Multi-level collaborative planning system and resource allocation method for EOD project

Abstract

The invention discloses an EOD project-oriented multi-level collaborative planning system and a resource allocation method, and relates to the technical field of project management and resource scheduling optimization. The method comprises the following steps: carrying out credible alignment and encrypted aggregation on heterogeneous data of a government chain, an enterprise chain and a community chain through a block chain, and generating a dynamic heterogeneous graph containing government task nodes and industrial development nodes; a government, enterprise and community three-party asymmetric game model is constructed, contribution degrees of all parties are calculated, and priority weights are determined. Through a gating time sequence attention mechanism, a node state is predicted, a coupling influence value of a governance efficiency attenuation factor and an industrial income fluctuation factor is calculated, a near-end strategy optimization algorithm is combined, and the investment proportion of funds, equipment and human resources is dynamically adjusted by taking comprehensive income maximization as a target. A disturbance sample is simulated by using a Monte Carlo method, the robustness of a priority weight is evaluated, a resource configuration strategy is formed, and the node state of the heterogeneous graph is iteratively updated in real time, so that resource configuration is optimized and the system cooperation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of project management and resource scheduling optimization, and in particular to a multi-level collaborative planning system and resource allocation method for EOD projects. Background Art

[0002] With the accelerating pace of urbanization and the growing conflict between the ecological and environmental landscape, the traditional extensive development model can no longer meet the dual demands of ecological protection and economic development. Large-scale ecological projects such as abandoned mine restoration and watershed management face challenges such as high governance costs and long payback periods. Relying solely on government funding or market-based single-industry development cannot achieve sustainable operation. Against this backdrop, the EOD model, by deeply integrating ecological governance with related industries to form a closed-loop mechanism whereby industrial profits feed back into ecological restoration, has become a key path to addressing the conflict between ecological governance funding gaps and sustainable industrial development. However, EOD projects involve complex issues such as multi-stakeholder collaboration, cross-departmental resource allocation, and dynamic environmental response, necessitating the establishment of a scientific and intelligent resource allocation system.

[0003] Currently, existing EOD projects have the following deficiencies in collaborative planning and resource allocation: Traditional models often use static weight allocation mechanisms and rely on manual experience to determine the correlation between governance tasks and industrial development. These models are unable to dynamically capture the natural decay of ecological restoration efficiency over time, nor are they able to quantify the feedback effects of industrial revenue fluctuations on resource allocation. Cross-departmental data integration remains at the offline processing stage based on centralized databases. The heterogeneity and real-time nature of data from governments, businesses, and communities are in stark contrast. Manual cleaning and alignment processes can easily lead to data tampering risks, reducing the credibility of decision-making. Resource allocation strategies often employ linear programming methods, which are unable to predict the exponential amplification effect of governance lags on industrial costs. They also struggle to verify the strategy's anti-interference capabilities through dynamic simulation, ultimately leading to the cumulative risks of resource mismatch, project delays, and revenue losses. Summary of the Invention

[0004] In view of the deficiencies of the existing technology, the present invention provides a multi-level collaborative planning system and resource allocation method for EOD projects, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-level collaborative planning system for EOD projects, including the following modules: a multi-source data dynamic fusion module, a multi-level collaborative decision-making module, and a flexible resource scheduling module; the multi-source data dynamic fusion module is used to perform trusted alignment and encrypted aggregation of heterogeneous data of government chains, enterprise chains, and community chains through blockchain, and generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes; the multi-level collaborative decision-making module is used to construct an asymmetric game model of the government, enterprises, and communities based on the dynamic heterogeneous graph, calculate the contribution of each party through the dual-dimensional discount factor of time and space, and generate the priority weights of governance tasks and industrial development; the flexible resource scheduling module is used to simulate multiple rounds of disturbance samples through the Monte Carlo method, evaluate the robustness of priority weights under uncertainty conditions, and generate a resource allocation strategy matrix.

[0006] Furthermore, the specific process of trustfully aligning and cryptographically aggregating heterogeneous data of government chains, enterprise chains, and community chains through blockchain to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes is as follows: cross-chain trusted verification of the ecological supervision data of the government chain, the engineering implementation data of the enterprise chain, and the environmental feedback data of the community chain is performed through blockchain, and data conflict items are eliminated through zero-knowledge proof; the desensitized multi-source data is aggregated through the homomorphic encryption algorithm to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes, in which the nodes are associated with the ecological sensitivity of the governance area and the spatiotemporal attributes of the industrial development compliance window.

[0007] Furthermore, based on the dynamic heterogeneous graph, the specific process of constructing an asymmetric game model among the government, enterprises, and communities is as follows: based on the geographical proximity between the nodes in the dynamic heterogeneous graph, the spatial correlation strength between the governance area and the industrial development nodes is quantified; combining the ecological restoration cycle with the time lag of industrial benefit release, a time decay function is constructed; with government supervision costs, enterprise investment risks, and community ecological demands as game branches, the contributions of the three parties are calculated through the Shapley value algorithm, and a set of priority weights of governance tasks and industrial development is output.

[0008] Furthermore, the Monte Carlo method is used to simulate multiple rounds of disturbance samples to evaluate the robustness of priority weights under uncertainty conditions. The specific process of generating a resource allocation strategy matrix is ​​as follows: based on the temporal causal graph network, the critical path dependencies between governance tasks and industrial development nodes are identified, and the probability of the cumulative impact of ecological restoration lags on industrial development costs is quantified; random disturbance samples with sudden changes in environmental parameters are injected through the Monte Carlo method to verify the stability threshold of priority weights under uncertainty conditions; combined with the restoration efficiency attenuation trend of dynamic heterogeneous graph nodes and the prediction of industrial revenue fluctuations, cross-stage resource allocation rules and risk response triggering mechanisms are output.

[0009] The resource allocation method for EOD projects includes the following steps: S1. According to the priority weights of governance tasks and industrial development, the gated temporal attention mechanism is used to perform temporal prediction on the node states of the heterogeneous graph, and the coupling impact value of the governance efficiency attenuation factor and the industrial income fluctuation factor is calculated; S2. The coupling impact value is input into the resource allocation strategy matrix, and through the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, the investment ratio of funds, equipment and human resources in the dynamic heterogeneous graph in governance tasks and industrial development is dynamically adjusted; S3. According to the resource allocation strategy matrix, the node states of the dynamic heterogeneous graph are iteratively updated, and the temporal properties of the governance tasks and industrial development nodes are corrected in real time.

[0010] Furthermore, according to the priority weights of governance tasks and industrial development, the specific process of temporal prediction of heterogeneous graph node states through the gated temporal attention mechanism is as follows: extract the dynamic change characteristics of environmental sensor data of governance task nodes and the temporal fluctuation patterns of real-time operation indicators of industrial development nodes; capture the cross-cycle lagged correlation between governance efficiency attenuation and industrial revenue fluctuations through a sliding window; and use a gating mechanism to screen high-confidence temporal features to eliminate the impact of occasional interference events.

[0011] Furthermore, the specific process of calculating the coupling impact value of the governance efficiency attenuation factor and the industrial income fluctuation factor is as follows: integrating historical governance compliance rate data and the satisfaction decline rate of community feedback to construct a dynamic model of governance efficiency attenuation; predicting the cyclical characteristics of income fluctuations based on the real-time operation data of industrial development nodes, introducing the environmental governance efficiency coefficient and the gain coefficient of technological iteration, and weightedly aggregating the spatiotemporal coupling impact value of governance and industry.

[0012] Furthermore, through the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, the specific process of dynamically adjusting the investment ratio of funds, equipment and human resources in governance tasks and industrial development in the dynamic heterogeneous graph is as follows: define a multidimensional state space including the governance task progress deviation rate, industrial benefit fluctuation amplitude, and community consensus index, and extract the environmental governance efficiency attenuation parameters and industrial operation indicator change characteristics based on the real-time node status data of the dynamic heterogeneous graph; design an action space based on the cross-chain resource transfer instructions and priority weights of the blockchain smart contract to dynamically correct the threshold, and generate the fund transfer ratio, equipment scheduling path and human resource allocation plan; construct a composite reward function that integrates the environmental governance efficiency improvement rate, industrial net present value growth rate, and community conflict event triggering frequency, optimize the network parameters through the policy gradient update mechanism, drive the resource allocation strategy to converge towards the direction of maximizing comprehensive benefits, and adjust the balance coefficient of the strategy in real time according to the changes in the temporal properties of the dynamic heterogeneous graph to ensure the robustness and convergence efficiency of the algorithm in complex scenarios.

[0013] Furthermore, according to the resource allocation strategy matrix, the node status of the dynamic heterogeneous graph is iteratively updated, and the specific process of real-time correction of the timing attributes of the governance tasks and industrial development nodes is as follows: when resource allocation causes the governance progress or industrial benefits to deviate from the preset tolerance range, the blockchain smart contract is triggered to automatically and synchronously update the node status of the dynamic heterogeneous graph; the cycle compression rate of the governance task and the income elasticity coefficient of the industrial development are reversely corrected according to the real-time environmental sensor data and industrial operation indicators; the contribution weights of the three-party data are recalculated through the Shapley value algorithm to verify the global stability of the resource allocation strategy.

[0014] The present invention has the following beneficial effects:

[0015] (1) A multi-level collaborative planning system for EOD projects dynamically integrates multi-source data and uses blockchain technology to align and encrypt heterogeneous data from government chains, enterprise chains, and community chains to ensure data security and reliability. At the same time, an asymmetric game model among the three parties of government, enterprise, and community built on a dynamic heterogeneous graph can scientifically calculate the contribution of each party, generate priority weights, and reasonably allocate resources and decision weights. Through the elastic resource scheduling module combined with the perturbation simulation of the Monte Carlo method, the robustness of the priority weights is evaluated to ensure that resource allocation under uncertain conditions can effectively respond to various external changes, thereby improving the implementation efficiency and governance effect of the EOD project.

[0016] (2) The resource allocation method for EOD projects uses a gated temporal attention mechanism to predict the state of heterogeneous graph nodes in a temporal manner, thereby accurately calculating the coupling impact value of governance efficiency decay and industrial revenue fluctuations, providing a basis for resource allocation. The proximal strategy optimization algorithm is used to find a balance point that maximizes the comprehensive benefits between the interests of multiple parties in governance tasks and industrial development, dynamically adjust the resource input ratio, and ensure the optimal allocation of resources. By iteratively updating the resource allocation strategy matrix, the temporal attributes of governance tasks and industrial development are corrected in real time, further improving the flexibility and stability of project implementation, and enhancing the adaptability and long-term sustainability of the system.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the multi-level collaborative planning system for EOD projects of the present invention.

[0019] Figure 2 This is a flow chart of the resource configuration method for EOD projects according to the present invention. DETAILED DESCRIPTION

[0020] The embodiment of the present application solves the problem of the lack of flexibility and accuracy of traditional resource allocation methods in dealing with complex and changing environments and multi-party interest games through a multi-level collaborative planning system and resource allocation method for EOD projects. By introducing dynamic fusion of multi-source data and blockchain technology to ensure the credibility and security of data, combined with the asymmetric game model between government, enterprises and communities, it can accurately calculate the contribution of each party and generate reasonable priority weights to optimize the resource allocation of governance tasks and industrial development. At the same time, the gated temporal attention mechanism and Monte Carlo method are used to model temporal prediction and uncertainty disturbances, ensuring the robustness and effectiveness of resource allocation strategies under complex and uncertain conditions, thereby improving the comprehensive benefits and implementation efficiency of EOD projects in the process of governance and industrial development.

[0021] The overall idea of ​​the solution in the embodiments of this application is as follows:

[0022] This multi-level collaborative planning system and resource allocation method for EOD projects provides an intelligent and efficient solution to the complex data, competitive advantages, and uncertainty inherent in governance tasks and industrial development. First, through a dynamic multi-source data fusion module, blockchain technology is leveraged to align and cryptographically aggregate heterogeneous data from government, enterprise, and community chains, generating a dynamic heterogeneous graph to ensure data security and consistency. Then, based on this heterogeneous graph, an asymmetric game model among the government, enterprises, and communities is constructed. Discount factors in both time and space are used to calculate the contributions of each party and determine the priority weights for governance tasks and industrial development. Finally, a flexible resource scheduling module, combined with Monte Carlo simulations, evaluates the robustness of these priority weights under uncertainty and generates an optimized resource allocation strategy matrix, ensuring stable and efficient resource allocation in complex environments. Within this resource allocation method, a gated temporal attention mechanism and a proximal policy optimization algorithm are combined to dynamically adjust the allocation ratios of funding, equipment, and human resources for governance tasks and industrial development, thereby improving the execution efficiency and overall benefits of EOD projects.

[0023] See also Figure 1, an embodiment of the present invention provides a technical solution: a multi-level collaborative planning system for EOD projects, including the following modules: a multi-source data dynamic fusion module, a multi-level collaborative decision-making module, and a flexible resource scheduling module; the multi-source data dynamic fusion module is used to perform trusted alignment and encrypted aggregation of heterogeneous data of government chains, enterprise chains, and community chains through blockchain, and generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes; the multi-level collaborative decision-making module is used to construct an asymmetric game model of the government, enterprises, and communities based on the dynamic heterogeneous graph, calculate the contribution of each party through the time-space dual-dimensional discount factor, and generate the priority weights of governance tasks and industrial development; the flexible resource scheduling module is used to simulate multiple rounds of disturbance samples through the Monte Carlo method, evaluate the robustness of priority weights under uncertainty conditions, and generate a resource allocation strategy matrix.

[0024] In this implementation, the multi-source data dynamic fusion module is primarily used to process data from different sources using blockchain technology. Specifically, the module aligns and cryptographically aggregates heterogeneous data from government, enterprise, and community chains to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes. Blockchain is a distributed data storage technology that ensures data immutability, traceability, and security. In this implementation, blockchain is used to ensure the authenticity and consistency of data from different sources. Heterogeneous data refers to data originating from different systems or platforms, which may differ in structure, format, and storage method. In this implementation, heterogeneous data includes data from multiple sources, such as government, enterprises, and communities. Governance task nodes and industrial development nodes: Governance task nodes refer to various governance-related task or activity nodes; industrial development nodes refer to nodes related to industrial development. By linking these two types of nodes in a dynamic heterogeneous graph, data integration and collaborative planning are enhanced. A multi-level collaborative decision-making module is also used: Based on the dynamic heterogeneous graph, this module constructs an asymmetric game model among the government, enterprises, and communities. The module calculates the contributions of each party using a dual-dimensional discount factor in time and space to determine the priority weights for governance tasks and industrial development. Asymmetric game model: This is a model in game theory that refers to a situation where there is inequality in information and decision-making power between participants. In this model, the decisions and goals of different roles such as government, enterprises, and communities may be different, so it is necessary to design a game model that can reflect this asymmetry. Time-space dual-dimensional discount factor: The discount factor is a factor used to discount future benefits or costs. In the present invention, the time-space dual-dimensional discount factor not only considers changes in time, but also includes the influence of spatial factors to more accurately reflect the contributions of each party under different conditions. Contribution degree: Contribution degree refers to the degree of influence of each party in governance tasks and industrial development during the decision-making process. In the present invention, the contribution degree of each party is calculated through a game model to determine the priority of governance tasks and industrial development. Flexible resource scheduling module: This module uses the Monte Carlo method to simulate multiple rounds of perturbation samples, evaluate the robustness of priority weights under uncertainty conditions, and thus generate a resource allocation strategy matrix. Monte Carlo method: A numerical calculation method that estimates results through random sampling and statistical analysis. In the present invention, the Monte Carlo method is used to simulate different uncertainty conditions and predict the changes in priority weights under various possible disturbance conditions. Disturbance samples: Disturbance samples refer to a variety of random change samples for the uncertainty factors (such as resource supply, demand fluctuations, etc.) existing in the system during the simulation process. Through these disturbance sample simulations, the robustness of the system can be better understood. Robustness: Robustness refers to the ability of a system to maintain its functionality and performance in the face of external disturbances or uncertainties. In resource scheduling, robustness refers to the stability of priority weights under different circumstances.Resource Allocation Strategy Matrix: The resource allocation strategy matrix is ​​a resource allocation plan generated based on simulation results. It determines how to allocate resources such as funds, equipment, and manpower to different tasks. This matrix can effectively guide the optimal allocation of resources.

[0025] Specifically, the specific process of using blockchain to perform trusted alignment and encrypted aggregation of heterogeneous data from government chains, enterprise chains, and community chains to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes is as follows: The ecological supervision data of the government chain, the engineering implementation data of the enterprise chain, and the environmental feedback data of the community chain are cross-chain trustedly verified through blockchain, and data conflict items are eliminated through zero-knowledge proof; the desensitized multi-source data are aggregated through homomorphic encryption algorithms to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes, in which the nodes are associated with the ecological sensitivity of the governance area and the spatiotemporal attributes of the industrial development compliance window.

[0026] In this implementation, blockchain technology is used to perform cross-chain trusted verification of data from different sources (government chains, enterprise chains, and community chains). Cross-chain trusted verification refers to the use of blockchain technology to interoperate data between different chains and verify their credibility. In this implementation, the government chain's ecological regulatory data, the enterprise chain's project implementation data, and the community chain's environmental feedback data come from different fields, each with its own data format and verification mechanism. Cross-chain technology ensures the integrity and consistency of this data across different chains and prevents data tampering. For example, suppose the government chain provides regulatory data on the ecological environment, the enterprise chain provides engineering data on project execution, and the community chain provides feedback from residents. Using cross-chain verification technology, these disparate data sources can be integrated without conflict, ensuring that the data is authentic and valid, and can be reliably used in subsequent processing and decision-making. Zero-knowledge proof eliminates data conflicts: Using zero-knowledge proof technology, data conflicts are eliminated, ensuring information consistency between different data sources. Zero-knowledge proof is a cryptographic protocol that allows one party to prove the truth of an assertion to another without revealing specific information. Zero-knowledge proofs can verify the consistency of data across different chains without exposing sensitive data, thereby eliminating data conflicts. For example, if a government chain and an enterprise chain have different interpretations or data records regarding a governance task, zero-knowledge proofs can verify the consistency of both data without revealing the specific content, ensuring that the final merged result is trustworthy and conflict-free. Data aggregation using a homomorphic encryption algorithm: Desensitized multi-source data is encrypted and aggregated using a homomorphic encryption algorithm. Homomorphic encryption is an encryption technique that allows computations to be performed directly on encrypted data without decrypting it. Homomorphic encryption protects data privacy while ensuring that the encrypted data can be processed and analyzed. For example, suppose project data in an enterprise chain contains sensitive business plans and technical details. Homomorphic encryption allows computations and analysis to be performed without revealing this sensitive information, resulting in the final data aggregation result. Users only see the complete data during the decryption phase, but data privacy is protected during the aggregation process. The encrypted and aggregated multi-source data is then used to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes. Dynamic Heterogeneous Graph: A dynamic heterogeneous graph is a graph structure consisting of multiple nodes and edges. Nodes represent different types of elements (such as governance task nodes and industrial development nodes), while edges represent the relationships or interactions between them. In this graph, node attributes can be dynamically adjusted over time, reflecting the spatiotemporal changes in governance tasks and industrial development. For example, governance task nodes might include environmental protection tasks and pollution control tasks, while industrial development nodes might include infrastructure construction and project implementation.Each node has associated spatiotemporal attributes, such as the ecological sensitivity of the governance area or the compliance window for industrial development. Dynamic heterogeneous graphs can visualize the complex relationships between these tasks and nodes, adjusting the priorities and resource allocation of these nodes under different temporal and spatial conditions. Nodes are associated with the spatiotemporal attributes of the ecological sensitivity of the governance area and the compliance window for industrial development. Spatiotemporal attributes: Spatiotemporal attributes refer to the characteristics or states of a task or node under specific temporal and spatial conditions. For example, the spatiotemporal attributes of a governance task might include the ecological sensitivity of the area where the task is located (such as whether it is an ecological protection zone) and the task's time window (such as whether it must be completed within a certain time period). The spatiotemporal attributes of an industrial development node might include whether the project complies with local regulations and the flexibility of the development timeline. For example, a governance task node might involve environmental protection tasks in a specific ecologically sensitive area. The implementation of these tasks must take into account the regional environmental carrying capacity and ecological protection regulations. An industrial development node might involve infrastructure construction tasks that must be completed within a specified compliance timeframe. These tasks must comply with local laws and regulations and be completed within an appropriate timeframe to avoid environmental damage or violations.

[0027] Specifically, based on the dynamic heterogeneous graph, the specific process of constructing an asymmetric game model among the government, enterprises, and communities is as follows: based on the geographical proximity between the nodes in the dynamic heterogeneous graph, the spatial correlation strength between the governance area and the industrial development nodes is quantified; combining the ecological restoration cycle with the time lag of industrial benefit release, a time decay function is constructed; with government supervision costs, enterprise investment risks, and community ecological demands as game branches, the contributions of the three parties are calculated through the Shapley value algorithm, and a set of priority weights of governance tasks and industrial development is output.

[0028] In this implementation plan, the spatial correlation strength between the governance area and the industrial development node is quantified based on the geographic proximity of the nodes in the dynamic heterogeneous graph. Explanation of the steps: Geographic proximity: refers to the physical distance or spatial correlation between different nodes (governance task nodes and industrial development nodes). This metric is usually evaluated by calculating the difference in geographic coordinates between nodes, or based on geographic information system (GIS) data. Nodes that are closer may have higher spatial dependence in actual execution, so the correlation between them is stronger. Quantification of spatial correlation strength: By calculating the distance or geographic proximity between nodes, the spatial correlation strength can be quantified by the following formula: Where: S ij Represents the spatial correlation strength between node i (governance task node) and node j (industry development node). ijRepresents the geographical distance between node i and node j. The function of this formula is that as the distance between nodes increases, the spatial correlation strength weakens. The closer the distance, the greater the correlation strength. Suppose we have two nodes: Governance task node: ecological restoration area, located in the suburbs of city A. Industrial development node: new industrial park, located in the industrial zone of city A. According to actual geographical data, the geographical distance from the suburbs of city A to the industrial zone is 50 kilometers. Constructing a time decay function Combining the ecological restoration cycle with the time lag of industrial benefit release, a time decay function is constructed. Time decay function: In EOD projects, the benefits of governance tasks (such as ecological restoration) and industrial development have a time lag effect. Governance tasks usually take a long time to see the results of ecological restoration, and the benefits of industrial development may also lag. Therefore, in order to accurately reflect the temporal correlation between tasks, a time decay function is designed. This function can describe the time delay between governance tasks and industrial development and its impact. The time decay function can be expressed as: D(t) = e -λt ; Wherein: D(t) represents the time decay factor. As time t increases, the relationship between governance tasks and industrial development will gradually decay. λ is the decay coefficient, which controls the decay rate. Its size depends on the time lag of the ecological restoration cycle and the release of industrial benefits. t is a time variable, which represents the time delay from the start of the governance task to the industrial development task. The decay function indicates that as time goes by, the influence of the governance task gradually weakens, and the benefits of industrial development are gradually released. Calculate the three-party contribution (using the Shapley value algorithm) with government supervision costs, corporate investment risks, and community ecological demands as game branches. The three-party contribution is calculated by the Shapley value algorithm, and finally the priority weight set of governance tasks and industrial development is output. Step explanation: Shapley value algorithm: Shapley value is a classic game theory method used to measure the marginal contribution of each participant in a cooperative game to the results of cooperation. In the present invention, Shapley value is used to calculate the contribution of the three subjects of government, enterprise and community in governance tasks and industrial development. Game branches: The three parties involved are the government, enterprises and communities, and they have different goals and costs in the game process: The government's goal: regulatory costs, mainly to ensure the implementation of ecological restoration tasks and safeguard public interests. The enterprise's goal: investment risk, the financial and market risks that enterprises need to bear in industrial development. The community's goal: ecological demands, focusing on ecological environmental protection and residents' well-being. The basic steps for calculating the Shapley value are as follows: For each subject (such as government, enterprise, community), the Shapley value is calculated using the following formula: Where: φ a(v) represents the Shapley value (i.e., contribution) of subject a (government, enterprise, or community) in the cooperative game. S is the set of all possible subsets, representing the combinations of other subjects in the absence of subject a. v(S) represents the total value of subset S, that is, the total benefit of subset S when cooperating in the current state. v(S∪{a}) represents the total value after adding subject a to subset S. The calculated Shapley value reflects the contribution of each subject to the entire system, ultimately generating a set of three-party contribution scores that serves as the basis for weighting governance tasks and industrial development priorities. Assume that for the three parties (government, enterprise, and community), their respective value functions are: v({G}) = 100 (total benefit when the government participates alone), v({E}) = 200 (total benefit when the enterprise participates alone), v({C}) = 150 (total benefit when the community participates alone), v({G,E}) = 300 (total benefit when the government and enterprise collaborate), v({G,C}) = 250 (total benefit when the government and community collaborate), v({E,C}) = 400 (total benefit when the enterprise and community collaborate), and v({G,E,C}) = 500 (total benefit when the three parties collaborate). The Shapley value calculation results provide the contribution (i.e., priority weight) of each participant: government's Shapley value: 50; enterprise's Shapley value: 100; community's Shapley value: 83.33. These weights will serve as the basis for prioritizing governance tasks and industrial development. In subsequent resource allocation, enterprises, due to their greatest contribution, may receive more resources to support industrial development, and the contributions of the community and government will also be appropriately reflected. Based on the three-party contribution calculated by the Shapley value algorithm, a set of priority weights for governance tasks and industrial development is output. Priority weight: Based on the contribution of the three-party game, the priority weight between governance tasks and industrial development is determined. The weight of each node (governance task or industrial development node) in the dynamic heterogeneous graph will be adjusted according to the contribution of the three parties. Weight set output: These weights reflect the importance of each party in their respective tasks or goals. Tasks or nodes with higher weights will receive more resources and priority. Ultimately, these priority weights will be used in subsequent resource scheduling and optimization to ensure that different tasks and nodes receive appropriate resource allocation and processing based on their importance.

[0029] Specifically, the Monte Carlo method is used to simulate multiple rounds of disturbance samples, evaluate the robustness of priority weights under uncertainty conditions, and generate the resource allocation strategy matrix. The specific process is as follows: according to the temporal causal graph network, the critical path dependencies between governance tasks and industrial development nodes are identified, and the probability of the cumulative impact of ecological restoration lag on industrial development costs is quantified; random disturbance samples with sudden changes in environmental parameters are injected through the Monte Carlo method to verify the stability threshold of priority weights under uncertainty conditions; combined with the restoration efficiency attenuation trend of dynamic heterogeneous graph nodes and the prediction of industrial revenue fluctuations, cross-stage resource allocation rules and risk response triggering mechanisms are output.

[0030] In this implementation plan, a temporal causal graph network identifies critical path dependencies. First, a temporal causal graph network needs to be constructed to identify and quantify the dependencies between governance task nodes and industrial development nodes. By tracking the execution order of task nodes and their mutual dependencies through a temporal causal graph, the critical path, that is, the causal relationship and time dependency between governance tasks and industrial development, is identified. Node identification: Each node represents a governance task or an industrial development node. Dependency analysis: Determine which tasks (such as ecological restoration tasks) have a direct or indirect impact on industrial development tasks (such as agricultural, cultural and tourism integration development). Quantify the probability of the cumulative impact of ecological restoration lag on industrial development costs. The lag effect of ecological restoration (that is, the time difference between the completion of the ecological restoration task and the beginning of the restoration benefits) will affect the process and cost of industrial development. By establishing a probability distribution model for the lag effect of ecological restoration, its cumulative impact on the cost of industrial development is quantified. Formula: Assuming that the lag effect of ecological restoration will affect the cost of industrial development within time T, the impact of restoration lag on costs can be modeled through cumulative probability: Where: P(T): The probability distribution of the repair hysteresis effect, which describes the impact of the repair effect on the industry development cost. f(C(T)): The industry development cost function in time period T under the hysteresis effect. C impact (T): The cumulative impact of lag effects on industrial development costs. This formula helps evaluate the impact of time lag effects on later industrial development costs, and thus reflects the cumulative relationship between repair cycles and costs. Monte Carlo simulation: random perturbation samples that inject environmental parameter mutations The Monte Carlo method simulates the impact of environmental parameter mutations and uncertainty conditions by generating a large number of random samples. For example, it may include mutation factors such as changes in market demand, climate change, and policy adjustments. By simulating the impact of different perturbations on the stability of priority weights, its robustness and threshold are evaluated. Specific process: Perturbation sample generation: Generate a set of random perturbation samples by setting the possible range of environmental parameters. Simulation: Each perturbation sample is simulated to calculate the change in priority weights. After each simulation, the corresponding resource allocation strategy is calculated. Formula representation: Set the probability distribution of the perturbation sample to D, and the perturbation sample to d b ∈D, the priority weight of each simulation is ω b The Monte Carlo simulation process is: Where: b (d b): Priority weight after the bth simulation. B: Total number of simulations. This step calculates the priority weight under each simulation result through multiple rounds of simulation, and outputs its stability under uncertain conditions. Verify the stability threshold of the priority weight Based on the Monte Carlo simulation results, evaluate the range of change of the priority weight under multiple rounds of disturbances to determine its stability threshold. If the disturbance causes the priority weight to exceed a certain threshold, it indicates that the system is more sensitive to environmental changes and the strategy needs to be adjusted. Formula: According to the simulation results of the perturbation sample, the standard deviation of the priority weight is calculated as a stability measure: Stability Threshold = σ(ω sim ); where: σ(ω sim ): The standard deviation of the priority weight, which reflects the fluctuation degree of the weight under different disturbances. If σ(ω sim ) exceeds a preset threshold, indicating system instability and requiring adjustment. Combining the restoration efficiency decay trend of dynamic heterogeneous nodes with industry revenue fluctuation predictions further optimizes resource allocation strategies. This process dynamically adjusts resource inputs for governance and industrial development tasks to ensure optimal resource allocation in the face of environmental disturbances. Specific operations: Restoration efficiency decay trend: Predict the decay trend of ecological restoration benefits using a time decay function. Industry revenue fluctuation prediction: Predict industrial development revenue fluctuations using time series analysis or regression models. Dynamic adjustment strategy: Dynamically adjust resource allocation based on the decay trend and revenue fluctuation predictions. Formula: Assume that at time T, the resource allocation strategy matrix R(T) can be updated using the following formula: R(T) = f(Efficiency(T), Volatility(T)); where: Efficiency(T) is the restoration efficiency at time T. Volatility(T) is the industry revenue fluctuation at time T. Output cross-stage resource allocation rules and risk response trigger mechanisms. Finally, through the above steps, the cross-stage resource allocation rules are derived and the risk response trigger mechanism is defined. When the environment mutates, adjust the resource allocation strategy based on the changes in disturbance samples and priority weights, and trigger the corresponding risk response mechanism (for example, increase repair resources, adjust investment direction, etc.). Specific operations: Cross-stage resource allocation rules: define how to adjust the resource input ratio based on repair efficiency and industry benefits at different stages. Risk response mechanism: When the standard deviation of the priority weight exceeds the set threshold, the risk response mechanism is triggered to adjust resource allocation or take additional remedial measures. Formula representation: The trigger mechanism can be expressed by the following formula: If σ(ωsim) exceeds a preset threshold, a risk response is triggered.

[0031] See also Figure 2, a resource allocation method for EOD projects, including the following steps: S1. According to the priority weights of governance tasks and industrial development, the gated temporal attention mechanism is used to perform temporal prediction on the node states of the heterogeneous graph, and the coupling influence value of the governance efficiency attenuation factor and the industrial income fluctuation factor is calculated; S2. The coupling influence value is input into the resource allocation strategy matrix, and through the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, the investment ratio of funds, equipment and human resources in the dynamic heterogeneous graph in governance tasks and industrial development is dynamically adjusted; S3. According to the resource allocation strategy matrix, the node states of the dynamic heterogeneous graph are iteratively updated, and the temporal attributes of the governance tasks and industrial development nodes are corrected in real time.

[0032] In this implementation, a gated temporal attention mechanism is used to perform time-series predictions on the states of heterogeneous graph nodes based on the priority weights of governance tasks and industrial development. The coupled impact value of the governance efficiency decay factor and the industrial revenue volatility factor is calculated. Priority weights: The priority weights of governance tasks and industrial development are derived based on prior decision-making models (such as game models and weight calculations). These weights represent the importance and influence of each party in the EOD project. A gated temporal attention mechanism: This mechanism incorporates gated units into neural networks to control the flow of information and is commonly used in models that process time series data. It allows the model to assign different attention weights to input information at different time steps in the time series data, enhancing the model's capture of important information. In time series data processing, the temporal attention mechanism can assign different weights based on the influence and relevance of historical data, enhancing the model's focus on key nodes or events. In this method, the temporal attention mechanism is used to capture changes in the states of governance tasks and industrial development nodes. The coupled impact value: This value represents the degree of interaction between the governance efficiency decay factor and the industrial revenue volatility factor. The governance efficiency decay factor describes the change in the efficiency of ecological restoration or governance tasks over time, while the industrial revenue volatility factor describes the fluctuation in revenue during industrial development. The coupled impact of these two factors reflects how the progress of ecological restoration affects the revenue of industrial development, and this impact is dynamic. Step S2: Resource Allocation Strategy Matrix: This matrix represents the allocation ratio of different resources (funds, equipment, and human resources) between different nodes (governance tasks and industrial development). This matrix is ​​dynamic and adjusts based on project progress and changes in priority weights. Proximal Policy Optimization (PPO): PPO is an optimization algorithm in reinforcement learning that optimizes by balancing a given policy with a new one. PPO limits the amplitude of policy updates to avoid excessive policy updates, thereby ensuring that the model does not become unstable during training. PPO is a policy gradient-based method that continuously improves resource allocation strategies over multiple trials. Maximizing Overall Benefit: The goal is to maximize the overall project benefit by adjusting the investment ratio of funds, equipment, and human resources. This benefit can be a comprehensive consideration of the benefits from governance task completion and the benefits from industrial development progress. Step S3: Iterative Update: After each round of resource allocation, the node states in the dynamic heterogeneous graph will change based on the new resource configuration results. Therefore, the node states need to be updated according to the new configuration, and the timing attributes of each node need to be corrected in real time. Timing Attributes: The timing attributes of governance task and industrial development nodes include task start time, completion time, resource consumption time, etc. As resource configuration is dynamically adjusted, timing attributes also need to be updated in real time to ensure that the project proceeds along the optimal path.

[0033] Specifically, according to the priority weights of governance tasks and industrial development, the specific process of temporal prediction of heterogeneous graph node states through the gated temporal attention mechanism is as follows: extract the dynamic change characteristics of environmental sensor data of governance task nodes and the temporal fluctuation patterns of real-time operation indicators of industrial development nodes; capture the cross-cycle lagged correlation between governance efficiency attenuation and industrial revenue fluctuations through a sliding window; and use a gating mechanism to screen high-confidence temporal features to eliminate the impact of occasional interference events.

[0034] In this implementation plan, step 1: extract the dynamic change characteristics of the environmental sensor data of the governance task node and the time series fluctuation law of the real-time operation index of the industrial development node. The goal of this step is to extract important features that can reflect the changes in the system operation status from the environmental sensor data and the operation data of the industrial development node. The specific process includes: Environmental sensor data feature extraction: the data obtained from the environmental monitoring system (such as temperature, humidity, air pressure, pollutant concentration, etc.) are processed and analyzed to capture the dynamic change characteristics in the time series. Real-time operation index time series fluctuation extraction: Industrial development nodes usually have some real-time operation indicators, such as production rate, resource consumption, operation cost, output, etc. By analyzing the time series data of these operation indicators, their fluctuation laws can be identified to reflect the operation status of industrial development. Mathematical expression: let the environmental sensor data of the governance task node be E(t), and the operation index of the industrial development node be P(t), where t represents time. The extracted features can be expressed as: F env (t)=Extract(E(t)), F op (t) = Extract(P(t)); where F env (t): The feature vector of the environmental sensor data of the governance task node extracted at time t, reflecting the dynamic change trend of the environment. op(t): The feature vector of the industrial development node's operational indicators extracted at time t, describing the fluctuations in real-time operational status. Extract(·): A feature extraction function that processes the raw time series data, including normalization, filtering, and feature engineering. E(t): Raw environmental sensor data collected at time t, such as temperature, humidity, and pollutant concentration. P(t): Operational indicator data for the industrial development node recorded at time t, such as output, yield, and energy consumption. Step 2: Capture the cross-period lagged correlation between governance efficiency decay and industrial revenue fluctuations using a sliding window. The key to this step is capturing the time-lagged relationship between the decay of governance task efficiency and fluctuations in industrial development revenue. To this end, a sliding window approach is used to capture the lagged effects: Governance efficiency decay: The efficiency of governance tasks may gradually decay over time. Using a sliding window, we can capture changes in governance efficiency at different time points and the impact of these changes on industrial development. Industrial revenue fluctuations: Industrial development revenues are also volatile. Tracking industrial revenues using a sliding window can identify the cyclical and lagged nature of revenue fluctuations. Mathematical expression: Assume the sliding window size is w, the governance efficiency decay is G(t), and the industry income is R(t). The time series data in the window is:

[0035] {G(tw+1), G(tw+2), …, G(t)}, {R(tw+1), R(tw+2), …, R(t)} can capture the lag effect by calculating the correlation of the data in the above window: Where w: the size of the sliding window, which indicates the number of consecutive time steps considered simultaneously in the time dimension. t: the current time step. z: the relative time step index within the window, ranging from 1 to w. Correlation(G,R): the lagged correlation between governance efficiency and industrial returns within the sliding window, which measures the degree of cyclical correlation between the two. Σ: the summation symbol, indicating the accumulation of each time step within the window. This formula calculates the lagged correlation between governance efficiency and industrial returns. Step 3: Use a gating mechanism to filter high-confidence time series features and eliminate the influence of occasional interference events. The gated time series attention mechanism automatically selects the most valuable time series features for prediction by learning weights and eliminates occasional interference events. The gating mechanism essentially weights the input sequence to enhance important time series features and suppress noise. Gating mechanism: The gating mechanism uses a gating function (such as the sigmoid activation function) to adjust the weight of each time step in the time series data to ensure attention to important information. High confidence time series feature screening: Based on the gate parameters obtained through training, features with high confidence are screened to reduce the impact of occasional interference events on the model. Mathematical expression: The gate function is set to g(t), and its output is a binary value indicating whether the feature is selected at that moment. The gate mechanism can be expressed as: g(t) = σ(W gF(t)+b g ); the feature representation after the sigmoid activation function is adjusted by the gating mechanism is: Among them: Parameter explanation g(t): The gate value at time t, ranging between (0, 1), represents the importance weight of the current feature. σ(·): Sigmoid activation function, in the form of Used to compress the gate output to the (0, 1) interval. g : Gating weight matrix, which represents the weight parameters when linearly transforming the input feature F(t), which needs to be learned through training. F(t): The time series feature vector extracted at time t, which can be F env (t), F op (t) or a combination of both. b g : Gating bias term, which helps adjust the baseline value of the gate output and is also learned through training. After gated screening, the time series features are weighted by importance, and only the valid features with high confidence are retained. ·: The dot product symbol represents element-by-element multiplication (Hadamard product), that is, the feature value is multiplied element-by-element by the corresponding gate weight. Step 4: Perform time series prediction through the gated time series attention mechanism. Through the high-confidence features extracted above, the gated time series attention mechanism is used to perform time series prediction to predict the node status of governance tasks and industrial development. The gated time series attention mechanism predicts the future state of the node by learning the dependency between nodes and the weighted sum of the time series features. Time series prediction: Using the gated time series attention mechanism to predict the future state of the node, the state change trend of the governance task and industrial development nodes can be obtained. Mathematical expression: Set the prediction of the node state to be The calculation formula of the temporal attention mechanism is: in: The predicted state value of the node at the future time t+1, such as the task success rate and industry revenue growth rate. H: The number of valid features at time t, that is, the number of feature vectors involved in the prediction calculation. α h : The attention weight of the h-th feature, which indicates the contribution of the feature to the overall prediction, is usually learned through the attention mechanism and satisfies The hth feature vector after being filtered by the gating mechanism. Σ: The summation symbol, indicating that the final prediction result is obtained by weighting the importance of all features. t+1: The future time step of the prediction.

[0036] Specifically, the specific process of calculating the coupling impact value of the governance efficiency attenuation factor and the industrial income fluctuation factor is as follows: integrating historical governance compliance rate data and the satisfaction decline rate of community feedback to construct a dynamic model of governance efficiency attenuation; predicting the cyclical characteristics of income fluctuations based on the real-time operation data of industrial development nodes, introducing the environmental governance efficiency coefficient and the gain coefficient of technological iteration, and weightedly aggregating the spatiotemporal coupling impact value of governance and industry.

[0037] In this implementation plan, step 1: integrate the governance compliance rate and the rate of decline in community satisfaction to construct a dynamic model of governance efficiency attenuation. The formula is as follows: D g (t) = λ1×(1-C s (t))+λ2×(1-F r (t)); parameter explanation, D g (t): The governance efficiency decay value at time t, reflecting the decline in governance effectiveness. λ1: The weight coefficient of the governance compliance rate, measuring the impact of the governance compliance rate on efficiency decay. λ2: The weight coefficient of community feedback, measuring the impact of changes in residents' satisfaction on governance efficiency decay. C s (t): The governance compliance rate at time t, defined as the proportion of completed governance tasks, generally ranging from [0,1]. r (t): Community satisfaction score at time t (normalized), generally in the range of [0,1]. s (t): Governance failure rate, which indicates the proportion of governance requirements that have not been met. r (t): The proportion of community satisfaction decline, indicating the degree of residents' dissatisfaction with the governance results. Step 2: Predict the periodic characteristics of revenue fluctuations based on the real-time operation data of the industrial development nodes, and predict the periodic fluctuation characteristics of revenue through short-term Fourier transform based on the real-time operation indicators collected by the industrial nodes (such as production volume, sales volume, energy consumption rate). Focus on extracting the characteristics of the cycle length, amplitude change and trend drift of revenue changes to provide revenue fluctuation parameter support for comprehensive coupling analysis. Step 3: Introduce the environmental governance efficiency coefficient and the technology iteration gain coefficient, and calculate the environmental governance efficiency coefficient based on governance-related indicators (air quality improvement rate, water quality improvement ratio) to reflect the positive impact of governance measures on industrial environment improvement. Combined with the technology upgrade records of industrial nodes, quantify the improvement in operational efficiency and revenue growth brought about by technology introduction or update to form a technology iteration gain coefficient. The environmental efficiency coefficient and technology gain coefficient will participate in the subsequent comprehensive weighting as positive adjustment factors. Step 4: Weighted aggregation of the spatiotemporal coupling impact value of governance and industry formula: I gp (t) = η1 × E d (t)+η2×P f (t)+η3×(φ e (t)+φ u (t)); Parameter explanation Igp (t): The impact value of the spatiotemporal coupling between governance and industry at time t, which comprehensively evaluates the interaction between changes in governance efficiency and fluctuations in industry returns. η1: The weight factor for governance efficiency decay, which controls the proportion of governance decay in the comprehensive impact. η2: The weight factor for industry return fluctuation, which controls the proportion of industry fluctuation characteristics in the comprehensive impact. η3: The weight factor for gain adjustment, which adjusts the contribution of environmental performance and technological upgrading in the overall coupling relationship. E d (t): Governance efficiency attenuation factor. P f (t): The characteristic parameter of the income fluctuation cycle, which represents the main periodic characteristics of income fluctuation during the industry operation process, comes from the analysis in step 2. e (t): Environmental governance effectiveness coefficient, which indicates the positive effect intensity of governance measures on the industrial ecological environment. u (t): Technology iteration gain coefficient, representing the positive impact of new technology applications on industrial efficiency. By weighting the governance attenuation factor, industry revenue volatility, and positive gain factors, this is integrated into a final spatiotemporal coupling impact indicator, providing a reference for macroeconomic regulation, risk warning, and revenue optimization.

[0038] Specifically, through the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, the specific process of dynamically adjusting the investment ratio of funds, equipment and human resources in governance tasks and industrial development in the dynamic heterogeneous graph is as follows: define a multidimensional state space including the governance task progress deviation rate, industrial benefit fluctuation amplitude, and community consensus index, and extract the environmental governance efficiency attenuation parameters and industrial operation indicator change characteristics based on the real-time node status data of the dynamic heterogeneous graph; design an action space based on the cross-chain resource transfer instructions and priority weights of the blockchain smart contract to dynamically correct the threshold, and generate fund transfer ratios, equipment scheduling paths and human resource allocation plans; construct a composite reward function that integrates the environmental governance efficiency improvement rate, industrial net present value growth rate, and community conflict event triggering frequency, optimize network parameters through the policy gradient update mechanism, drive the resource allocation strategy to converge towards the direction of maximizing comprehensive benefits, and adjust the balance coefficient of the strategy in real time according to the changes in the temporal properties of the dynamic heterogeneous graph to ensure the robustness and convergence efficiency of the algorithm in complex scenarios.

[0039] In this implementation plan, step one: define a multidimensional state space, quantify the dynamic characteristics of governance tasks and industrial development processes into state variables, and define a multidimensional state space containing the following elements: governance task progress deviation rate (indicates the deviation ratio of the actual completion of the governance task relative to the planned progress); industrial income fluctuation range (indicates the range of income change per unit time); community consensus index (reflects the community's support for the current governance and development plan). At the same time, extract in real time from the dynamic heterogeneous graph node state: environmental governance efficiency attenuation parameter; industrial operation indicator change characteristics. Through the above state quantities, the overall picture of the environment when making resource allocation decisions is portrayed. Step two: Design the action space and resource allocation plan to generate a formula to represent the action space definition, Parameter Explanation Action space, defined as the set of allocation ratios of funds, equipment, and human resources in governance and industry; f : Fund transfer ratio, which indicates the share of funds allocated to governance or development; r e : Equipment scheduling ratio, which indicates the ratio of equipment resources used for management or development; r h : Human resource allocation ratio, representing the proportion of manpower invested in governance or development; the sum of the three is 1, ensuring the conservation of total resource allocation. The action space is limited by the cross-chain resource transfer instructions set in the blockchain smart contract and the dynamic adjustment threshold of the priority weight of each resource category. Step 3: Construct a composite reward function formula: Q = δ1·Δη+δ2·ΔNPV-δ3·γ c Parameter explanation: Q: immediate reward value, which measures the comprehensive benefit effect of resource allocation decisions; δ1, δ2, δ3: weight coefficients, used to balance the importance of different goals; Δη: environmental governance efficiency improvement rate, which represents the positive change in governance efficiency per unit time; ΔNPV: industry net present value growth rate, which represents the growth rate of economic benefits from industrial development; γ c : The frequency of community conflict events, which indicates the number of community dissent events caused by resource allocation per unit time. The reward function comprehensively considers environmental, economic, and social factors, and forms an overall benefit evaluation index through weighted summation. Step 4: Optimize resource allocation strategy through policy gradient update. Parameter explanation, The gradient of the policy parameter θ indicates how to adjust the policy to improve the expected return; θ (a t |s t ): In state s t Next take action a t Strategy probability distribution; τ: represents a complete trajectory (i.e., state-action sequence); Q t: Reward value at time t; T: Length of decision time window. Through the proximal policy optimization algorithm framework, by limiting the amplitude of single-step policy updates, the stability of convergence is maintained. Step 5: Dynamically adjust the policy balance coefficient based on the temporal properties of the heterogeneous graph. Since the node states in the dynamic heterogeneous graph have obvious temporal change characteristics, it is necessary to introduce an adaptive mechanism to dynamically adjust the weight coefficients δ1, δ2, and δ3 in the reward function. If environmental fluctuations intensify (for example, the governance efficiency decreases significantly), the weight of δ1 will be automatically increased to give priority to environmental governance tasks; if industrial returns fluctuate significantly, δ2 will be increased in a timely manner; if community conflicts occur frequently, δ3 will be increased to strengthen the constraints on community stability.

[0040] Specifically, according to the resource allocation strategy matrix, the node status of the dynamic heterogeneous graph is iteratively updated, and the specific process of real-time correction of the timing attributes of the governance tasks and industrial development nodes is as follows: when resource allocation causes the governance progress or industrial benefits to deviate from the preset tolerance range, the blockchain smart contract is triggered to automatically and synchronously update the node status of the dynamic heterogeneous graph; the cycle compression rate of the governance task and the income elasticity coefficient of the industrial development are reversely corrected according to the real-time environmental sensor data and industrial operation indicators; the contribution weights of the three-party data are recalculated through the Shapley value algorithm to verify the global stability of the resource allocation strategy.

[0041] In this implementation plan, after the resource allocation strategy matrix is ​​executed, the progress of governance tasks and industrial benefits are monitored in real time. If the governance progress deviation rate or industrial benefit deviation rate exceeds the preset tolerance range threshold, the blockchain smart contract mechanism will automatically trigger the synchronization update of the heterogeneous graph node status. The update includes: task completion status marking of the governance node, benefit cycle marking of the industrial node, and resource transfer log record. Step 2: Reverse correction of the governance task cycle compression rate and industrial benefit elasticity coefficient formula, the governance task cycle compression rate correction formula is: β g ′=β g -μ1×δ g ; Modification formula of industrial development income elasticity coefficient: ∈ p ′=∈ p +μ2×δ p ; Parameter explanation, β g ′: The revised compression rate of the governance task cycle, reflecting the compression range of the new cycle due to the change in actual resource allocation; β g : The compression rate of the originally set governance task cycle; μ1: Environmental response correction coefficient, which represents the sensitivity of the impact of real-time environmental changes on the governance progress; δ g :Governance progress deviation (the difference between actual completion and preset planned completion). ∈ p ′: the modified industry income elasticity coefficient, which measures the sensitivity of income changes to changes in resource allocation; ∈ p: the original set industrial development income elasticity coefficient; μ2: industrial operation response correction coefficient, which represents the amplification or suppression effect of resource fluctuations on industrial income; δ p Industry return deviation (the difference between actual and target returns). Based on real-time environmental sensor data and feedback from industry operational indicators, governance progress and industry return elasticity are dynamically fine-tuned to adapt to actual scenario changes. The Shapley value is used to re-evaluate the actual contribution of governance data, industry operational data, and community feedback to the stability of the overall decision-making results. If the contribution of any party is too low or too high, the data input weighting is adjusted in a timely manner to ensure the global stability of the resource allocation strategy.

[0042] In summary, this application has at least the following effects:

[0043] A multi-level collaborative planning system and resource allocation method for EOD projects leverages blockchain technology to implement trusted alignment and encrypted aggregation of heterogeneous data from government, enterprise, and community chains. This significantly improves the security and real-time nature of multi-source data fusion, ensuring data consistency and tamper resistance during the generation of dynamic heterogeneous graphs. By constructing an asymmetric game model among the government, enterprises, and communities, and combining spatial and temporal discount factors to quantitatively calculate contributions, this system enables multi-level collaborative decision-making under complex stakeholder relationships, enhancing the rationality and scientific nature of resource allocation plans. By employing Monte Carlo methods to perform multiple rounds of perturbation simulations on priority weights, the system systematically evaluates the robustness of resource allocation strategies under uncertainty, effectively enhancing the overall system's adaptability to external environmental changes. By incorporating a gated temporal attention mechanism to extract and predict temporal features of node states in dynamic heterogeneous graphs, the system accurately models the coupled relationship between governance efficiency decay and industrial revenue fluctuations, thereby improving the accuracy of resource allocation strategies in responding to future trends. The Proximal Policy Optimization (PPO) algorithm is used to dynamically adjust the investment ratios of funds, equipment, and human resources, and the node states of the dynamic heterogeneous graph are iteratively updated in real time during the optimization process, ensuring that the resource allocation strategy adaptively converges towards maximizing comprehensive benefits, significantly improving resource utilization efficiency and the overall benefits of the system.

[0044] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-level collaborative planning system for EOD projects, characterized by: It includes the following modules: multi-source data dynamic fusion module, multi-level collaborative decision-making module, and elastic resource scheduling module; The multi-source data dynamic fusion module is used to align and encrypt heterogeneous data from government chains, enterprise chains, and community chains through blockchain, generating a dynamic heterogeneous graph containing governance task nodes and industrial development nodes. The multi-level collaborative decision-making module is used to build an asymmetric game model among the government, enterprises, and communities based on a dynamic heterogeneous graph. It calculates the contribution of each party through a dual-dimensional discount factor in time and space, and generates priority weights for governance tasks and industrial development. The elastic resource scheduling module is used to simulate multiple rounds of disturbance samples through the Monte Carlo method, evaluate the robustness of priority weights under uncertainty conditions, and generate a resource allocation strategy matrix.

2. The multi-level collaborative planning system for EOD projects according to claim 1, characterized in that: The specific process of using blockchain to align and encrypt heterogeneous data from government chains, enterprise chains, and community chains to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes is as follows: Through blockchain, cross-chain trusted verification is performed on the ecological supervision data of the government chain, the engineering implementation data of the enterprise chain, and the environmental feedback data of the community chain, and data conflicts are eliminated through zero-knowledge proof; The desensitized multi-source data is aggregated through a homomorphic encryption algorithm to generate a dynamic heterogeneous graph containing governance task nodes and industrial development nodes, in which the nodes are associated with the spatiotemporal attributes of the ecological sensitivity of the governance area and the industrial development compliance window.

3. The multi-level collaborative planning system for EOD projects according to claim 2, characterized in that: Based on the dynamic heterogeneous graph, the specific process of constructing an asymmetric game model among the government, enterprises, and communities is as follows: Based on the geographical proximity between nodes in the dynamic heterogeneous graph, the spatial correlation strength between governance regions and industrial development nodes is quantified; Combining the ecological restoration cycle with the time lag of industrial revenue release, a time decay function is constructed; Taking government regulatory costs, corporate investment risks, and community ecological demands as game branches, the contribution of the three parties is calculated through the Shapley value algorithm, and the priority weight set of governance tasks and industrial development is output.

4. The multi-level collaborative planning system for EOD projects according to claim 3, characterized in that: The Monte Carlo method is used to simulate multiple rounds of perturbation samples, evaluate the robustness of priority weights under uncertainty conditions, and generate the resource allocation strategy matrix. The specific process is as follows: Based on the temporal causal graph network, the critical path dependencies between governance tasks and industrial development nodes are identified, and the cumulative impact probability of ecological restoration delays on industrial development costs is quantified. The Monte Carlo method is used to inject random perturbation samples of environmental parameter mutations to verify the stability threshold of priority weights under uncertainty conditions. Combining the attenuation trend of repair efficiency of dynamic heterogeneous graph nodes with the prediction of industrial revenue fluctuations, we output cross-stage resource allocation rules and risk response triggering mechanisms.

5. A resource allocation method for EOD projects, using the multi-level collaborative planning system for EOD projects according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1. Based on the priority weights of governance tasks and industrial development, a gated temporal attention mechanism is used to perform temporal prediction of heterogeneous graph node states and calculate the coupled impact value of the governance efficiency attenuation factor and the industrial revenue volatility factor. S2. Input the coupling impact value into the resource allocation strategy matrix. Using the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, dynamically adjust the investment ratio of funds, equipment, and human resources in governance tasks and industrial development in the dynamic heterogeneous graph. S3. Based on the resource allocation strategy matrix, iteratively update the node status of the dynamic heterogeneous graph and correct the timing attributes of the governance tasks and industrial development nodes in real time.

6. The multi-level collaborative planning system for EOD projects according to claim 5, characterized in that: Based on the priority weights of governance tasks and industrial development, the specific process of temporal prediction of heterogeneous graph node states through the gated temporal attention mechanism is as follows: Extract the dynamic change characteristics of environmental sensor data of governance task nodes and the time series fluctuation patterns of real-time operation indicators of industrial development nodes; A sliding window is used to capture the cross-cycle lagged correlation between the decline in governance efficiency and the fluctuation of industrial returns; a gating mechanism is used to screen high-confidence time series features and eliminate the impact of occasional interference events.

7. The multi-level collaborative planning system for EOD projects according to claim 6, characterized in that: The specific process of calculating the coupling impact value of the governance efficiency attenuation factor and the industrial income volatility factor is as follows: By integrating historical governance compliance rate data with the satisfaction decline rate reported by the community, a dynamic model of governance efficiency attenuation is constructed. The cyclical characteristics of revenue fluctuations are predicted based on the real-time operational data of industrial development nodes. The environmental governance efficiency coefficient and the gain coefficient of technological iteration are introduced to weightedly aggregate the spatiotemporal coupling impact value of governance and industry.

8. The multi-level collaborative planning system for EOD projects according to claim 7, characterized in that: Through the proximal strategy optimization algorithm, with the goal of maximizing comprehensive benefits, the specific process of dynamically adjusting the investment ratio of funds, equipment, and human resources in governance tasks and industrial development in the dynamic heterogeneous graph is as follows: Define a multidimensional state space that includes the governance task progress deviation rate, industry revenue fluctuation range, and community consensus index. Extract environmental governance efficiency attenuation parameters and industry operation indicator change characteristics based on real-time node status data of the dynamic heterogeneous graph. Design an action space based on the cross-chain resource transfer instructions and priority weights of blockchain smart contracts to dynamically modify the threshold, generating fund transfer ratios, equipment scheduling paths, and human resource allocation plans; A composite reward function is constructed that integrates the environmental governance efficiency improvement rate, industry net present value growth rate, and community conflict event triggering frequency. The network parameters are optimized through the policy gradient update mechanism, driving the resource allocation strategy to converge towards maximizing the overall benefit. The balance coefficient of the strategy is adjusted in real time according to the changes in the temporal properties of the dynamic heterogeneous graph to ensure the robustness and convergence efficiency of the algorithm in complex scenarios.

9. The multi-level collaborative planning system for EOD projects according to claim 8, characterized in that: The specific process of iteratively updating the status of dynamic heterogeneous graph nodes based on the resource allocation strategy matrix and correcting the timing attributes of governance tasks and industrial development nodes in real time is as follows: When resource allocation causes governance progress or industry benefits to deviate from the preset tolerance range, the blockchain smart contract is triggered to automatically and synchronously update the node status of the dynamic heterogeneous graph; Reversely revise the cycle compression rate of governance tasks and the profit elasticity coefficient of industrial development based on real-time environmental sensor data and industrial operation indicators; The contribution weights of the three-party data are recalculated using the Shapley value algorithm to verify the global stability of the resource allocation strategy.

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