Universe regulation cognition and decision support method and system

Abstract

The invention relates to the technical field of smart territorial and artificial intelligence, in particular to a global improvement cognition and decision support method and system. The global improvement cognition and decision support method comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data of a global improvement scene to obtain multi-modal data; s2, constructing a digital twinborn model of a global renovation scene, and fusing the multi-modal data by adopting an attention mechanism to obtain fused data; s3, a multi-modal large model is adopted to extract semantic features and association relationships in the fusion data; s4, acquiring historical data and experience knowledge in a global improvement process, performing time sequence prediction based on a space-time memory network, and evaluating prediction results of different decisions by means of anti-fact reasoning; and S5, generating a decision suggestion based on a prediction result, and displaying the decision suggestion. The cognitive accuracy and interpretability of a global renovation scene can be effectively improved.

Description

Technical Field

[0001] This invention relates to the fields of smart land management and artificial intelligence technology, specifically to a method and system for comprehensive governance cognition and decision support. Background Technology

[0002] Comprehensive land consolidation is a crucial measure for optimizing the spatial pattern of the national territory and promoting rural revitalization. Currently, several digital solutions exist. For example, Guangxi's 3D-based monitoring and assessment technology has reached international advanced levels, and its cloud-based modeling capabilities are even internationally leading. Jinhua's Pujiang has constructed a "smart brain" for comprehensive land consolidation and restoration, enabling precise site selection and accelerated approval processes. Nanning Normal University released "Dizhitong," a large-scale AI model for natural resource governance, integrating an intelligent auxiliary system for village planning and a 3D visualization platform for comprehensive land consolidation.

[0003] In terms of patented technologies, Ningbo Ward has applied for a patent for a multimodal digital twin system, realizing the acquisition, processing, fusion analysis, and feedback control of multimodal information. Zhongyifeng Digital has applied for a patent for a method for 3D reconstruction of building digital twins based on large models, employing multi-source data fusion and an adaptive deep network model. Caijiang Intelligent has applied for a patent for a design method for industrial digital twin systems based on dedicated models and large models, solving problems such as inaccurate real-time response in traditional digital twin systems through hierarchical collaboration between dedicated and large models.

[0004] Despite some progress in existing technologies, most remain at the level of data visualization and simple analysis, lacking in-depth understanding of the overall governance scenario and unable to comprehend complex semantic relationships and dynamic changes, resulting in low accuracy and interpretability in the understanding of the overall governance scenario. Summary of the Invention

[0005] The present invention aims to provide a method and system for comprehensive governance cognition and decision support, which can effectively improve the accuracy and interpretability of cognition of comprehensive governance scenarios.

[0006] To address the aforementioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a comprehensive governance cognition and decision support method, comprising the following steps: S1: Collect and preprocess multi-source heterogeneous data from the whole-domain governance scenario to obtain multimodal data; S2: Construct a digital twin model of the entire governance scenario and use an attention mechanism to fuse multimodal data to obtain fused data; S3: Employ a multimodal large model to extract semantic features and relationships from the fused data; S4: Obtain historical data and experience knowledge during the overall governance process, make time-series predictions based on spatiotemporal memory networks, and evaluate the prediction results of different decisions with the help of counterfactual reasoning; S5: Generate and display decision recommendations based on the prediction results.

[0007] By adopting the above technical solution, when conducting comprehensive governance, the first step is to acquire various types of data related to the comprehensive governance scenario. Since these data come from different sources, preprocessing is necessary to meet subsequent processing requirements. Then, a corresponding digital twin model is constructed based on the comprehensive governance scenario, and an attention mechanism is used to fuse the multi-source heterogeneous data to obtain fused data. Next, semantic features and relationships are extracted from the fused data using a multimodal large model, thereby improving the depth of understanding and comprehension of the comprehensive governance scenario. Then, historical data and experiential knowledge from previous governance processes are acquired, and time-series prediction is performed using a spatiotemporal memory network, thereby improving the cognitive accuracy and interpretability of the comprehensive governance scenario. Finally, counterfactual reasoning is used to evaluate the prediction results of different decisions and generate decision-making suggestions for decision-makers' reference.

[0008] Optionally, the multimodal data in S1 includes remote sensing image data, geospatial data, land use data, and socioeconomic data.

[0009] Optionally, step S2 uses an attention weight calculation formula to calculate the fused data: in, This represents the query vector, indicating the semantics of the current task. and These represent the feature representations of each modality of data; Representing the The fusion weights for each modality take values ​​in the range [0, 1], and .

[0010] Optionally, the digital twin model in S2 includes terrain, buildings, vegetation, and water features.

[0011] Optionally, the fused data in S2 includes image data, text data, and geographic data.

[0012] Optionally, the structure of the multimodal large model in S3 is as follows: in, Represents the PTM model; These represent the query, key, and value matrices, respectively, derived from feature projections of different modalities. Represents a multi-head attention mechanism; Represents a feedforward neural network; Represents the normalization layer; This represents fused data.

[0013] Optionally, the state update formula for the spatiotemporal memory network in S4 is: in, represent The hidden state at any given time, with dimensions of ; represent The memory state at any given moment, with dimensions of ; represent The input feature vector at time step 1.

[0014] Optionally, the formula for calculating the prediction result is: in, This represents the prediction result under counterfactual conditions; This represents the outcome under current factual conditions; Input conditions representing counterfactual assumptions; Represents the current actual input conditions; This represents the gradient of the output with respect to the input.

[0015] Optionally, the following further exists between S4 and S5: The computing tasks are dynamically allocated to the cloud and edge devices, with the optimization goal of minimizing the total computing cost. Real-time inference and feedback are performed on the edge devices to execute lightweight inference, while large-scale model updates and collaborative optimizations are carried out in the cloud. The formula for calculating the minimum total computational cost is: in, Represents a binary variable, indicating a task. Assign to device ; Representative task In the equipment The computational cost on top; Representative equipment Maximum task capacity.

[0016] Secondly, the present invention provides a comprehensive governance cognition and decision support system for implementing the comprehensive governance cognition and decision support method described in the first aspect, comprising: an intelligent perception layer, a digital twin layer, a cognitive decision layer, and an application service layer; The intelligent sensing layer includes an integrated air-space monitoring network, an adaptive data acquisition engine, and edge computing nodes. The digital twin layer includes a holographic visualization modeling engine, a multimodal data fusion module, and a spatiotemporal memory network; The cognitive decision-making layer includes a multimodal large model, a dynamic prediction module, and an interpretability engine; The application service layer includes a natural language interactive interface.

[0017] In summary, the present invention has at least the following beneficial technical effects: 1. Cognitive Depth: Employing multimodal large model technology, it can deeply understand the complex semantic relationships and dynamic changes in the governance scenario, with a cognitive accuracy rate of over 95%.

[0018] 2. Real-time performance: Through the cloud-edge-device collaborative computing framework, near real-time evaluation and dynamic prediction are achieved, shortening the evaluation cycle from 3-6 months in traditional methods to within 24 hours.

[0019] 3. Adaptability: Possessing self-learning and adaptive capabilities, the system can continuously optimize the evaluation model as data accumulates, adapting to the characteristics of different regions and different types of remediation projects.

[0020] 4. Interactivity: It provides a variety of application methods such as natural language interaction and augmented reality display, enabling decision-makers to easily understand and use the evaluation results.

[0021] 5. Explainability: The explainability engine provides a comprehensive explanation of the evaluation process, enhancing decision-makers' understanding and trust in the results. Attached Figure Description

[0022] Figure 1 This is a flowchart of a comprehensive governance cognition and decision support method according to an embodiment of the present invention; Figure 2 This is an overall architecture diagram of a comprehensive governance cognition and decision support system according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] The terminology used in the following embodiments of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification and appended claims of the present invention, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in the present invention refers to and includes any or all possible combinations of one or more of the listed items. The terms “first” and “second” are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as “first” or “second” may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of the present invention, unless otherwise stated, “a plurality” means two or more.

[0025] This invention provides a method for comprehensive governance cognition and decision support.

[0026] refer to Figure 1 A comprehensive governance cognition and decision support method includes the following steps: S1: Collect and preprocess multi-source heterogeneous data from the whole-domain governance scenario to obtain multimodal data.

[0027] The intelligent sensing layer utilizes an integrated air-space-ground monitoring network to collect multi-source heterogeneous data from comprehensive environmental remediation scenarios. This network includes high-resolution satellites, drones, and IoT devices. The multi-source heterogeneous data includes remote sensing imagery, geospatial data, land use data, and socioeconomic data.

[0028] The adaptive data acquisition engine of the intelligent sensing layer dynamically optimizes the acquisition plan and parameter configuration based on reinforcement learning algorithms, aiming to minimize energy consumption and maximize information gain, and adjusts the acquisition plan and equipment parameters in real time.

[0029] Edge computing nodes in the intelligent sensing layer preprocess multi-source heterogeneous data to obtain multimodal data. Preprocessing includes data cleaning, noise reduction, and format unification to reduce data transmission volume and improve system real-time performance.

[0030] S2: Construct a digital twin model of the whole-domain governance scenario, and use an attention mechanism to fuse multimodal data to obtain fused data.

[0031] The holographic visualization modeling engine of the digital twin layer constructs a data twin model of the entire governance scenario based on real-scene 3D technology. Specifically, it uses oblique photography and LiDAR point cloud data to construct a digital twin model that includes elements such as terrain, buildings, vegetation, and water bodies.

[0032] The multimodal data fusion module of the digital twin layer adopts an attention-based multimodal fusion algorithm to fuse remote sensing image data, geospatial data, land use data, and socioeconomic data to obtain fused data.

[0033] It should be understood that fused data includes image data, text data, geographic data, etc. The formula for calculating attention weights is: in, This represents the query vector, indicating the semantics of the current task. and These represent the feature representations of each modality of data; Representing the The fusion weights for each modality take values ​​in the range [0, 1], and .

[0034] S3: Use a multimodal large model to extract semantic features and relationships from the fused data.

[0035] The multimodal large-scale model of the cognitive decision layer performs multimodal feature alignment and encoding, embedding image data, text data, geographic data, etc., into a unified semantic space. The multimodal large-scale model adopts a Transformer-based PTM model.

[0036] The following model structure is used for cross-modal reasoning to obtain semantic features and associations.

[0037] in, Represents the PTM model; These represent the query, key, and value matrices, respectively, derived from feature projections of different modalities. This represents a multi-head attention mechanism used to capture long-range dependencies; This represents a feedforward neural network, designed to enhance nonlinear expressive power; This represents a normalization layer to improve training stability; This represents fused data.

[0038] S4: Obtain historical data and experiential knowledge during the overall governance process, make time-series predictions based on spatiotemporal memory networks, and evaluate the prediction results of different decisions with the help of counterfactual reasoning.

[0039] The spatiotemporal memory network of the digital twin layer stores and manages historical data and experiential knowledge from the overall governance process, supporting spatiotemporal correlation analysis. Its updates and predictions utilize LSTM and external memory units to model spatiotemporal dynamics; the state update formula is: in, represent The hidden state at any given time, with dimensions of ; represent The memory state at any given moment, with dimensions of ; represent The input feature vector at time step 1.

[0040] The interpretable engine of the cognitive decision-making layer uses counterfactual reasoning to evaluate and comprehensively interpret the predictive outcomes of different decisions, presenting the results in natural language. This comprehensive interpretation effectively enhances decision-makers' understanding and trust in the predictive outcomes.

[0041] The formula for calculating the prediction result is: in, This represents the prediction result under counterfactual conditions; This represents the outcome under current factual conditions; Input conditions representing counterfactual assumptions; Represents the current actual input conditions; This represents the gradient of the output with respect to the input, indicating sensitivity.

[0042] S5: Generate and display decision recommendations based on the prediction results.

[0043] The interpretable engine in the cognitive decision-making layer generates decision recommendations during the evaluation process and displays them through natural language interaction interfaces, augmented reality displays, and automated report generation in the application service layer.

[0044] To improve the real-time performance of this comprehensive governance cognition and decision support method, the method also includes the following components between S4 and S5: Computational tasks are dynamically allocated to cloud and edge devices, with the optimization goal of minimizing total computational cost. Real-time inference and feedback are implemented, with lightweight inference performed on edge devices and large-scale model updates and collaborative optimization conducted in the cloud. The formula for calculating the minimum total computational cost is: in, Represents a binary variable, indicating a task. Assign to device ; Representative task In the equipment The computational cost on top; Representative equipment Maximum task capacity.

[0045] This invention also provides a comprehensive governance cognition and decision support system.

[0046] refer to Figure 2 A comprehensive governance cognition and decision support system includes: an intelligent perception layer, a digital twin layer, a cognitive decision layer, and an application service layer.

[0047] The intelligent perception layer includes an integrated air-space monitoring network, an adaptive data acquisition engine, and edge computing nodes. The digital twin layer includes a holographic visualization modeling engine, a multimodal data fusion module, and a spatiotemporal memory network. The cognitive decision-making layer includes a multimodal large model, a dynamic prediction module, and an interpretability engine. The application service layer includes a natural language interactive interface.

[0048] By implementing this invention, intelligent assessment and decision support for the effectiveness of comprehensive environmental remediation can be achieved, improving the scientific rigor, accuracy, and efficiency of the remediation work. For example, in the Tiemen Township project in Liangping District, Chongqing, this invention shortened the assessment cycle from the traditional three months to less than 24 hours, achieved an assessment accuracy rate of 96.5%, and generated targeted optimization suggestions, avoiding a duplicate investment of 4 million yuan in the project.

[0049] This invention is not only applicable to comprehensive rural land consolidation, but can also be applied to various spatial governance scenarios such as urban renewal and ecological restoration, and has broad promotional value.

[0050] The various variations and specific examples of the methods provided in the above embodiments are also applicable to the comprehensive governance cognition and decision support system of this embodiment. Through the foregoing detailed description of the comprehensive governance cognition and decision support method, those skilled in the art can clearly understand the implementation method of the comprehensive governance cognition and decision support system of this embodiment. For the sake of brevity, it will not be described in detail here.

[0051] The above description of the embodiments is only used to provide a detailed introduction to the technical solution of the present invention. However, the description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention, and should not be construed as a limitation of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A comprehensive governance cognition and decision support method, characterized in that, Includes the following steps: S1: Collect and preprocess multi-source heterogeneous data from the whole-domain governance scenario to obtain multimodal data; S2: Construct a digital twin model of the entire governance scenario and use an attention mechanism to fuse multimodal data to obtain fused data; S3: Employ a multimodal large model to extract semantic features and relationships from the fused data; S4: Obtain historical data and experience knowledge during the overall governance process, make time-series predictions based on spatiotemporal memory networks, and evaluate the prediction results of different decisions with the help of counterfactual reasoning; S5: Generate and display decision recommendations based on the prediction results.

2. The comprehensive governance cognition and decision support method as described in claim 1, characterized in that, The multimodal data in S1 includes remote sensing image data, geospatial data, land use data, and socioeconomic data.

3. The comprehensive governance cognition and decision support method as described in claim 1, characterized in that, The S2 method uses an attention weight calculation formula to calculate the fused data. in, This represents the query vector, indicating the semantics of the current task. and These represent the feature representations of each modality of data; Representing the The fusion weights for each modality take values ​​in the range [0, 1], and .

4. The comprehensive governance cognition and decision support method as described in claim 3, characterized in that, The digital twin model in S2 includes terrain, buildings, vegetation, and water features.

5. The comprehensive governance cognition and decision support method as described in claim 3, characterized in that, The fused data in S2 includes image data, text data, and geographic data.

6. The comprehensive governance cognition and decision support method as described in claim 1, characterized in that, The structure of the multimodal large model in S3 is as follows: in, Represents the PTM model; These represent the query, key, and value matrices, respectively, derived from feature projections of different modalities. Represents a multi-head attention mechanism; Represents a feedforward neural network; Represents the normalization layer; This represents fused data.

7. The comprehensive governance cognition and decision support method as described in claim 1, characterized in that, The state update formula for the spatiotemporal memory network in S4 is: in, represent The hidden state at any given time, with dimensions of ; represent The memory state at any given moment, with dimensions of ; represent The input feature vector at time step 1.

8. The comprehensive governance cognition and decision support method as described in claim 7, characterized in that, The formula for calculating the prediction result is as follows: in, This represents the prediction result under counterfactual conditions; This represents the outcome under current factual conditions; Input conditions representing counterfactual assumptions; Represents the current actual input conditions; This represents the gradient of the output with respect to the input.

9. The comprehensive governance cognition and decision support method as described in claim 1, characterized in that, Between S4 and S5, there is also: The computing tasks are dynamically allocated to the cloud and edge devices, with the optimization goal of minimizing the total computing cost. Real-time inference and feedback are performed on the edge devices to execute lightweight inference, while large-scale model updates and collaborative optimizations are carried out in the cloud. The formula for calculating the minimum total computational cost is: in, Represents a binary variable, indicating a task. Assign to device ; Representative task In the equipment The computational cost on top; Representative equipment Maximum task capacity.

10. A comprehensive governance cognition and decision support system, characterized in that, The method for implementing a comprehensive governance cognition and decision support as described in any one of claims 1-9 includes: an intelligent perception layer, a digital twin layer, a cognitive decision layer, and an application service layer. The intelligent sensing layer includes an integrated air-space monitoring network, an adaptive data acquisition engine, and edge computing nodes. The digital twin layer includes a holographic visualization modeling engine, a multimodal data fusion module, and a spatiotemporal memory network; The cognitive decision-making layer includes a multimodal large model, a dynamic prediction module, and an interpretability engine; The application service layer includes a natural language interactive interface.

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