Dynamic optimization and risk early warning platform for weapon escorting route

Abstract

The invention relates to the field of intelligent security and path planning, and discloses a weapon escorting route dynamic optimization and risk early warning platform, which comprises a multi-modal data acquisition module, integrates traffic flow, geographic elevation and threat public opinion data through space-time alignment and tensor filling, and constructs a multi-channel space-time feature tensor; the dynamic risk assessment module is used for solving and generating a dynamic risk field based on an adversarial game model and Nash equilibrium, and quantifying a risk gradient under an attacker strategy; the quantum collaborative optimization module is used for generating quantum parameters through tensor hidden variable decomposition and solving a multi-target optimal route in combination with a quantum annealing algorithm; the hypergraph emergency response module is used for reconstructing a mutual exclusion redundant path and feeding back and updating a strategy space when a threshold exceeding risk is detected; and the strategy collaborative updating module fuses the risk gradient and the game utility to generate a joint reward signal, and drives the reinforcement learning model to dynamically optimize the decision strategy. The safety, efficiency and reliability of the escorting task are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent security and path planning, and in particular to a dynamic optimization and risk warning platform for weapon escort routes. Background Art

[0002] Traditional weapons escort route planning systems rely heavily on static risk assessment models and fixed-path optimization algorithms, which present significant limitations when responding to complex dynamic threats. Existing technologies typically use a single sensor data source (such as GPS positioning or manual inspection reports) for risk assessment. This lacks the fusion analysis of multimodal data such as traffic flow, geographic environment, and dark web public opinion, resulting in a single risk identification dimension and difficulty capturing the spatiotemporal correlation characteristics of sudden threats. Furthermore, traditional path optimization algorithms (such as Dijkstra and A* algorithms) perform shortest path searches based on fixed weight parameters and are unable to dynamically adjust weight distribution based on real-time risk gradients. This can easily lead to global path failures due to local high-risk areas.

[0003] In terms of emergency response, existing systems often rely on manually pre-set backup paths, which lack redundancy and mutual exclusivity, making it difficult to quickly generate effective alternatives in the event of a hijacking. More critically, the attack and defense strategy update process lacks a closed-loop feedback mechanism. The dynamic game relationship between the escort's strategy and the attacker's behavior has not been quantitatively modeled, resulting in limited adaptability of the decision-making model and a gradual decline in security and efficiency during long-term mission execution.

[0004] The above defects make it difficult for the existing system to meet the core requirements of real-time risk perception, dynamic path optimization and adaptive strategy adjustment in high-value weapons escort missions. Summary of the Invention

[0005] In response to the shortcomings of existing technologies, the present invention provides a dynamic optimization and risk warning platform for weapons escort routes to solve the problems of insufficient multimodal data fusion, delayed risk response, and lack of dynamic coordination between path optimization and emergency strategies in existing weapons escort systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a weapons escort route dynamic optimization and risk warning platform, including:

[0007] A multimodal data acquisition module is used to collect traffic flow data, meteorological data, geographic elevation data, and threat public opinion text data in real time, and map the collected data to a unified spatiotemporal coordinate system to construct a spatiotemporal feature tensor containing multi-channel features;

[0008] A dynamic risk assessment module receives the spatiotemporal feature tensor from the multimodal data acquisition module, calculates the dynamic risk value of each spatiotemporal node in the escort route through an adversarial game model, and outputs a dynamic risk field;

[0009] A quantum collaborative optimization module receives the spatiotemporal feature tensor and the dynamic risk field, generates quantum optimization parameters based on the latent variable decomposition results of the spatiotemporal feature tensor, and solves the multi-objective optimal escort route in combination with the gradient constraints of the dynamic risk field;

[0010] The Hypergraph Emergency Response Module monitors the dynamic risk field output by the Dynamic Risk Assessment Module in real time. When the risk value exceeds a preset threshold, it reconstructs redundant paths based on the original road network data and risk distribution, and feeds the reconstructed paths back to the Dynamic Risk Assessment Module to update the attack and defense strategy.

[0011] The strategy collaborative update module receives the utility function data of the dynamic risk field and the adversarial game model, and dynamically adjusts the escort route selection strategy through the joint reward function.

[0012] Preferably, the multimodal data acquisition module includes:

[0013] The spatiotemporal alignment unit maps traffic flow data to a spatiotemporal grid coordinate system, with the grid resolution dynamically adjusted based on the area of ​​the escort zone;

[0014] The tensor filling unit fills data in a preset channel order, which includes at least:

[0015] Channel 1: Traffic density value, sampled by IoT sensors and normalized to the [0, 1] range;

[0016] The second channel is terrain relief, which is generated by the elevation variance within the LiDAR point cloud computing grid;

[0017] The third channel: Threat public opinion vector, which generates a d-dimensional keyword distribution after parsing dark web text through the NLP model.

[0018] Preferably, the dynamic risk assessment module performs the following operations:

[0019] Construct the attacker's strategy space, defining the hijacking point location, attack time window, and available weapon type parameters;

[0020] Construct the escort strategy space, define the set of optional routes, the number of vehicle formations, and the armor protection level parameters;

[0021] The optimal strategy pair is iteratively calculated through the Nash equilibrium solver, and a dynamic risk field is output, where the risk value of each grid is the partial derivative of the utility function under the attacker's optimal strategy.

[0022] Preferably, the dynamic risk assessment module further includes:

[0023] a risk weight adjustment unit, which generates a dynamic weight coefficient according to the number N of time windows when the hijacking risk parameter shows a monotonically increasing trend within N consecutive time windows, wherein the weight coefficient increase multiple is in a piecewise linear relationship with N;

[0024] The public opinion amplification unit performs an exponential amplification operation on the risk value of the associated geographic grid when it detects that the threat public opinion vector contains preset high-risk keywords.

[0025] Preferably, the quantum collaborative optimization module includes:

[0026] The tensor decomposition unit performs CP decomposition on the spatiotemporal feature tensor to extract the time factor matrix, space factor matrix and modal factor matrix;

[0027] A coupling parameter generation unit generates a correlation strength parameter between quantum bits according to the inner product of the row vectors of the time factor matrix and the space factor matrix;

[0028] The annealing solver combines the gradient distribution and correlation strength parameters of the dynamic risk field to solve the multi-objective optimization route that meets vehicle capacity and road height restrictions.

[0029] Preferably, the coupling parameter generating unit performs:

[0030] Calculate the Euclidean distance between nodes based on the corresponding row vectors of the time factor matrix and the space factor matrix;

[0031] When the Euclidean distance is greater than the preset similarity threshold, the coupling strength between the associated quantum bits is forced to zero.

[0032] Preferably, the hypergraph emergency response module performs the following operations:

[0033] When the risk value of any grid in the dynamic risk field exceeds the preset safety threshold, the associated road network edge is marked as a high-risk edge;

[0034] After removing high-risk edges from the original road network graph, a hypergraph convolutional network is used to generate a redundant road network containing at least three non-overlapping backup paths;

[0035] The redundant road network is input into the attacker's strategy space of the dynamic risk assessment module to update the candidate set of hijacking point locations and time windows.

[0036] Preferably, the strategy collaborative update module includes:

[0037] The joint reward calculation unit generates a reward signal based on the real-time value of the dynamic risk field and the partial derivative of the adversarial game utility function to the escort strategy;

[0038] The policy gradient update unit adjusts the weight parameters of the route selection neural network through the policy gradient algorithm, so that the probability of selecting the high reward path increases monotonically.

[0039] Preferably, it also includes:

[0040] Secure communication interface, homomorphically encrypting spatiotemporal feature tensors and dynamic risk fields, with the encryption key bound to the escort mission identifier;

[0041] The decryption verification unit verifies the consistency between the encryption tag and the current escort task identifier before the data is used. If there is any inconsistency, an alarm will be triggered.

[0042] The present invention also provides a method for dynamic optimization and risk warning of weapon escort routes, comprising the following steps:

[0043] Collect multimodal data in real time and construct a spatiotemporal feature tensor, which includes at least traffic density, terrain relief, and threat sentiment vector channels;

[0044] The dynamic risk value of each spatiotemporal node in the escort route is calculated through an adversarial game model, and a dynamic risk field bound to the road network coordinates is output;

[0045] The quantum optimization parameters are generated based on the latent variable decomposition results of the spatiotemporal characteristic tensor, and the multi-objective optimal route is solved by combining the gradient constraints of the dynamic risk field;

[0046] When the risk value is detected to exceed the safety threshold, the redundant path is reconstructed and fed back to the update process of the adversarial game strategy;

[0047] A joint reward signal is generated based on the risk gradient and strategy utility derivative to dynamically optimize the escort decision model.

[0048] The present invention provides a dynamic optimization and risk warning platform for weapons escort routes. It has the following beneficial effects:

[0049] 1. This invention uses the spatiotemporal alignment unit and tensor filling unit of the multimodal data acquisition module to map traffic flow, terrain relief, and threat public opinion data into a unified spatiotemporal coordinate system, constructing a multidimensional feature tensor. The multi-channel structure of this tensor (traffic density, terrain relief, and keyword vectors) enables joint modeling of environmental characteristics and semantic threats, overcoming the incomplete coverage of traditional single-modal data and significantly improving the comprehensiveness and accuracy of potential risk identification.

[0050] 2. This invention utilizes the adversarial game model and Nash equilibrium solver in the dynamic risk assessment module to quantify the gradient distribution of the risk field under the attacker's optimal strategy. Combined with the risk weight adjustment unit's linear response mechanism to continuous risk escalation and the public opinion amplification unit's exponential amplification of high-risk keywords, this system achieves differentiated real-time responses to both gradual and sudden risks, ensuring the dynamic adaptability of the escort strategy.

[0051] 3. This invention leverages the CP tensor decomposition and quantum annealing solution mechanisms of the quantum collaborative optimization module to transform latent variables of spatiotemporal characteristics into qubit coupling parameters. This approach, combined with the gradient constraints of a dynamic risk field, constructs a multi-objective optimization model. Leveraging the parallel search capabilities of quantum computing, this method rapidly solves the problem of traditional heuristic algorithms being prone to local optimality by rapidly finding the global optimal path within the constraints of complex road networks.

[0052] 4. This invention utilizes the hypergraph emergency response module's high-risk edge detection and hypergraph convolutional networks to generate a set of non-overlapping redundant paths after removing high-risk sections. These redundant paths dynamically update the attacker's strategy space through a closed-loop feedback mechanism, ensuring that the escort can still obtain available paths in unexpected risk scenarios, avoiding global mission interruptions caused by the failure of traditional single paths.

[0053] 5. This invention utilizes the joint reward calculation unit and policy gradient update algorithm of the policy collaborative update module to fuse dynamic risk gradients and partial derivatives of game utility into reinforcement learning signals. Through online policy iteration within the actor-critic framework, it continuously optimizes the escort route selection strategy, enabling the system to autonomously evolve its decision-making capabilities in complex adversarial environments and improving the safety and stability of long-term mission execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the system architecture of the present invention;

[0055] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] Please see the attached Figure 1This paper provides a dynamic optimization and risk warning platform for weapons escort routes. Through multimodal data fusion, dynamic risk field modeling, and quantum collaborative optimization, it achieves highly secure escort route planning. The platform includes a multimodal data acquisition module, a dynamic risk assessment module, a quantum collaborative optimization module, a hypergraph emergency response module, and a policy collaborative update module.

[0058] In the multimodal data acquisition module, by collecting traffic flow data, meteorological data, geographic elevation data and threat public opinion text data in real time, and performing spatiotemporal alignment and fusion processing on heterogeneous data, a spatiotemporal feature tensor containing multi-channel features is constructed, providing a unified data foundation for subsequent dynamic risk assessment and route optimization.

[0059] Specifically, the multimodal data acquisition module first connects to various sensors and data interfaces to acquire raw, heterogeneous data. Traffic flow data is periodically collected by IoT sensors deployed at key nodes along the escort route, meteorological data is acquired through satellite remote sensing interfaces, geographic elevation data is scanned using LiDAR equipment to generate point cloud data, and threat sentiment text is captured in real time using a dark web crawler. These multimodal data have discrepancies in timestamps and spatial coordinates, necessitating spatiotemporal alignment.

[0060] Furthermore, the multimodal data acquisition module includes a spatiotemporal alignment unit and a tensor filling unit.

[0061] The spatiotemporal alignment unit maps multi-source data onto a unified spatiotemporal grid coordinate system. The spatiotemporal grid division rules are dynamically adjusted based on the size of the escort mission: a high-resolution grid is used for smaller escort areas to improve positioning accuracy, while a lower resolution is used for larger areas to optimize computational efficiency. The spatiotemporal alignment unit fills in missing data using an interpolation algorithm and employs a sliding time window mechanism to align the data in the temporal dimension, ensuring consistency across all modal data in both spatiotemporal and temporal dimensions.

[0062] After completing the spatiotemporal alignment, the tensor filling unit writes the normalized data into the three-dimensional spatiotemporal feature tensor. The spatiotemporal feature tensor contains multiple preset channels, each corresponding to the feature representation of a modal data. The traffic density channel is calculated using the raw traffic flow data collected by IoT sensors. Specifically, the maximum-minimum normalization method is used to map the raw data to the [0, 1] interval. The formula is expressed as:

[0063]

[0064] Among them, RawCount(s,t) is the raw traffic flow count in the spatiotemporal grid (s,t), and MinCount and MaxCount are the minimum and maximum values ​​of historical data statistics.

[0065] The terrain relief channel is generated from LiDAR point cloud data. Specifically, the elevation variance within each spatiotemporal grid is calculated to characterize the terrain complexity. The formula is:

[0066]

[0067] Among them, z i is the elevation value of the i-th point in the grid s, is the mean elevation of all points within the grid, and N is the number of point clouds within the grid. This channel data reflects the concealment and difficulty of the route, providing geographical environmental characteristics for subsequent risk assessment.

[0068] The threat sentiment vector channel generates this information by parsing dark web text using a natural language processing (NLP) model. Specifically, a pre-trained semantic model is used to extract keywords (e.g., "hijacking" and "explosives") from the text. A d-dimensional keyword distribution vector is constructed using the term frequency-inverse document frequency (TF-IDF) algorithm. This vector is normalized and written into a spatiotemporal feature tensor, which is used to quantify the potential threat level of different regions.

[0069] Preferably, the multimodal data acquisition module also includes a secure communication interface for homomorphically encrypting the spatiotemporal feature tensors during transmission. This encryption process is tied to the escort mission identifier, ensuring data tamper-proofing during transmission and storage. Before data is used, a decryption verification unit verifies the consistency of the encrypted tag with the current mission identifier. Any anomalies trigger a security alarm.

[0070] Through the above implementation method, the multimodal data acquisition module realizes the efficient fusion and standardized processing of multi-source heterogeneous data. The constructed spatiotemporal feature tensor completely covers multi-dimensional features such as traffic, geography, and public opinion, providing reliable data input for subsequent dynamic risk assessment and path optimization. The spatiotemporal alignment mechanism and dynamic grid division rules effectively balance data accuracy and computing efficiency, and adapt to the needs of escort tasks of different scales.

[0071] In the dynamic risk assessment module, based on the spatiotemporal feature tensor provided by the multimodal data acquisition module, the dynamic risk value of each spatiotemporal node in the escort route is quantified through an adversarial game model, and a dynamic risk field bound to the road network coordinates is generated, providing real-time risk situation awareness for subsequent path optimization and emergency response.

[0072] Specifically, the dynamic risk assessment module first constructs a strategy space for the attacker and escort. The attacker's strategy space defines the locations of potential hijacking points, attack time windows, and available weapon types. The hijacking point location set is dynamically updated based on the distribution of historical hijacking incidents and real-time threat sentiment keywords. The escort's strategy space defines the set of optional routes, the number of vehicle formations, and armor protection level parameters. The route set is generated based on the original road network and includes travel time and concealment characteristics for each path.

[0073] Furthermore, the module iteratively calculates the optimal attack and defense strategy pair through a Nash equilibrium solver. The solution process is based on a preset utility function, which comprehensively considers the secrecy of the escort route, resource consumption, and the expected benefits of the attacker. The formula is expressed as:

[0074] U(d j ,a i )=α·Stealth(P j )+β·Energy(P j )-γ·Gain(a i )

[0075] Among them, Stealth (P j ) is route P j The concealment score is calculated by weighted summation of traffic density and terrain relief, using the formula:

[0076]

[0077] Energy(P j ) is the route energy consumption, which is modeled based on the path length and vehicle load; Gain(a i ) is the attacker's profit, which is negatively correlated with the target value and security level. The formula is:

[0078] Gain(a i )=TargetValue·exp(-λ·DefenseLevel)

[0079] After obtaining the Nash equilibrium strategy pair, the dynamic risk field is calculated through the risk field generator. The generation of the dynamic risk field is based on the partial derivative of the utility function under the attacker's optimal strategy, reflecting the sensitivity of the hijacking point position change to the attack benefit. The formula is:

[0080]

[0081] The partial derivatives are calculated using automatic differentiation techniques, and the output is a risk gradient distribution bound to a spatiotemporal grid, which is used to identify high-risk areas.

[0082] Preferably, the module also includes a risk weight adjustment unit for dynamically enhancing the quantitative weight of the continuously escalating risk. When it is detected that the risk value of a certain area shows a monotonically increasing trend in N consecutive time windows, a piecewise linear weight coefficient is generated according to the number of time windows N, and the formula is expressed as:

[0083]

[0084] Among them, k1 and k2 are preset coefficients, N th The updated risk value r'(x,y,t) = K·R(x,y,t) strengthens the response priority for persistent threats.

[0085] In addition, the module also includes a public opinion amplification unit, which is used to detect high-risk keywords in threat public opinion vectors. When a preset high-risk keyword (such as "explosives" or "armed hijacking") is identified, an exponential amplification operation is performed on the risk value of the associated geographic grid. The formula is:

[0086] R'(x,y,t)=R(x,y,t)·exp(μ·KeywordLevel)

[0087] Where μ is the amplification factor, and KeywordLevel is set based on the keyword risk level classification. This operation maps textual semantic threats to spatial risk distribution, enhancing the ability to respond to sudden threats in real time.

[0088] Through this implementation, the dynamic risk assessment module achieves dynamic game equilibrium and risk field quantification for attack and defense strategies. The dynamic risk field serves as the core intermediate variable, driving the coordinated operation of subsequent path optimization and emergency response modules. Weight adjustment and public opinion amplification mechanisms ensure refined classification of persistent and sudden threats, enhancing the system's adaptability and security.

[0089] In the quantum collaborative optimization module, based on the dynamic risk field and spatiotemporal feature tensor output by the dynamic risk assessment module, the spatiotemporal latent variable characteristics are extracted through tensor decomposition, quantum optimization parameters are generated, and the multi-objective optimal escort route is solved to achieve efficient path planning under risk constraints.

[0090] The dynamic risk assessment module includes a tensor decomposition unit, a coupling parameter generation unit and an annealing solver.

[0091] Specifically, the quantum collaborative optimization module first performs a canonical multivariate (CP) decomposition of the space-time feature tensor through the tensor decomposition unit. The decomposition process breaks down the three-dimensional space-time tensor into a combination of a time factor matrix, a space factor matrix, and a modal factor matrix, which can be expressed as follows:

[0092]

[0093] in, is the time factor vector, representing the potential patterns of different time windows; is a spatial factor vector that characterizes the spatial correlation characteristics of road network nodes; R is the decomposition rank, which is dynamically determined according to the energy accumulation ratio of the tensor eigenvalue.

[0094] Furthermore, the module maps the time-space factor matrix to the quantum bit association network through the coupling parameter generation unit. For any two nodes p and q in the road network, the coupling strength between their quantum bits is calculated by the inner product of the row vectors of the time factor and the space factor, as follows:

[0095]

[0096] Among them, A (1) and A (2) The ReLU function is used to eliminate the interference of negative correlation on the optimization process. This calculation method converts the spatiotemporal correlation characteristics into physical interaction parameters between quantum bits, ensuring that the optimization process is strongly coupled with the data characteristics.

[0097] Preferably, the module also includes an invalid association elimination mechanism. By calculating the Euclidean distance between nodes:

[0098]

[0099] When d pq When the similarity exceeds the preset threshold, J pq This mechanism can filter out the false correlations of spatially irrelevant nodes and reduce the computational complexity of quantum annealing solution.

[0100] After the parameters are generated, the module constructs the Hamiltonian model through the quantum annealing solver, which is expressed as follows:

[0101]

[0102] Among them, σ p ∈{-1,+1} represents the quantum bit spin state, and the bias term h p It is generated by the cumulative risk value of the dynamic risk field at node p, and the formula is:

[0103]

[0104] The Hamiltonian transforms the path optimization problem into an energy minimization problem and combines it with the quantum annealing algorithm to search for the global optimal solution.

[0105] The multi-objective optimization model of this module takes into account time cost, risk integral and energy consumption constraints at the same time. The objective function is defined as:

[0106]

[0107] Among them, t e is the travel time of section e, e e is the energy consumption coefficient, and λ1, λ2, and λ3 are weight parameters. The model uses hard constraints to limit physical limitations such as the maximum vehicle load and minimum turning radius to ensure the feasibility of the generated path.

[0108] Preferably, the quantum annealing solution process uses a hybrid quantum-classical computing framework. After the quantum processor performs the annealing operation, the candidate solution is locally optimized using a classical optimization algorithm to further improve the quality and stability of the solution.

[0109] Through this implementation, the quantum collaborative optimization module deeply integrates spatiotemporal data features with quantum computing to achieve risk-sensitive multi-objective path optimization. The tensor decomposition and coupled parameter generation mechanisms effectively extract spatiotemporal correlations from the data, while the quantum annealing solution overcomes the vulnerability of traditional optimization algorithms to local optimality, providing highly secure route decision support for weapons escort missions.

[0110] In the Hypergraph emergency response module, by real-time monitoring of changes in the dynamic risk field, when it is detected that the risk value exceeds the safety threshold, redundant paths are reconstructed based on the original road network data and risk distribution, and the reconstructed paths are fed back to the dynamic risk assessment module to update the attack and defense strategy, forming a risk-adaptive closed-loop control mechanism.

[0111] Specifically, the module first scans the dynamic risk field in real time using a high-risk edge detector. The detector compares the risk value of each spatiotemporal grid in the risk field with the preset safety threshold. When the following conditions are met:

[0112] R(x,y,t)>R crit

[0113] When the associated road network edge is marked as a high-risk edge, R crit is the percentile threshold calculated based on historical risk data. The high-risk edge set will be removed from the original road network graph, forming a partially broken sub-road network structure.

[0114] Furthermore, the module constructs redundant paths in the broken sub-network through a hypergraph generator. The generator uses a hypergraph convolutional network (HyperGCN) model, taking as input a fusion of the remaining network node features and spatiotemporal feature tensors, and generates a set of mutually exclusive paths covering multiple nodes through hyperedge expansion. A hyperedge is defined as a superset of at least three nodes, and its generation rule satisfies:

[0115]

[0116] in For the generated hyperedge set, ensure that there are no overlapping sections between redundant paths. Preferably, the hypergraph convolutional network includes a multi-layer graph attention mechanism, which generates candidate paths that meet the constraints of concealment and traffic efficiency by aggregating the risk characteristics and traffic status of neighboring nodes.

[0117] After redundant paths are generated, the module injects the new path set into the attacker strategy space of the dynamic risk assessment module through the strategy feedback unit. Specifically, the updated attacker candidate hijacking point location set is:

[0118]

[0119] At the same time, the attack time window parameters are dynamically adjusted according to the estimated travel time of the redundant paths, ensuring that the strategy space of the attack and defense game model is updated synchronously with the real-time road network status.

[0120] Preferably, this module also includes a path feasibility verification unit. Before outputting redundant paths, it verifies whether they meet vehicle physical constraints (such as minimum turning radius and maximum slope limit), and performs pruning operations on paths that do not meet the conditions. The pruning process is achieved by iteratively removing hyperedges that violate the constraints to ensure the feasibility of the final path set.

[0121] Through the aforementioned implementation, the Hypergraph Emergency Response Module enables risk-driven dynamic path reconstruction and policy feedback. The high-order correlation modeling capabilities of the Hypergraph Convolutional Network effectively address the limitations of traditional graph models in path redundancy design. The closed-loop feedback mechanism ensures rapid system response to unexpected risks and adaptive policy adjustments, thereby enhancing the overall safety of escort missions.

[0122] In the strategy collaborative update module, through the collaborative analysis of the dynamic risk field and the adversarial game utility function, a joint reward signal is generated and drives the real-time optimization of the escort route selection strategy, realizing the closed-loop iterative update of the risk perception and decision-making model.

[0123] The policy collaborative update module includes a joint reward calculation unit and a policy gradient update unit.

[0124] Specifically, this module first integrates the real-time value of the dynamic risk field and the utility function derivative of the adversarial game model through the joint reward calculation unit. The calculation formula of the reward signal is:

[0125]

[0126] Among them, the numerator R(x t ,y t ,t) is the dynamic risk value of the current time-space node, which is used to suppress the selection tendency of high-risk paths; the denominator Escort strategy The partial derivative of the utility function under reflects the impact of strategy adjustments on attack and defense gains. The reward function normalizes risk constraints and game utility to the same dimension, providing an interpretable feedback signal for strategy optimization.

[0127] Furthermore, the module adjusts the weight parameters of the route selection neural network through the policy gradient update unit. The policy gradient algorithm based on the Actor-Critic framework is adopted, where the Actor network π θ (a|s) Output route selection probability distribution, Critic network V φ (s) Evaluate the state value function. The gradient update formula is:

[0128]

[0129] Among them, the advantage function A(S t ,a t ) is calculated from the timing difference error:

[0130] S(s t ,a t )=r t +γV φ (s t+1 )-V φ (s t )

[0131] The update process stores the historical state-action-reward sequence through the experience replay buffer and uses the stochastic gradient descent method to update the network parameters so that the probability of selecting the high reward path shows a monotonically increasing trend.

[0132] Preferably, the module also includes a policy distillation mechanism. After each iterative update, the current policy network π θ The output distribution of is constrained by the KL divergence with the distribution of the historical optimal strategy to prevent oscillation deviation during the strategy update process. The formula is:

[0133]

[0134] The constraint term is weighted and summed with the original policy gradient loss to ensure the stability and convergence of the policy update.

[0135] Furthermore, this module operates in conjunction with a secure communication interface. During data transmission, the dynamic risk field and utility function data are homomorphically encrypted, with the encryption key bound to the escort mission identifier. Before policy updates, a decryption verification unit verifies data integrity. If the encryption tag and mission identifier do not match, the update process is aborted and a security alert is triggered.

[0136] Through this implementation, the strategy collaborative update module achieves adaptive optimization driven by both risk and utility. The joint reward mechanism deeply integrates real-time perception of the dynamic risk landscape with game utility analysis, while the policy gradient algorithm guides the directed evolution of route selection strategies through the advantage function, ensuring the system can generate safe and reliable escort decisions even in high-risk and complex environments.

[0137] In general, the present invention uses a multimodal data acquisition module to integrate traffic flow, geographic elevation, and threat public opinion data in real time to construct a spatiotemporal feature tensor. The dynamic risk assessment module quantifies the risk field based on the adversarial game model and calculates the dynamic risk value by combining the Nash equilibrium of the attacker's strategy space and the escort's strategy space. The quantum collaborative optimization module uses tensor decomposition to extract spatiotemporal latent variable characteristics to generate quantum parameters and solves the multi-objective optimal path through the annealing algorithm. The hypergraph emergency response module reconstructs mutually exclusive redundant paths when super-threshold risks are detected and feeds back to the attack and defense strategy update. The strategy collaborative update module fuses the risk field gradient and the game utility partial derivative to generate a joint reward signal, driving the reinforcement learning model to dynamically adjust the route selection strategy. Each module forms a dynamic collaborative mechanism through closed-loop feedback to achieve full-process control of risk perception, path optimization, and strategy adaptation.

[0138] like Figure 2 As shown, the present invention also provides a method for dynamic optimization and risk warning of weapon escort routes, which realizes dynamic control of the entire process based on the platform system. The specific implementation steps include:

[0139] S1, multimodal data fusion and feature modeling;

[0140] Traffic flow, geographic elevation, and dark web sentiment data are collected in real time through a distributed sensor network. After spatial and temporal alignment, a multi-channel spatiotemporal feature tensor is constructed. This tensor is populated with traffic density, terrain complexity, and threat semantic vectors according to pre-set rules, forming a unified data representation structure that provides input for subsequent analysis.

[0141] S2, dynamic risk field generation and attack and defense simulation;

[0142] The spatiotemporal feature tensor is input into the adversarial game model, which defines the attacker's hijacking strategy (hijacking point location, weapon type) and the escort's defense strategy (route selection, vehicle configuration). The optimal strategy pair is iteratively calculated using a Nash equilibrium solver. The output is mapped to the spatiotemporal coordinate system of the road network, generating a dynamically updated risk field distribution map that quantifies the real-time risk level of each area.

[0143] S3, quantum optimization to find the optimal route;

[0144] Based on the latent variable decomposition results of the spatiotemporal characteristic tensor, the time factor and space factor matrices are extracted to generate the inter-qubit coupling strength parameters. Combined with the gradient constraints of the dynamic risk field, a quantum annealing algorithm is used to solve for the optimal set of routes that meet physical conditions such as vehicle capacity and road height limits, and output a multi-objective decision-making solution that balances risk and energy consumption.

[0145] S4, redundant path emergency reconstruction and feedback;

[0146] When the risk value of any area in the dynamic risk field exceeds the preset safety threshold, the hypergraph emergency response mechanism is triggered: after removing high-risk sections, the hypergraph convolutional network is used to generate at least three non-overlapping backup paths. This new set of paths is fed back into the attacker's strategy space, and the set of candidate hijacking points and time window parameters are updated, forming a risk-adaptive closed-loop control.

[0147] S5, Strategy collaborative optimization and dynamic iteration;

[0148] Based on the gradient distribution of the dynamic risk field and the policy partial derivative of the game utility function, a joint reward signal is generated to drive the reinforcement learning model. The weight parameters of the route selection neural network are adjusted through a policy gradient algorithm to continuously improve the decision probability of high-security paths, achieving online iterative optimization of the escort strategy.

[0149] The above steps form a dynamic closed-loop process of "data collection-risk assessment-route optimization-emergency response-strategy update". Through the collaborative operation of multiple modules, it ensures the real-time and reliability of escort missions in responding to complex risks throughout the entire life cycle.

[0150] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Dynamic optimization and risk warning platform for weapons escort routes, characterized by: include: A multimodal data acquisition module is used to collect traffic flow data, meteorological data, geographic elevation data, and threat public opinion text data in real time, and map the collected data to a unified spatiotemporal coordinate system to construct a spatiotemporal feature tensor containing multi-channel features; A dynamic risk assessment module receives the spatiotemporal feature tensor from the multimodal data acquisition module, calculates the dynamic risk value of each spatiotemporal node in the escort route through an adversarial game model, and outputs a dynamic risk field; A quantum collaborative optimization module receives the spatiotemporal feature tensor and the dynamic risk field, generates quantum optimization parameters based on the latent variable decomposition results of the spatiotemporal feature tensor, and solves the multi-objective optimal escort route in combination with the gradient constraints of the dynamic risk field; The Hypergraph Emergency Response Module monitors the dynamic risk field output by the Dynamic Risk Assessment Module in real time. When the risk value exceeds a preset threshold, it reconstructs redundant paths based on the original road network data and risk distribution, and feeds the reconstructed paths back to the Dynamic Risk Assessment Module to update the attack and defense strategy. The strategy collaborative update module receives the utility function data of the dynamic risk field and the adversarial game model, and dynamically adjusts the escort route selection strategy through the joint reward function.

2. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: The multimodal data acquisition module includes: The spatiotemporal alignment unit maps traffic flow data to a spatiotemporal grid coordinate system, with the grid resolution dynamically adjusted based on the area of ​​the escort zone; The tensor filling unit fills data in a preset channel order, which includes at least: Channel 1: Traffic density value, sampled by IoT sensors and normalized to the [0, 1] range; The second channel is terrain relief, which is generated by the elevation variance within the LiDAR point cloud computing grid; The third channel: Threat public opinion vector, which generates a d-dimensional keyword distribution after parsing dark web text through the NLP model.

3. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: The dynamic risk assessment module performs the following operations: Construct the attacker's strategy space, defining the hijacking point location, attack time window, and available weapon type parameters; Construct the escort strategy space, define the set of optional routes, the number of vehicle formations, and the armor protection level parameters; The optimal strategy pair is iteratively calculated through the Nash equilibrium solver, and a dynamic risk field is output, where the risk value of each grid is the partial derivative of the utility function under the attacker's optimal strategy.

4. The weapon escort route dynamic optimization and risk warning platform according to claim 3 is characterized in that: The dynamic risk assessment module further includes: a risk weight adjustment unit, which generates a dynamic weight coefficient according to the number N of time windows when the hijacking risk parameter shows a monotonically increasing trend within N consecutive time windows, wherein the weight coefficient increase multiple is in a piecewise linear relationship with N; The public opinion amplification unit performs an exponential amplification operation on the risk value of the associated geographic grid when it detects that the threat public opinion vector contains preset high-risk keywords.

5. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: The quantum collaborative optimization module includes: The tensor decomposition unit performs CP decomposition on the spatiotemporal feature tensor to extract the time factor matrix, space factor matrix and modal factor matrix; A coupling parameter generation unit generates a correlation strength parameter between quantum bits according to the inner product of the row vectors of the time factor matrix and the space factor matrix; The annealing solver combines the gradient distribution and correlation strength parameters of the dynamic risk field to solve the multi-objective optimization route that meets vehicle capacity and road height restrictions.

6. The weapon escort route dynamic optimization and risk warning platform according to claim 5 is characterized in that: The coupling parameter generation unit performs: Calculate the Euclidean distance between nodes based on the corresponding row vectors of the time factor matrix and the space factor matrix; When the Euclidean distance is greater than the preset similarity threshold, the coupling strength between the associated quantum bits is forced to zero.

7. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: The Hypergraph emergency response module performs the following operations: When the risk value of any grid in the dynamic risk field exceeds the preset safety threshold, the associated road network edge is marked as a high-risk edge; After removing high-risk edges from the original road network graph, a hypergraph convolutional network is used to generate a redundant road network containing at least three non-overlapping backup paths; The redundant road network is input into the attacker's strategy space of the dynamic risk assessment module to update the candidate set of hijacking point locations and time windows.

8. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: The strategy collaborative update module includes: The joint reward calculation unit generates a reward signal based on the real-time value of the dynamic risk field and the partial derivative of the adversarial game utility function to the escort strategy; The policy gradient update unit adjusts the weight parameters of the route selection neural network through the policy gradient algorithm, so that the probability of selecting the high reward path increases monotonically.

9. The weapon escort route dynamic optimization and risk warning platform according to claim 1 is characterized in that: Also includes: Secure communication interface, homomorphically encrypting spatiotemporal feature tensors and dynamic risk fields, with the encryption key bound to the escort mission identifier; The decryption verification unit verifies the consistency between the encryption tag and the current escort task identifier before the data is used. If there is any inconsistency, an alarm will be triggered.

10. A weapon escort route dynamic optimization and risk warning method, based on the weapon escort route dynamic optimization and risk warning platform according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect multimodal data in real time and construct a spatiotemporal feature tensor, which includes at least traffic density, terrain relief, and threat sentiment vector channels; The dynamic risk value of each spatiotemporal node in the escort route is calculated through an adversarial game model, and a dynamic risk field bound to the road network coordinates is output; The quantum optimization parameters are generated based on the latent variable decomposition results of the spatiotemporal characteristic tensor, and the multi-objective optimal route is solved by combining the gradient constraints of the dynamic risk field; When the risk value is detected to exceed the safety threshold, the redundant path is reconstructed and fed back to the update process of the adversarial game strategy; A joint reward signal is generated based on the risk gradient and strategy utility derivative to dynamically optimize the escort decision model.

Images