Integrated law enforcement business governance and decision assistance method and platform across layers of cascading

Abstract

This invention discloses a cross-level collaborative method and platform for comprehensive law enforcement business governance and decision support, relating to the field of law enforcement business management technology. The method includes: acquiring law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers; constructing a hierarchical topology based on the law enforcement entity identifiers; matching law enforcement matter identifiers with nodes in the topology to construct a set of associated nodes, selecting the connection path with the smallest hierarchical span as the collaborative path; designating the highest-level node in the collaborative path as the decision node, and the rest as execution nodes; extracting law enforcement rules to generate a decision scheme and decomposing it into execution tasks, allocating tasks according to the hierarchical attributes of the execution nodes; extracting law enforcement resource identifiers from the execution nodes to generate an execution plan; performing resource conflict detection on the execution plan, and reallocating resources according to the node hierarchical position to generate a collaborative execution scheme. This invention achieves cross-level collaboration in law enforcement business, improving the efficiency of law enforcement collaboration.

Description

Technical Field

[0001] This invention relates to the field of law enforcement business management technology, specifically to a comprehensive law enforcement business governance and decision support method and platform that involves cross-level collaboration. Background Technology

[0002] With the modernization of urban governance, the complexity of integrated law enforcement operations is increasing, involving the coordination of multiple law enforcement entities. Existing law enforcement systems mainly manage single law enforcement entities, lacking a linkage mechanism between law enforcement entities at different levels, resulting in inefficient allocation of law enforcement resources and low law enforcement efficiency.

[0003] Currently, law enforcement operations management lacks a systematic organizational structure management method, making it difficult to quickly determine the processing path for law enforcement matters; law enforcement rules are scattered across different law enforcement entities, lacking a unified rule extraction and decision-making scheme generation mechanism, affecting the uniformity of law enforcement standards; the allocation of law enforcement tasks lacks hierarchical attribute constraints, easily leading to unreasonable task allocation and low execution efficiency; the law enforcement resource scheduling method is singular, failing to consider the hierarchical differences between law enforcement entities, easily causing resource allocation conflicts.

[0004] Law enforcement operations management primarily employs a static, hierarchical management model, lacking a dynamic collaboration mechanism among law enforcement entities. While some systems have introduced task allocation functions, they are often limited to fixed task allocation rules, failing to flexibly adjust task allocation schemes according to the characteristics of law enforcement matters. Simultaneously, the utilization efficiency of law enforcement resources is low, and the resource sharing and allocation mechanisms among various law enforcement entities are imperfect, making it difficult to meet the needs of complex law enforcement scenarios.

[0005] Existing law enforcement management methods rarely consider the hierarchical differences between law enforcement entities, and do not fully utilize hierarchical characteristics in task allocation and resource scheduling, resulting in low collaboration efficiency. Especially in cross-level law enforcement scenarios, the lack of a systematic collaboration mechanism easily leads to problems such as unreasonable task allocation and resource usage conflicts. Summary of the Invention

[0006] The purpose of this invention is to provide a method and platform for integrated law enforcement business governance and decision support that involves cross-level collaboration, aiming to solve at least one of the technical problems existing in the prior art.

[0007] The technical solution of this invention is: a cross-level collaborative integrated law enforcement business governance and decision support method, comprising the following steps:

[0008] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology based on the law enforcement entity identifiers;

[0009] The law enforcement matter identifier is associated with the nodes of the hierarchical topology to construct a set of associated nodes. The connection paths between nodes in the set of associated nodes are retrieved in the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path.

[0010] Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes;

[0011] Extract the enforcement rules corresponding to the enforcement matters identifier, generate a decision plan based on the enforcement rules by the decision node, decompose the decision plan into multiple execution tasks, and assign multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path;

[0012] Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the preset law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme;

[0013] The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, the resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, and a collaborative execution plan is generated.

[0014] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology structure based on the law enforcement entity identifiers, including:

[0015] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and extract law enforcement business information from the law enforcement matter identifiers and organizational information from the law enforcement entity identifiers, respectively;

[0016] The law enforcement business information is decomposed into main responsibilities, the law enforcement entity association information and law enforcement duty information are extracted, the responsibility boundaries of the law enforcement entity association information and law enforcement duty information are divided, and a law enforcement business mapping table is generated.

[0017] Based on the responsibility boundaries in the law enforcement business mapping table, a law enforcement business network is constructed. Subordinate level information and business authority information are extracted from the organizational information. The subordinate level information and business authority information are combined to construct the organizational structure of the law enforcement entity.

[0018] The law enforcement business network is imported into the law enforcement entity organizational structure. Based on the business permission information, the responsibility boundaries in the law enforcement business network are constrained, and a law enforcement entity association matrix is ​​generated.

[0019] Extract the connection relationships of law enforcement entities from the law enforcement entity association matrix, impose hierarchical constraints on the connection relationships of law enforcement entities based on the hierarchical information, and construct a hierarchical topology structure.

[0020] The law enforcement matter identifier is associated with nodes in the hierarchical topology to construct a set of associated nodes. Connection paths between nodes within this set are retrieved within the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path.

[0021] Extract law enforcement business information from law enforcement matter identifiers, perform semantic analysis on the law enforcement business information to obtain business features, and construct a law enforcement business feature matrix from the business features;

[0022] The law enforcement business feature matrix is ​​mapped to a vector space, and the business function information of the nodes in the hierarchical topology is mapped to a vector space. The matching degree between the law enforcement business feature matrix and the business function information in the vector space is calculated. Nodes with matching degrees exceeding a preset matching threshold are extracted, and a set of associated nodes is constructed.

[0023] Traverse the connection relationships between nodes in the associated node set, obtain the connection paths between nodes in the associated node set, extract the level identifiers of adjacent nodes in the connection paths, calculate the level span of adjacent nodes, and sum the level spans of adjacent nodes to obtain the total level span of each connection path.

[0024] Compare the overall hierarchical span of all connection paths and select the connection path with the smallest overall hierarchical span as the collaborative path.

[0025] Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes, including:

[0026] Traverse all nodes in the collaborative path, extract the identification information of each node, parse the hierarchical code value and the functional type value from the identification information, and combine the hierarchical code value and the functional type value to construct a node attribute table;

[0027] Read the hierarchical encoding value from the node attribute table, compare the hierarchical encoding values ​​of all nodes, and filter the node group with the highest hierarchical encoding value;

[0028] Select nodes with the function type value of management function from the node group with the highest hierarchical coding value and designate them as decision nodes. Designate all nodes in the collaboration path except decision nodes as execution nodes.

[0029] Extract the enforcement rules corresponding to the enforcement matter identifier, generate a decision plan based on the enforcement rules from the decision node, decompose the decision plan into multiple execution tasks, and assign the multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path, including:

[0030] Extract the law enforcement element dimension table from the law enforcement matter identifier, parse the business attributes of the law enforcement matter identifier based on the law enforcement element dimension table, extract the law enforcement rules corresponding to the law enforcement matter identifier according to the business attributes, and generate law enforcement rule content containing execution conditions and execution constraints;

[0031] The decision node loads the enforcement rules, constructs the execution conditions and constraints into an execution rule chain, generates an execution step sequence based on the execution rule chain, and generates a decision plan containing execution responsibilities and resource requirements based on the execution step sequence.

[0032] The execution responsibilities and resource requirements in the decision-making plan are grouped and classified. The responsibility boundaries of the execution tasks are determined according to the grouping results. The execution tasks are divided based on the responsibility boundaries, and multiple execution tasks with execution requirements are generated.

[0033] Extract execution node hierarchical attributes from the collaborative path, construct task matching rules based on execution node hierarchical attributes, perform adaptation calculations between the execution requirements of the execution tasks and the task matching rules, generate an allocation scheme for the execution tasks, and allocate the execution tasks to the corresponding execution nodes according to the allocation scheme.

[0034] Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the pre-set law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme, including:

[0035] Each execution node receives the assigned execution task, parses the execution task to obtain the task execution instruction, and decomposes the task execution instruction to obtain the resource requirement item;

[0036] Resource attribute information and resource quantity information are extracted from resource demand items, and the resource attribute information and resource quantity information are combined to construct resource retrieval rules;

[0037] Input the resource retrieval rules into the preset law enforcement resource database, and retrieve law enforcement resource identifiers that match the resource retrieval rules from the law enforcement resource database;

[0038] Extract the execution rules from the decision-making scheme, match the execution rules with law enforcement resource identifiers, generate a resource allocation scheme, and generate a scheduling arrangement based on the resource allocation scheme;

[0039] The scheduling and execution tasks are integrated to generate a sequence of execution steps. The sequence of execution steps is then arranged in sequence according to the execution rules to generate an execution plan.

[0040] The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, generating a collaborative execution plan including:

[0041] Summarize the execution plans of each execution node, parse the execution plans to extract law enforcement resource identifiers and execution times, and combine the law enforcement resource identifiers and execution times to generate a resource occupancy table;

[0042] Analyze the execution time of law enforcement resource identifiers in the resource occupancy table, identify law enforcement resource identifiers with conflicting execution times, generate a resource conflict list, and extract resource conflict items from the resource conflict list;

[0043] Extract the hierarchical position of the execution node in the collaborative path, construct priority rules based on the hierarchical position, match the priority rules with resource conflict items, and generate a resource allocation scheme;

[0044] Based on the resource allocation plan, the law enforcement resource identifiers in the execution plan are reallocated, and the reallocated execution plans are integrated to generate a collaborative execution plan.

[0045] This invention provides a cross-level collaborative integrated law enforcement business governance and decision support platform, the platform comprising:

[0046] The topology construction module is used to acquire law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and to construct a hierarchical topology structure based on the law enforcement entity identifiers;

[0047] The collaborative path generation module is used to associate and match law enforcement matter identifiers with nodes in the hierarchical topology, construct a set of associated nodes, retrieve connection paths between nodes in the set of associated nodes in the hierarchical topology, and select the connection path with the smallest hierarchical span as the collaborative path.

[0048] The node designation module is used to designate the highest-level node in the collaborative path as the decision node and the remaining nodes as execution nodes.

[0049] The task allocation module is used to extract the law enforcement rules corresponding to the law enforcement matter identifier. The decision-making node generates a decision plan based on the law enforcement rules, decomposes the decision plan into multiple execution tasks, and allocates multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path.

[0050] The execution plan generation module is used to enable each execution node to receive the assigned execution tasks, extract law enforcement resource identifiers from the preset law enforcement resource library, and generate an execution plan based on the law enforcement resource identifiers and decision-making schemes;

[0051] The collaborative solution generation module is used to summarize the execution plans of each execution node, perform conflict detection on the law enforcement resource identifiers in the execution plan, and when a resource conflict is detected, reallocate resources according to the hierarchical position of the execution node in the collaborative path to generate a collaborative execution plan.

[0052] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0053] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0054] This invention achieves systematic management among law enforcement entities by constructing a hierarchical topology. It selects collaborative paths based on the principle of minimizing hierarchical span, reducing cross-level coordination costs and improving collaborative efficiency among law enforcement entities. By designating the highest-level node in the collaborative path as the decision-making node, it achieves centralized management and unified decision-making of law enforcement rules, ensuring consistency in law enforcement standards. Task allocation based on the hierarchical attributes of execution nodes makes task allocation more rational, avoiding inefficiencies caused by task mismatch. Conflict detection and resource reallocation through law enforcement resource identification enable dynamic scheduling and optimized configuration of law enforcement resources, improving resource utilization efficiency. The hierarchical location-based resource reallocation method ensures that resource scheduling conforms to the hierarchical management requirements of law enforcement entities, enhancing the standardization of law enforcement collaboration. Attached Figure Description

[0055] Figure 1 A flowchart of a cross-level collaborative integrated law enforcement business governance and decision support method provided in an embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram of the structure of the cross-level collaborative integrated law enforcement business governance and decision support platform provided in an embodiment of the present invention. Detailed Implementation

[0057] like Figure 1 As shown, Figure 1 A flowchart of a cross-level collaborative integrated law enforcement business governance and decision support method provided in an embodiment of the present invention, the method comprising the following steps:

[0058] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology based on the law enforcement entity identifiers;

[0059] The law enforcement matter identifier is associated with the nodes of the hierarchical topology to construct a set of associated nodes. The connection paths between nodes in the set of associated nodes are retrieved in the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path.

[0060] Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes;

[0061] Extract the enforcement rules corresponding to the enforcement matters identifier, generate a decision plan based on the enforcement rules by the decision node, decompose the decision plan into multiple execution tasks, and assign multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path;

[0062] Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the preset law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme;

[0063] The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, the resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, and a collaborative execution plan is generated.

[0064] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology structure based on the law enforcement entity identifiers, including:

[0065] Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and extract law enforcement business information from the law enforcement matter identifiers and organizational information from the law enforcement entity identifiers, respectively;

[0066] The law enforcement business information is decomposed into main responsibilities, the law enforcement entity association information and law enforcement duty information are extracted, the responsibility boundaries of the law enforcement entity association information and law enforcement duty information are divided, and a law enforcement business mapping table is generated.

[0067] Based on the responsibility boundaries in the law enforcement business mapping table, a law enforcement business network is constructed. Subordinate level information and business authority information are extracted from the organizational information. The subordinate level information and business authority information are combined to construct the organizational structure of the law enforcement entity.

[0068] The law enforcement business network is imported into the law enforcement entity organizational structure. Based on the business permission information, the responsibility boundaries in the law enforcement business network are constrained, and a law enforcement entity association matrix is ​​generated.

[0069] Extract the connection relationships of law enforcement entities from the law enforcement entity association matrix, impose hierarchical constraints on the connection relationships of law enforcement entities based on the hierarchical information, and construct a hierarchical topology structure.

[0070] First, acquire law enforcement business data containing enforcement matter identifiers and enforcement entity identifiers. Enforcement matter identifiers generally adopt the format of "category-region-number", such as "environmental protection-east district-2023001", while enforcement entity identifiers usually adopt the format of "organization code-department code-personnel number", such as "12345-ENV-008". The law enforcement business information extracted from the enforcement matter identifiers includes business type, jurisdiction, and processing time limit, while the organizational information extracted from the enforcement entity identifiers includes organizational affiliation, departmental functional positioning, and personnel positions.

[0071] By retrieving the law enforcement business database, information on the law enforcement entities associated with each law enforcement matter is extracted, including the competent authority, assisting departments, and supervising departments. Simultaneously, corresponding law enforcement responsibility information is extracted, including leading responsibility, coordinating responsibility, and supervisory responsibility. This information is then divided into core responsibility areas, auxiliary responsibility areas, and supervisory responsibility areas, generating a law enforcement business mapping table. The law enforcement business mapping table includes fields such as law enforcement matter identifier, responsibility type, responsibility boundary, and law enforcement entity identifier, providing data support for the subsequent construction of the law enforcement business network.

[0072] When constructing a law enforcement business network based on the responsibility boundaries in the law enforcement business mapping table, each law enforcement entity is treated as a node, and the relationship of responsibilities is considered as a connecting line. Hierarchical information, including superior-subordinate relationships and peer relationships, as well as business authority information, including decision-making authority, execution authority, and supervisory authority, are extracted from organizational information. The hierarchical information and business authority information are combined to construct the organizational structure of the law enforcement entities. During the construction process, the division of responsibilities between different levels needs to be considered to ensure that superiors have the right to guide and supervise subordinates, peers have collaborative relationships, and subordinates have a reporting obligation to superiors.

[0073] After importing the law enforcement operational network into the organizational structure of law enforcement entities, permission constraints are applied to the boundaries of responsibilities within the network based on operational authorization information. Specifically, this involves checking whether the connections of each law enforcement entity within the network conform to its operational authorization requirements within the organizational structure. If a law enforcement entity's operational authorization does not support its responsibilities within the network, its position or scope of authorization must be adjusted. This permission constraint generates an operational entity association matrix, where each element represents the strength of the responsibility association between two law enforcement entities.

[0074] The connections between law enforcement entities are extracted from the law enforcement entity association matrix, including direct and indirect connections. Direct connections refer to direct interaction of duties between two law enforcement entities, while indirect connections involve interaction of duties between two entities through other law enforcement entities. Hierarchical constraints are applied to these connections based on hierarchical information to ensure they comply with hierarchical management requirements. For example, guidance and supervision from a higher-level department to a lower-level department is permitted, while direct guidance from a lower-level department to a higher-level department is restricted. Through these hierarchical constraints, a hierarchical topology structure conforming to the principles of administrative management is constructed.

[0075] In practical implementation, relational databases can be used to store basic data such as law enforcement business data and law enforcement entity information. Relevant data can be extracted using structured query language, and a hierarchical topology can be constructed using a graph database. For example, a graph database containing node tables and edge tables can be designed, where the node tables store law enforcement entity information and the edge tables store the connections between law enforcement entities. When constructing the hierarchical topology, a depth-first search algorithm is used to traverse the organizational structure of law enforcement entities, determine the hierarchical position of each entity, and establish connections between nodes based on responsibility boundaries and business permission information.

[0076] To improve the accuracy of the hierarchical topology, weight parameters can be set to quantify the connection strength between law enforcement entities. Connection strength can be calculated based on factors such as the frequency of historical law enforcement cooperation and the importance of law enforcement activities. By setting thresholds to filter weak connections and retain strong connections, the hierarchical topology becomes clearer and more practical.

[0077] For example, when law enforcement needs to handle an environmental pollution incident, it first obtains the incident identifier "Environmental Protection-South District-2023056" and the identifiers of relevant law enforcement entities. By extracting law enforcement business information and organizational information, it identifies the environmental protection department as the competent authority, the urban management department as the assisting department, and the discipline inspection and supervision department as the supervising department. The responsibilities of these departments are then decomposed, clarifying that the environmental protection department is responsible for pollution source investigation and penalty decisions, the urban management department is responsible for on-site control and cleanup, and the discipline inspection and supervision department is responsible for full-process supervision. Based on the affiliation and business authority of these departments, a hierarchical topology is constructed to ensure that each department works collaboratively according to its responsibilities, avoiding overlapping or missing responsibilities.

[0078] This invention, by constructing a hierarchical topology, clearly defines the boundaries of responsibilities among law enforcement entities and establishes a collaborative mechanism, thereby improving the efficiency and quality of comprehensive law enforcement. The hierarchical topology provides data support for law enforcement decision-making, making decisions more scientific and rational. This invention breaks through the limitations of independent operations by various departments in the traditional law enforcement model, establishing a cross-level and cross-departmental linkage mechanism, effectively solving problems such as information asymmetry and unclear responsibilities in the law enforcement process.

[0079] The law enforcement matter identifier is associated with nodes in the hierarchical topology to construct a set of associated nodes. Connection paths between nodes within this set are retrieved within the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path.

[0080] Extract law enforcement business information from law enforcement matter identifiers, perform semantic analysis on the law enforcement business information to obtain business features, and construct a law enforcement business feature matrix from the business features;

[0081] The law enforcement business feature matrix is ​​mapped to a vector space, and the business function information of the nodes in the hierarchical topology is mapped to a vector space. The matching degree between the law enforcement business feature matrix and the business function information in the vector space is calculated. Nodes with matching degrees exceeding a preset matching threshold are extracted, and a set of associated nodes is constructed.

[0082] Traverse the connection relationships between nodes in the associated node set, obtain the connection paths between nodes in the associated node set, extract the level identifiers of adjacent nodes in the connection paths, calculate the level span of adjacent nodes, and sum the level spans of adjacent nodes to obtain the total level span of each connection path.

[0083] Compare the overall hierarchical span of all connection paths and select the connection path with the smallest overall hierarchical span as the collaborative path.

[0084] First, enforcement business information is extracted from the enforcement matter identifier. Enforcement matter identifiers typically contain several key pieces of information, such as the type of matter, geographical scope, and timeframe. Taking the enforcement matter identifier "Food Safety - Retail Industry - Market Inspection - 20231201" as an example, "Food Safety" can be extracted as the business area, "Retail Industry" as the industry attribute, "Market Inspection" as the enforcement method, and "20231201" as the enforcement time. Semantic analysis is then performed on this extracted information. Natural language processing techniques can be used to transform the text information into a structured feature vector. In practice, methods such as word frequency statistics and keyword extraction are used to extract core business features from the text. In this example, the extracted business features include keywords such as "food hygiene inspection," "retail store supervision," and "on-site inspection enforcement." These business features are then constructed into an enforcement business feature matrix. Each row of the matrix represents an enforcement matter, each column represents a business feature, and the matrix element values ​​represent the weight of that feature within the enforcement matter.

[0085] After the law enforcement business feature matrix is ​​constructed, it is mapped to a vector space. Word embedding technology is used to transform the business features into vector representations in a high-dimensional vector space, ensuring that semantically similar features are also located close together in the vector space. Simultaneously, the business function information of nodes in the hierarchical topology is also mapped to the same vector space. The business function information of a node includes its scope of responsibility, law enforcement authority, and professional field. For example, the business function information of a food safety supervision and inspection station node includes "food production license review," "food sales supervision," and "food safety inspection." This business function information is also vectorized, transforming it into vector representations in the vector space.

[0086] The cosine similarity method is used to calculate the matching degree between the law enforcement business feature matrix and the business function information in the vector space. Cosine similarity can effectively measure the similarity between two vector directions, and its value ranges from [-1, 1]. The larger the value, the higher the matching degree. A preset matching threshold of 0.7 is set, that is, when the cosine similarity between the law enforcement business feature matrix and the business function information of a certain node is greater than or equal to 0.7, the node is considered to be associated with the law enforcement matter. There may be multiple nodes whose matching degree with the law enforcement matter exceeds the threshold. These nodes together constitute the set of associated nodes. Taking the aforementioned food safety inspection matter as an example, nodes such as food safety supervision and inspection stations, market comprehensive law enforcement brigades, and consumer rights protection centers may be identified as associated nodes because their business functions are related to food safety inspections.

[0087] When traversing the connections between nodes within a set of associated nodes, a graph traversal algorithm is used to find all possible connection paths between nodes in the hierarchical topology. Depth-first search or breadth-first search algorithms can be employed. For each connection path, the hierarchical identifiers of adjacent nodes are extracted. The hierarchical identifier represents the node's position in the organizational structure, usually represented numerically, such as 1 indicating the highest level, with larger values ​​indicating lower levels. The hierarchical span between adjacent nodes is calculated, which is the absolute value of the difference in their hierarchical identifiers. For example, if node A has a hierarchical identifier of 2 and node B has a hierarchical identifier of 4, then the hierarchical span between them is 2. The hierarchical spans of all adjacent node pairs in the connection path are summed to obtain the overall hierarchical span of the connection path.

[0088] When multiple connection paths with the same overall hierarchical span exist, other evaluation indicators, such as path length and node weight, can be introduced for further screening. Paths with a smaller overall hierarchical span mean fewer hierarchical jumps in the collaboration process, which is beneficial for improving enforcement efficiency and reducing communication costs. Taking food safety inspections as an example, if there are two connection paths: Path 1 includes a food safety supervision and inspection station (hierarchical identifier 3) – a market comprehensive law enforcement brigade (hierarchical identifier 3) – a consumer rights protection center (hierarchical identifier 3), its overall hierarchical span is 0; Path 2 includes a food safety supervision and inspection station (hierarchical identifier 3) – a district-level law enforcement supervision office (hierarchical identifier 2) – a consumer rights protection center (hierarchical identifier 3), its overall hierarchical span is 2. Comparing the overall hierarchical spans of the two paths, Path 1 is selected as the collaboration path.

[0089] In constructing collaborative paths, it's also necessary to consider the business relevance and collaborative efficiency between nodes. Business relevance weights can be introduced into connection paths to reflect the tightness of business collaboration between nodes. These weights can be determined based on factors such as historical collaboration records and business similarity. Time constraints can be introduced to ensure that the selected collaborative path can complete the collaborative task within a specified timeframe. For law enforcement matters with high timeliness requirements, the matching threshold can be appropriately lowered or the weight of the hierarchical span can be adjusted to ensure that a suitable collaborative path can be found promptly.

[0090] This invention achieves intelligent collaboration and optimized resource allocation for cross-level law enforcement operations by associating and matching law enforcement matter identifiers with nodes in a hierarchical topology, constructing a set of associated nodes, and selecting the optimal collaborative path. This invention breaks away from the traditional fragmented approach of different departments in law enforcement, establishing an intelligent matching mechanism based on business characteristics, enabling law enforcement resources to accurately meet law enforcement needs. By minimizing the hierarchical span, it reduces the cost and time of cross-level communication, thereby improving law enforcement efficiency.

[0091] Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes, including:

[0092] Traverse all nodes in the collaborative path, extract the identification information of each node, parse the hierarchical code value and the functional type value from the identification information, and combine the hierarchical code value and the functional type value to construct a node attribute table;

[0093] Read the hierarchical encoding value from the node attribute table, compare the hierarchical encoding values ​​of all nodes, and filter the node group with the highest hierarchical encoding value;

[0094] Select nodes with the function type value of management function from the node group with the highest hierarchical coding value and designate them as decision nodes. Designate all nodes in the collaboration path except decision nodes as execution nodes.

[0095] A collaborative path refers to a path connecting various law enforcement entities in a hierarchical topology. It consists of multiple nodes, each representing a law enforcement entity. When traversing all nodes in a collaborative path, either depth-first traversal or breadth-first traversal methods can be used to visit each node in the path sequentially. The identification information of a node typically contains multiple fields, including a hierarchy code and a functional type.

[0096] Node identification information typically follows the format "hierarchical code-department code-functional type-serial number," such as "01-ENF-M-001," which represents a first-level node, a law enforcement department, a management function, and a serial number of 001. Extracting the hierarchical code value from the identification information requires extracting the hierarchical code portion. Hierarchical codes are usually represented by numbers, with smaller numbers indicating higher levels. For example, "01" represents the highest level, "02" represents the second-highest level, and so on. The functional type value indicates the node's functional positioning. Common functional types include management functions (M), execution functions (E), and supervisory functions (S). Extracting the functional type value from the identification information allows understanding the node's role and responsibilities within the law enforcement system.

[0097] Building a node attribute table by combining hierarchical coding values ​​and functional type values ​​is the process of structurally storing node characteristics. The structure of the node attribute table includes fields such as node ID, hierarchical coding value, functional type value, department, and business scope. Taking three nodes in a collaborative path as an example, the content of the constructed node attribute table may be: Node 1 (ID: N001, hierarchical coding value: 01, functional type value: M, department: law enforcement team, business scope: comprehensive management); Node 2 (ID: N002, hierarchical coding value: 02, functional type value: E, department: law enforcement brigade, business scope: on-site law enforcement); Node 3 (ID: N003, hierarchical coding value: 02, functional type value: S, department: supervision office, business scope: law enforcement supervision).

[0098] Reading the hierarchical code value from the node attribute table requires traversing all records in the table and extracting the hierarchical code value field for each record. The hierarchical code values ​​of all nodes are compared, and the node with the smallest hierarchical code value is identified. These nodes form the node group with the highest hierarchical code value. In the example above, node 1's hierarchical code value is "01", which is the smallest; therefore, node 1 constitutes the node group with the highest hierarchical code value. If multiple nodes have the same and smallest hierarchical code value, then these nodes all belong to the node group with the highest hierarchical code value. For example, if node 4 exists (ID: N004, hierarchical code value: 01, function type value: E, department: directly subordinate team, business scope: special enforcement), then node 1 and node 4 together constitute the node group with the highest hierarchical code value.

[0099] From the node group with the highest hierarchical coding value, select nodes whose function type is "management". Examine the function type value of each node in the node group and retain nodes with the function type value "M". In the example above, node 1's function type value is "M", which meets the management function requirement, so node 1 is designated as the decision node. If multiple nodes in the node group with the highest hierarchical coding value have the function type value "M", further filtering can be performed based on other conditions, such as the matching degree of business scope and historical decision effects. If there are no nodes with the function type value "M" in the node group with the highest hierarchical coding value, the node group with the second lowest hierarchical coding value can be selected, and the above filtering process can be repeated.

[0100] Designating nodes other than decision-making nodes as execution nodes in the collaborative path is the process of defining the roles of the remaining nodes as executors. In this example, nodes 2 and 3 are designated as execution nodes. Execution nodes can be further subdivided into different types of execution roles based on their function type values. For example, nodes with a function type value of "E" are responsible for specific law enforcement actions, while nodes with a function type value of "S" are responsible for supervising the law enforcement process. This refined role division helps clarify the specific responsibilities and task assignments of execution nodes in the collaborative process.

[0101] The division between decision-making and execution nodes also needs to consider the characteristics and complexity of law enforcement operations. For comprehensive law enforcement operations that cross regions and departments, decision-making nodes typically possess higher management authority and decision-making capabilities, enabling them to coordinate resources from multiple parties and formulate enforcement strategies and plans. Execution nodes, on the other hand, carry out specific enforcement activities within their respective responsibilities, based on the instructions of the decision-making nodes. This division of labor between decision-making and execution is conducive to improving enforcement efficiency and quality.

[0102] The construction and maintenance of the node attribute table are dynamic. As the law enforcement system and operations change, the hierarchical coding values ​​and functional type values ​​of nodes may change. Therefore, each time a law enforcement operation is initiated, it is necessary to re-acquire the latest node identification information and update the node attribute table to ensure that the division of decision-making nodes and execution nodes conforms to the current law enforcement system structure and operational needs. The node attribute table can also serve as part of the law enforcement operation history for subsequent law enforcement effectiveness evaluation and law enforcement system optimization.

[0103] This invention achieves an organic combination of scientific decision-making and efficient execution in law enforcement by designating the highest-level node in the collaborative path as the decision-making node and the remaining nodes as execution nodes. The clear division between decision-making and execution nodes solves the problems of decision-making confusion and unclear responsibilities in traditional law enforcement processes, providing a clear organizational framework for law enforcement operations. The decision-making node selection method based on node hierarchy and functional characteristics ensures the matching of decision-making authority and management level, improving the scientific nature and authority of decisions. The detailed classification of execution nodes enables the rational allocation of law enforcement resources according to functional characteristics, improving the accuracy and collaborative efficiency of law enforcement.

[0104] Extract the enforcement rules corresponding to the enforcement matter identifier, generate a decision plan based on the enforcement rules from the decision node, decompose the decision plan into multiple execution tasks, and assign the multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path, including:

[0105] Extract the law enforcement element dimension table from the law enforcement matter identifier, parse the business attributes of the law enforcement matter identifier based on the law enforcement element dimension table, extract the law enforcement rules corresponding to the law enforcement matter identifier according to the business attributes, and generate law enforcement rule content containing execution conditions and execution constraints;

[0106] The decision node loads the enforcement rules, constructs the execution conditions and constraints into an execution rule chain, generates an execution step sequence based on the execution rule chain, and generates a decision plan containing execution responsibilities and resource requirements based on the execution step sequence.

[0107] The execution responsibilities and resource requirements in the decision-making plan are grouped and classified. The responsibility boundaries of the execution tasks are determined according to the grouping results. The execution tasks are divided based on the responsibility boundaries, and multiple execution tasks with execution requirements are generated.

[0108] Extract execution node hierarchical attributes from the collaborative path, construct task matching rules based on execution node hierarchical attributes, perform adaptation calculations between the execution requirements of the execution tasks and the task matching rules, generate an allocation scheme for the execution tasks, and allocate the execution tasks to the corresponding execution nodes according to the allocation scheme.

[0109] The enforcement element dimension table is extracted from the enforcement matter identifier. Enforcement matter identifiers typically contain information across multiple dimensions, such as enforcement area, enforcement target, enforcement method, and enforcement time. Taking the enforcement matter identifier "FSC-RT-INS-20240115" as an example, this identifier includes the enforcement area "FSC" (food safety inspection), the enforcement target "RT" (retailer), the enforcement method "INS" (on-site inspection), and the enforcement time "20240115". When constructing the enforcement element dimension table, this identifier information is mapped to predefined dimension categories, forming a structured enforcement element table. The enforcement element dimension table includes fields such as dimension name, dimension value, and dimension weight, providing support for subsequent business attribute parsing.

[0110] Based on the business attributes identified by the law enforcement element dimension table, semantic parsing technology is used to transform the dimension values ​​into specific business attributes. These business attributes include multiple aspects such as the law enforcement subject, the law enforcement object, the law enforcement scenario, and the timeliness of law enforcement. A combination of keyword matching and semantic association analysis is employed to map the dimension values ​​to a predefined business attribute library, extracting a set of matching business attributes. Taking the aforementioned example, the parsed business attributes include "food business supervision," "retailer inspection," and "on-site enforcement." These business attributes constitute the business characteristics of the law enforcement matter, laying the foundation for the extraction of law enforcement rules.

[0111] The enforcement rule base stores information such as enforcement basis, enforcement procedures, and enforcement standards for various enforcement scenarios. Using a multi-dimensional matching algorithm, the parsed business attributes are matched with rule entries in the enforcement rule base to extract a set of enforcement rules applicable to the current enforcement matter. For food safety inspection enforcement matters, the extracted enforcement rules may include "Food Business License Inspection Standards," "Food Safety Sampling and Inspection Standards," etc. These rules are then structured and transformed into enforcement rule content containing execution conditions and constraints.

[0112] An execution rule chain is a sequence of rule entries organized according to the logical order of law enforcement, reflecting the conditional judgments and execution paths in the law enforcement process. Decision nodes employ rule engine technology, representing execution conditions as "IF" conditions and execution constraints as "THEN" results, constructing an execution rule chain in the form of a decision tree. Taking food safety inspection as an example, the execution rule chain may include stages such as "pre-inspection preparation → on-site inspection → inspection result processing → follow-up," with multiple conditional branches under each stage, such as "discovery of illegal behavior → evidence collection → order for rectification." Based on the execution rule chain, decision nodes generate a sequence of execution steps, clarifying the sequential relationship and resource requirements of each execution step.

[0113] The decision-making nodes generate decision plans based on the sequence of execution steps, taking into account factors such as enforcement efficiency, resource allocation, and risk management. The decision plan includes two parts: execution responsibilities and resource requirements. Execution responsibilities clarify the tasks and objectives to be completed during enforcement activities; resource requirements specify the types and quantities of personnel, equipment, materials, and other resources needed for enforcement activities. Taking food safety inspections as an example, the execution responsibilities in the decision plan might include "inspecting the licenses and qualifications of food operators" and "inspecting whether food storage conditions meet requirements"; resource requirements might include "two enforcement personnel" and "one testing device."

[0114] The execution responsibilities and resource requirements in the decision-making scheme are grouped and categorized. The responsibility boundaries of the execution tasks are determined according to the grouping results, based on factors such as responsibility relevance, resource sharing, and execution continuity. A hierarchical clustering algorithm is used to group execution responsibilities with high similarity together, forming responsibility clusters. Based on the type and quantity of resource requirements, the optimal resource allocation scheme is calculated to ensure maximum resource utilization efficiency. Execution tasks are divided based on responsibility boundaries, generating multiple execution tasks with specific requirements. Execution tasks are the refined units of the decision-making scheme, including task descriptions, execution standards, completion deadlines, and reporting requirements.

[0115] The execution node hierarchical attributes are extracted from the collaborative path to obtain information such as the hierarchical code, functional type, and business scope of the execution nodes. These attributes reflect the node's position and responsibilities within the organizational structure. Task matching rules are constructed based on these attributes, primarily considering factors such as hierarchical adaptability, functional matching degree, and business coverage. These rules can be expressed as conditional statements, such as "If the task type is on-site inspection and the resource requirements do not exceed the node's resource limit, then the node can execute this type of task." The execution requirements of the tasks are then matched with the task matching rules using a fuzzy comprehensive evaluation method to calculate the adaptability score of each execution node for each task. A higher adaptability score indicates that the node is more suitable for executing the task.

[0116] The process involves generating an allocation scheme for execution tasks, which is essentially the optimal combination of tasks and execution nodes. Tasks are then assigned to their corresponding execution nodes according to this scheme, taking into account dependencies and timing requirements. For tasks with sequential dependencies, it's crucial to ensure that the execution node of the preceding task can promptly transmit its results to the execution nodes of the subsequent tasks, guaranteeing smooth task flow. Tasks with high timeliness requirements are prioritized for allocation to execution nodes with fast response times and strong processing capabilities. After task allocation, each execution node conducts corresponding enforcement activities based on its assigned tasks and feeds back the enforcement process and results to the decision-making node, forming a closed-loop enforcement process.

[0117] This invention achieves scientific decision-making and precise execution in law enforcement by extracting the enforcement rules corresponding to enforcement matters. Decision nodes then generate decision schemes based on these rules and decompose them into multiple execution tasks. The extraction and parsing process of enforcement rules ensures the compliance and standardization of enforcement activities. The decision schemes generated by the decision nodes provide comprehensive guidance and planning for enforcement activities, avoiding blindness and arbitrariness in the enforcement process. The division and allocation of execution tasks optimizes the allocation of enforcement resources and maximizes enforcement efficiency. The introduction of hierarchical attributes for execution nodes ensures the matching degree between task allocation and node capabilities, reducing the complexity of cross-level collaboration.

[0118] Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the pre-set law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme, including:

[0119] Each execution node receives the assigned execution task, parses the execution task to obtain the task execution instruction, and decomposes the task execution instruction to obtain the resource requirement item;

[0120] Resource attribute information and resource quantity information are extracted from resource demand items, and the resource attribute information and resource quantity information are combined to construct resource retrieval rules;

[0121] Input the resource retrieval rules into the preset law enforcement resource database, and retrieve law enforcement resource identifiers that match the resource retrieval rules from the law enforcement resource database;

[0122] Extract the execution rules from the decision-making scheme, match the execution rules with law enforcement resource identifiers, generate a resource allocation scheme, and generate a scheduling arrangement based on the resource allocation scheme;

[0123] The scheduling and execution tasks are integrated to generate a sequence of execution steps. The sequence of execution steps is then arranged in sequence according to the execution rules to generate an execution plan.

[0124] Each execution node receives assigned execution tasks, which are transmitted in structured data format, including task number, task description, execution standards, and completion deadline. For food safety inspection tasks, the data structure is: "TASK-FSC-001: Retailer Food Business License Qualification Inspection," specifying the inspection target, content, and standards. Execution nodes break down the task execution instructions, extracting key information elements. This breakdown process decomposes the task execution instructions into multiple resource requirements. For the above task, the decomposed resource requirements include "law enforcement personnel," "license inspection form," "law enforcement recorder," and "mobile law enforcement terminal," serving as the basis for subsequent resource allocation.

[0125] The execution node extracts resource attribute information and resource quantity information from the resource requirement item. Resource attribute information describes the resource type and characteristics, such as "law enforcement personnel with food safety inspection qualifications"; resource quantity information specifies the resource requirement, such as "2 people". These two types of information are combined to construct resource retrieval rules. Resource retrieval rules follow the pattern of "resource type + attribute condition + quantity requirement", such as "find 2 law enforcement personnel with food safety inspection qualifications", facilitating precise queries in the resource database.

[0126] The resource retrieval rules are input into a pre-defined law enforcement resource database. This database retrieves matching law enforcement resource identifiers. The database stores information on various law enforcement resources, including detailed information on law enforcement personnel, equipment, tools, and locations. Each law enforcement resource identifier is a unique identifier within the database; for example, "PERSON-FS-0023" represents a law enforcement officer with food safety inspection qualifications. The retrieval process compares the conditions in the resource retrieval rules with the resource attributes in the law enforcement resource database, identifying the set of resource identifiers that meet all conditions. For the aforementioned retrieval rules, two law enforcement officer identifiers, "PERSON-FS-0023" and "PERSON-FS-0045," may be retrieved for subsequent resource configuration.

[0127] The execution node extracts the execution rules from the decision-making scheme. These rules define the constraints and standards for law enforcement activities, such as "at least two law enforcement personnel must be present" and "law enforcement credentials must be presented before the inspection." For the rule "at least two law enforcement personnel must be present," the retrieved identifiers of two law enforcement personnel satisfy this rule. The resource allocation scheme is the optimal matching scheme between law enforcement resources and execution tasks, including resource assignment information and resource scheduling order. For food safety inspection tasks, the resource allocation scheme includes "PERSON-FS-0023 as the lead inspector," "PERSON-FS-0045 as the assistant inspector," and "EQUIP-REC-0078 for on-site evidence collection," among other things.

[0128] A scheduling arrangement is generated based on the resource allocation plan, taking into account the time availability and spatial reachability of resources. The scheduling arrangement specifies the usage time and execution location of each resource, such as "PERSON-FS-0023 will go to the inspection location at 9:00 AM on May 10th". The generation process of the scheduling arrangement includes time point division, resource allocation and conflict detection to ensure that the same resource is not assigned to different tasks at the same time, and finally forms a resource scheduling table.

[0129] By integrating scheduling and task execution, a sequence of execution steps is generated. This sequence is the core of the execution plan, detailing each step of the enforcement activity and the resources required. For food safety inspection tasks, the sequence includes steps such as "Preparation Phase: Receiving enforcement equipment → Proceeding to the inspection location," "Execution Phase: Presenting credentials → Inspecting licenses → Inspecting the premises → Inspecting products → Recording inspection results," and "Conclusion Phase: On-site feedback → Completing enforcement documents → Returning to the enforcement agency."

[0130] The execution node sequentially arranges the execution steps according to execution rules. This sequential arrangement considers the dependencies between steps and the constraints of the execution rules, ensuring that the order of the execution steps conforms to law enforcement logic and regulatory requirements. For steps with preconditions, it ensures that these preconditions are met during execution. The generated execution plan is a complete guidance document for law enforcement activities, including task information, resource allocation, execution steps, time nodes, and quality standards. The execution plan uses a combination of graphical and textual methods for easy understanding and execution.

[0131] During the execution of the plan, execution nodes monitor the progress and resource status in real time, identifying problems and risks. For unexpected situations, such as non-cooperation by the inspected party or equipment malfunction, execution nodes handle them according to the contingency plan. The contingency plan is an important supplement to the execution plan, providing guidance for execution nodes to respond to emergencies. After completing the task, each execution node generates an execution report, recording the results, existing problems, and suggested solutions, and provides feedback to the decision-making node for reference in subsequent law enforcement decisions.

[0132] This invention ensures the targeted and precise allocation of resources by breaking down task execution instructions and extracting resource requirements. Through the construction of resource retrieval rules and the matching of law enforcement resources, it achieves efficient utilization and rational allocation of law enforcement resources. By matching execution rules with law enforcement resources, it ensures the compliance and standardization of law enforcement activities. Through the generation of scheduling arrangements and the temporal arrangement of execution steps, it improves the coordination and execution efficiency of law enforcement activities.

[0133] The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, generating a collaborative execution plan including:

[0134] Summarize the execution plans of each execution node, parse the execution plans to extract law enforcement resource identifiers and execution times, and combine the law enforcement resource identifiers and execution times to generate a resource occupancy table;

[0135] Analyze the execution time of law enforcement resource identifiers in the resource occupancy table, identify law enforcement resource identifiers with conflicting execution times, generate a resource conflict list, and extract resource conflict items from the resource conflict list;

[0136] Extract the hierarchical position of the execution node in the collaborative path, construct priority rules based on the hierarchical position, match the priority rules with resource conflict items, and generate a resource allocation scheme;

[0137] Based on the resource allocation plan, the law enforcement resource identifiers in the execution plan are reallocated, and the reallocated execution plans are integrated to generate a collaborative execution plan.

[0138] The execution plans of each execution node are aggregated, and execution plan data submitted by all participating execution nodes in the collaborative enforcement are obtained. Execution plan data typically includes information such as execution node identifiers, task content, enforcement resource identifiers, and execution time periods. For joint food safety inspection actions, the execution plan submitted by regional execution node A may include the enforcement resource identifier "PERSON-FS-0023" and the execution time "2023-05-10 09:00-12:00", while the execution plan submitted by regional execution node B may include the enforcement resource identifier "PERSON-FS-0023" and the execution time "2023-05-10 10:00-13:00". When parsing the execution plans to extract the enforcement resource identifiers and execution times, data parsing methods are used to structure the execution plans, converting execution plans of different formats into a unified standard data format. The resource occupancy table is generated by combining the law enforcement resource identifier extracted from the execution plan with the execution time. The table format is: resource identifier, occupancy time period, occupancy node, such as "PERSON-FS-0023, 2023-05-10 09:00-12:00, node A" and "PERSON-FS-0023, 2023-05-10 10:00-13:00, node B".

[0139] Execution time analysis is performed on the law enforcement resource identifiers in the resource occupancy table to check whether the occupancy time of each law enforcement resource identifier overlaps in different execution nodes. Time overlap judgment is based on the comparison of the start and end times of time periods. When two time periods overlap, it is determined to be a time overlap. For the aforementioned resource occupancy table, the law enforcement resource "PERSON-FS-0023" occupies "2023-05-10 09:00-12:00" and "2023-05-10 10:00-13:00" at node A and node B respectively, and there is a time overlap "2023-05-10 10:00-12:00". After identifying the law enforcement resource identifiers with conflicting execution times, a resource conflict list is generated. The list includes: conflicting resource identifier, conflicting time period, and involved nodes, such as "PERSON-FS-0023, 2023-05-10 10:00-12:00, node A / node B". Extract resource conflict items from the resource conflict list. Each resource conflict item contains a specific resource conflict situation, including detailed information about the conflicting resource, the specific time range of the conflict, and the execution nodes involved in the conflict.

[0140] Extract the hierarchical position of execution nodes in the collaborative path to obtain the hierarchical relationship of each execution node participating in collaborative law enforcement within the organizational structure. The hierarchical position of an execution node can be represented by a numerical value or a hierarchical identifier, such as node A being "regional level-2" and node B being "regional level-3". Construct priority rules based on the hierarchical position of the execution nodes. The priority rules are determined comprehensively based on factors such as hierarchical level, task urgency, and resource scarcity. Priority rules can be expressed as "higher-level nodes have priority in using resources" or "urgent tasks have priority in using resources," etc. Match the priority rules with resource conflict items to determine which execution node should be allocated the conflicting resource. For the aforementioned conflict items, if node A's hierarchical level is higher than node B's, then resource "PERSON-FS-0023" is preferentially allocated to node A during the conflict period; if task urgency B is higher than A's, then it is considered to be preferentially allocated to node B. Generate a resource allocation scheme based on the matching results, clarifying the allocation of each conflicting resource during the conflict period.

[0141] The enforcement resource identifiers in the execution plan are reallocated according to the resource allocation scheme, and the execution plans of execution nodes that have not been given priority are modified. Modification methods include: replacing enforcement resources, adjusting execution times, and task splitting. For the example above, if resource "PERSON-FS-0023" is assigned to node A, then resources need to be reallocated to node B or its execution time adjusted. When reallocating resources, alternative resources similar to the original resource can be retrieved from the enforcement resource database; when adjusting the execution time, the execution time can be moved to a later time period than the conflicting time period; when splitting tasks, the original task can be divided into multiple sub-tasks, which are then scheduled to be executed in different time periods. The reallocated execution plans are then integrated to generate a collaborative execution plan, which includes the final execution plan for each execution node, as well as the collaborative execution timeline and resource allocation table.

[0142] After the collaborative execution plan is generated, it is verified to check if resource conflicts still exist in the reassigned execution plan. If conflicts still exist, the above process must be repeated until all resource conflicts are eliminated. Once the plan is verified, it is sent to each execution node for execution. Upon receiving the collaborative execution plan, each execution node adjusts its execution schedule accordingly to ensure resource usage conforms to the allocation plan. If new circumstances arise during execution that prevent the original plan from being executed, the execution node must promptly report the issue and restart the collaborative plan generation process.

[0143] After the collaborative execution plan is completed, each execution node submits an execution result report, including execution status, resource usage, and existing problems. The execution result reports are summarized, and the effectiveness and problems of collaborative execution are analyzed to provide improvement suggestions for subsequent collaborative law enforcement. The analysis results are recorded in the collaborative law enforcement case library as experience accumulation for future similar collaborative law enforcement activities. After the collaborative law enforcement activity concludes, the use of law enforcement resources is evaluated to analyze resource utilization efficiency and allocation rationality, providing a basis for optimizing the law enforcement resource library.

[0144] In the process of conflict detection and resource reallocation, the complexity of actual law enforcement scenarios must be considered. For example, some law enforcement resources may be irreplaceable, some execution tasks may have strict time constraints, and some execution nodes may face special law enforcement environments. Therefore, resource allocation plans must be comprehensively considered in conjunction with these actual circumstances to ensure the feasibility of the plan and the effectiveness of law enforcement. An emergency resource allocation mechanism also needs to be established to deal with unforeseen circumstances during execution. This emergency allocation mechanism, based on pre-set contingency plans, clarifies the priority and procedures for resource scheduling in emergency situations, ensuring that critical law enforcement tasks are not affected by resource conflicts.

[0145] This invention aggregates the execution plans of each execution node, performs conflict detection on the law enforcement resource identifiers in the execution plans, and reallocates resources based on the hierarchical position of the execution nodes in the collaborative path, thereby achieving coordinated use and optimized allocation of law enforcement resources across levels. This invention effectively identifies and resolves spatiotemporal conflicts of law enforcement resources, improving the efficiency of law enforcement resource utilization and the degree of collaboration in law enforcement activities. Through hierarchical position analysis and the application of priority rules, it ensures resource support for key nodes and important tasks, enhancing the overall effectiveness of law enforcement activities. It establishes a standardized process for law enforcement resource allocation, reducing interference from human factors and improving the fairness and transparency of resource allocation.

[0146] like Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a cross-level collaborative integrated law enforcement business governance and decision support platform provided in an embodiment of the present invention. The platform includes:

[0147] The topology construction module is used to acquire law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and to construct a hierarchical topology structure based on the law enforcement entity identifiers;

[0148] The collaborative path generation module is used to associate and match law enforcement matter identifiers with nodes in the hierarchical topology, construct a set of associated nodes, retrieve connection paths between nodes in the set of associated nodes in the hierarchical topology, and select the connection path with the smallest hierarchical span as the collaborative path.

[0149] The node designation module is used to designate the highest-level node in the collaborative path as the decision node and the remaining nodes as execution nodes.

[0150] The task allocation module is used to extract the law enforcement rules corresponding to the law enforcement matter identifier. The decision-making node generates a decision plan based on the law enforcement rules, decomposes the decision plan into multiple execution tasks, and allocates multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path.

[0151] The execution plan generation module is used to enable each execution node to receive the assigned execution tasks, extract law enforcement resource identifiers from the preset law enforcement resource library, and generate an execution plan based on the law enforcement resource identifiers and decision-making schemes;

[0152] The collaborative solution generation module is used to summarize the execution plans of each execution node, perform conflict detection on the law enforcement resource identifiers in the execution plan, and when a resource conflict is detected, reallocate resources according to the hierarchical position of the execution node in the collaborative path to generate a collaborative execution plan.

[0153] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0154] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0155] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A cross-level collaborative method for integrated law enforcement governance and decision support, characterized in that: Includes the following steps: Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology based on the law enforcement entity identifiers; The law enforcement matter identifier is associated with the nodes of the hierarchical topology to construct a set of associated nodes. The connection paths between nodes in the set of associated nodes are retrieved in the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path. Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes; Extract the enforcement rules corresponding to the enforcement matters identifier, generate a decision plan based on the enforcement rules by the decision node, decompose the decision plan into multiple execution tasks, and assign multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path; Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the preset law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme; The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, the resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, and a collaborative execution plan is generated.

2. The method according to claim 1, characterized in that, Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and construct a hierarchical topology structure based on the law enforcement entity identifiers, including: Obtain law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and extract law enforcement business information from the law enforcement matter identifiers and organizational information from the law enforcement entity identifiers, respectively; The law enforcement business information is decomposed into main responsibilities, the law enforcement entity association information and law enforcement duty information are extracted, the responsibility boundaries of the law enforcement entity association information and law enforcement duty information are divided, and a law enforcement business mapping table is generated. Based on the responsibility boundaries in the law enforcement business mapping table, a law enforcement business network is constructed. Subordinate level information and business authority information are extracted from the organizational information. The subordinate level information and business authority information are combined to construct the organizational structure of the law enforcement entity. The law enforcement business network is imported into the law enforcement entity organizational structure. Based on the business permission information, the responsibility boundaries in the law enforcement business network are constrained, and a law enforcement entity association matrix is ​​generated. Extract the connection relationships of law enforcement entities from the law enforcement entity association matrix, impose hierarchical constraints on the connection relationships of law enforcement entities based on the hierarchical information, and construct a hierarchical topology structure.

3. The method according to claim 1, characterized in that, The law enforcement matter identifier is associated with nodes in the hierarchical topology to construct a set of associated nodes. Connection paths between nodes within this set are retrieved within the hierarchical topology, and the connection path with the smallest hierarchical span is selected as the collaborative path. Extract law enforcement business information from law enforcement matter identifiers, perform semantic analysis on the law enforcement business information to obtain business features, and construct a law enforcement business feature matrix from the business features; The law enforcement business feature matrix is ​​mapped to a vector space, and the business function information of the nodes in the hierarchical topology is mapped to a vector space. The matching degree between the law enforcement business feature matrix and the business function information in the vector space is calculated. Nodes with matching degrees exceeding a preset matching threshold are extracted, and a set of associated nodes is constructed. Traverse the connection relationships between nodes in the associated node set, obtain the connection paths between nodes in the associated node set, extract the level identifiers of adjacent nodes in the connection paths, calculate the level span of adjacent nodes, and sum the level spans of adjacent nodes to obtain the total level span of each connection path. Compare the overall hierarchical span of all connection paths and select the connection path with the smallest overall hierarchical span as the collaborative path.

4. The method according to claim 1, characterized in that, Designate the highest-level node in the collaborative path as the decision node, and designate the remaining nodes as execution nodes, including: Traverse all nodes in the collaborative path, extract the identification information of each node, parse the hierarchical code value and the functional type value from the identification information, and combine the hierarchical code value and the functional type value to construct a node attribute table; Read the hierarchical encoding value from the node attribute table, compare the hierarchical encoding values ​​of all nodes, and filter the node group with the highest hierarchical encoding value; Select nodes with the function type value of management function from the node group with the highest hierarchical coding value and designate them as decision nodes. Designate all nodes in the collaboration path except decision nodes as execution nodes.

5. The method according to claim 1, characterized in that, Extract the enforcement rules corresponding to the enforcement matter identifier, generate a decision plan based on the enforcement rules from the decision node, decompose the decision plan into multiple execution tasks, and assign the multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path, including: Extract the law enforcement element dimension table from the law enforcement matter identifier, parse the business attributes of the law enforcement matter identifier based on the law enforcement element dimension table, extract the law enforcement rules corresponding to the law enforcement matter identifier according to the business attributes, and generate law enforcement rule content containing execution conditions and execution constraints; The decision node loads the enforcement rules, constructs the execution conditions and constraints into an execution rule chain, generates an execution step sequence based on the execution rule chain, and generates a decision plan containing execution responsibilities and resource requirements based on the execution step sequence. The execution responsibilities and resource requirements in the decision-making plan are grouped and classified. The responsibility boundaries of the execution tasks are determined according to the grouping results. The execution tasks are divided based on the responsibility boundaries, and multiple execution tasks with execution requirements are generated. Extract execution node hierarchical attributes from the collaborative path, construct task matching rules based on execution node hierarchical attributes, perform adaptation calculations between the execution requirements of the execution tasks and the task matching rules, generate an allocation scheme for the execution tasks, and allocate the execution tasks to the corresponding execution nodes according to the allocation scheme.

6. The method according to claim 1, characterized in that, Each execution node receives the assigned execution task, extracts the law enforcement resource identifier from the pre-set law enforcement resource database, and generates an execution plan based on the law enforcement resource identifier and the decision-making scheme, including: Each execution node receives the assigned execution task, parses the execution task to obtain the task execution instruction, and decomposes the task execution instruction to obtain the resource requirement item; Resource attribute information and resource quantity information are extracted from resource demand items, and the resource attribute information and resource quantity information are combined to construct resource retrieval rules; Input the resource retrieval rules into the preset law enforcement resource database, and retrieve law enforcement resource identifiers that match the resource retrieval rules from the law enforcement resource database; Extract the execution rules from the decision-making scheme, match the execution rules with law enforcement resource identifiers, generate a resource allocation scheme, and generate a scheduling arrangement based on the resource allocation scheme; The scheduling and execution tasks are integrated to generate a sequence of execution steps. The sequence of execution steps is then arranged in sequence according to the execution rules to generate an execution plan.

7. The method according to claim 1, characterized in that, The execution plans of each execution node are aggregated, and conflict detection is performed on the law enforcement resource identifiers in the execution plans. When a resource conflict is detected, resources are reallocated according to the hierarchical position of the execution nodes in the collaborative path, generating a collaborative execution plan including: Summarize the execution plans of each execution node, parse the execution plans to extract law enforcement resource identifiers and execution times, and combine the law enforcement resource identifiers and execution times to generate a resource occupancy table; Analyze the execution time of law enforcement resource identifiers in the resource occupancy table, identify law enforcement resource identifiers with conflicting execution times, generate a resource conflict list, and extract resource conflict items from the resource conflict list; Extract the hierarchical position of the execution node in the collaborative path, construct priority rules based on the hierarchical position, match the priority rules with resource conflict items, and generate a resource allocation scheme; Based on the resource allocation plan, the law enforcement resource identifiers in the execution plan are reallocated, and the reallocated execution plans are integrated to generate a collaborative execution plan.

8. A cross-level integrated law enforcement business governance and decision support platform, used to implement the method described in any one of claims 1-7, characterized in that, The platform includes: The topology construction module is used to acquire law enforcement business data containing law enforcement matter identifiers and law enforcement entity identifiers, and to construct a hierarchical topology structure based on the law enforcement entity identifiers; The collaborative path generation module is used to associate and match law enforcement matter identifiers with nodes in the hierarchical topology, construct a set of associated nodes, retrieve connection paths between nodes in the set of associated nodes in the hierarchical topology, and select the connection path with the smallest hierarchical span as the collaborative path. The node designation module is used to designate the highest-level node in the collaborative path as the decision node and the remaining nodes as execution nodes. The task allocation module is used to extract the law enforcement rules corresponding to the law enforcement matter identifier. The decision-making node generates a decision plan based on the law enforcement rules, decomposes the decision plan into multiple execution tasks, and allocates multiple execution tasks to the corresponding execution nodes according to the hierarchical attributes of each execution node in the collaborative path. The execution plan generation module is used to enable each execution node to receive the assigned execution tasks, extract law enforcement resource identifiers from the preset law enforcement resource library, and generate an execution plan based on the law enforcement resource identifiers and decision-making schemes; The collaborative solution generation module is used to summarize the execution plans of each execution node, perform conflict detection on the law enforcement resource identifiers in the execution plan, and when a resource conflict is detected, reallocate resources according to the hierarchical position of the execution node in the collaborative path to generate a collaborative execution plan.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 7.

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