determined. Specifically, the departments in the collaboration requirements list are treated as nodes in the graph network, and the collaboration relationships between each department are edges, with collaboration frequency values ​​annotated on the edges. Based on the node-edge mapping, the number of connected components in the preliminary framework is calculated to ensure that all department nodes are connected.

[0080] For example, in a collaboration scenario among regulatory departments, assume there are three departments A, B, and C. The list shows that the collaboration frequency between A and B is 5, and that between B and C is 3. When constructing the topology, the edge weight of A connecting B is 5, and that of B connecting C is 3, forming a chain structure. This structure helps identify collaboration bottlenecks and improve the efficiency of authority sharing.

[0081] Step S503: Analyze the connection strength and path length between nodes in the preliminary framework to obtain the distribution of key areas for path optimization. Specifically, use the Dijkstra algorithm to calculate the shortest path length between any two nodes, where path length is defined as the sum of the inverses of the edge weights. Calculate the connection strength as the average of the edge weights. If the average is below a threshold of 0.5, mark it as a weakly connected area. Calculate the distribution density of weakly connected areas to form a list of key areas.

[0082] It should be noted that connection strength refers to the sum of the weights of the edges between nodes divided by the number of edges, and the path length is obtained by accumulating the distance values ​​on the edges. This analysis can reveal areas of inefficient collaboration. For example, in authority allocation, if the path length from A to C is 8 (via B) and the direct strength is low, then focus on optimizing this area. This can improve the overall regulatory response speed and reduce information delays.

[0083] Step S504 adjusts the edge weight parameters in the graph network based on the distribution of key areas. If the connection strength of a path falls below a preset threshold, the information processing module recalculates the path priority to obtain an optimized collaborative path set. Specifically, for key areas, the weak edge weights are increased, for example, adding 1 to the original weight of 3 to become 4. If the strength of a path remains below the threshold of 0.6 after adjustment, the Floyd-Warshall algorithm is used to recalculate the priorities of all paths, with the priority being the inverse of the path length. Paths with high priority are aggregated to form a set.

[0084] For example, assume there is a path ABC in a key area with an initial strength of 0.4, lower than 0.6. After adjusting the weight of edge BC from 3 to 5, the priority is recalculated from 0.125 to 0.2. This optimization ensures the efficient flow of permission data between departments and avoids regulatory delays caused by bottlenecks. In another embodiment, for a larger network, such as involving department D, the strength of path ABD is 0.3, which is lower than the threshold. After adjustment, the priority is increased, supporting rapid collaboration in complex permission scenarios and bringing greater system stability.

[0085] Step S505 analyzes the resource allocation ratios for the optimized collaborative path set. If the allocation ratios are uneven, the resource balancing tool adjusts the allocation rules to determine the final collaborative mechanism model. Specifically, the resource utilization ratio of each path, such as CPU usage, is calculated. If one path's ratio exceeds 30% and another's is less than 10%, resources are reallocated to a balanced state, forming a model. This adjustment ensures resource fairness and improves overall collaborative efficiency.

[0086] Step 600: Based on the collaborative path optimization result, use data stream processing technology to obtain information sharing data and generate information sharing efficiency indicators. Specifically, step S600 may include the following steps:

[0087] Step 601: Based on the collaborative path optimization results, real-time data stream processing technology is used to acquire information sharing-related data from regulatory activities. This data is then pre-processed using a data cleansing module to obtain a preliminary, consolidated information sharing dataset. Specifically, data flows within regulatory activities are monitored, and real-time data stream processing technology is used to capture information sharing-related data, such as regulatory report transmissions and inter-departmental query records. The data cleansing module then removes noise and invalid entries, such as duplicates or malformed data, to form a preliminary, consolidated information sharing dataset.

[0088] Step S602 involves using a classification tool to stratify the initially organized information sharing dataset, determine the priority distribution of data across different categories, and generate a categorized shared dataset. Specifically, the dataset is input into the classification tool, stratified by data type (e.g., text, numeric, or time series), and the priority distribution of each category is calculated, for example, by assigning priority values ​​based on frequency and relevance. The categorized shared dataset is then output.

[0089] Step S603, the classified shared data set is screened by a preset threshold. If the relevance of a certain type of data is lower than the preset threshold, the data weight is adjusted through the information processing module to obtain a filtered shared data subset. Specifically, the relevance score of each category of the classified shared data set is calculated, for example, the Pearson correlation coefficient is used to evaluate the relevance of the data with the shared target; if the relevance score is lower than a preset threshold such as 0.5, the weight is adjusted by applying a weighted average method through the information processing module, for example, the weight of low-relevance data is reduced by 20%, thereby retaining the high-relevance subset as the filtered shared data subset. This adjustment can improve data quality, ensure that subsequent processing focuses on efficient information, and is beneficial to improving sharing efficiency.

[0090] For example, in a regulatory scenario, for financial regulatory activities, the classified shared data set includes transaction records and risk alerts. If the relevance of the transaction record is lower than the threshold, its weight is adjusted to 0.8 of the original value to generate a filtered subset to avoid irrelevant data interfering with path optimization.

[0091] In one embodiment, for another regulatory path such as environmental monitoring, the preset threshold in step S603 is set to 0.6. If the correlation of the alarm data is 0.4, it is weighted and adjusted to above 0.5 to obtain a subset. This helps to prioritize key data when resources are limited and reduce computing overhead.

[0092] Step S604: Use information integration tools to structure the filtered shared data subset to generate a data framework that meets the requirements of the sharing mechanism. For example, the subset is mapped to a standard field structure, such as adding a timestamp and source tag, to form a data framework.

[0093] In step S605, a regression analysis model is used to quantify key fields based on the data framework to determine a preliminary estimate of information sharing efficiency. Specifically, key fields from the data framework, such as sharing latency and transmission volume, are selected and a multivariate linear regression model is applied. Regression analysis involves fitting the model using the least squares method, calculating a preliminary estimate such as efficiency = β0 + β1 * latency + β2 * transmission volume, where β0, β1, and β2 are coefficients. This model can quantify the impact of fields and reveal efficiency bottlenecks, facilitating accurate assessments.

[0094] For example, in financial supervision, the key fields of the data framework are delay of 10 seconds and transmission volume of 500KB. The preliminary evaluation value calculated by the regression model is 85%, indicating a high sharing efficiency.

[0095] In one embodiment, for the medical supervision pathway, the same regression model is used in step S605. If the delay is 15 seconds and the transmission volume is 300 KB, the preliminary evaluation value is 70%. This shows through coefficient analysis that the delay has a greater negative contribution to efficiency, which facilitates targeted optimization.

[0096] In step S606, the preliminary evaluation value is adjusted based on real-time feedback to obtain the final information sharing efficiency indicator. Specifically, real-time feedback data on regulatory activities is collected, such as the actual sharing success rate. In step S62, if the feedback indicates a deviation, such as a success rate that is 10% lower than the preliminary value, the evaluation value is adjusted, for example, by taking a weighted average of the difference between the preliminary value and the feedback, to obtain the final indicator. This feedback loop ensures dynamic and accurate indicators, which facilitates real-time path adjustments.

[0097] For example, in environmental supervision, the preliminary assessment value is 80%, the real-time feedback success rate is 75%, and the final indicator after adjustment is 77.5%, reflecting the actual efficiency.

[0098] In one embodiment, for traffic supervision, if the feedback deviation is 15% in step S606 , the final indicator is adjusted to 90% of the initial value, which helps maintain the stability of the optimization result of the collaborative path.

[0099] Step 700: Based on the information sharing efficiency index and contribution index, a classification algorithm is used to perform a hierarchical evaluation of the regulatory effectiveness to obtain an effectiveness evaluation result. Specifically, step S700 may include the following steps:

[0100] Step 701: Use data integration tools to obtain regulatory-related datasets from multiple sources and generate a preliminary shared information library. Specifically, use data integration tools to obtain regulatory-related datasets from multiple sources and generate a preliminary shared information library. Specifically, relevant information is extracted from the functional departments' historical regulatory data and authority allocation records, and these datasets are merged into a unified library to ensure that the diversity of data sources covers different regulatory activities. Fields related to information sharing efficiency indicators, such as sharing frequency and response time, are screened from the preliminary shared information library to form a preliminary dataset.

[0101] Step S702: Preprocess the preliminary shared information database using a data cleaning module to obtain a consolidated information set. Specifically, deduplication and format standardization are applied to the preliminary shared information database to remove duplicate records and unify the data format. During preprocessing, a hash algorithm is used to detect and delete duplicate regulatory records to ensure the uniqueness of the information set.

[0102] In step S703, if any fields are missing or abnormal in the collated information set, they are repaired using a data completion tool to obtain a repaired information dataset. Specifically, the data completion tool first identifies the location of the missing fields. For example, if the efficiency indicator field for a regulatory event is empty, it is filled based on the average of adjacent records. For outliers, such as contribution indicators that fall outside the normal range, median replacement is used to correct them to ensure the integrity and accuracy of the dataset. This repair process helps maintain data quality and prevents subsequent classification algorithms from being biased due to incomplete data.

[0103] When repairing missing fields, the contextual relevance of the missing values ​​is calculated. For example, for the missing information sharing efficiency indicator, the nearest neighbor algorithm is used to find the corresponding values ​​of similar regulatory records for interpolation.

[0104] For anomaly detection, the threshold range is set such as the normal interval of the contribution index is 0.5 to 0.95. If it exceeds the range, it is marked as an anomaly and replaced with the median of the interval 0.725, thereby generating a repaired information dataset.

[0105] Step S704, based on the repaired information data set, a decision tree algorithm is used to classify the data to obtain the classified performance level distribution. Specifically, the decision tree algorithm is a supervised learning method that constructs a tree structure by recursively splitting the data set. Each internal node represents a test condition, and the leaf node represents the classification result. In this embodiment, the information sharing efficiency index and the contribution index are used as input features to construct a decision tree: first, the contribution index is selected as the splitting attribute of the root node. If the contribution is greater than 0.7, it enters the high-efficiency branch, otherwise it enters the low-efficiency branch; then further splits on the child node, such as the split point based on the sharing efficiency index is 85%, and recursively until the stopping condition is met, such as the minimum number of samples is 5, thereby classifying the regulatory effectiveness into three levels: high, medium, and low, and obtaining the performance level distribution, such as 40% for high level, 35% for medium level, and 25% for low level.

[0106] When constructing a decision tree, information gain is used as the splitting criterion. The information gain of each feature is calculated. For example, the information gain of the contribution index is 0.32, and the sharing efficiency index is 0.28. The highest one is selected for splitting to optimize the classification accuracy of the tree.

[0107] After training the decision tree, the test subset is classified to obtain the performance level distribution, ensuring that the distribution reflects the performance differences in real regulatory scenarios.

[0108] In one possible implementation, assume that the repaired information dataset contains 100 regulatory records, each record has a contribution index such as 0.6 and a sharing efficiency index such as 80%. The decision tree first checks the contribution at the root node. If it is less than 0.7, it is classified into the medium-level branch. The sharing efficiency is further checked. If it is greater than 85%, it is adjusted to a high level. The final distribution shows that high-level records account for 45%, which helps to identify the positive impact of authority allocation on regulatory efficiency and improve the accuracy of the evaluation.

[0109] In one embodiment, for different regulatory scenarios, such as data sets under a centralized authority model, the decision tree may use sharing efficiency as the preferred splitting attribute, resulting in a distribution of 60% high level and 40% low level. This adjustment demonstrates the adaptability of the algorithm to business scenarios and brings more accurate performance grading results.

[0110] In step S705, if the distribution of the classified performance levels is uneven, adjustments are made through the data balancing module to obtain an adjusted level distribution result. Specifically, an uneven distribution refers to too few samples of certain levels, such as low levels accounting for only 10%. The data balancing module uses oversampling methods such as the SMOTE algorithm to generate synthetic samples: for low-level samples, its k-nearest neighbors are found, such as k=5, and new samples are randomly generated along the connecting lines until the proportion of each level is balanced, such as 33% each. Alternatively, undersampling is used to randomly delete samples of the majority class to ensure a uniform distribution after adjustment, thereby improving the fairness of subsequent evaluations and the generalization ability of the model.

[0111] During the oversampling process, the difference in the feature vectors of the minority class samples is calculated, for example, the contribution of the low class is on average 0.4 and that of its neighbors is 0.5, and the contribution of the new sample generated is 0.45. This process is repeated until the number of low class samples increases to be comparable to that of other classes.

[0112] Verify the balance of the distribution after adjustment. If the high level drops from the original 50% to 33%, it is confirmed that the adjustment is effective and classification bias is avoided.

[0113] In one possible implementation, the initial distribution is 70% high-level, 20% medium-level, and 10% low-level. SMOTE oversampling is used to generate 30 new samples for the low-level. The sharing efficiency index of each new sample is based on the original sample average value of 82% and random perturbation generation, such as 80% to 84%. After adjustment, the distribution is balanced to 33% each. This improves the classification accuracy of the decision tree, reduces the risk of ignoring low-level efficiency due to imbalance, and enhances the comprehensiveness of regulatory evaluation.

[0114] In one embodiment, for the uneven distribution of authority dispersion scenarios, such as 5% of the low level, undersampling is used to delete redundant high-level samples, and 100 samples are randomly removed from the original 200 high-level samples to ensure that the number of samples at each level is equal after adjustment. This method simplifies processing and maintains data authenticity, bringing more reliable performance grading results.

[0115] Step S706: Use a data mapping tool to associate the adjusted grade distribution results with the sharing mechanism to generate a final performance evaluation dataset. Specifically, the grade distribution, such as the proportion of high grades, is mapped to the path optimization results of the sharing mechanism. The associated dataset contains the performance grades and corresponding sharing rules.

[0116] In step S707, the final performance evaluation data set is compared and saved with the historical records using a storage module to obtain an evaluation reference library. Specifically, the current data set is compared with the historical library for similarity. If the matching degree is greater than 90%, they are merged and saved to form a queryable reference library.

[0117] Step 800: Adjust the collaboration mechanism model according to the performance evaluation result to generate an optimized collaboration mechanism solution. Specifically, step S800 may include the following steps:

[0118] In step S801, if the efficiency index of a collaborative path in the performance evaluation results is below a preset threshold, the feedback loop mechanism is used to obtain path-related data records and determine the deviation range of the path efficiency. Specifically, the efficiency index value in the performance evaluation results is examined. For example, if the index of path A is 0.65 and the preset threshold is 0.80, the feedback loop mechanism is triggered to pull the 10 most recent execution records of this path from the supervision activity, including timestamps and performance logs. Based on the pulled records, the difference between the actual efficiency and the threshold is calculated, for example, the deviation range is 0.15, which is then used as the input basis for subsequent extraction.

[0119] In step S802, a data integration tool is used to extract path performance information from historical collaboration records based on the deviation range to obtain fluctuation characteristics of path efficiency. Specifically, the data integration tool first aggregates performance information within the deviation range from historical records. For example, the tool extracts the efficiency value sequence of path A over the past month, such as 0.70, 0.62, and 0.68, to form time series data. The tool then calculates fluctuation characteristics, including a standard deviation of 0.04 and a peak-to-valley difference of 0.08, which reflect the stability of efficiency.

[0120] In one possible implementation, obtaining such fluctuation characteristics helps identify potential problem patterns. For example, if the standard deviation exceeds 0.05, it indicates that the path is unstable, providing a data basis for subsequent corrections.

[0121] In step S803, if the fluctuation characteristics indicate that certain links are persistently inefficient, the configuration parameters of the collaborative path are modified through the adjustment module to obtain a modified parameter set. Specifically, the fluctuation characteristics are analyzed. For example, if the standard deviation of a link in path A, such as the data transmission link, is 0.06 and is lower than the average value of 0.70 three times in a row, it is determined to be persistently inefficient. The adjustment module then applies the gradient descent method to modify the parameters. For example, the transmission bandwidth parameter is adjusted from the initial value of 50Mbps to 60Mbps, generating a modified parameter set such as {bandwidth: 60, delay: 20ms}. The gradient descent method is implemented by iteratively minimizing a loss function defined as the sum of squared efficiency deviations. The parameters are updated with each iteration until convergence. This modification ensures that the parameters are more adapted to the actual inefficient links, improving overall path efficiency.

[0122] For example, in regulatory data sharing scenarios, this correction can reduce delays caused by transmission bottlenecks and help improve collaborative response times.

[0123] In step S804, a logical comparison tool is used to analyze the degree of match between the revised parameter set and the historical optimization solution to determine the direction of updating the collaborative mechanism. Specifically, the logical comparison tool first loads a library of historical optimization solutions, for example, containing previously successful parameter sets such as {bandwidth: 55, latency: 25ms}. It then calculates the Euclidean distance between the revised parameters and each historical solution. For example, if the distance is 5.0, if the distance is less than a threshold of 10, the match is high and the update direction is to move closer to the historical solution, such as increasing latency tolerance.

[0124] It should be noted that the Euclidean distance calculation process treats the parameters as vectors and calculates the square root of the sum of the squares of the differences in each dimension. This comparison helps to learn from historical experience, avoid blind adjustments, and thus determine the update direction such as "enhanced bandwidth priority."

[0125] In one embodiment, for different path types such as information query paths, this comparison can reveal matching patterns. For example, if historical solutions are mostly low-latency oriented, the update direction will tend to be real-time optimization, resulting in more stable performance improvement.

[0126] In step S805, the data mapping module associates the modified parameter set with the actual scenario to obtain an updated mechanism model framework. Specifically, the data mapping module maps the modified parameter, such as {bandwidth: 60}, to the variables of the actual scenario. For example, in the network load scenario between regulatory departments, the associated load of department B is increased by 10%. Based on this mapping, a framework is constructed, for example, a graph structure consisting of nodes (departments) and edges (parameter associations) is generated, where the edge weights reflect the impact of the modification. This framework integrates parameters and scenarios to ensure that the model reflects the actual collaborative dynamics.

[0127] For example, this correlation can reveal the impact of parameter changes on the scene, such as bandwidth improvement reduces the waiting time in the scene, which is beneficial to the robustness of the mechanism.

[0128] Step S806: A decision tree algorithm is used to predict and analyze the efficiency indicators in the updated mechanism model framework, determine whether the prediction results meet the preset threshold range, and generate a final optimization solution. Specifically, a decision tree is constructed, in which the root node is split based on key parameters such as bandwidth, and the child nodes are further split based on scenario variables such as load. For example, if the bandwidth is greater than 55, the left subtree predicts an efficiency of 0.85, otherwise the right subtree predicts 0.70. The decision tree is implemented by selecting split points based on information gain, which is calculated as the amount of entropy reduction. Entropy is defined as the negative logarithm of the sample purity. The framework input decision tree is predicted. For example, the framework data of path A is input, and the tree is traversed to obtain a predicted efficiency of 0.82. If it is within the threshold range of 0.80-0.90, an optimization solution is generated, such as the final parameters {bandwidth: 60, delay: 20ms}.

[0129] In one embodiment, for the permission allocation path, this predictive analysis can cover multiple scenarios. For example, in a high-load scenario, the decision tree may predict that 0.78 is lower than the threshold, and the iteration returns to step S803 for correction; in a low-load scenario, the prediction 0.85 reaches the threshold, and the solution is directly output. This method captures nonlinear relationships through the tree structure, which is beneficial for accurately estimating performance and avoiding the persistent existence of inefficient paths.

[0130] For example, in the historical regulatory data, the decision tree splitting process first calculates that the bandwidth gain is 0.3, which is higher than the delay of 0.2, so the bandwidth is split first, and then reliable predictions are generated, enhancing the adaptability of the collaborative mechanism.

[0131] Step 900: Extract information sharing rules from the optimized collaboration mechanism solution, update the inter-departmental data sharing agreement through distributed storage technology, and build a regulatory information processing system. Specifically, step S900 may include the following steps:

[0132] Step S901: obtaining specific content of information sharing rules from the optimized collaboration mechanism solution, performing structured processing using a data analysis tool, and obtaining a structured sharing rule set.

[0133] Step S902: storing and classifying the shared rule set through distributed database technology to determine the priority configuration of data interaction between departments.

[0134] Specifically, distributed database technology refers to a storage system that distributes data across multiple nodes, enabling high availability and load balancing. In this step, the structured set of shared rules is first distributed to different database nodes, with each node responsible for storing some of the rules. For example, rules involving regulatory data sharing are assigned to node A, and permission-related rules are assigned to node B. These rules are then categorized and processed, with query language used to group rule attributes such as urgency and data type. The interaction frequency of each group is calculated to determine the priority configuration. For example, rules with high urgency are assigned priority 1 to ensure priority processing during data interaction between departments.

[0135] Step S903: According to the priority configuration, a mapping tool is used to associate the configuration with the data protocol update requirement to obtain an updated data protocol framework.

[0136] Step S904: If the interaction permissions in the updated data protocol framework do not match the department collaboration requirements, the permission allocation is adjusted through a permission verification tool to obtain an adjusted permission set.

[0137] For example, the permission verification tool is a rule-based verification mechanism that identifies mismatches by comparing the permission settings in the protocol framework with a predefined list of departmental collaboration requirements. The specific process includes: loading the updated data protocol framework, extracting the interactive permission fields, such as read and write permissions, and then comparing them item by item with the departmental collaboration requirements. If a permission is found to not allow cross-departmental sharing of key regulatory data, it will be automatically adjusted to a permitted state, generating an adjusted permission set. This can improve the flexibility of regulatory information processing and reduce permission conflicts in collaboration.

[0138] In one possible implementation, for different mismatch scenarios, such as when interactive permissions restrict real-time data access while departmental collaboration needs require instant sharing, the permission verification tool will increase the access level based on the priority of the needs to ensure that the adjusted permission set supports efficient collaboration.

[0139] Step 905: Use a logical comparison tool to analyze the compatibility of the adjusted permission set with the data flow records to determine the final permission allocation plan. Specifically, the logical comparison tool is a mechanism that evaluates the degree of matching through logical operations. It takes the adjusted permission set as input and compares it with the historical data flow records. First, the allocation rules in the permission set are extracted, such as the access path of a specific department to the data, and then aligned with the actual flow path in the data flow record to calculate the adaptability score. For example, if the matching degree exceeds 80%, the plan is confirmed; otherwise, it is iteratively adjusted until it is compatible.

[0140] It should be noted that data flow records refer to the logs of data transmission between departments during regulatory activities, including timestamps and transmission content. Through this comparison, it is ensured that the authority allocation plan is consistent with the actual flow, thereby improving the reliability of the regulatory system.

[0141] For example, in a regulatory scenario, the adjusted permission set allows Department A to share historical data with Department B, but the data flow records show frequent cross-departmental queries. The logical comparison tool will analyze this adaptability and confirm that the solution can support high-frequency interactions, bringing the beneficial effect of reducing delays.

[0142] In one possible implementation, if data flow records show that certain paths have security vulnerabilities, the logical comparison tool will reduce the allocation weight of the corresponding permissions, generate a more secure final solution, and support the stable processing of regulatory information.

[0143] Step S906: The final authority allocation plan is combined with the supervision system construction requirements through the data integration module to build a final supervision information processing system.

[0144] The above description is only a preferred embodiment of the present invention. Therefore, any equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the protection scope of the patent application of the present invention.

Claims

1. An information optimization method for collaborative supervision by functional departments, characterized in that: include: Obtain historical regulatory data and authority allocation records from functional departments, cleanse and standardize the historical regulatory data and authority allocation records, and generate a structured regulatory data set; For the structured regulatory data set, an analytical algorithm is used to classify the authority allocation pattern and determine the authority feature distribution; Based on the distribution of authority characteristics, a correlation model between authority configuration and regulatory efficiency is constructed, and a contribution index of authority configuration to efficiency is obtained through quantitative analysis technology; based on the contribution index, the authority allocation plan is adjusted to generate a dynamic authority configuration plan; Extract authority allocation rules from the dynamic authority configuration scheme, combine with departmental collaboration needs, build a collaboration mechanism model through network analysis technology, and obtain collaboration path optimization results; based on the collaboration path optimization results, use data stream processing technology to obtain information sharing data and generate information sharing efficiency indicators; based on the information sharing efficiency indicators and contribution indicators, conduct a graded evaluation of regulatory effectiveness through a classification algorithm to obtain effectiveness evaluation results; adjust the collaboration mechanism model according to the effectiveness evaluation results to generate an optimized collaboration mechanism scheme; extract information sharing rules from the optimized collaboration mechanism scheme, update the inter-departmental data sharing agreement through distributed storage technology, and build a regulatory information processing system.

2. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method of obtaining historical regulatory data and authority allocation records from functional departments, cleaning and standardizing the historical regulatory data and authority allocation records, and generating a structured regulatory data set includes: extracting the historical regulatory data and authority allocation records from functional departments through a preset interface protocol to obtain an original data set; using a deduplication processing method for the original data set, detecting duplicate records through a hash algorithm, and generating a deduplication data set; performing field mapping and type conversion on the deduplication data set according to format standardization rules to obtain a format-unified data set; if the field consistency of the format-unified data set is lower than a preset threshold, correcting missing values ​​through a field completion algorithm to obtain a field-consistent data set; using a data storage structure design to store the field-consistent data set according to a predefined pattern to generate the structured regulatory data set.

3. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The structured regulatory data set is targeted at, and an analysis algorithm is used to classify the authority allocation patterns and determine the distribution of authority characteristics, including: extracting core fields related to authority allocation from the structured regulatory data set through preset field screening rules to obtain an authority allocation subset; based on the authority allocation subset, a clustering analysis method is used to group the allocation patterns to obtain preliminary classification results of authority concentration and authority dispersion; if the distribution characteristics of the preliminary classification results do not match the preset threshold, the field comparison tool is used to adjust the boundaries of authority concentration and authority dispersion to obtain an adjusted classification set; based on the adjusted classification set, statistical indicators of distribution characteristics are obtained to judge the concentration or dispersion tendency of the authority allocation pattern; through a data mapping method, the identification results of the authority allocation pattern are associated with other fields to obtain an associated feature set; and a data analysis tool is used to deeply mine the associated feature set to determine the final authority pattern distribution law.

4. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: According to the authority feature distribution, a correlation model between authority configuration and regulatory efficiency is constructed, and a contribution index of authority configuration to efficiency is obtained through quantitative analysis technology, including: standardizing the authority configuration parameters in the authority feature distribution through a data preprocessing tool to obtain a standardized authority configuration data set; based on the standardized authority configuration data set, using an efficiency evaluation module to measure the regulatory efficiency under each authority configuration scenario to obtain efficiency evaluation result data; identifying the correlation strength between the authority configuration parameters and regulatory efficiency through an association analysis method, and determining a set of significantly correlated configuration factors; based on the set of configuration factors, constructing a multivariate linear regression model to establish a mathematical correlation relationship between authority configuration and regulatory efficiency; if the fit of the regression model is lower than a preset threshold, adjusting the regression parameters and variable selection strategy through a model optimization tool to obtain an optimized correlation model; based on the optimized correlation model, calculating the efficiency contribution value of each configuration factor to generate a set of authority configuration efficiency contribution indicators.

5. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method of adjusting the permission allocation scheme according to the contribution index and generating a dynamic permission configuration scheme includes: detecting configuration items in the contribution index that are lower than a preset threshold by a threshold comparator to obtain a list of permission configurations to be optimized; using a permission resource analysis module to count the resource occupancy of each permission type according to the list of permission configurations to be optimized, and determining resource allocation constraints; if there is a conflict in the resource allocation constraints, adjusting the constraint parameter range by a constraint relaxation processor to obtain a set of feasible constraints; constructing a fitness function of a genetic algorithm for the set of feasible constraints, and establishing a mathematical target model for permission configuration optimization; using a genetic algorithm to generate an initial permission configuration population according to the mathematical target model, and obtaining a coding sequence of configuration parameters; evaluating the configuration effect of the coding sequence by a fitness calculator to judge the pros and cons of the configuration scheme; generating a new generation of permission configuration population through selection, crossover and mutation operations, and obtaining an adaptive permission configuration strategy.

6. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method extracts authority allocation rules from the dynamic authority configuration scheme, combines them with departmental collaboration needs, constructs a collaboration mechanism model through network analysis technology, and obtains collaboration path optimization results, including: obtaining the specific content of the authority allocation rules from the dynamic authority configuration scheme, extracting data for departmental collaboration terms, and obtaining a list of inter-departmental collaboration needs; based on the list of collaboration needs, constructs a topological structure of departmental collaboration using graph network technology, and determines a preliminary framework of the collaboration mechanism through relationship mapping of nodes and edges; for the preliminary framework, analyzes the connection strength and path length between nodes to obtain the distribution of key areas for path optimization; based on the distribution of key areas, adjusts the edge weight parameters in the graph network; if the connection strength of a path is lower than a preset threshold, recalculates the path priority through the information processing module to obtain an optimized collaboration path set; analyzes the resource allocation ratio for the optimized collaboration path set; if the allocation ratio is unbalanced, adjusts the allocation rules through a resource balancing tool to determine the final collaboration mechanism model.

7. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method adopts data stream processing technology to obtain information sharing data and generate information sharing efficiency indicators based on the collaborative path optimization results, including: based on the collaborative path optimization results, adopting real-time data stream processing technology to obtain information sharing related data from regulatory activities, pre-processing through a data cleaning module, and obtaining a preliminary organized information sharing data set; based on the preliminary organized information sharing data set, adopting a classification tool to perform layered processing on the data, determine the priority distribution of different categories of data, and generate a classified shared data set; screening the classified shared data set through a preset threshold, if the relevance of a certain type of data is lower than the preset threshold, adjusting the data weight through the information processing module, and obtaining a screened shared data subset; for the screened shared data subset, adopting an information integration tool to perform structured processing to generate a data framework that meets the requirements of the sharing mechanism; based on the data framework, adopting a regression analysis model to quantify key fields and determine a preliminary evaluation value of information sharing efficiency; adjusting the preliminary evaluation value through real-time feedback to obtain a final information sharing efficiency indicator.

8. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method of performing a graded assessment of regulatory effectiveness through a classification algorithm based on the information sharing efficiency index and contribution index to obtain an effectiveness assessment result includes: using a data integration tool to obtain regulatory-related data sets from multiple sources to generate a preliminary shared information library; based on the preliminary shared information library, performing preprocessing through a data cleaning module to obtain a sorted information set; if there are missing or abnormal fields in the sorted information set, repairing them through a data completion tool to obtain a repaired information data set; based on the repaired information data set, using a decision tree algorithm to classify the data to obtain a classified effectiveness level distribution; if the classified effectiveness level distribution is unbalanced, adjusting it through a data balancing module to obtain an adjusted level distribution result; and associating the adjusted level distribution result with a sharing mechanism through a data mapping tool to generate a final effectiveness evaluation data set.

9. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method of adjusting the collaboration mechanism model according to the performance evaluation result to generate an optimized collaboration mechanism solution includes: if the efficiency index of a collaboration path in the performance evaluation result is lower than a preset threshold, obtaining path-related data records through a feedback loop mechanism to determine the deviation range of the path efficiency; according to the deviation range, extracting path performance information from historical collaboration records using a data integration tool to obtain the fluctuation characteristics of the path efficiency; if the fluctuation characteristics show that certain links are continuously inefficient, correcting the configuration parameters of the collaboration path through an adjustment module to obtain a corrected parameter set; using a logical comparison tool to analyze the degree of matching between the corrected parameter set and the historical optimization solution to determine the update direction of the collaboration mechanism; associating the corrected parameter set with the actual scenario through a data mapping module to obtain an updated mechanism model framework; using a decision tree algorithm to predict and analyze the efficiency index in the updated mechanism model framework to determine whether the prediction result reaches the preset threshold range and generate a final optimization solution.

10. The information optimization method for collaborative supervision by functional departments according to claim 1, characterized in that: The method extracts information sharing rules from the optimized collaboration mechanism scheme, updates the inter-departmental data sharing agreement through distributed storage technology, and constructs a regulatory information processing system, including: obtaining the specific content of the information sharing rules from the optimized collaboration mechanism scheme, using data analysis tools for structured processing, and obtaining a structured sharing rule set; storing and classifying the sharing rule set through distributed database technology, and determining the priority configuration of inter-departmental data interaction; according to the priority configuration, using a mapping tool to associate the configuration with the data protocol update requirements, and obtain an updated data protocol framework; if the interaction permissions in the updated data protocol framework do not match the department collaboration requirements, adjusting the permission allocation through a permission verification tool to obtain an adjusted permission set; using a logical comparison tool to analyze the adaptability of the adjusted permission set to data flow records, and determine the final permission allocation scheme; combining the final permission allocation scheme with the regulatory system construction requirements through a data integration module to construct a final regulatory information processing system.

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