Natural resource right confirmation service intelligent collaboration method, device, equipment and medium

Abstract

The invention relates to the technical field of data processing. The natural resource right confirmation service intelligent collaboration method, device and equipment and the medium are provided, and the method comprises the following steps: analyzing a cross-department right confirmation service through a process modeling technology, and generating a visual flow chart comprising a responsibility boundary and a collaboration node; performing packaging processing on the right confirmation link through a micro-service packaging technology, generating an independent micro-service unit, and constructing a template library associated with resource types and policy labels; sorting the independent micro-service units to generate an optimized execution path; performing visual monitoring processing on the execution state through a workflow engine to generate a blocking point report; triggering resource allocation processing based on the blocking point report, and generating a dynamic resource allocation scheme; and updating the template library and the rule library to generate the adaptive collaborative system, so as to achieve the technical effects of improving the dynamic adaptive capacity of the process, reducing the rule conflict rate, optimizing the resource scheduling efficiency and enhancing the collaborative consistency of the template library and the rule library.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to intelligent collaborative methods, devices, equipment and media for natural resource title confirmation services. Background Art

[0002] With the deepening of digital development, the confirmation and registration of natural resource rights has become a core component of modern land and space governance. In cross-departmental collaborative confirmation, business processes involving the intertwined responsibilities of multiple departments, such as land, forestry, grassland, and environmental protection, require intelligent technology to achieve efficient collaboration and dynamic optimization.

[0003] However, related technologies have the following problems: static process modeling technology lacks dynamic adaptability and is unable to cope with the real-time updates of policies and regulations and the flexible demands of sudden business scenarios, resulting in delayed process reconstruction and a vacuum in responsibility attribution; the rule engine mechanism is limited by a hard-coded architecture and cannot adapt to multi-dimensional regional policy differences. In addition, the lack of feedback-driven closed-loop optimization capabilities leads to an increase in the rule conflict rate and a continuous decline in processing efficiency; the system assembly mode produces a collaborative island effect due to interface heterogeneity, resulting in delayed resource scheduling responses and inconsistent status between the template library and the rule library. Summary of the Invention

[0004] Based on this, it is necessary to provide intelligent collaborative methods, devices, equipment and media for natural resource rights confirmation services to address the above-mentioned technical issues, so as to achieve the technical effects of improving the dynamic adaptability of the process, reducing the rule conflict rate, optimizing resource scheduling efficiency and enhancing the collaborative consistency between the template library and the rule library.

[0005] In the first aspect, this application provides an intelligent collaborative method for natural resource rights confirmation services, which includes:

[0006] Analyze cross-departmental property rights confirmation business through process modeling technology to generate a visual flowchart including responsibility boundaries and collaboration nodes;

[0007] Based on a visual flowchart, the rights confirmation process is encapsulated through microservice encapsulation technology to generate independent microservice units and build a template library that associates resource types with policy tags.

[0008] Based on the policy and regulatory constraints in the template library and the preset rule library, the independent microservice units are sorted and processed to generate an optimized execution path;

[0009] Based on the optimized execution path, the workflow engine performs visual monitoring of the execution status and generates a blockage point report.

[0010] Trigger resource allocation processing based on choke point reports and generate dynamic resource allocation plans;

[0011] Based on the execution feedback data of the dynamic resource allocation solution, the template library and rule library are updated to generate an adaptive collaborative system.

[0012] Furthermore, based on the policy and regulatory constraints in the template library and the preset rule library, the independent microservice units are sorted and an optimized execution path is generated, including:

[0013] Use the following formula to perform combination pattern matching on independent microservice units based on the resource type tags in the template library to generate the initial service sequence:

[0014]

[0015] Among them, M(S i ,T j ) represents the matching degree between the i-th microservice unit and the j-th template resource, K represents the number of label features, ω k represents the weight of the kth feature, sim(s ik ,t jk ) represents the similarity between the k-th feature of the i-th microservice unit and the k-th feature of the j-th template resource;

[0016] Extract the policy and regulatory constraints corresponding to the current business scenario from the rule base, perform conflict detection on the initial service sequence, and generate a constraint correction sequence;

[0017] Based on the ecological priority weight and business urgency coefficient, the microservice units in the constraint correction sequence are dynamically rearranged to generate an optimized execution path.

[0018] Furthermore, the policy and regulatory constraints corresponding to the current business scenario are extracted from the rule base, conflict detection is performed on the initial service sequence, and a constraint correction sequence is generated, including:

[0019] Perform feature recognition processing on the spatial attributes and policy attributes of the current business scenario to generate a scenario feature vector;

[0020] Based on the scenario feature vector, multi-dimensional similarity matching is performed from the rule library, and policy and regulatory constraint rules are screened out based on the degree of relevance;

[0021] Compare policy and regulatory constraints with the initial service sequence node by node to identify service nodes with conflicting rules.

[0022] The service nodes with conflicting rules are replaced or reorganized through the rule compatibility algorithm to generate a constraint correction sequence.

[0023] Furthermore, based on the optimized execution path, the workflow engine performs visual monitoring of the execution status and generates a blockage point report, including:

[0024] Based on the optimized service node sequence in the execution path, the workflow engine collects the start timestamp and completion status of each node in real time to generate an execution status log;

[0025] Based on the preset node processing time threshold, the execution status log is processed for retention time and service nodes that have timed out and are not completed are identified.

[0026] Based on the responsibility boundary information in the visual flowchart, the timed-out and unfinished service nodes are associated with the responsible departments and a blocking point report is generated.

[0027] Furthermore, based on the responsibility boundary information in the visual flowchart, the timed-out and unfinished service nodes are associated with the responsible departments and a blocking point report is generated, including:

[0028] Based on the responsibility boundary information in the visual flow chart, the relationship between the responsible departments and service nodes is structured and parsed to generate a department-node responsibility map;

[0029] Perform topological matching on the timed-out and unfinished service nodes and the department-node responsibility map to locate the responsible departments and mark the department collaboration paths.

[0030] Based on the node dependencies in the departmental collaboration path, the root causes of blockages are classified into three dimensions and a blockage point report is generated.

[0031] Furthermore, based on the execution feedback data of the dynamic resource allocation solution, the template library and the rule library are updated to generate an adaptive collaborative system, including:

[0032] Perform multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation plan to generate an efficiency feature vector;

[0033] Based on the compliance indicators in the effectiveness feature vector, the policy and regulatory constraint rules in the rule base are iteratively optimized through the rule evolution algorithm to generate an updated rule base;

[0034] Based on the resource allocation efficiency index in the effectiveness feature vector, the service unit combination logic in the template library is topologically reorganized to generate an updated template library;

[0035] The updated rule base and the updated template base are cross-validated to generate an adaptive collaborative system.

[0036] Furthermore, the execution feedback data of the dynamic resource allocation scheme is subjected to multi-dimensional feature analysis to generate an efficiency feature vector, including:

[0037] Perform time series analysis on the task processing time in the execution feedback data to generate a time efficiency feature vector;

[0038] Performing distribution statistics on the computing resource occupancy rate in the execution feedback data to generate a resource utilization feature vector;

[0039] Perform pattern recognition on policy and regulation violation records in execution feedback data to generate compliance feature vectors;

[0040] The time efficiency feature vector, resource utilization feature vector and compliance feature vector are orthogonally fused to generate the effectiveness feature vector.

[0041] In a second aspect, the present application provides an intelligent collaborative device for natural resource rights confirmation services, which includes:

[0042] The business process modeling module is used to analyze and process cross-departmental rights confirmation business through process modeling technology, and generate a visual flow chart including responsibility boundaries and collaboration nodes;

[0043] The microservice encapsulation module is used to encapsulate the rights confirmation process based on a visual flowchart and microservice encapsulation technology, generate independent microservice units, and build a template library that associates resource types with policy tags;

[0044] The dynamic orchestration module is used to sort independent microservice units based on the policy and regulatory constraints in the template library and the preset rule library to generate an optimized execution path;

[0045] Real-time monitoring module, which is used to visualize the execution status and generate blockage point reports based on the optimized execution path through the workflow engine;

[0046] Resource scheduling module, used to trigger resource allocation processing based on the blockage point report and generate dynamic resource allocation plan;

[0047] The closed-loop optimization module is used to update the template library and rule library based on the execution feedback data of the dynamic resource allocation plan to generate an adaptive collaborative system.

[0048] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any method in the first aspect of the present application are implemented.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any method in the first aspect of the present application when the computer program is executed by a processor.

[0050] The present application provides an intelligent collaborative method, device, equipment and medium for natural resource title confirmation services. The method includes: parsing and processing cross-departmental title confirmation business through process modeling technology to generate a visual flowchart including responsibility boundaries and collaboration nodes; based on the visual flowchart, encapsulating the title confirmation link through microservice encapsulation technology to generate independent microservice units, and building a template library that associates resource types and policy tags; based on the policy and legal constraints in the template library and the preset rule library, sorting the independent microservice units to generate an optimized execution path; based on the optimized execution path, visually monitoring the execution status through the workflow engine to generate a blockage point report; triggering resource allocation based on the blockage point report to generate a dynamic resource allocation plan; based on the execution feedback data of the dynamic resource allocation plan, updating the template library and the rule library to generate an adaptive collaborative system, so as to achieve the technical effects of improving the dynamic adaptability of the process, reducing the rule conflict rate, optimizing resource scheduling efficiency and enhancing the collaborative consistency between the template library and the rule library. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flow chart of an intelligent collaborative method for natural resource rights confirmation services in one embodiment of the present invention;

[0053] Figure 2 A flowchart of an embodiment of the present invention for extracting policy and regulatory constraint rules corresponding to the current business scenario from a rule base, performing conflict detection on the initial service sequence, and generating a constraint modification sequence;

[0054] Figure 3 This is a structural diagram of an intelligent collaborative device for natural resource title confirmation services in one embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more clearly understood, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to fully understand the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0056] like Figure 1As shown, this application provides an intelligent collaborative method for natural resource rights confirmation services, which includes:

[0057] S101: Analyze and process cross-departmental property rights confirmation business through process modeling technology to generate a visual flowchart including responsibility boundaries and collaboration nodes.

[0058] Specifically, data related to cross-departmental property rights confirmation business is collected, including departmental responsibilities, business processes, policies and regulations. The business processes are then sorted out to clarify the main links and input and output content. Departmental responsibility boundaries are divided in each link, the leading and supporting departments are determined, and collaborative nodes that require multi-departmental collaboration are identified. Using process modeling tools, a process model containing responsibility boundaries and collaborative nodes is constructed based on the sorting results, and logical verification and optimization are performed to make it more efficient and reasonable. Afterwards, the optimized process model is converted into a visual flowchart, with annotations added to ensure clarity and ease of understanding. After verification, it is applied to actual business and continuously updated and improved based on business development and policy changes.

[0059] S102: Based on the visual flowchart, the property rights confirmation process is encapsulated through microservice encapsulation technology to generate independent microservice units, and a template library that associates resource types and policy tags is constructed.

[0060] Specifically, based on a visual flowchart, the various steps in the title confirmation process are identified and broken down. Relevant microservice encapsulation technologies are selected to encapsulate the aforementioned steps into independent microservice units, each with a clear interface and functionality. At the same time, the types of natural resources and relevant policies and regulations are sorted out to generate a resource type and policy labeling system. A template library is then constructed, into which the encapsulated microservice units are stored. Each unit is associated with the corresponding resource type and policy label, clarifying their applicable scenarios and business attributes. The template library is then verified and optimized to ensure that the microservice units can operate normally, interface interactions are smooth, and the association between resource types and policy labels is accurate.

[0061] S103: Based on the policy and regulatory constraints in the template library and the preset rule library, the independent microservice units are sorted and an optimized execution path is generated.

[0062] Specifically, we select microservice units relevant to the current business from the template library. Combining resource type tags and business requirements, we perform a combination pattern match between the microservice units and template resources, preliminarily determining how the microservice units should be combined, laying the foundation for subsequent sorting. We then sort the microservice units based on their matching degree with the template resources, prioritizing those with high matching degrees to ensure the consistency and rationality of the business process. Furthermore, we further optimize the sorting results based on the business logic sequence and dependencies.

[0063] Extract policy and regulatory constraints that match the current business scenario from the rule base and perform conflict detection on the initial service sequence. Through feature recognition, generate a scenario feature vector. This vector is then matched against the rule base for similarity, screening out highly relevant policy and regulatory constraints to ensure that business processes comply with policy and regulatory requirements. These selected policy and regulatory constraints are then compared node by node with the initial service sequence to identify service nodes with conflicting rules. Apply a rule compatibility algorithm to replace or reorganize conflicting nodes, generating a constraint correction sequence to resolve rule conflicts in the initial sequence.

[0064] Dynamically reorder microservice units within the constraint correction sequence based on ecological priority weights and business urgency coefficients. Adjust the execution order of microservice units based on business needs and policy guidance to generate optimized execution paths, improving the efficiency and adaptability of business processes. Verify and optimize these generated optimized execution paths to ensure they conform to business logic, policy, and regulatory requirements, while also meeting dynamic adaptability requirements and enabling efficient and coordinated processing of natural resource rights confirmation services.

[0065] S104: Based on the optimized execution path, the execution status is visually monitored and processed through the workflow engine to generate a blocking point report.

[0066] Specifically, the optimized execution path is imported into the workflow engine, which analyzes the microservice units, execution order, and inter-node dependencies within the path, clarifying the basic information and execution logic of each node. Based on the parsed results of the optimized execution path, the workflow engine initializes the execution status of each microservice unit, providing a foundation for subsequent monitoring and processing.

[0067] During the execution of microservice units, the workflow engine collects execution status information for each node in real time, including the start timestamp, end timestamp, and current status. This information is graphically displayed using visualization tools, allowing monitoring personnel to intuitively understand the execution status of business processes. This collected execution status information is recorded in the execution status log for subsequent analysis. By analyzing the execution status log, we can understand information such as the execution duration and frequency of each node, providing a data foundation for identifying bottlenecks.

[0068] Based on preset node processing time thresholds, the execution status log is processed for retention time and identified service nodes that have timed out and not completed. Based on the responsibility boundary information in the visual flowchart, these service nodes are mapped to the responsible departments and a blockage point report is generated. This blockage point report includes information such as the location of the blocked node, the cause of the blockage, and the responsible department, providing a basis for subsequent resource allocation and process optimization.

[0069] S105: triggering resource allocation processing based on the congestion point report and generating a dynamic resource allocation plan.

[0070] Specifically, the blocking point report is analyzed to identify the location, cause, and responsible department of the blocked node. The resource requirements of the blocked node are then assessed, including human, material, financial, and computing resources. The organization's available resources are also inventoried, integrating idle and shared resources to create a resource pool. Based on resource needs and urgency, and taking into account the status of the resource pool, a resource allocation strategy is developed to determine the priority, quantity, and method of resource allocation. Based on the strategy, a dynamic resource allocation plan is generated, issued, and executed to ensure timely allocation of resources to the blocked node, facilitating the smooth recovery of business processes.

[0071] S106: Based on the execution feedback data of the dynamic resource allocation solution, the template library and the rule library are updated to generate an adaptive collaborative system.

[0072] Specifically, feedback data from the dynamic resource allocation plan is collected during its execution. This data includes multi-dimensional information such as task processing time, computing resource utilization, and policy and regulatory violations. This feedback data is then analyzed for multi-dimensional features to generate an efficiency feature vector. This vector includes a time efficiency feature vector, a resource utilization feature vector, and a compliance feature vector, providing data support for subsequent optimization processes.

[0073] The generated performance feature vectors are thoroughly analyzed to extract compliance and resource allocation efficiency indicators. Based on these compliance indicators, a rule evolution algorithm is used to iteratively optimize the policy and regulatory constraints in the rule base, correcting or updating rules that don't align with actual business needs to improve the accuracy and applicability of the rule base. Furthermore, based on the resource allocation efficiency indicator, the service unit combination logic in the template library is topologically reorganized, optimizing the call relationships and combination methods between service units to improve the rationality and efficiency of resource allocation.

[0074] The updated rule base and template library are cross-validated to ensure consistency and business logic coherence. This validated rule base and template library form an adaptive collaborative system that automatically adjusts and optimizes based on feedback from the execution of dynamic resource allocation plans to adapt to the ever-changing and evolving natural resource rights confirmation business, enabling intelligent collaborative processing of business processes.

[0075] An embodiment of the present application provides an intelligent collaborative method for natural resource title confirmation services, including: parsing and processing cross-departmental title confirmation business through process modeling technology to generate a visual flowchart including responsibility boundaries and collaboration nodes; based on the visual flowchart, encapsulating the title confirmation link through microservice encapsulation technology to generate independent microservice units, and constructing a template library that associates resource types and policy tags; based on the policy and legal constraint rules in the template library and the preset rule library, sorting the independent microservice units to generate an optimized execution path; based on the optimized execution path, visually monitoring the execution status through the workflow engine to generate a blockage point report; triggering resource allocation based on the blockage point report to generate a dynamic resource allocation plan; based on the execution feedback data of the dynamic resource allocation plan, updating the template library and the rule library to generate an adaptive collaborative system, so as to achieve the technical effects of improving the dynamic adaptability of the process, reducing the rule conflict rate, optimizing resource scheduling efficiency and enhancing the collaborative consistency between the template library and the rule library.

[0076] Furthermore, based on the policy and regulatory constraints in the template library and the preset rule library, the independent microservice units are sorted and an optimized execution path is generated, including:

[0077] Use the following formula to perform combination pattern matching on independent microservice units based on the resource type tags in the template library to generate the initial service sequence:

[0078]

[0079] Among them, M(S i ,T j ) represents the matching degree between the i-th microservice unit and the j-th template resource, K represents the number of label features, ω k represents the weight of the kth feature, sim(s ik ,t jk ) represents the similarity between the k-th feature of the i-th microservice unit and the k-th feature of the j-th template resource;

[0080] Extract the policy and regulatory constraints corresponding to the current business scenario from the rule base, perform conflict detection on the initial service sequence, and generate a constraint correction sequence;

[0081] Based on the ecological priority weight and business urgency coefficient, the microservice units in the constraint correction sequence are dynamically rearranged to generate an optimized execution path.

[0082] Specifically, resource type tags in the template library are used to perform combined pattern matching on independent microservice units. A matching algorithm calculates the similarity between each microservice unit and the template resource, taking into account the number and weight of tag features to generate an initial service sequence. Microservice units with high matching scores are prioritized in the sequence, ensuring the consistency and rationality of business processes.

[0083] Extract policy and regulatory constraints that match the current business scenario from the rule base. Perform conflict detection on the initial service sequence to identify service nodes that conflict with policies and regulations. Based on the conflict detection results, generate a constraint correction sequence to correct conflicts and ensure that the service sequence complies with policy and regulatory requirements.

[0084] By combining ecological priority weights and business urgency coefficients, the microservice units in the constraint correction sequence are dynamically rearranged. Based on business needs and ecological priority principles, the execution order of microservice units is adjusted to generate an optimized execution path. This optimized execution path improves the efficiency of business process execution and adapts to dynamically changing business needs.

[0085] like Figure 2 As shown, the policy and regulatory constraints corresponding to the current business scenario are extracted from the rule base, conflict detection is performed on the initial service sequence, and a constraint correction sequence is generated, including:

[0086] S201: Perform feature recognition processing on the spatial attributes and policy attributes of the current business scenario to generate a scenario feature vector;

[0087] S202: Based on the scenario feature vector, multi-dimensional similarity matching is performed from the rule library, and policy and regulatory constraint rules are screened out according to the correlation degree;

[0088] S203: Compare the policy and regulatory constraint rules with the initial service sequence node by node to identify service nodes with conflicting rules;

[0089] S204: Replace or reorganize the service nodes with conflicting rules through a rule compatibility algorithm to generate a constraint correction sequence.

[0090] Specifically, the spatial and policy attributes of the current business scenario are processed for feature recognition to generate a scenario feature vector. Spatial attributes include information such as the geographic location and scope of the business, while policy attributes include applicable laws, regulations, and policy documents. These spatial and policy attributes are analyzed and extracted using relevant algorithms or models to generate a vector that characterizes the current business scenario.

[0091] Based on the generated scenario feature vector, a multi-dimensional similarity matching process is performed within the rule base. The policy and regulatory constraints in the rule base are analyzed from multiple dimensions, including the scope of application, business type, and resource type. The similarity between these rules and the scenario feature vector is then calculated. Based on this similarity, the policy and regulatory constraints with the highest relevance to the current business scenario are selected.

[0092] The selected policy and regulatory constraints are compared node by node with the initial service sequence. Each node in the initial service sequence is compared with the matched policy and regulatory constraints to check for any violations. If a node's processing logic or business operations are inconsistent with the policy and regulatory constraints, it is identified as a service node with a rule violation.

[0093] A rule compatibility algorithm replaces or reorganizes conflicting service nodes to generate a constraint correction sequence. The rule compatibility algorithm is a method specifically designed to resolve rule conflicts. It analyzes the type and cause of rule conflicts and then adjusts the conflicting nodes based on a specific strategy. This involves replacing and reorganizing conflicting nodes to ensure that the adjusted service sequence complies with policy and regulatory constraints, thereby generating a constraint correction sequence.

[0094] Furthermore, based on the optimized execution path, the workflow engine performs visual monitoring of the execution status and generates a blockage point report, including:

[0095] Based on the optimized service node sequence in the execution path, the workflow engine collects the start timestamp and completion status of each node in real time to generate an execution status log;

[0096] Based on the preset node processing time threshold, the execution status log is processed for retention time and service nodes that have timed out and are not completed are identified.

[0097] Based on the responsibility boundary information in the visual flowchart, the timed-out and unfinished service nodes are associated with the responsible departments and a blocking point report is generated.

[0098] Specifically, the workflow engine collects key information such as the start timestamp and completion status of each node in real time based on the service node sequence defined in the optimized execution path, organizes and records the above information in the execution status log, and ensures more accurate tracking of business process execution.

[0099] Using a preset node processing time threshold, we analyze data from the execution status log and calculate the duration of each service node. By comparing this duration with the threshold, we identify timed-out service nodes that have not completed beyond the preset timeframe, providing critical data support for subsequent process optimization.

[0100] By combining the responsibility boundary information in the visual flowchart, we can map timed-out, unfinished service nodes to the corresponding responsible departments. By analyzing the dependencies and collaborative processes between nodes, we can generate a detailed blockage point report, clearly identifying the link where the blockage occurred, the departments involved, and the possible causes, providing clear guidance and basis for efficiently resolving process blockages.

[0101] Furthermore, based on the responsibility boundary information in the visual flowchart, the timed-out and unfinished service nodes are associated with the responsible departments and a blocking point report is generated, including:

[0102] Based on the responsibility boundary information in the visual flow chart, the relationship between the responsible departments and service nodes is structured and parsed to generate a department-node responsibility map;

[0103] Perform topological matching on the timed-out and unfinished service nodes and the department-node responsibility map to locate the responsible departments and mark the department collaboration paths.

[0104] Based on the node dependencies in the departmental collaboration path, the root causes of blockages are classified into three dimensions and a blockage point report is generated.

[0105] Specifically, we extract responsibility boundary information from the visual flowchart and perform a structured analysis of the relationship between responsible departments and service nodes. By analyzing the responsible departments for each service node and the collaborative relationships between departments, we generate a department-node responsibility map containing department-node association information, clarifying the specific responsibilities of each department in the business process and the distribution of service nodes.

[0106] Topologically match timed-out, unfinished service nodes with the department-node responsibility map. By matching the timed-out node's position in the process map with the department-node relationship in the responsibility map, the responsible department for the timed-out node is located and the inter-departmental collaboration path is marked, visually demonstrating the timed-out node's position in the departmental collaboration process and the departments involved.

[0107] Based on the node dependencies within departmental collaboration paths, we categorize the root causes of blockages across three dimensions. We analyze the causes of blockages from the perspectives of departmental collaboration processes, resource allocation, and business logic, and provide a detailed classification and description of the blocking factors within each dimension. Based on the categorization results, we generate a blockage point report containing the location of the timeout node, the responsible department, the collaboration path, and a detailed breakdown of the root cause of the blockage, providing clear guidance for subsequent optimization of business processes and resolution of blockage issues.

[0108] Furthermore, based on the execution feedback data of the dynamic resource allocation solution, the template library and the rule library are updated to generate an adaptive collaborative system, including:

[0109] Perform multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation plan to generate an efficiency feature vector;

[0110] Based on the compliance indicators in the effectiveness feature vector, the policy and regulatory constraint rules in the rule base are iteratively optimized through the rule evolution algorithm to generate an updated rule base;

[0111] Based on the resource allocation efficiency index in the effectiveness feature vector, the service unit combination logic in the template library is topologically reorganized to generate an updated template library;

[0112] The updated rule base and the updated template base are cross-validated to generate an adaptive collaborative system.

[0113] Specifically, we perform multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation plan, extract key performance indicators, and generate a performance feature vector. This vector includes multiple features such as time efficiency, resource utilization, and compliance, providing data support for subsequent optimization.

[0114] Based on the compliance indicators in the effectiveness feature vector, a rule evolution algorithm is used to iteratively optimize the policy and regulatory constraints in the rule base. By analyzing the match between the compliance indicators and existing rules, rules that need adjustment or updating are identified, and an updated rule base is generated to improve the accuracy and applicability of the rules.

[0115] Based on the resource allocation efficiency indicators in the performance feature vector, the service unit combination logic in the template library is topologically reorganized. Based on the analysis results of the resource allocation efficiency indicators, the combination relationship and call sequence between service units are adjusted to optimize resource allocation and generate an updated template library to improve the overall system operation efficiency.

[0116] The updated rule base and template library are then cross-validated to ensure consistency and business logic coherence. This validated rule base and template library form an adaptive collaborative system that automatically adjusts and optimizes based on feedback from the execution of dynamic resource allocation plans to adapt to the ever-changing and evolving natural resource rights confirmation business, enabling intelligent collaborative processing of business processes.

[0117] Furthermore, the execution feedback data of the dynamic resource allocation scheme is subjected to multi-dimensional feature analysis to generate an efficiency feature vector, including:

[0118] Perform time series analysis on the task processing time in the execution feedback data to generate a time efficiency feature vector;

[0119] Performing distribution statistics on the computing resource occupancy rate in the execution feedback data to generate a resource utilization feature vector;

[0120] Perform pattern recognition on policy and regulation violation records in execution feedback data to generate compliance feature vectors;

[0121] The time efficiency feature vector, resource utilization feature vector and compliance feature vector are orthogonally fused to generate the effectiveness feature vector.

[0122] Specifically, we perform time series analysis on the task processing times in the execution feedback data. By analyzing the changing trends, periodic characteristics, and abnormal fluctuations in task processing time, we generate a time efficiency feature vector. This feature vector reflects the time efficiency characteristics of task processing and provides a basis for evaluating the time efficiency of business processes.

[0123] We perform statistical processing on the computing resource utilization rates in the execution feedback data. By analyzing the distribution, central tendency, and dispersion of computing resource utilization rates, we generate a resource utilization feature vector. This feature vector reflects the efficiency of computing resource utilization and the rationality of its allocation, providing data support for optimizing resource allocation.

[0124] Pattern recognition is performed on policy and regulatory violation records in execution feedback data. By identifying common patterns, frequently violated regulatory provisions, and the severity of violations, a compliance feature vector is generated. This feature vector reflects the compliance level of business processes and provides guidance for improving compliance.

[0125] The time efficiency, resource utilization, and compliance feature vectors are orthogonally fused, eliminating correlations and redundant information between the feature vectors to generate a comprehensive performance feature vector. This performance feature vector integrates the characteristics of time efficiency, resource utilization, and compliance, providing a more comprehensive and objective reflection of the execution effect of the dynamic resource allocation solution and providing a comprehensive performance evaluation basis for subsequent template and rule library updates.

[0126] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0127] In one embodiment, Figure 3 As shown, the present application provides a natural resource rights confirmation service intelligent collaboration device 300, which includes:

[0128] The business process modeling module 301 is used to analyze and process cross-departmental property rights confirmation business through process modeling technology and generate a visual flow chart including responsibility boundaries and collaboration nodes;

[0129] The microservice encapsulation module 302 is used to encapsulate the rights confirmation process based on a visual flowchart using microservice encapsulation technology, generate independent microservice units, and build a template library that associates resource types with policy tags;

[0130] Dynamic orchestration module 303, used to sort independent microservice units based on the policy and regulatory constraints in the template library and the preset rule library, and generate an optimized execution path;

[0131] A real-time monitoring module 304 is used to perform visual monitoring of the execution status through the workflow engine based on the optimized execution path and generate a blocking point report;

[0132] Resource scheduling module 305, for triggering resource allocation processing based on the choke point report and generating a dynamic resource allocation plan;

[0133] The closed-loop optimization module 306 is used to update the template library and the rule library based on the execution feedback data of the dynamic resource allocation solution to generate an adaptive collaborative system.

[0134] Specifically, the business process modeling module 301 uses process modeling technology to analyze cross-departmental rights confirmation operations and generate a visual flowchart that includes responsibility boundaries and collaboration nodes. This process includes a relatively detailed analysis of the business process, clarifying the responsible parties and collaboration requirements for each link, and presenting it graphically, laying the foundation for subsequent steps.

[0135] Based on a visual flowchart, the microservice encapsulation module 302 uses microservice encapsulation technology to encapsulate the rights confirmation process, generate independent microservice units, and build a template library that associates resource types with policy tags. This step breaks down the business process into independently run microservice units, improving the system's flexibility and maintainability. The template library also facilitates the management of resource and policy linkages.

[0136] The dynamic orchestration module 303 uses the policy and regulatory constraints in the template library and the preset rule library to sort the independent microservice units and generate an optimized execution path. Through matching algorithms and rule screening, it determines the optimal execution order of the microservice units, ensuring the compliance and efficiency of the business process.

[0137] Based on the optimized execution path, the real-time monitoring module 304 uses the workflow engine to visually monitor the execution status and generate a report on bottleneck points. During the monitoring process, it collects node execution information in real time, identifies nodes that have timed out and are incomplete, and generates a report based on responsibility boundary information to provide a basis for resource allocation.

[0138] The resource scheduling module 305 triggers resource allocation based on the blockage report and generates a dynamic resource allocation plan, which includes analyzing the blockage report, evaluating resource requirements, and formulating and executing the resource allocation plan to alleviate process congestion and improve execution efficiency.

[0139] Based on feedback from the execution of the dynamic resource allocation solution, the closed-loop optimization module 306 updates the template and rule bases to generate an adaptive collaborative system. By analyzing the feedback data, it optimizes the policy and regulatory constraints in the rule base and the service unit combination logic in the template base, achieving continuous improvement and optimization of the system to adapt to business changes and form a closed-loop management system.

[0140] The dynamic arrangement module 303 is further used to:

[0141] Use the following formula to perform combination pattern matching on independent microservice units based on the resource type tags in the template library to generate the initial service sequence:

[0142]

[0143] Among them, M(S i ,T j ) represents the matching degree between the i-th microservice unit and the j-th template resource, K represents the number of label features, ω k represents the weight of the kth feature, sim(s ik ,t jk ) represents the similarity between the k-th feature of the i-th microservice unit and the k-th feature of the j-th template resource;

[0144] Extract the policy and regulatory constraints corresponding to the current business scenario from the rule base, perform conflict detection on the initial service sequence, and generate a constraint correction sequence;

[0145] Based on the ecological priority weight and business urgency coefficient, the microservice units in the constraint correction sequence are dynamically rearranged to generate an optimized execution path.

[0146] The dynamic arrangement module 303 is further used to:

[0147] Perform feature recognition processing on the spatial attributes and policy attributes of the current business scenario to generate a scenario feature vector;

[0148] Based on the scenario feature vector, multi-dimensional similarity matching is performed from the rule library, and policy and regulatory constraint rules are screened out based on the degree of relevance;

[0149] Compare policy and regulatory constraints with the initial service sequence node by node to identify service nodes with conflicting rules.

[0150] The service nodes with conflicting rules are replaced or reorganized through the rule compatibility algorithm to generate a constraint correction sequence.

[0151] The real-time monitoring module 304 is further used to:

[0152] Based on the optimized service node sequence in the execution path, the workflow engine collects the start timestamp and completion status of each node in real time to generate an execution status log;

[0153] Based on the preset node processing time threshold, the execution status log is processed for retention time and service nodes that have timed out and are not completed are identified.

[0154] Based on the responsibility boundary information in the visual flowchart, the timed-out and unfinished service nodes are associated with the responsible departments and a blocking point report is generated.

[0155] The real-time monitoring module 304 is further used to:

[0156] Based on the responsibility boundary information in the visual flow chart, the relationship between the responsible departments and service nodes is structured and parsed to generate a department-node responsibility map;

[0157] Perform topological matching on the timed-out and unfinished service nodes and the department-node responsibility map to locate the responsible departments and mark the department collaboration paths.

[0158] Based on the node dependencies in the departmental collaboration path, the root causes of blockages are classified into three dimensions and a blockage point report is generated.

[0159] The closed-loop optimization module 306 is further configured to:

[0160] Perform multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation plan to generate an efficiency feature vector;

[0161] Based on the compliance indicators in the effectiveness feature vector, the policy and regulatory constraint rules in the rule base are iteratively optimized through the rule evolution algorithm to generate an updated rule base;

[0162] Based on the resource allocation efficiency index in the effectiveness feature vector, the service unit combination logic in the template library is topologically reorganized to generate an updated template library;

[0163] The updated rule base and the updated template base are cross-validated to generate an adaptive collaborative system.

[0164] The closed-loop optimization module 306 is further configured to:

[0165] Perform time series analysis on the task processing time in the execution feedback data to generate a time efficiency feature vector;

[0166] Performing distribution statistics on the computing resource occupancy rate in the execution feedback data to generate a resource utilization feature vector;

[0167] Perform pattern recognition on policy and regulation violation records in execution feedback data to generate compliance feature vectors;

[0168] The time efficiency feature vector, resource utilization feature vector and compliance feature vector are orthogonally fused to generate the effectiveness feature vector.

[0169] In one embodiment, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0170] In one embodiment, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method embodiments when the computer program is executed by a processor.

[0171] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0172] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. An intelligent collaborative method for natural resource rights confirmation services, characterized by: The method comprises: Analyze cross-departmental property rights confirmation business through process modeling technology to generate a visual flowchart including responsibility boundaries and collaboration nodes; Based on the visual flowchart, the rights confirmation process is encapsulated through microservice encapsulation technology to generate independent microservice units, and a template library associating resource types and policy tags is constructed; Based on the policy and regulatory constraints in the template library and the preset rule library, the independent microservice units are sorted and an optimized execution path is generated; Based on the optimized execution path, the execution status is visually monitored and processed by the workflow engine to generate a blocking point report; triggering resource allocation processing based on the choke point report to generate a dynamic resource allocation plan; Based on the execution feedback data of the dynamic resource allocation solution, the template library and the rule library are updated to generate an adaptive collaborative system.

2. The intelligent collaborative method for natural resource rights confirmation services according to claim 1 is characterized in that: The process of sorting the independent microservice units based on the policy and regulatory constraint rules in the template library and the preset rule library to generate an optimized execution path includes: The following formula is used to perform combination pattern matching on the independent microservice units based on the resource type tags in the template library to generate an initial service sequence: Among them, M(S i ,T j ) represents the matching degree between the i-th microservice unit and the j-th template resource, K represents the number of label features, ω k represents the weight of the kth feature, sim(s ik ,t jk ) represents the similarity between the k-th feature of the i-th microservice unit and the k-th feature of the j-th template resource; Extracting policy and regulatory constraint rules corresponding to the current business scenario from the rule base, performing conflict detection processing on the initial service sequence, and generating a constraint correction sequence; Based on the ecological priority weight and the business urgency coefficient, the microservice units in the constraint correction sequence are dynamically rearranged to generate the optimized execution path.

3. The intelligent collaborative method for natural resource rights confirmation services according to claim 2 is characterized in that: The step of extracting policy and regulatory constraint rules corresponding to the current business scenario from the rule base, performing conflict detection processing on the initial service sequence, and generating a constraint correction sequence includes: Perform feature recognition processing on the spatial attributes and policy attributes of the current business scenario to generate a scenario feature vector; Based on the scenario feature vector, multi-dimensional similarity matching is performed from the rule library, and policy and regulatory constraint rules are screened out according to the relevance; Comparing the policy and regulatory constraint rules with the initial service sequence node by node to identify service nodes with conflicting rules; The service nodes with conflicting rules are replaced or reorganized using a rule compatibility algorithm to generate the constraint modification sequence.

4. The intelligent collaborative method for natural resource rights confirmation services according to claim 1 is characterized in that: Based on the optimized execution path, the execution status is visually monitored and processed by the workflow engine to generate a blocking point report, including: Based on the order of service nodes in the optimized execution path, the workflow engine collects the start timestamp and completion status of each node in real time to generate an execution status log; Calculate the retention time of the execution status log according to the preset node processing time threshold to identify the service nodes that have timed out and not completed; Based on the responsibility boundary information in the visual flow chart, the timed-out and unfinished service nodes are associated with the responsible departments and mapped to generate the blocking point report.

5. The intelligent collaborative method for natural resource rights confirmation services according to claim 4 is characterized in that: The process of associating and mapping the timed-out and unfinished service nodes with the responsible departments based on the responsibility boundary information in the visual flowchart to generate the blocking point report includes: Based on the responsibility boundary information in the visual flowchart, a structured analysis is performed on the ownership relationship between the responsible departments and the service nodes to generate a department-node responsibility map; Perform topological matching on the timed-out and unfinished service nodes and the department-node responsibility map, locate the responsible departments and mark the department collaboration paths; Based on the node dependencies in the department collaboration path, the root causes of the blockage are classified in three dimensions to generate the blockage point report.

6. The intelligent collaborative method for natural resource rights confirmation services according to claim 1 is characterized in that: The updating process of the template library and the rule library based on the execution feedback data of the dynamic resource allocation scheme to generate an adaptive collaborative system includes: Performing multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation scheme to generate an efficiency feature vector; Based on the compliance indicators in the effectiveness feature vector, the policy and regulatory constraint rules in the rule base are iteratively optimized by a rule evolution algorithm to generate an updated rule base; Based on the resource allocation efficiency index in the effectiveness feature vector, topologically reorganize the service unit combination logic in the template library to generate an updated template library; The updated rule library and the updated template library are cross-validated to generate the adaptive collaborative system.

7. The intelligent collaborative method for natural resource rights confirmation services according to claim 6 is characterized in that: The performing multi-dimensional feature analysis on the execution feedback data of the dynamic resource allocation scheme to generate an efficiency feature vector includes: Performing time series analysis on the task processing time in the execution feedback data to generate a time efficiency feature vector; Performing distribution statistics on the computing resource occupancy rate in the execution feedback data to generate a resource utilization feature vector; Performing pattern recognition processing on the policy and regulation violation records in the execution feedback data to generate a compliance feature vector; The time efficiency feature vector, the resource utilization feature vector and the compliance feature vector are orthogonally fused to generate the effectiveness feature vector.

8. The intelligent collaborative device for natural resource rights confirmation services is characterized by: The device comprises: The business process modeling module is used to analyze and process cross-departmental rights confirmation business through process modeling technology, and generate a visual flow chart including responsibility boundaries and collaboration nodes; A microservice encapsulation module is used to encapsulate the right confirmation process based on the visual flowchart using microservice encapsulation technology, generate independent microservice units, and build a template library that associates resource types with policy tags; A dynamic orchestration module, configured to sort the independent microservice units based on the policy and regulatory constraints in the template library and the preset rule library, and generate an optimized execution path; A real-time monitoring module is used to perform visual monitoring of the execution status through the workflow engine based on the optimized execution path and generate a blocking point report; A resource scheduling module, configured to trigger resource allocation processing based on the choke point report and generate a dynamic resource allocation plan; The closed-loop optimization module is used to update the template library and the rule library based on the execution feedback data of the dynamic resource allocation solution to generate an adaptive collaborative system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent collaborative method for natural resource title confirmation services described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent collaborative method for natural resource title confirmation services described in any one of claims 1 to 7 are implemented.

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