Environmental protection management intelligent collaborative management method based on data element flow

Abstract

The invention discloses an intelligent collaborative management method for environmental protection treatment based on data element flow, and the method comprises the following steps: S1, collecting environment original data, carrying out the structural processing and standardized conversion, generating a data element entry set, and carrying out the right confirmation processing; s2, establishing a data element circulation mechanism, and setting a data element circulation path; s3, constructing a state vector and a behavior gene chain, and calculating a corresponding task scheduling weight; s4, generating a governance strategy graph based on the state vector and the behavior gene chain; s5, performing deduction simulation on each governance path, performing effect evaluation, and screening out an optimal strategy path; and S6, distributing the governance tasks to the corresponding participants, and collecting governance response feedback data to update the state vectors. Based on the data element flow modeling method, an environmental protection treatment cooperation mechanism is constructed, strategy intelligent generation and closed-loop optimization are realized, and the method has the advantages of being accurate, efficient, flexible in response and high in self-adaption.

Description

Technical Field

[0001] The present invention relates to the field of environmental governance, and in particular to an intelligent collaborative management method for environmental governance based on the flow of data elements. Background Art

[0002] In recent years, with the development of information technology, especially the application of the Internet of Things, cloud computing, big data analysis and other means, environmental governance has gradually evolved towards data-driven, platform collaboration and intelligent decision-making. Environmental protection regulatory departments have deployed a large number of environmental monitoring equipment to collect various environmental raw data such as air, water, noise, and emissions; enterprises and third-party testing agencies have also actively participated in environmental data collection and governance plan implementation, and jointly built an environmental governance pattern with multi-source and multi-subject participation.

[0003] In existing technologies, mainstream environmental data management systems often adopt a centralized or department-based structure, with data stored in a dispersed manner across different information platforms. There is a lack of standard and unified data modeling and rights confirmation mechanisms, resulting in obvious barriers to data exchange between different entities and difficulty in achieving effective cross-platform and cross-institutional circulation. Although some places have tried to build a unified "environmental big data platform" or "ecological environment cloud" to aggregate multi-source data for analysis and supervision, its core is still limited to data collection and indicator display, and a fluid governance mechanism with "data elements" as the driving force of governance has not yet been formed. Traditional practices focus more on collection and storage, and ignore value release links such as data circulation, rights confirmation, and controllable use, resulting in a large amount of environmental data in a "sleeping" state in the actual governance process.

[0004] In addition, the existing environmental management system mostly uses static analysis in data application and cannot dynamically reflect the timeliness, reliability and responsiveness of the data. On the one hand, the credibility and applicability of the data are difficult to quantify; on the other hand, there is a lack of a systematic tracking mechanism for the use records, feedback results and governance effects of data in multiple governance tasks. This makes the data still exist as "isolated samples" in subsequent strategy formulation and cannot form a behavioral feedback loop. Even if some systems have data scoring or weight analysis functions, most of them are static preset weights, and lack a mechanism to dynamically generate scores based on data behavior trajectories and feedback, and cannot accurately evaluate the actual value of each type of data in environmental governance.

[0005] The existing environmental protection strategy formulation process generally uses expert experience, qualitative analysis or rule presets to construct governance paths. The strategy path lacks learning from historical execution effects and simulation and deduction of response performance in multiple environmental scenarios. Even if some simulation technologies are introduced, the model is relatively rough and often relies on single or average environmental conditions. It lacks dynamic scoring and strategy optimization mechanisms driven by path level and environmental status. At the task scheduling level, the existing system generally divides tasks by department or region, ignoring the comprehensive evaluation of data sources, data value, and the capabilities and response intentions of participating entities, resulting in inefficient task allocation and low resource utilization. There is even a phenomenon of "negative response" or "avoidance of execution" of the task execution entities to the governance strategy, affecting the governance effect.

[0006] In terms of multi-subject collaboration, the current data usage permission control in environmental protection governance is mainly concentrated at the access restriction level, lacking flexible compliance control and path-level usage audit capabilities. The compliance judgment, permission mapping and dynamic verification mechanisms of data in the circulation process are still relatively weak, making it difficult for regulatory authorities to track how data flows between multiple participants and to control the "extra-scope" use of data. At the same time, the feedback on governance results usually fails to be effectively closed-loop reflected in the policy model, resulting in the system being unable to automatically optimize path selection or identify failed strategies, thereby hindering the adaptive evolution capability of the governance system.

[0007] Based on the above status quo, the current environmental protection governance process still faces a series of technical bottlenecks and systematic deficiencies: First, data elements cannot be confirmed and circulate in an disorderly manner, and governance behaviors lack real data support; second, the strategy path construction and evaluation mechanism lags behind, and there is a lack of path response simulation and optimization models under multiple environmental conditions; third, task scheduling fails to be intelligently allocated based on data value and behavioral feedback, and the collaborative efficiency and execution accuracy are not high; fourth, data feedback has not formed a closed loop, and the system learning and self-optimization capabilities are insufficient, making it difficult to achieve long-term strategy evolution and continuous governance improvement. Summary of the Invention

[0008] One purpose of the present invention is to propose an intelligent collaborative management method for environmental protection governance based on the flow of data elements. The present invention is based on the data element flow modeling method to construct an environmental protection governance collaborative management mechanism with the participation of multiple subjects, comprehensively covering the entire process of data rights confirmation, path generation, strategy optimization and feedback update, and fully improving the efficiency of data circulation and the level of intelligent governance. By introducing state vectors and behavioral gene chains, the data value is quantified and task scheduling is assisted. Combined with the environment-driven graph evolution and intention recognition mechanism, high-precision and high-adaptability of strategy generation and execution closed-loop optimization are achieved, with significant advantages such as precision and efficiency, flexible response, and strong adaptability.

[0009] According to an embodiment of the present invention, an intelligent collaborative management method for environmental protection based on the flow of data elements includes the following steps:

[0010] S1. Collect raw environmental data, perform structured processing and standardized conversion on the raw environmental data, generate a set of data element entries, confirm the ownership of the set of data element entries, and add a unique identifier;

[0011] S2. Establish a data element circulation mechanism, set data element circulation paths, and configure permission control rules and compliance verification strategies;

[0012] S3. Construct a state vector and a behavior gene chain based on each data element entry in the data element entry set, score each data element entry based on the state vector, and calculate the corresponding task scheduling weight;

[0013] S4. Generate a governance strategy graph based on the state vector and behavior gene chain;

[0014] S5. Simulate each governance path in a virtual environment, evaluate its effectiveness in combination with environmental variables, and select the optimal strategy path from the governance strategy map;

[0015] S6. Distribute governance tasks to corresponding participating entities according to the preferred strategy path, collect governance response feedback data, and update the state vector.

[0016] Optionally, the environmental raw data includes air quality data, water quality detection data, noise monitoring data and emission record data.

[0017] Optionally, the S2 specifically includes:

[0018] S21. Establish a data element circulation mechanism and construct a data element circulation graph represented by a graph structure G = (V, E), where V represents a node, representing a participating entity, and E represents an edge, representing the circulation path of a data element entry. The participating entities include regulatory agencies, pollutant-discharging enterprises, third-party platforms, and data service providers;

[0019] S22. Configure permission control rules and establish permission control vectors for each data element entry:

[0020] A i =[r i ,s i ,d i ];

[0021] Among them, A i represents the permission control vector of the i-th data element entry, r i Indicates the read permission level, s i Indicates the sharing permission level, d iIndicates the use of range tags;

[0022] S23. Set up compliance verification strategies and perform compliance assessments on the circulation paths of each data element entry:

[0023]

[0024] Among them, C i represents the compliance judgment value of the i-th data element entry, Indicates that the logical "AND" operation is performed on the 1st to kth nodes in the path, r i Indicates the read permission level, R r (v j ) represents node v j The minimum read permission requirement, ∧ represents the logical "AND" operation, d i Indicates the use of range labels, R d (v j ) represents node v j The set of allowed usage scopes, P i Indicates the path label, R p Represents the system's predefined compliance path whitelist set. If C i =1, the path is considered compliant.

[0025] Optionally, the state vector includes a credibility factor, a feedback consistency factor, a prediction deviation factor and a response influence factor.

[0026] Optionally, the behavioral gene chain consists of a source feature segment, a governance participation segment, a feedback bias segment, and an associated propagation segment.

[0027] Optionally, the S3 specifically includes:

[0028] S31. Construct a state vector based on each data element entry in the data element entry set, wherein the state vector represents the quality and response characteristics of the data element entry in the environmental governance process:

[0029] V i =[q i ,c i ,e i ,r i ];

[0030] Among them, V i Represents the state vector of the i-th data element entry, q i Represents the credibility factor, reflecting the reliability of the data source, c i It represents the feedback consistency factor, which measures the consistency between the judgment of the historical data during the participation task and the governance feedback. iIt represents the prediction deviation factor, which reflects the error between the data and the model prediction value, r i represents the response impact factor, which indicates the sensitivity of the data to the results of the governance strategy selection;

[0031] S32. Construct a behavior gene chain for each data element entry. The behavior gene chain represents the behavior trajectory of the data in the historical governance task:

[0032] G i ={γ1,γ2,γ3,γ4};

[0033] Among them, G i represents the behavioral gene chain of the i-th data element entry, γ1 represents the source feature segment, which describes the data collection method and spatial source, γ2 represents the governance participation segment, which describes the historical strategy identification of the data participation, γ3 represents the feedback deviation segment, which reflects the residual performance of the data in the decision-making task, and γ4 represents the association propagation segment, which describes the influence propagation relationship between the data and the data element entry;

[0034] S33. Calculate a score value for each data element entry based on the state vector:

[0035]

[0036] Among them, S i Indicates the score value of the i-th data element entry, q i represents the credibility factor, r i represents the response impact factor, e i represents the prediction deviation factor, λ represents the deviation sensitivity coefficient, which is used to control the penalty amplitude of the error on the score; τ represents the prediction deviation threshold, e represents the base of the natural logarithm, ρ i represents the behavioral gene chain influence factor, which is calculated from the historical participation frequency and transmission intensity of fragments γ2 and γ4, c i represents the feedback consistency factor, θ represents the consistency enhancement index, which is used to adjust the enhancement intensity of the feedback consistency factor in the rating;

[0037] S34. Map the score value to the task scheduling weight.

[0038] Optionally, the S4 specifically includes:

[0039] S41. Obtain a state vector and a behavior gene chain for each data element entry in the data element entry set;

[0040] S42. Perform feature encoding on each segment in the behavioral gene chain, specifically including:

[0041] Use one-hot encoding on the source feature fragments to form the collection mode and regional position encoding vectors;

[0042] Count the number of participations, successes, and failures for each governance participation segment and normalize them to generate a frequency vector;

[0043] After calculating the average deviation and variance of the feedback deviation fragment, normalization is performed to generate the deviation feature vector;

[0044] Calculate the communication influence score of the associated communication segment and normalize it to generate the associated communication vector;

[0045] S43. Concatenate the state vector and the behavior gene chain encoding result to generate a fusion feature vector:

[0046] x i =[q i ,c i ,e i ,r i ]‖g 1i ‖g 2i ‖g 3i ‖g 4i ;

[0047] Among them, x i represents the fused feature vector of the i-th data element entry, ‖ represents the vector splicing operation, d represents the total dimension after splicing, g 1i Indicates the acquisition method and regional position encoding vector, g 2i represents the frequency vector, g 3i represents the deviation eigenvector, g 4i represents the associated propagation vector;

[0048] S44. Construct a strategy node with each fused feature vector to generate a strategy node set, and construct an edge set based on the similarity between the fused feature vectors, and simultaneously construct a graph adjacency matrix:

[0049]

[0050] Among them, a ij Represents the elements of the graph adjacency matrix, describing node n i With n j The similarity edge weight, ‖·‖ represents the Euclidean distance, σ represents the width control coefficient of the Gaussian kernel function, exp represents the natural exponential function, x i represents the fused feature vector of the i-th data element entry, x j represents the fused feature vector of the jth data element entry;

[0051] S45. Generate governance strategy graph G s =(N,A), where G s Represents the governance strategy graph, N represents the set of strategy nodes constructed by data element entries, A=[aij ] represents the graph adjacency matrix, describing the edge set.

[0052] Optionally, the S5 specifically includes:

[0053] S51, construct a virtual environment state set E = {e k}, where e k Represents the environmental state vector, describing the pollutant concentration, meteorological factors, geographic tags and governance history feedback parameters under different governance scenarios;

[0054] S52. For each environment state vector, perform node feature adjustment and graph structure reconstruction of the governance strategy graph to generate an environment-driven strategy graph, including:

[0055] Node feature adjustment: Based on the credibility factor and prediction deviation factor in the node's state vector, combined with the current environment state vector e k , calculate the adjusted factor:

[0056]

[0057] in, Represents node n i The credibility factor under the environment state vector, q i represents the credibility factor, α represents the credibility adjustment coefficient, ‖e k -x i ‖ represents the Euclidean distance between the environment state vector and the fusion feature vector, x i represents the fused feature vector, represents the adjusted forecast bias factor, e i represents the prediction deviation factor, λ represents the deviation amplification coefficient, e k represents the environment state vector;

[0058] Edge weight update: Based on the fused feature vector and the environment state vector, recalculate the edge weight of each pair of nodes under the environment state vector:

[0059]

[0060] in, Represents node n i With n j The edge weight under the environment state vector, x i and x j Represents node n i and n j The fusion feature vector, β represents the similarity weight factor of the environmental variables, η ij represents the historical average environment state vector of the node pair, ‖e k -η ij‖ represents the Euclidean distance between the current environment vector and the historical average environment state vector, σ represents the width control parameter of the Gaussian kernel function;

[0061] Edge structure pruning: remove edges whose edge weights are insufficient to support policy coordination in the current environment state:

[0062]

[0063] in, represents the set of edges whose weights are lower than the edge pruning threshold under the environment state vector, τ k Indicates that the environment state vector e k The edge pruning threshold set below;

[0064] New edge generation: for unconnected node pairs Calculate the new edge similarity score:

[0065]

[0066] in, represents the new edge similarity score of the node pair under the environment state vector, γ represents the fusion factor between node similarity and environment similarity, cos(x i ,x j ) represents node n i and n j The cosine similarity between the fused feature vectors, cos(e k ,η ij ) represents the cosine similarity between the environment state vector and the historical average environment state vector;

[0067] when When , a new edge is generated, δ represents the threshold for generating new edges;

[0068] S53. Extracting a set of policy paths based on the environment-driven policy graph, where each path in the set of policy paths represents a governance path, and each node in the governance path carries a fusion feature vector and a task scheduling weight;

[0069] S54. Under the environmental state vector, calculate the path response score and generate a weighted score:

[0070]

[0071] in, represents the weighted score of the path in the kth state, x jt represents the fusion feature vector, W represents the feature-environment interaction weight matrix, Sigmoid represents the activation function, and m represents the total number of path nodes. Represents node n jt Task scheduling weight;

[0072] S55. Take the average score of the path under all environmental state vectors:

[0073]

[0074] in, represents the average score of the path in all environmental states, n represents the number of environmental states, represents the weighted score of the path in the kth state;

[0075] S56: Set a screening threshold, and select paths with an average score greater than the screening threshold to obtain a preferred strategy path.

[0076] Optionally, the S6 specifically includes:

[0077] S61. Distribute the governance tasks to the corresponding participating entities according to the optimal strategy path of the task nodes;

[0078] S62. After each remediation task is completed, collect the remediation response feedback data submitted by the participating entities. The remediation response feedback data includes the actual disposal results, response time, execution deviation, task completion level, and changes in pollution factors. The remediation response feedback data is then structured and standardized.

[0079] S63. Update the state vector based on the standardized governance response feedback data;

[0080] S64. Re-inject the updated state vector into the governance strategy graph.

[0081] Optionally, the updated state vector includes a reassessment credibility factor, a feedback consistency factor, a prediction deviation factor, and a response impact factor.

[0082] The beneficial effects of the present invention are:

[0083] First of all, the present invention provides an intelligent collaborative management method for environmental protection governance based on the flow of data elements, which breaks through the key bottlenecks of data fragmentation, collaborative lag and inaccurate response in the existing environmental protection governance system, and establishes a complete closed-loop governance mechanism with data elements as the core driving force, which runs through data rights confirmation, intelligent analysis, strategy formulation, task distribution and feedback optimization. By constructing a set of data element entries with identity identification and permission tags, it realizes the fundamental transformation of environmental protection data from "passive collection" to "active rights confirmation and controllable circulation", and significantly improves the traceability, security and reuse value of environmental protection data.

[0084] Secondly, unlike the traditional model that mainly focuses on platform integration and static data archiving, the present invention introduces a data element circulation graph structure for the first time, marking the participating entities and circulation paths in the graph. Combined with the compliance policy verification mechanism, it can determine the authority adaptability and path legitimacy of the data in real time before it is used, thereby strengthening the data collaborative management and control capabilities across departments and platforms. On this basis, by constructing a state vector composed of credibility factors, feedback consistency factors, prediction deviation factors and response influence factors, and superimposing a behavioral gene chain, the system realizes dynamic scoring and behavioral trajectory modeling for each data element entry, providing a quantifiable data foundation for governance strategy map construction and path optimization.

[0085] In addition, the present invention also proposes a strategy path simulation and optimization mechanism for multi-environment state space, which performs predictive deduction on each governance path in a virtual environment, calculates the comprehensive score of the path by combining the response characteristics of the strategy node and the scheduling weight, and introduces a scoring threshold to realize the optimal path screening, which effectively avoids the limitations of traditional governance strategy formulation based on expert experience or fixed rules, and improves the scientificity and adaptability of strategy design. The strategy graph not only has the ability to construct paths driven by data, but also has the ability to evolve dynamically according to the environmental state, providing technical support for realizing intelligent and real-time governance response.

[0086] Finally, the present invention generates task scheduling weights based on the scoring results, and realizes the intelligent allocation of tasks among participating entities by modeling the response capabilities and historical behaviors of the node responsible entities. Especially after the introduction of the intention recognition model, the system not only considers the technical feasibility of the strategy path, but also introduces the behavioral preferences and response willingness of each responsible entity as feedback adjustment factors to re-optimize the final governance path, thereby improving the actual adaptability of strategy execution and avoiding the problem of strategy failure caused by inconsistency between task arrangement and entity execution intention. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0088] Figure 1 This is a flow chart of an intelligent collaborative management method for environmental protection based on the flow of data elements proposed by the present invention;

[0089] Figure 2 This is a schematic diagram of the governance strategy diagram construction process and feature vector splicing process of an intelligent collaborative management method for environmental governance based on data element flow proposed by the present invention;

[0090] Figure 3A logical structure diagram of the data state vector and behavior gene chain construction for an intelligent collaborative management method for environmental protection based on the flow of data elements proposed in the present invention. DETAILED DESCRIPTION

[0091] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0092] refer to Figure 1-3 , an intelligent collaborative management method for environmental protection based on the flow of data elements, comprising the following steps:

[0093] S1. Collect raw environmental data, perform structured processing and standardized conversion on the raw environmental data, generate a set of data element entries, confirm the ownership of the set of data element entries, and add a unique identifier;

[0094] S2. Establish a data element circulation mechanism, set data element circulation paths, and configure permission control rules and compliance verification strategies;

[0095] S3. Construct a state vector and a behavior gene chain based on each data element entry in the data element entry set, score each data element entry based on the state vector, and calculate the corresponding task scheduling weight;

[0096] S4. Generate a governance strategy graph based on the state vector and behavior gene chain;

[0097] S5. Simulate each governance path in a virtual environment, evaluate its effectiveness in combination with environmental variables, and select the optimal strategy path from the governance strategy map;

[0098] S6. Distribute governance tasks to corresponding participating entities according to the preferred strategy path, collect governance response feedback data, and update the state vector.

[0099] The present invention realizes an intelligent environmental protection governance process driven by data elements by constructing a full-process governance method including collection, title confirmation, circulation, modeling, deduction, distribution and feedback, thereby improving the efficiency, coordination and adaptive optimization capabilities of governance responses.

[0100] In this embodiment, the environmental raw data includes air quality data, water quality detection data, noise monitoring data and emission record data.

[0101] This invention expands the data input dimension by introducing multiple types of environmental original data sources such as air quality, water quality testing, noise monitoring and emission records, providing a high-quality foundation for the rich modeling of subsequent data element entries and the accuracy of strategy maps.

[0102] In this embodiment, S2 specifically includes:

[0103] S21. Establish a data element circulation mechanism and construct a data element circulation graph represented by a graph structure G = (V, E), where V represents a node, representing a participating entity, and E represents an edge, representing the circulation path of a data element entry. The participating entities include regulatory agencies, pollutant-discharging enterprises, third-party platforms, and data service providers;

[0104] S22. Configure permission control rules and establish permission control vectors for each data element entry:

[0105] A i =[r i ,s i ,d i ];

[0106] Among them, A i represents the permission control vector of the i-th data element entry, r i Indicates the read permission level, s i Indicates the sharing permission level, d i Indicates the use of range tags;

[0107] S23. Set up compliance verification strategies and perform compliance assessments on the circulation paths of each data element entry:

[0108]

[0109] Among them, C i represents the compliance judgment value of the i-th data element entry, Indicates that the logical "AND" operation is performed on the 1st to kth nodes in the path, r i Indicates the read permission level, R r (v j ) represents node v j The minimum read permission requirement, ∧ represents the logical "AND" operation, d i Indicates the use of range labels, R d (v j ) represents node v j The set of allowed usage scopes, P i Indicates the path label, R p Represents the system's predefined compliance path whitelist set. If C i =1, the path is considered compliant.

[0110] This invention constructs a data element circulation mechanism with graph structure as the core, combines authority control and compliance verification, ensures the safe circulation, compliant sharing and controllable use of environmental protection data among multiple subjects, and enhances the basic support capabilities of data collaborative governance.

[0111] In this embodiment, the state vector includes a credibility factor, a feedback consistency factor, a prediction deviation factor, and a response impact factor.

[0112] By defining key indicators such as credibility, feedback consistency, prediction deviation and response impact in the state vector, the present invention realizes a multi-dimensional evaluation of data quality and strategy participation capability, providing systematic support for precise scoring and task scheduling.

[0113] In this embodiment, the behavioral gene chain consists of a source feature segment, a governance participation segment, a feedback deviation segment, and an associated propagation segment.

[0114] The present invention utilizes source characteristics, participation records, feedback bias, and propagation influence to construct a behavioral gene chain, forming an evolutionary trajectory model of data element entries in governance tasks, and improving the system's ability to track data dynamic behavior and historical performance.

[0115] In this embodiment, S3 specifically includes:

[0116] S31. Construct a state vector based on each data element entry in the data element entry set, wherein the state vector represents the quality and response characteristics of the data element entry in the environmental governance process:

[0117] V i =[q i ,c i ,e i ,r i ];

[0118] Among them, V i Represents the state vector of the i-th data element entry, q i Represents the credibility factor, reflecting the reliability of the data source, c i It represents the feedback consistency factor, which measures the consistency between the judgment of the historical data during the participation task and the governance feedback. i It represents the prediction deviation factor, which reflects the error between the data and the model prediction value, r i It represents the response impact factor, which indicates the sensitivity of the data to the results of the governance strategy selection;

[0119] S32. Construct a behavior gene chain for each data element entry. The behavior gene chain represents the behavior trajectory of the data in the historical governance task:

[0120] G i ={γ1,γ2,γ3,γ4};

[0121] Among them, G irepresents the behavioral gene chain of the i-th data element entry, γ1 represents the source feature segment, which describes the data collection method and spatial source, γ2 represents the governance participation segment, which describes the historical strategy identification of the data participation, γ3 represents the feedback deviation segment, which reflects the residual performance of the data in the decision-making task, and γ4 represents the association propagation segment, which describes the influence propagation relationship between the data and the data element entry;

[0122] S33. Calculate a score value for each data element entry based on the state vector:

[0123]

[0124] Among them, S i Indicates the score value of the i-th data element entry, q i represents the credibility factor, r i represents the response impact factor, e i represents the prediction deviation factor, λ represents the deviation sensitivity coefficient, which is used to control the penalty amplitude of the error on the score; τ represents the prediction deviation threshold, e represents the base of the natural logarithm, ρ i represents the behavioral gene chain influence factor, which is calculated from the historical participation frequency and transmission intensity of fragments γ2 and γ4, c i represents the feedback consistency factor, θ represents the consistency enhancement index, which is used to adjust the enhancement intensity of the feedback consistency factor in the rating;

[0125] S34. Map the score value to the task scheduling weight.

[0126] In this embodiment, the S4 specifically includes:

[0127] S41. Obtain a state vector and a behavior gene chain for each data element entry in the data element entry set;

[0128] S42. Perform feature encoding on each segment in the behavioral gene chain, specifically including:

[0129] Use one-hot encoding on the source feature fragments to form the collection mode and regional position encoding vectors;

[0130] Count the number of participations, successes, and failures for each governance participation segment and normalize them to generate a frequency vector;

[0131] After calculating the average deviation and variance of the feedback deviation fragment, normalization is performed to generate the deviation feature vector;

[0132] Calculate the communication influence score of the associated communication segment and normalize it to generate the associated communication vector;

[0133] S43. Concatenate the state vector and the behavior gene chain encoding result to generate a fusion feature vector:

[0134] x i =[q i ,c i ,e i ,r i ]‖g 1i ‖g 2i ‖g 3i ‖g 4i ;

[0135] Among them, x i represents the fused feature vector of the i-th data element entry, ‖ represents the vector splicing operation, d represents the total dimension after splicing, g 1i Indicates the acquisition method and regional position encoding vector, g 2i represents the frequency vector, g 3i represents the deviation eigenvector, g 4i represents the associated propagation vector;

[0136] S44. Construct a strategy node with each fused feature vector to generate a strategy node set, and construct an edge set based on the similarity between the fused feature vectors, and simultaneously construct a graph adjacency matrix:

[0137]

[0138] Among them, a ij Represents the elements of the graph adjacency matrix, describing node n i With n j The similarity edge weight, ‖·‖ represents the Euclidean distance, σ represents the width control coefficient of the Gaussian kernel function, exp represents the natural exponential function, x i represents the fused feature vector of the i-th data element entry, x j represents the fused feature vector of the jth data element entry;

[0139] S45. Generate governance strategy graph G s =(N,A), where G s Represents the governance strategy graph, N represents the set of strategy nodes constructed by data element entries, A=[a ij ] represents the graph adjacency matrix, describing the edge set.

[0140] The present invention extracts multi-source features and constructs a strategy node graph by fusing state vectors and behavior gene chains, and introduces a Gaussian kernel similarity edge weight modeling mechanism into the strategy graph, effectively improving the accuracy and controllability of strategy path generation.

[0141] In this embodiment, the S5 specifically includes:

[0142] S51, construct a virtual environment state set E = {e k}, where ek Represents the environmental state vector, describing the pollutant concentration, meteorological factors, geographic tags and governance history feedback parameters under different governance scenarios;

[0143] S52. For each environment state vector, perform node feature adjustment and graph structure reconstruction of the governance strategy graph to generate an environment-driven strategy graph, including:

[0144] Node feature adjustment: Based on the credibility factor and prediction deviation factor in the node's state vector, combined with the current environment state vector e k , calculate the adjusted factor:

[0145]

[0146] in, Represents node n i The credibility factor under the environment state vector, q i represents the credibility factor, α represents the credibility adjustment coefficient, ‖e k -x i ‖ represents the Euclidean distance between the environment state vector and the fusion feature vector, x i represents the fused feature vector, represents the adjusted forecast bias factor, e i represents the prediction deviation factor, λ represents the deviation amplification coefficient, e k represents the environment state vector;

[0147] Edge weight update: Based on the fused feature vector and the environment state vector, recalculate the edge weight of each pair of nodes under the environment state vector:

[0148]

[0149] in, Represents node n i With n j The edge weight under the environment state vector, x i and x j Represents node n i and n j The fusion feature vector, β represents the similarity weight factor of the environmental variables, η ij represents the historical average environment state vector of the node pair, ‖e k -η ij ‖ represents the Euclidean distance between the current environment vector and the historical average environment state vector, σ represents the width control parameter of the Gaussian kernel function;

[0150] Edge structure pruning: remove edges whose edge weights are insufficient to support policy coordination in the current environment state:

[0151]

[0152] in, represents the set of edges whose weights are lower than the edge pruning threshold under the environment state vector, τ k Indicates that the environment state vector e k The edge pruning threshold set below;

[0153] New edge generation: for unconnected node pairs Calculate the new edge similarity score:

[0154]

[0155] in, represents the new edge similarity score of the node pair under the environment state vector, γ represents the fusion factor between node similarity and environment similarity, cos(x i ,x j ) represents node n i and n j The cosine similarity between the fused feature vectors, cos(e k ,η ij ) represents the cosine similarity between the environment state vector and the historical average environment state vector;

[0156] when When , a new edge is generated, δ represents the threshold for generating new edges;

[0157] S53. Extracting a set of policy paths based on the environment-driven policy graph, where each path in the set of policy paths represents a governance path, and each node in the governance path carries a fusion feature vector and a task scheduling weight;

[0158] S54. Under the environmental state vector, calculate the path response score and generate a weighted score:

[0159]

[0160] in, represents the weighted score of the path in the kth state, x jt represents the fusion feature vector, W represents the feature-environment interaction weight matrix, Sigmoid represents the activation function, and m represents the total number of path nodes. Represents node n jt Task scheduling weight;

[0161] S55. Take the average score of the path under all environmental state vectors:

[0162]

[0163] in, represents the average score of the path in all environmental states, n represents the number of environmental states, represents the weighted score of the path in the kth state;

[0164] S56: Set a screening threshold, and select paths with an average score greater than the screening threshold to obtain a preferred strategy path.

[0165] The present invention introduces a path simulation and scoring method under multiple environmental conditions, which improves the adaptability, stability and prediction accuracy of the governance path in complex environments through node feature adjustment, edge structure reconstruction and strategic path screening.

[0166] In this embodiment, S6 specifically includes:

[0167] S61. Distribute the governance tasks to the corresponding participating entities according to the optimal strategy path of the task nodes;

[0168] S62. After each remediation task is completed, collect the remediation response feedback data submitted by the participating entities. The remediation response feedback data includes the actual disposal results, response time, execution deviation, task completion level, and changes in pollution factors. The remediation response feedback data is then structured and standardized.

[0169] S63. Update the state vector based on the standardized governance response feedback data;

[0170] S64. Re-inject the updated state vector into the governance strategy graph.

[0171] The present invention constructs an intelligent task scheduling mechanism based on the optimal strategy path, accurately distributes tasks to responsible entities, and collects governance response feedback data, realizing full-chain closed-loop management and control from strategy design to execution supervision.

[0172] In this embodiment, the updated state vector includes a re-evaluation credibility factor, a feedback consistency factor, a prediction deviation factor, and a response impact factor.

[0173] Example 1:

[0174] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the scenario of joint air pollution control in a certain industrial park. This area is a heavy chemical industry-intensive area with a relatively high annual average PM2.5 concentration, multiple emission entities and complex pollution sources. Conventional control models have typical problems such as data fragmentation, delayed response, and unclear strategy effects.

[0175] In actual application, the environmental monitoring platform first deploys environmental monitoring terminals in conjunction with five emission enterprises, two ground monitoring stations, and one third-party monitoring agency in the park. These terminals collect air quality data regularly and simultaneously access the VOCs and particulate matter automatic monitoring data from the enterprise emission systems. The data sampling period is 5 minutes. After all raw data is uploaded to the system of the present invention, it automatically enters the structured processing and standardized conversion process, generates a set of data element entries in a unified format, and binds a unique identifier and ownership entity to each piece of data. The system confirms the ownership of each piece of data based on the time, space, device ID, and source entity contained in the data entry, establishes permission control rules, and then automatically constructs a state vector and behavior gene chain for each piece of data based on historical participation behavior, feedback deviation, and strategy contribution value.

[0176] After integrating the above factors, the system generates policy nodes for all data entries and constructs edge sets based on feature similarity to form an initial governance strategy graph. Subsequently, the system simulates a typical winter heavy pollution weather scenario in Taizhou City, loading real-time meteorological parameters and pollution background, dynamically simulating the governance strategy graph, and optimizing it based on the path response score. By scoring each node in the path and identifying the intention of the responsible party, a highly matched strategy path is ultimately formed. After the governance task is issued, the system collects feedback data in real time and conducts closed-loop tracking of the execution of each task. After the governance is completed, the system automatically uses the feedback results to update the state vector and reconstruct the behavior gene chain to generate a new round of strategy graphs, realizing intelligent governance self-evolution.

[0177] Table 1 Comparison of the treatment effects of the method of the present invention and the traditional method

[0178]

[0179] In terms of pollutant concentration control, within 24 hours before and after treatment, the average reduction of PM2.5 was increased from 42% of the traditional method to 55.2%. After 24 hours, the concentration was reduced from 94μg / m 3 Further reduced to 73 μg / m 3 , the decrease rate reached 22.3%. Other pollutants such as NO2 and PM10 also showed faster response speed and more obvious numerical decrease, which fully demonstrated that the present invention has stronger control ability in the coordinated control of multiple pollution factors, especially within 3 hours after control, the PM2.5 concentration can be reduced from 163μg / m 3 Down to 109 μg / m 3 , which fully demonstrates the value of strategy path simulation and task scheduling accuracy.

[0180] In terms of response efficiency, the data scoring mechanism and task scheduling weight mechanism introduced in the present invention significantly shorten the task response delay. The average task response time in traditional governance is 8.4 minutes, while the present invention compresses the average response time to 3.1 minutes through scoring guidance and node matching optimization, and the response speed is increased by nearly 63%. In addition, the slowest response time is also reduced from 15.2 minutes to 6.4 minutes, indicating that the response performance of nodes with higher scheduling weights in the governance strategy path is stable, and the system has stronger anti-fluctuation capabilities.

[0181] In terms of governance execution quality, this invention automatically adjusts the strategic path for nodes that lack execution enthusiasm by integrating intent recognition and behavior trajectory evaluation, thereby increasing the task completion rate from the traditional 82% to 96.5%, an increase of 14.5 percentage points. In terms of the participation rate of high-value data elements, it has jumped from the original 39% to 78%, fully demonstrating that the system is highly effective in data value identification and scheduling.

[0182] In terms of intelligent scheduling, through the integration of node-level scoring, multi-environment path simulation and subject intention recognition mechanism, the success rate of enterprise subject matching has been increased from 73% of the traditional system to 93%. The system automatically replaced nodes with poor historical response willingness 7 times, effectively avoiding the execution interruption of the strategy path due to insufficient response from the participants, and improving the stability of strategy implementation and the rationality of matching.

[0183] In terms of system operation efficiency, the data processing efficiency of the present invention has almost doubled, from the traditional 620 items per minute to 1190 items per minute, ensuring real-time flow and scoring in monitoring scenarios with intensive data frequency. At the same time, the system supports 5.4 path simulation and scoring cycles per minute, combined with the automatic update mechanism of the state vector, to achieve rapid self-evolution and feedback learning of the strategy map. The system has good scalability and continuous optimization capabilities.

[0184] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent collaborative management method for environmental protection based on the flow of data elements, characterized by: The steps include: S1. Collect raw environmental data, perform structured processing and standardized conversion on the raw environmental data, generate a set of data element entries, confirm the ownership of the set of data element entries, and add a unique identifier; S2. Establish a data element circulation mechanism, set data element circulation paths, and configure permission control rules and compliance verification strategies; S3. Construct a state vector and a behavior gene chain based on each data element entry in the data element entry set, score each data element entry based on the state vector, and calculate the corresponding task scheduling weight; S4. Generate a governance strategy graph based on the state vector and behavior gene chain; S5. Simulate each governance path in a virtual environment, evaluate its effectiveness in combination with environmental variables, and select the optimal strategy path from the governance strategy map; S6. Distribute governance tasks to corresponding participating entities according to the preferred strategy path, collect governance response feedback data, and update the state vector.

2. The method for intelligent collaborative management of environmental protection based on the flow of data elements according to claim 1 is characterized in that: The environmental raw data includes air quality data, water quality detection data, noise monitoring data and emission record data.

3. The intelligent collaborative management method for environmental protection based on data element flow according to claim 1 is characterized in that: The S2 specifically includes: S21. Establish a data element circulation mechanism and construct a data element circulation graph represented by a graph structure G = (V, E), where V represents a node, representing a participating entity, and E represents an edge, representing the circulation path of a data element entry. The participating entities include regulatory agencies, pollutant-discharging enterprises, third-party platforms, and data service providers; S22. Configure permission control rules and establish a permission control vector for each data element entry; S23. Set up compliance verification strategies and perform compliance judgment on the circulation path of each data element entry.

4. The method for intelligent collaborative management of environmental protection based on the flow of data elements according to claim 1 is characterized in that: The state vector includes a credibility factor, a feedback consistency factor, a prediction deviation factor and a response influence factor.

5. The method for intelligent collaborative management of environmental protection based on data element flow according to claim 1 is characterized in that: The behavioral gene chain consists of source feature fragments, governance participation fragments, feedback bias fragments and associated propagation fragments.

6. The method for intelligent collaborative management of environmental protection based on the flow of data elements according to claim 1 is characterized in that: The S3 specifically includes: S31. Constructing a state vector based on each data element entry in the data element entry set, wherein the state vector represents the quality and response characteristics of the data element entry in the environmental governance process; S32. Construct a behavior gene chain for each data element entry, where the behavior gene chain represents the behavior trajectory of the data in historical governance tasks; S33. Calculate a score value for each data element entry based on the state vector: Among them, S i Indicates the score value of the i-th data element entry, q i represents the credibility factor, r i represents the response impact factor, e i represents the prediction deviation factor, λ represents the deviation sensitivity coefficient, which is used to control the penalty amplitude of the error on the score; τ represents the prediction deviation threshold, e represents the base of the natural logarithm, ρ i represents the behavioral gene chain influence factor, which is calculated from the historical participation frequency and transmission intensity of fragments γ2 and γ4, c i represents the feedback consistency factor, θ represents the consistency enhancement index, which is used to adjust the enhancement intensity of the feedback consistency factor in the rating; S34. Map the score value to the task scheduling weight.

7. The method for intelligent collaborative management of environmental protection based on the flow of data elements according to claim 1 is characterized in that: The S4 specifically includes: S41. Obtain a state vector and a behavior gene chain for each data element entry in the data element entry set; S42. Perform feature encoding on each segment in the behavioral gene chain, specifically including: Use one-hot encoding on the source feature fragments to form the collection mode and regional position encoding vectors; Count the number of participations, successes, and failures for each governance participation segment and normalize them to generate a frequency vector; After calculating the average deviation and variance of the feedback deviation fragment, normalization is performed to generate the deviation feature vector; Calculate the communication influence score of the associated communication segment and normalize it to generate the associated communication vector; S43, concatenating the state vector with the behavior gene chain encoding result to generate a fusion feature vector; S44, constructing a strategy node with each fused feature vector to generate a strategy node set, and constructing an edge set based on the similarity between the fused feature vectors, and constructing a graph adjacency matrix at the same time; S45. Generate governance strategy graph G s =(N,A), where G s Represents the governance strategy graph, N represents the set of strategy nodes constructed by data element entries, A=[a ij ] represents the graph adjacency matrix, describing the edge set.

8. The method for intelligent collaborative management of environmental protection based on the flow of data elements according to claim 1 is characterized in that: The S5 specifically includes: S51, construct a virtual environment state set E = {e k }, where e k Represents the environmental state vector, describing the pollutant concentration, meteorological factors, geographic tags and governance history feedback parameters under different governance scenarios; S52. For each environment state vector, perform node feature adjustment and graph structure reconstruction of the governance strategy graph to generate an environment-driven strategy graph, including: Node feature adjustment: Based on the credibility factor and prediction deviation factor in the node's state vector, combined with the current environment state vector e k , calculate the adjusted factor: in, Represents node n i The credibility factor under the environment state vector, q i represents the credibility factor, α represents the credibility adjustment coefficient, ‖e k -x i ‖ represents the Euclidean distance between the environment state vector and the fusion feature vector, x i represents the fused feature vector, represents the adjusted forecast bias factor, e i represents the prediction deviation factor, λ represents the deviation amplification coefficient, e k represents the environment state vector; Edge weight update: Based on the fused feature vector and the environment state vector, recalculate the edge weight of each pair of nodes under the environment state vector: in, Represents node n i With n j The edge weight under the environment state vector, x i and x j Represents node n i and n j The fusion feature vector, β represents the similarity weight factor of the environmental variables, η ij represents the historical average environment state vector of the node pair, ‖e k -η ij ‖ represents the Euclidean distance between the current environment vector and the historical average environment state vector, σ represents the width control parameter of the Gaussian kernel function; Edge structure pruning: remove edges whose edge weights are insufficient to support policy coordination in the current environment; New edge generation: for unconnected node pairs Calculate the new edge similarity score: in, represents the new edge similarity score of the node pair under the environment state vector, γ represents the fusion factor between node similarity and environment similarity, cos(x i ,x j ) represents node n i and n j The cosine similarity between the fused feature vectors, cos(e k ,η ij ) represents the cosine similarity between the environment state vector and the historical average environment state vector; when When , a new edge is generated, δ represents the threshold for generating new edges; S53. Extracting a set of policy paths based on the environment-driven policy graph, where each path in the set of policy paths represents a governance path, and each node in the governance path carries a fusion feature vector and a task scheduling weight; S54. Calculate the path response score under the environmental state vector and generate a weighted score; S55, taking the average score of the path under all environmental state vectors; S56: Set a screening threshold, and select paths with an average score greater than the screening threshold to obtain a preferred strategy path.

9. The method for intelligent collaborative management of environmental protection based on data element flow according to claim 1 is characterized in that: The S6 specifically includes: S61. Distribute the governance tasks to the corresponding participating entities according to the optimal strategy path of the task nodes; S62. After each remediation task is completed, collect the remediation response feedback data submitted by the participating entities. The remediation response feedback data includes the actual disposal results, response time, execution deviation, task completion level, and changes in pollution factors. The remediation response feedback data is then structured and standardized. S63. Update the state vector based on the standardized governance response feedback data; S64. Re-inject the updated state vector into the governance strategy graph.

10. The method for intelligent collaborative management of environmental protection based on data element flow according to claim 9 is characterized in that: The updated state vector includes a reassessment credibility factor, a feedback consistency factor, a prediction deviation factor, and a response influence factor.

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