Dike self-adaptive cooperative regulation and control system and method based on distributed group intelligence

Abstract

The invention discloses a dike self-adaptive cooperative regulation and control system and method based on distributed group intelligence, and belongs to the field of water conservancy project protection. The system comprises a three-level framework formed by basic nodes, regional control nodes and a central controller which are deployed in a distributed mode, and cooperative monitoring and regulation from local to global are achieved. A mechanism-data fusion health diagnosis model is adopted, embankment engineering physical constraint rules are embedded into a neural network, and real-time and credible health state diagnosis is achieved. And when a dangerous case is diagnosed, a collaborative response mechanism based on a multi-agent game is triggered, a node calculates a utility value according to task relevancy, a self-state and collaborative cost containing earthwork transportation energy consumption, and an optimal collaborative response combination is formed through self-organization of a VCG consensus algorithm. According to the invention, the conversion of embankment safety monitoring from centralized passive response to distributed active self-healing is realized, and the method has the characteristics of real-time early warning, physical credibility and high efficiency in cooperation.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering protection, specifically to an adaptive collaborative control system and method for dikes based on distributed swarm intelligence. Background Technology

[0002] With the continuous development of water conservancy projects in my country, dikes, as crucial infrastructure for flood control and disaster reduction, play a vital role in safeguarding national economic development and the safety of people's lives and property. Traditional dike safety monitoring and control systems primarily rely on a centralized architecture. This involves deploying various sensors at key locations along the dike, transmitting the collected data to a central control center for processing and analysis, followed by manual assessment and the development of appropriate countermeasures. However, this model often suffers from delayed response and poor coordination when facing complex and ever-changing natural disasters, making it difficult to meet the needs of modern dike safety management.

[0003] In recent years, with the rapid development of technologies such as the Internet of Things, artificial intelligence, and distributed systems, the application of multi-agent cooperative control and swarm intelligence in engineering fields has gradually become a research hotspot. CN115840892B discloses a hierarchical autonomous decision-making method and system for multi-agent systems in complex environments. This system constructs a decision-making and control model through agent modeling, achieving goal-oriented overall behavioral emergence and forming swarm intelligence control decisions. It possesses the resilience, robustness, and high dynamic topology adaptability of a self-organizing network. This provides an important reference for the distributed architecture design of dike safety monitoring systems.

[0004] In the area of ​​risk monitoring and early warning, CN121032726A proposes an intelligent control system and method for urban drainage network overflow risk based on the Internet of Things (IoT). This system integrates IoT sensing and data acquisition, nonlinear dynamics algorithms for real-time diagnosis and early warning, graph neural networks and causal discovery algorithms for risk causal inference, and multi-agent adaptive game theory algorithms for collaborative decision-making and execution. This approach of combining physical models with data-driven methods is inspiring for the design of levee safety monitoring systems.

[0005] In the field of swarm intelligence decision-making, CN121032758A introduces a method and system for emergency decision-making in transportation hubs with large passenger flow based on swarm intelligence emergence and large-scale models. This system achieves a shift from traditional single-center decision-making to distributed collaborative autonomous decision-making through four modules: distributed perception, swarm intelligence emergence, large-scale model meta-agent evaluation, and emergency decision-making linkage execution. CN117725832A proposes an adaptive swarm intelligence decision-making method based on unmanned surface vessel (USV) swarms. This method collects environmental information using multiple sensors, fuses the data using distributed algorithms to form a global situational awareness, and then employs a collaborative decision-making algorithm to achieve intelligent coordination of the swarm.

[0006] In the field of bio-like control and data-driven fusion, CN119292300A discloses a method and related equipment for unmanned swarm cooperative control based on the fusion of bio-like control and data-driven fusion. This method changes the fixed decision parameters in the bio-like control model to adaptive time-varying decision parameters that change with environmental perception, and trains them through a deep reinforcement learning framework to achieve cooperative control of unmanned swarms.

[0007] However, existing technologies still have the following shortcomings in the monitoring and control of dike safety:

[0008] First, traditional dike safety monitoring systems primarily rely on a "data acquisition-central transmission-manual assessment" model or a simple threshold alarm mode, resulting in delayed responses and an inability to promptly identify the "golden window" when danger first appears, thus impacting rescue efficiency. Second, existing systems are highly dependent on central servers and communication networks, making them prone to paralysis under extreme disasters. This weakens the dike safety monitoring system's rigidity, hindering reliable real-time monitoring and timely response. Third, existing algorithmic early warning systems lack engineering and physical basis, resulting in low reliability and difficulty in guiding practical operations, as well as accurately assessing and identifying complex dangers. Fourth, in multi-agent collaborative handling processes, the lack of an effective collaborative decision-making mechanism makes it difficult to balance collaborative efficiency and safety, affecting the overall collaborative working capability of the dike system. Finally, existing systems lack in-depth analysis and real-time monitoring mechanisms of the dike's engineering physical characteristics, failing to accurately reflect the actual operating status and potential risks of the dike, thus affecting the scientific rigor and accuracy of rescue decisions.

[0009] Therefore, there is an urgent need to develop a dike adaptive collaborative control system based on distributed swarm intelligence, which combines physical models with data-driven methods to construct a multi-layered distributed architecture, enabling real-time monitoring of dike safety status, accurate risk assessment, and efficient collaborative emergency response, thereby improving the intelligence level of dike safety management and emergency response capabilities. Summary of the Invention

[0010] To address the problems of traditional dike safety monitoring systems, such as delayed response, high system vulnerability, lack of physical basis for early warning, insufficient collaborative decision-making mechanism, and lack of in-depth analysis of the physical characteristics of dike projects, and to realize the paradigm shift of dike safety monitoring from "centralized passive response" to "distributed active self-healing," this paper provides a dike adaptive collaborative control system and method based on distributed swarm intelligence.

[0011] The technical solution adopted by this invention to solve its technical problem is:

[0012] An adaptive collaborative control system for dikes based on distributed swarm intelligence, comprising:

[0013] Multiple distributed basic nodes constitute an information processing layer, used to collect dike environmental data and perform local control operations; multiple regional control nodes constitute a regional control node layer, each of which is communicatively connected to and collaboratively manages a group of the aforementioned basic nodes.

[0014] The central controller forms the global control layer and communicates with each of the aforementioned regional control nodes.

[0015] A mechanism-data fusion health diagnosis model is used to perform real-time diagnosis of the health status of dikes based on data collected from the basic nodes and regional control nodes, combined with physical constraint rules of the dike engineering; wherein, the physical constraint rules of the dike engineering are embedded in the health diagnosis model in the form of executable code;

[0016] A distributed collaborative control module is used to trigger a collaborative response mechanism based on multi-agent game theory when the health diagnosis model detects a hazard. Multiple nodes responding to the hazard make game decisions based on a preset utility function, self-organizing to form a collaborative response combination. The utility function is determined at least by the node's task relevance, its own state, and the collaboration cost. The physical constraints of the embankment engineering include at least one of seepage stability constraints, outflow slope boundary constraints, seepage line morphology smoothness constraints, structural stability constraints, and material strength constraints.

[0017] The distributed collaborative control module is configured to use the VCG consensus algorithm for game decision-making, selecting the node combination with the largest total declared utility value as the collaborative responder.

[0018] The collaboration cost dimension in the utility function is calculated using an earthwork transportation energy consumption coefficient μ, where μ = k. 1·ρ +k 2· d, where ρ is the soil density, d is the standardized haul distance, and k1 and k2 are weighting coefficients determined by fitting historical earthwork transportation task data, and k 1+ k 2＝ 1.

[0019] The earthwork transportation energy consumption coefficient μ is calculated according to the formula μ=0.25·ρ+0.65·d+0.1·γ, where γ is the environmental humidity correction coefficient, and the weighting coefficient is determined based on the test data of the influence of humidity on earthwork transportation energy consumption.

[0020] An adaptive collaborative control method for dikes based on distributed swarm intelligence includes:

[0021] Multidimensional sensing data of the dike is collected through multiple distributed basic nodes and regional control nodes.

[0022] A health diagnosis model based on mechanism-data fusion is used to analyze the multidimensional sensing data in real time, combined with the physical constraint rules of the dike engineering, in order to diagnose the health status of the dike; wherein the physical constraint rules of the dike engineering are embedded in the health diagnosis model through gradient truncation technology or loss function weighting.

[0023] When a dangerous situation is diagnosed, a collaborative response mechanism based on multi-agent game theory is triggered, enabling relevant nodes to engage in game theory based on a utility function determined by task relevance, their own state, and collaboration costs, and to self-organize and form a collaborative response combination.

[0024] The coordinated response combination is controlled to perform the control task corresponding to the emergency. The embedding method includes gradient zeroing operation. The physical constraint rules of the embankment project include at least one of the following: seepage stability constraint based on Darcy's law, outflow slope boundary constraint based on critical slope of soil, seepage line morphology smoothing constraint based on slope limit, structural stability constraint based on moment balance, and material strength constraint based on allowable stress of material.

[0025] The beneficial effects of this invention are as follows: By constructing a three-level distributed architecture, the dike possesses the ability to identify local risks in milliseconds and to enable neighboring nodes to self-organize and coordinate in seconds, realizing a paradigm shift from "centralized passive response" to "distributed proactive self-healing." This effectively solves the problem of delayed response in traditional systems, enabling timely seizing of the "golden window" when danger first appears and improving rescue efficiency. The distributed network architecture avoids dependence on central servers and communication networks, overcoming the rigidity and fragility of traditional systems, improving system reliability and stability, and ensuring normal operation even in extreme disaster situations, thus guaranteeing the sustainability of the dike safety monitoring system. Furthermore, it innovatively embeds the physical laws of dike engineering as a "rule base" hard constraint or soft loss function into the neural network, establishing an interpretable and reliable... The real-time health diagnosis model addresses the lack of engineering and physical basis in existing algorithmic early warning systems, improving the accuracy and reliability of warnings and providing a scientific basis for the identification and assessment of complex emergencies. The distributed collaborative control algorithm based on multi-agent game theory, by introducing dike-specific parameters such as the energy consumption coefficient of earthwork transportation to optimize the utility function, achieves more practical and cost-effective collaborative decision-making among multiple nodes. This solves the problem of traditional systems lacking effective collaborative decision-making mechanisms in multi-agent collaborative handling, achieving a balance between collaborative efficiency and safety, and enhancing the collaborative working capability of the entire dike system. By comprehensively considering factors such as task relevance, self-state, and collaboration costs, it achieves optimized scheduling and collaborative allocation of node resources, improving the overall efficiency of the system and its ability to cope with complex emergencies. Attached Figure Description

[0026] Figure 1This is a diagram showing the overall structure of the dike adaptive collaborative control system in this scheme.

[0027] Figure 2 This is a schematic diagram illustrating the health diagnosis process in this plan;

[0028] Figure 3 is a schematic diagram of the emergency triggering process in this plan; Figure 4 A flowchart illustrating the game-theoretic decision-making process when a dangerous situation is diagnosed. Detailed Implementation

[0029] The present application will be further described in detail below with reference to the accompanying drawings, so that those skilled in the art can better understand and implement the present invention, but the embodiments described are not intended to limit the present invention.

[0030] Example 1

[0031] like Figure 1-2 As shown, this invention provides an adaptive collaborative control system for dikes. The system adopts a three-layer architecture, including an information processing layer, a regional control node layer, and a global control layer, to achieve real-time monitoring, health diagnosis, and collaborative control of the dikes. The information processing layer consists of multiple basic nodes deployed on the dike. Each basic node is equipped with a sensing module, a decision-making module, and an execution module. The sensing module includes sensors for monitoring water level, seepage, soil pressure, and stress, collecting environmental data and the node's own status data in real time. The decision-making module performs local decision analysis based on the collected sensing data and generates corresponding execution commands. The execution module includes micro-mechanical actuators responsible for performing corresponding operations, such as local repairs and grouting, according to the commands from the decision-making module.

[0032] The regional control node layer consists of multiple regional cluster head nodes. Each regional cluster head node communicatively connects to multiple basic nodes within its jurisdiction, receiving and collaboratively processing data from each basic node within that region, performing local risk assessments and coordinated responses. Equipped with drones and large mechanical equipment, regional cluster head nodes can perform larger-scale operational tasks, such as regional dam reinforcement and construction. Regional cluster head nodes possess strong computing and communication capabilities, enabling them to group and manage basic nodes within their jurisdiction, achieving localized collaborative data processing.

[0033] The global control layer consists of a central controller that communicates with each regional cluster head node. The central controller receives global information, performs system-level learning and policy optimization, and issues policy commands to the regional control node layers. The central controller possesses supercomputing capabilities and is responsible for long-term system learning and policy optimization. Data exchange between the central controller and the regional cluster head nodes is conducted via 5G and satellite communication to ensure communication stability and reliability.

[0034] The system also includes a mechanism-data fusion health diagnosis model, used for real-time diagnosis and early warning of the embankment's health status based on the perceived data from each node and in conjunction with pre-established physical constraint rules for the embankment engineering. This health diagnosis model is a neural network model, and the pre-established physical constraint rules for the embankment engineering are embedded in this neural network model in the form of hard constraints or soft loss functions. The physical constraint rules include seepage stability constraints, outflow slope threshold constraints, seepage line morphology smoothness constraints, structural stability constraints, and material strength constraints.

[0035] The seepage stability constraint establishes a continuity constraint condition based on Darcy's law; the outflow slope threshold constraint determines whether the outflow slope of any section is less than the critical slope of the soil; the smoothness constraint of the phreatic line requires that the slope of the phreatic line be within a certain range; the structural stability constraint, based on the principles of soil mechanics, determines whether the anti-sliding moment and the sliding moment on the sliding surface reach a dynamic equilibrium; the material strength constraint determines whether the stress σ at the monitoring point satisfies σ < [σ], where [σ] is the allowable stress of the material.

[0036] like Figure 3 As shown, when the health diagnosis model detects a dangerous situation, the system triggers a distributed collaborative control mechanism based on multi-agent game theory. Multiple nodes within the relevant area engage in game-theoretic decision-making based on a preset utility function, self-organizing to form the optimal collaborative response combination. Each node's utility function is constructed based on three dimensions: task relevance, its own state, and collaboration cost. The collaboration cost dimension, tailored to the characteristics of dike emergency rescue tasks, introduces an earthwork transportation energy consumption coefficient (μ), calculated using the formula μ = 0.3·ρ + 0.7·d, where ρ is the soil density and d is the standardized transport distance. The weighting coefficients a = 0.3 and b = 0.7 are empirical values ​​determined by analyzing and fitting energy consumption data from over 1000 historical earthwork transportation tasks recorded by the system. This coefficient significantly increases the cost of transporting heavy earthwork over long distances, thus naturally favoring nearby or high-efficiency nodes in the algorithm to optimize overall energy consumption. In another preferred embodiment, to account for the impact of ambient humidity on transportation energy consumption, the formula μ = 0.25·ρ + 0.65·d + 0.1·γ can be used, where γ is the ambient humidity correction coefficient (e.g., the normalized humidity value). The game-theoretic decision-making employs the VCG consensus algorithm to select the node combination with the highest total utility as the collaborative responders.

[0037] In a preferred embodiment, when the system detects an abnormal seepage in a certain area of ​​the dike, the health diagnosis model analyzes data such as water level, seepage velocity, and earth pressure, combined with physical constraint rules, to determine that there is a potential hazard in that area. The system then triggers a distributed collaborative control mechanism. Basic nodes within the relevant area calculate their respective utility values, including their relevance to the seepage treatment task, their own power consumption and robotic arm status, and the comprehensive collaborative cost (e.g., distance × μ) after considering the earthwork transportation energy consumption coefficient μ. Using the VCG consensus algorithm, the system selects the node combination with the highest total utility, including 10 basic nodes equipped with micro-robotic arms and 1 regional cluster head node equipped with a drone, forming a collaborative response team. These nodes work collaboratively; the basic nodes are responsible for local grouting and repair, while the drone at the regional cluster head node provides real-time monitoring and material transportation support, successfully handling the seepage hazard.

[0038] Through the synergistic effect of this three-layer architecture and intelligent algorithms, the dike adaptive collaborative control system can realize real-time monitoring, health diagnosis and collaborative control of dike status, effectively prevent and deal with various risks, and improve dike safety and management efficiency.

[0039] Example 2

[0040] This embodiment provides a dike adaptive collaborative control method based on distributed swarm intelligence, which is applied to the dike adaptive collaborative control system described in Embodiment 1.

[0041] The method includes the following steps:

[0042] Step 1: Data Collection

[0043] Through the basic nodes of the information processing layer, environmental data and node status data of the dike are collected in real time. Environmental data includes parameters such as water level, seepage velocity, soil pressure, and stress, while node status data includes power level and robotic arm status. This data is collected by the sensing module described in Example 1, which is equipped with corresponding sensor devices.

[0044] Step Two: Health Check

[0045] A health diagnosis model based on mechanism and data fusion is used to perform real-time diagnosis of the health status of dikes, based on collected data and combined with physical constraint rules of the dike project. This health diagnosis model is the neural network model described in Example 1, in which physical constraint rules are embedded in the model in the form of hard constraints or soft loss functions.

[0046] During the health diagnosis process, the system also performs the following operations:

[0047] 1) Based on the acquired data stream, multidimensional dynamic features are extracted using nonlinear dynamics methods, including the maximum Lyapunov exponent (λ).m ax), Kolmogorov-Sinai entropy and attractor geometric complexity;

[0048] 2) Calculate the fluid chaos index based on these multidimensional dynamic characteristics;

[0049] 3) The fluid chaos index is compared with a preset warning threshold to generate a warning signal. The preset warning threshold, in particular λ... m The threshold for ax is determined based on statistical analysis of historical dam failure and major emergency data. For example, the mapping relationship established based on historical data shows that when λ m When ax < 0.05, the system is stable (normal); when 0.05 ≤ λ m When ax < 0.15, the fluid exhibits weak chaos, indicating a risk (warning); when λ m When ax ≥ 0.15, a strong chaotic state is indicated, with a high probability of danger. The system sets tiered early warning thresholds based on this mapping table.

[0050] In the health diagnosis model, the establishment of physical constraint rules for dike engineering includes:

[0051] 1) Establish seepage stability constraints and satisfy the continuity equation according to Darcy's law.

[0052] 2) Establish the exit slope threshold constraint and determine whether the exit slope Ji of any cross section satisfies Ji < η·Jc, where Jc is the critical slope of the soil and η is the safety factor;

[0053] 3) Establish a smoothness constraint for the morphology of the wetting line, requiring the slope of the wetting line to meet the following conditions. Where M is the maximum permissible slope;

[0054] 4) Establish structural stability constraints and determine the anti-slip moment Σ(τi·Ai) and sliding moment on the sliding surface. Does it meet the requirements? Where φ is the internal friction angle of the soil, and k is the stability coefficient;

[0055] 5) Establish material strength constraints and determine whether the stress σ at the monitoring point satisfies σ<[σ], where [σ] is the allowable stress of the material.

[0056] Step 3: Formation of Synergistic Response Combinations

[0057] When a danger is diagnosed, a distributed cooperative control mechanism based on multi-agent game theory is used to make game decisions among relevant nodes based on a preset utility function, thereby self-organizing a cooperative response combination. The game decision-making specifically includes:

[0058] 1) Calculate the utility value for each node based on the type, location, and urgency of the emergency. The utility value comprehensively considers task relevance, the node's own status, and collaboration costs. Collaboration cost C c The computational optimization for ollaborate is: C c ollaborate = f(distance, task type) × μ, where μ is the earthwork transportation energy consumption coefficient of the node, which can be calculated by the formula μ = 0.3·ρ + 0.7·d or μ = 0.25·ρ + 0.65·d + 0.1·γ.

[0059] 2) The VCG consensus algorithm is used to screen all reporting nodes, and the node combination that maximizes the total reporting utility value is selected as the collaborative responders.

[0060] Step 4: Task Execution

[0061] The control and coordination mechanism enables nodes in the coordinated response system to perform corresponding emergency rescue or repair tasks. Based on the results of the game theory decision, each node performs coordinated operations according to its assigned tasks, such as local levee reinforcement, grouting, and construction work.

[0062] In a preferred embodiment, when the system detects abnormal seepage in a certain area of ​​the dike, the health diagnosis model analyzes data such as water level, seepage velocity, and earth pressure, combined with physical constraint rules, to determine that a hazard exists in that area. The system then triggers a distributed collaborative control mechanism, where basic nodes within the relevant areas calculate their respective utility values. Using the VCG consensus algorithm, the system selects the node combination with the highest total utility to form a collaborative response team. These nodes work together to successfully handle the seepage hazard.

[0063] Through this distributed swarm intelligence collaborative control method, the system can achieve real-time monitoring, health diagnosis and collaborative control of the dike status, effectively prevent and deal with various risks, and improve the safety and management efficiency of the dike.

[0064] Example 3

[0065] This embodiment elaborates in detail the implementation of embedding physical constraints in the mechanism-data fusion health diagnosis model, especially the specific technical implementation of hard constraints and soft loss functions.

[0066] Hard constraint implementation and examples:

[0067] For physical rules that must be strictly satisfied (such as material strength constraints σ < [σ]), gradient truncation techniques are used to implement hard constraints during neural network training. The specific steps are as follows:

[0068] In forward propagation, predicted values ​​(such as predicted stress σ) are calculated. p red).

[0069] Determine if the constraint is violated: If σ p If red≥[σ], it is considered a violation of the hard constraint.

[0070] During backpropagation, the gradients corresponding to neurons that cause constraint violations are truncated or reduced to zero, i.e.: grad n ew=0ifσ p red≥[σ]elsegrad o Original.

[0071] This method can effectively prevent network parameters from updating in a direction that violates physical laws, ensuring that the model output always lies within the physically feasible region.

[0072] Demonstration of the gradient zeroing operation effect using seepage stability constraints as an example:

[0073] Suppose a neural network model needs to predict the seepage velocity v at a certain cross section. p red. Physical constraint rules require that, according to Darcy's law and the difference in water level between upstream and downstream, v p red should be within a reasonable range [v] m in,v m Within [ax]. During training:

[0074] Forward propagation yields v p red = 1.5 m / s.

[0075] The reasonable maximum velocity calculated by the physics rule base based on the current head difference is v. m ax = 1.2 m / s. Determine: v p red>v m ax, violation of constraints.

[0076] During backpropagation to update parameters, all factors that led to this prediction v are included. p The gradient of neurons with a large red value is set to zero. This means that in this training iteration, the model will not update in the direction of increasing the seepage prediction value, but will be forced to find other parameter update paths that can reduce the prediction value or make it conform to the physical laws.

[0077] After this gradient zeroing operation, the parameter updates allow for adjustments to the model's predicted output value during the next forward propagation, such as v. p The red line is corrected to 1.1 m / s, thus satisfying v. p red≤v m Physical constraints of ax.

[0078] Soft loss function weight optimization:

[0079] For physical rules that can be tolerated by deviations (such as wetting line smoothing constraints), they are constructed as soft constraint loss functions L. physics, and the data fitting loss L d The ata functions together form the total loss function for joint optimization.

[0080] The total loss function is defined as: L t otal=α·L p hysics+β·L d ata.

[0081] Here, α and β are weighting coefficients that control the relative importance of physical constraints and data fitting during training, respectively.

[0082] Example of weight optimization process:

[0083] Initialization: Set α = 1.0, β = 1.0.

[0084] Evaluate model performance on the validation set, focusing on the physical rule compliance rate (e.g., the proportion of wetted line slopes that meet the standard) and the data fitting accuracy (e.g., mean squared error).

[0085] Using grid search or Bayesian optimization methods, α and β are adjusted within a preset range (e.g., α, β ∈ [0.1, 10]). The α and β values ​​that optimize the overall performance index (e.g., 0.6 * compliance rate + 0.4 * (1 - standardized error)) are selected as the final weights. Through this process, the model's adherence to physical laws and its ability to fit actual observation data can be adaptively balanced.

[0086] Example 4

[0087] This embodiment adds a case study on the detection and collaborative reinforcement of structural instability risks to more comprehensively demonstrate the system's handling capabilities.

[0088] When the health diagnosis model is based on real-time monitored soil pressure, stress, and displacement data, combined with the structural stability constraints... During the calculation, it was found that the anti-sliding moment on the sliding surface of a certain section of the embankment was continuously approaching and about to be less than the sliding moment multiplied by the stability coefficient k. It was determined that there was a risk of structural instability due to the imbalance of the sliding surface moment at that location, and a high-level warning was immediately generated.

[0089] The system then triggers a distributed collaborative control mechanism. Basic nodes and their respective regional control nodes (regional cluster head nodes) within the vicinity of the hazard point receive the early warning and initiate utility value calculations. The task relevance of each node is determined by its engineering capability graph. This engineering capability graph is a skill matrix maintained by each node, continuously trained and updated through reinforcement learning in historical collaborative tasks. For example, as... Figure 4As shown, in an area with a dike height of 5m and a slope of 1:2.5, the skill vector of the control node can be represented as: [Earthwork Reinforcement: 0.9, Piling: 0.8, Grouting: 0.6, Material Transportation: 0.95]. Higher values ​​indicate higher proficiency and efficiency in that skill. For the current structural reinforcement task, the system will match nodes requiring high skill values ​​in "Earthwork Reinforcement" and "Piling".

[0090] Self-status: Each node reports its current battery level, robotic arm operating status, and inventory of materials (such as sandbags and concrete). Collaboration cost: Calculates the time and energy cost required for each node to move to the emergency location. The earthwork transportation energy consumption coefficient (μ) is calculated based on the transportation distance and current soil conditions (using the formula μ = 0.3·ρ + 0.7·d or an extended formula considering humidity) and then incorporated.

[0091] Through multiple rounds of game and consensus-building using the VCG consensus algorithm, the system ultimately selects an optimal collaborative response combination: 5 neighboring basic nodes with high "earthwork reinforcement" skill values: immediately dispatched to the potential area of ​​the sliding surface, using a micro-robotic arm to perform rapid soil compaction and local sandbag stacking for initial anti-slip.

[0092] One regional control node with high "pile driving" and "material transportation" skill values: dispatches the large pile drivers and concrete mixing equipment under its management to the site, and directs two drones to conduct precise mapping and real-time monitoring.

[0093] The other two more distant area control nodes, with high "material transportation" skill values, are responsible for transporting additional reinforcement materials and providing backup power support, respectively, according to global resource allocation instructions.

[0094] Collaborative execution process:

[0095] The basic node cluster was put in place first, and emergency local reinforcement was carried out to stabilize the development trend of the danger.

[0096] Once the regional control node arrives with large equipment, it immediately carries out pile driving and concrete pouring at key locations on the sliding surface to implement fundamental reinforcement.

[0097] The drone monitors the stress and displacement changes throughout the reinforcement process, and the data is transmitted back to the health diagnosis model in real time for reassessment until the model determines that the structural stability constraints are met again and the danger is eliminated.

[0098] This case demonstrates the complete response ability of the system to non-seepage structural dangers, from precise detection, rapid collaborative decision-making based on a skill matrix to multi-level equipment linkage execution, reflecting the universality and effectiveness of the present invention under various levee dangers. The above are all preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. A dike adaptive collaborative control system based on distributed swarm intelligence, characterized in that, include: Multiple basic nodes deployed in a distributed manner constitute an information processing layer, which is used to collect dike environmental data and perform local control operations. Multiple regional control nodes constitute a regional control node layer, and each regional control node is communicatively connected and collaboratively manages a group of the aforementioned basic nodes; The central controller forms the global control layer and communicates with each of the aforementioned regional control nodes. A mechanism-data fusion health diagnosis model is used to perform real-time diagnosis of the health status of dikes based on data collected from the basic nodes and regional control nodes, combined with physical constraint rules of the dike engineering; wherein, the physical constraint rules of the dike engineering are embedded in the health diagnosis model in the form of executable code; A distributed collaborative control module is used to trigger a collaborative response mechanism based on multi-agent game theory when the health diagnosis model diagnoses a dangerous situation. The multiple nodes responding to the dangerous situation make game decisions based on a preset utility function to form a collaborative response combination through self-organization. The utility function is determined at least by the task relevance of the nodes, their own state, and the collaboration cost.

2. The system according to claim 1, characterized in that, The physical constraint rules for the dike project are embedded in one of the following ways: a) Hard constraint method: Gradient cutoff technique is used in model training to block gradient backpropagation when the predicted value violates physical rules; b) Soft constraint method: Constructing physical rules as physical constraint loss terms Loss term of data fitting Weighted summation is used for joint optimization.

3. The system according to claim 2, characterized in that, When the hard constraint method is adopted, it is achieved through a gradient zeroing operation, specifically: when the model predicts the seepage velocity... Violating the reasonable maximum speed calculated based on Darcy's law When this happens, it will cause the gradient of the relevant neuron in the prediction to be set to zero, that is... .

4. The system according to claim 1 or 2, characterized in that, The physical constraints of the embankment project include at least one of the following: seepage stability constraints, outflow slope boundary constraints, phreatic line morphology smoothness constraints, structural stability constraints, and material strength constraints.

5. The system according to claim 1, characterized in that, The distributed collaborative control module is configured to use the VCG consensus algorithm for game decision-making, selecting the node combination with the largest total declared utility value as the collaborative responder.

6. The system according to claim 5, characterized in that, The collaboration cost dimension in the utility function is calculated using the earthwork transportation energy consumption coefficient μ, μ = k1·ρ + k2·d, where ρ is the soil density, d is the standardized transport distance, k1 and k2 are weight coefficients determined by fitting historical earthwork transportation task data, and k1 + k2 = 1.

7. The system according to claim 6, characterized in that, The earthwork transportation energy consumption coefficient μ is calculated according to the formula μ = 0.25·ρ + 0.65·d + 0.1·γ, where γ is the environmental humidity correction coefficient, and the weighting coefficient is determined based on the test data of the influence of humidity on earthwork transportation energy consumption.

8. A dike adaptive collaborative control method based on distributed swarm intelligence, characterized in that, include: Multidimensional sensing data of the dike is collected through multiple distributed basic nodes and regional control nodes. A health diagnosis model based on mechanism-data fusion is used to analyze the multidimensional sensing data in real time, combined with the physical constraint rules of the dike engineering, in order to diagnose the health status of the dike; wherein the physical constraint rules of the dike engineering are embedded in the health diagnosis model through gradient truncation technology or loss function weighting. When a dangerous situation is diagnosed, a collaborative response mechanism based on multi-agent game theory is triggered, enabling relevant nodes to engage in game theory based on a utility function determined by task relevance, their own state, and collaboration costs, and to self-organize and form a collaborative response combination. The coordinated response combination is controlled to perform the control tasks corresponding to the danger.

9. The method according to claim 8, characterized in that, The embedding method includes a gradient zeroing operation, specifically: when the model predicts the seepage velocity... Violating the reasonable maximum speed calculated based on Darcy's law When this happens, the gradient of the relevant neuron in the prediction will be set to zero.

10. The method according to claim 8 or 9, characterized in that, The physical constraint rules for the embankment project include at least one of the following: seepage stability constraint based on Darcy's law, exit slope boundary constraint based on critical slope of soil, smooth morphology constraint based on slope limit, structural stability constraint based on moment balance, and material strength constraint based on allowable stress of material.

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