Logistics support dynamic scheduling and emergency response system based on intelligent data fusion

Abstract

The invention provides a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, and relates to the technical field of logistics support. Comprising a data acquisition module, a data fusion and processing module, a scene prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, a self-adaptive network optimization module and a moral priority scheduling module, according to the logistics resource dynamic scheduling system, global efficient optimization of logistics resource dynamic scheduling and intelligent analysis and strategy optimization of real-time data are realized through advanced technologies such as a variable component optimization algorithm, fusion of quantum annealing and a genetic algorithm, deep reinforcement learning and combination of Transform and a genetic algorithm; and the rationality of resource allocation and the scheduling efficiency are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics support, and in particular to a logistics support dynamic scheduling and emergency response system based on intelligent data fusion. Background Art

[0002] In the field of modern logistics support, military operations, natural disaster relief, and large-scale public event organization have put forward extremely high requirements for logistics support. In the face of increasingly complex environments and diversified needs, efficient and intelligent logistics support scheduling systems are essential. With the rapid development of information technology, such as the widespread application of technologies such as big data, cloud computing, and the Internet of Things, logistics support work has gradually shifted towards digitalization and intelligence, data collection technology has continued to advance, and various types of sensors have been widely used to collect information related to logistics resources. In terms of data processing and analysis, traditional algorithms and models, such as linear programming and heuristic algorithms, have also been used to optimize the scheduling process to a certain extent. These algorithms try to improve resource allocation efficiency and respond to emergencies by analyzing historical data and current partial data. For example, a material distribution plan is formulated based on the analysis of historical disaster relief data, or a transportation route is arranged based on logistics experience in previous military operations. At the same time, the development of network technology and security technology has also provided certain means for resource tracking and data privacy protection. Network technology enables logistics information in different regions and different links to be interconnected, realizing the sharing and collaborative management of resource information.

[0003] The existing emergency logistics support system has the following problems in actual application:

[0004] 1. Insufficient data fusion and processing:

[0005] Traditional data processing algorithms are often inefficient and difficult to achieve a global optimal solution when faced with complex global optimization problems such as the dynamic scheduling of logistics resources. For the large amount of real-time data collected, there is a lack of sufficiently intelligent analysis capabilities and strategy optimization methods, and it is impossible to fully tap the value of the data and adapt to the ever-changing situation in the complex logistics network. In particular, the evaluation and optimization of bottleneck nodes lack real-time and accuracy. When constructing scenario prediction models for emergency events, existing technologies often cannot integrate a variety of complex factors well. For example, the dynamic scenario prediction of complex emergency events is not accurate and comprehensive enough. The multi-scenario deduction and dynamic scheduling simulation capabilities are limited, and cannot provide sufficiently multi-dimensional strategy references for emergency response, making it difficult to meet the needs of complex and changing emergency situations;

[0006] 2. In the coordinated scheduling of resources among multiple regions and institutions, there is a contradiction between ensuring data privacy and achieving efficient scheduling. Traditional methods are prone to data privacy leakage when integrating multi-party data for collaborative scheduling, and the generation of optimal scheduling solutions under a distributed architecture is not dynamic enough and cannot adapt to changes in different regions and institutions in a timely manner. In the scheduling of logistics resources involving multiple parties, the methods for resolving resource conflicts and optimizing scheduling paths are not effective enough. Traditional algorithms are difficult to take into account both the efficiency and fairness of resource allocation at the same time, and cannot well support the dynamic collaborative decision-making of multiple participants, resulting in the inability to reasonably allocate logistics resources when faced with complex multi-party resource allocation problems;

[0007] 3. Existing resource tracking technologies make it difficult to achieve accurate traceability of logistics resources throughout the entire process, and are insufficient in ensuring that dynamic scheduling plans cannot be tampered with. At the same time, there is a lack of comprehensive transparent auditing of the scheduling process, which cannot meet the requirements of logistics support work for transparency and credibility of resource tracking. In the process of building a self-organizing network of logistics resources, traditional network optimization algorithms have limited capabilities for efficient resource allocation and dynamic path optimization in complex network environments, and cannot adapt well to the dynamic changes of logistics resource scheduling networks, resulting in inefficient resource allocation and failure to meet the needs of actual logistics support work. Existing logistics resource allocation models often ignore ethical factors and lack resource allocation considerations based on ethical priorities, which may lead to resource allocation decisions in some special cases that do not meet social ethical standards, affecting the social acceptance and sustainability of logistics support work.

[0008] Therefore, a dynamic logistics scheduling and emergency response system based on intelligent data fusion is needed to solve the above problems. Summary of the invention

[0009] Technical issues solved

[0010] In view of the shortcomings of the prior art, the present invention provides a dynamic scheduling and emergency response system for logistics support based on intelligent data fusion, which solves the following problems:

[0011] 1. It solves the problem that traditional data processing algorithms are often inefficient and difficult to achieve global optimal solutions when facing complex global optimization problems such as dynamic scheduling of logistics resources. For the large amount of real-time data collected, there is a lack of sufficiently intelligent analysis capabilities and strategy optimization methods, which makes it impossible to fully explore the value of data and difficult to adapt to the ever-changing situation in complex logistics networks, especially for the lack of real-time and accuracy in the evaluation and optimization of bottleneck nodes.

[0012] 2. It solves the contradiction between ensuring data privacy and achieving efficient scheduling in the coordinated scheduling of resources among multiple regions and institutions. Traditional methods are prone to data privacy leakage when integrating multi-party data for coordinated scheduling.

[0013] 3. It solves the problem that the existing resource tracking technology is difficult to achieve accurate traceability of logistics resources throughout the process, and is insufficient in ensuring that the dynamic scheduling plan cannot be tampered with. At the same time, it lacks a comprehensive transparent audit function for the scheduling process, and cannot meet the requirements of logistics support work for resource tracking transparency and credibility. In the process of building a self-organizing network of logistics resources, traditional network optimization algorithms have limited capabilities for efficient resource allocation and dynamic path optimization in complex network environments.

[0014] Technical Solution

[0015] To achieve the above objectives, the present invention is implemented through the following technical solutions: a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, including a data acquisition module, a data fusion and processing module, a scenario prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, an adaptive network optimization module and a moral priority scheduling module, the data acquisition module is responsible for collecting relevant data, the data fusion and processing module is used to integrate and process data, the scenario prediction and simulation module predicts and simulates different scenarios, the privacy protection collaborative scheduling module ensures data privacy security, the game optimization scheduling module optimizes the scheduling process, the transparent resource tracking module tracks resource usage, the adaptive network optimization module optimizes network performance, and the moral priority scheduling module regulates the morality of decision-making.

[0016] Preferably, the data acquisition module is configured with multi-source sensors to collect dynamic information of logistics resources, wherein the dynamic information of logistics resources includes material inventory, geographical location, demand intensity and traffic status, and transmits the collected data to the central data processing center through distributed nodes, supporting real-time updates and high-frequency data transmission.

[0017] Preferably, the data fusion and processing module constructs an optimization algorithm framework by integrating variational quantum optimization algorithm, quantum annealing and genetic algorithm, optimizes the global dynamic scheduling of logistics resources, uses a combination of deep reinforcement learning, Transformer and genetic algorithm to perform intelligent analysis and strategy optimization on the collected real-time data, and performs real-time evaluation and optimization of bottleneck nodes in complex logistics networks through knowledge graph embedding, random forest and multi-scale deep neural network.

[0018] Preferably, the scenario prediction and simulation module will integrate generative adversarial networks, graph neural networks and long short-term memory networks to construct dynamic scenario predictions of complex emergency events, and realize multi-scenario deduction and dynamic scheduling simulation, providing multi-dimensional strategy references for emergency response. The privacy-preserving collaborative scheduling module will integrate federated learning, deep reinforcement learning and Kalman filtering algorithms to achieve collaborative resource scheduling among multiple regions and institutions, while ensuring data privacy and security, and dynamically generating optimal scheduling plans in a distributed architecture.

[0019] Preferably, the game optimization scheduling module utilizes a hybrid algorithm of ant colony optimization, game theory and Monte Carlo tree search to solve multi-party resource conflicts and scheduling path optimization problems, and supports dynamic collaborative decision-making of multiple parties to optimize the allocation efficiency and fairness of logistics resources. The transparent resource tracking module is based on blockchain technology, combined with smart contracts and conditional random field algorithms, to achieve full traceability of logistics resources and non-tamperability of dynamic scheduling plans, and provide a transparent audit function for the scheduling process.

[0020] Preferably, the adaptive network optimization module integrates dynamic programming, simulated annealing and graph embedding algorithms to construct a self-organizing network of logistics resources and realize efficient resource allocation and dynamic path optimization in complex networks.

[0021] Preferably, the ethical priority scheduling module integrates the algorithmic framework of multi-objective optimization, Bayesian reasoning and hierarchical analysis method, constructs a resource allocation model based on ethical priorities, and implements it in the resource allocation model.

[0022] Preferably, the data fusion and processing module further includes an abnormal data detection and repair function, which performs high-precision identification of abnormal values ​​in the collected data by combining generative adversarial networks, support vector machines and conditional random field algorithms, and dynamically repairs abnormal data through an adaptive algorithm based on Bayesian reasoning, thereby ensuring the integrity and accuracy of subsequent processed data.

[0023] Preferably, the scenario prediction and simulation module further realizes real-time prediction of complex emergency scenarios and iterative optimization of optimal scheduling schemes through the integration of quantum computing-based dynamic programming algorithms, variational autoencoders and adaptive neural fuzzy inference systems, and supports the self-learning and scenario adaptation capabilities of the prediction schemes.

[0024] Preferably, the privacy-preserving collaborative scheduling module enhances the federated learning mechanism and combines the blockchain consensus mechanism with quantum key distribution technology to achieve full-link encryption and privacy protection of data during transmission, storage and calculation, and dynamically optimizes the computing efficiency and security level of each regional resource scheduling scheme.

[0025] Beneficial Effects

[0026] The present invention provides a dynamic scheduling and emergency response system for logistics support based on intelligent data fusion.

[0027] It has the following beneficial effects:

[0028] 1. The present invention realizes efficient optimization of the global dynamic scheduling of logistics resources and intelligent analysis and strategy optimization of real-time data through the advanced technologies such as variational quantum optimization algorithm, fusion of quantum annealing and genetic algorithm, deep reinforcement learning, combination of Transformer and genetic algorithm, etc., which greatly improves the rationality of resource allocation and scheduling efficiency. At the same time, by using knowledge graph embedding, random forest and multi-scale deep neural network, it can accurately evaluate and optimize the bottleneck nodes in the complex logistics network in real time, ensure the smooth operation of the logistics support network, and use the abnormal data detection method combining generative adversarial network, support vector machine and conditional random field algorithm, and adaptive repair algorithm based on Bayesian reasoning, it can identify and dynamically repair abnormal values ​​in the collected data with high precision, ensure the integrity and accuracy of the data, provide a reliable basis for subsequent scheduling decisions, and effectively avoid decision-making errors caused by data problems.

[0029] 2. The present invention integrates generative adversarial networks, graph neural networks and long short-term memory networks to build high-precision dynamic scenario predictions for complex emergency events, and realizes multi-scenario deduction and dynamic scheduling simulation. This provides a rich multi-dimensional strategy reference for emergency response, enabling the logistics support system to better cope with various complex and changeable emergency situations, enhance emergency handling capabilities, and achieve real-time prediction of complex emergency scenarios and iterative optimization of optimal scheduling plans through the integration of quantum computing-based dynamic programming algorithms, variational autoencoders and adaptive neural fuzzy inference systems, so that the prediction plan has self-learning and scenario adaptation capabilities, and can be quickly adjusted according to actual conditions, further improving the timeliness and effectiveness of emergency response.

[0030] 3. The present invention integrates federated learning, deep reinforcement learning and Kalman filtering algorithms, and combines blockchain consensus mechanism with quantum key distribution technology to achieve resource coordination and scheduling among multiple regions and institutions, while ensuring full-link encryption and privacy protection of data during transmission, storage and calculation. It can not only effectively prevent data privacy leakage, but also dynamically generate optimal scheduling plans in a distributed architecture, better adapt to changes in different regions and institutions, improve the efficiency and security of coordinated scheduling, and dynamically optimize the computing efficiency and security level of resource scheduling plans in each region by enhancing the federated learning mechanism, so that the entire logistics support system can respond to changes in resource allocation needs more flexibly while ensuring privacy, and ensure that resources are reasonably and efficiently allocated. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1It is the overall flow chart of the system of the present invention;

[0032] Figure 2 A logic diagram of the system application of the present invention;

[0033] Figure 3 A mind map for the present invention;

[0034] Figure 4 It is a system simulation diagram of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention. Specific embodiment one:

[0037] like Figure 1-4 As shown in the figure, the logistics support dynamic scheduling and emergency response system based on intelligent data fusion includes a data acquisition module, a data fusion and processing module, a scenario prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, an adaptive network optimization module and a moral priority scheduling module. The data acquisition module is responsible for collecting relevant data, the data fusion and processing module is used to integrate and process data, the scenario prediction and simulation module predicts and simulates different scenarios, the privacy protection collaborative scheduling module ensures data privacy security, the game optimization scheduling module optimizes the scheduling process, the transparent resource tracking module tracks resource usage, the adaptive network optimization module optimizes network performance, and the moral priority scheduling module regulates the morality of decision-making.

[0038] The data acquisition module is equipped with multi-source sensors to collect dynamic information of logistics resources, including material inventory, geographical location, demand intensity and traffic status. The collected data is transmitted to the central data processing center through distributed nodes, supporting real-time updates and high-frequency data transmission.

[0039] The data fusion and processing module constructs an optimization algorithm framework by integrating variational quantum optimization algorithm, quantum annealing and genetic algorithm, optimizes the global dynamic scheduling of logistics resources, and uses a combination of deep reinforcement learning, Transformer and genetic algorithms to perform intelligent analysis and strategy optimization on the collected real-time data. It also uses knowledge graph embedding, random forest and multi-scale deep neural networks to perform real-time evaluation and optimization of bottleneck nodes in complex logistics networks.

[0040] The scenario prediction and simulation module will integrate generative adversarial networks, graph neural networks and long short-term memory networks to construct dynamic scenario predictions for complex emergency events, and realize multi-scenario deduction and dynamic scheduling simulation, providing multi-dimensional strategy references for emergency response. The privacy-preserving collaborative scheduling module will integrate federated learning, deep reinforcement learning and Kalman filtering algorithms to achieve collaborative resource scheduling among multiple regions and institutions, while ensuring data privacy and security, and dynamically generating optimal scheduling plans in a distributed architecture.

[0041] The game optimization scheduling module uses a hybrid algorithm of ant colony optimization, game theory and Monte Carlo tree search to solve multi-party resource conflicts and scheduling path optimization problems, and supports dynamic collaborative decision-making among multiple participants to optimize the efficiency and fairness of logistics resource allocation. The transparent resource tracking module is based on blockchain technology, combining smart contracts and conditional random field algorithms to achieve full traceability of logistics resources and the immutability of dynamic scheduling plans, and provide transparent audit functions for the scheduling process.

[0042] The adaptive network optimization module integrates dynamic programming, simulated annealing and graph embedding algorithms to build a self-organizing network of logistics resources and realize efficient resource allocation and dynamic path optimization in complex networks.

[0043] The ethical priority scheduling module integrates the algorithmic framework of multi-objective optimization, Bayesian reasoning and hierarchical analysis method to construct a resource allocation model based on ethical priorities and implement it in the resource allocation model.

[0044] The data fusion and processing module further includes abnormal data detection and repair functions. By combining generative adversarial networks, support vector machines and conditional random field algorithms, it can identify abnormal values ​​in the collected data with high precision, and dynamically repair abnormal data through an adaptive algorithm based on Bayesian reasoning to ensure the integrity and accuracy of subsequent processed data.

[0045] The scenario prediction and simulation module further achieves real-time prediction of complex emergency scenarios and iterative optimization of optimal scheduling plans through the integration of quantum computing-based dynamic programming algorithms, variational autoencoders and adaptive neural fuzzy inference systems, and supports the self-learning and scenario adaptation capabilities of prediction plans.

[0046] The privacy-preserving collaborative scheduling module enhances the federated learning mechanism and combines the blockchain consensus mechanism with quantum key distribution technology to achieve full-link encryption and privacy protection of data during transmission, storage and calculation, and dynamically optimizes the computing efficiency and security level of each regional resource scheduling plan. Specific embodiment 2:

[0048] like Figure 1-4As shown in the figure, the data acquisition module adds dynamic monitoring of environmental variables such as climate change, energy supply and demand, and infrastructure status, and combines low-power remote communication based on LoRa and NB-IoT technology to expand the acquisition range. It supports a multi-level sampling frequency adjustment mechanism, combined with a bandwidth allocation algorithm based on adaptive optimization, to give priority to the transmission of high-priority information when data traffic is limited. In addition, it integrates real-time data preprocessing functions based on 5G and edge computing to achieve localized processing capabilities and reduce data transmission delays. The data fusion and processing module expands the time series data prediction and dynamic modeling functions, and uses time series embedding and deep variational autoencoders to model long-term trends. A multimodal data alignment mechanism is introduced, and the robustness of heterogeneous data fusion is enhanced through data distribution evaluation based on Wasserstein distance optimization. In addition, the module adopts a distributed scheduling strategy driven by reinforcement learning to dynamically optimize computing resource allocation and improve real-time and scalability. The scenario prediction and simulation module adds virtual reality (VR)-assisted scenario simulation and interaction functions to provide emergency decision makers with more intuitive scenario demonstration and interactive experience; introduces a multimodal event deduction mechanism, combines reinforcement learning and generative adversarial networks (GAN) to realize the automatic adjustment of scenario simulation parameters; evaluates key nodes in complex scenarios through a multi-layer dynamic attention mechanism to improve the accuracy and coverage of scenario simulation. The privacy-preserving collaborative scheduling module adds a hybrid protection scheme based on homomorphic encryption and differential privacy on the basis of federated learning to achieve multi-level privacy protection of data. Combined with zero-knowledge proof technology, the credibility and privacy of cross-regional scheduling schemes are enhanced. In addition, the module ensures the security of resource scheduling permissions in multi-party participation scenarios through an extensible dynamic access control mechanism. The game optimization scheduling module adds a multi-agent collaborative game strategy, combines a hierarchical game model to dynamically adjust the priority of multi-party resource allocation; introduces an intelligent conflict resolution algorithm based on multi-objective optimization and evolutionary games to ensure the fairness and efficiency of scheduling decisions in multi-party demand scenarios. The module also realizes adaptive path planning optimization through a scheduling scheme generation mechanism based on genetic programming. The transparent resource tracking module adds a dynamic binding function for IoT device identification codes to resource tracking, and combines distributed ledger technology to ensure the real-time and verifiability of resource status updates. The module improves processing efficiency in multi-node environments through blockchain sharding technology, and introduces an auxiliary mechanism for smart contracts to automatically trigger resource scheduling. The adaptive network optimization module adds a cross-layer network optimization mechanism, optimizes the multi-layer network architecture based on a dynamic grouping optimization algorithm; combined with a network self-healing mechanism based on reinforcement learning, it responds to and repairs node failures and link anomalies in real time to ensure high network reliability. The module also supports distributed resource load balancing based on traffic prediction to improve network performance during peak loads. The moral priority scheduling module enhances the regional adaptability of ethical norms, and provides personalized resource allocation decisions under different cultural or policy backgrounds through a dynamic adjustment algorithm for moral weights based on social factor analysis.The module supports the automatic generation of ethical assessment reports, provides decision-making interpretability through natural language generation (NLG) algorithms, and improves the transparency and social acceptance of scheduling plans. The above supplementary content strengthens the comprehensiveness and advancement of the system in terms of functions, especially the technical depth in data processing, scheduling optimization and ethical norms, providing the system with higher innovation value and practicality. Specific embodiment three:

[0050] like Figure 1-4 As shown, the key algorithm mentioned in Example 1 is analyzed in detail below, including its core mathematical formula and explanation:

[0051] 1. Variational quantum optimization algorithm is used for resource allocation and path optimization problems.

[0052] Core formula

[0053]

[0054] Where E(θ) is the objective function, which represents the total cost of the current resource allocation and path design scheme; |ψ(θ) is a parameterized quantum state, which is a candidate solution for logistic resource allocation and is generated by adjusting the parameter θ; is the Hamiltonian, which represents the cost function of the logistics scheduling problem, and its structure is:

[0055]

[0056] where c ij is the transportation cost between two nodes i and j (such as path distance, time consumption, etc.); x i represents the distribution status of resources at node i (binary variable, 0 means no resources, 1 means there are resources); b i is the self-demand cost of node i (such as the additional cost required to meet the demand); <·|·|·> is the inner product of the quantum state, which is used to calculate the expected value of the objective function.

[0057] Combined with the scene

[0058] Resource allocation: The quantum state |ψ(θ) represents all possible resource allocation schemes. By optimizing the parameter θ, the allocation with the lowest cost is found.

[0059] Path Optimization: Hamiltonian c ij By generating traffic data of the logistics network, the path selection is ensured to have the lowest cost.

[0060] 2. Deep reinforcement learning (DRL) for dynamic decision making and resource allocation:

[0061] The core formula is:

[0062] Where π(s) is the optimal policy function, which indicates the optimal action a selected in state s; Q(s,a) is the Q-value, which indicates the value function of selecting action a in state s; r is the immediate reward function, which indicates the immediate benefit brought by the current action a, such as meeting logistics needs or reducing transportation costs; γ is the discount factor, which is used to balance short-term and long-term benefits, γ∈[0,1]; s is the system state, such as the inventory of the warehouse and the urgency of the demand node; a is the action, which indicates the operation of allocating a certain amount of resources from a warehouse to a demand node; s ′ is the new state after action a is executed, such as the state after inventory and demand change; a ′ In the new state s ′ All possible actions that can be taken.

[0063] Combined with the scene

[0064] Dynamic scheduling: At each moment, Q(s,a) is updated based on the inventory and demand data collected in real time, and the optimal action a is selected.

[0065] Resource allocation: Weigh the fairness and efficiency of resource allocation in r to ensure that the needs of each demand node are met as much as possible.

[0066] 3. Federated learning + Kalman filter fusion for distributed data sharing and demand forecasting:

[0067] Core formula

[0068]

[0069] in is the state estimate at time step k, representing the current predicted demand; is the predicted demand at the previous time step k-1; K k K is the Kalman gain, which weighs the update weight of the predicted value and the actual observed value. k The state noise covariance matrix P k-1 , measurement noise covariance matrix R k and the measurement model matrix H k OK; k is the observed value of the current time step, such as the actual demand obtained by the sensor; H k is the measurement model matrix, which is used to map the state to the observation value space; P k-1 is the noise covariance matrix of the predicted state at the previous time step; R k is the measurement noise covariance matrix at the current time step.

[0070] Combined with the scene

[0071] Federated learning: Each region uploads local data prediction model parameters, which are integrated through the federated server to update the global model; dynamic prediction: Kalman filtering is used to correct the demand prediction model in real time, and the resource scheduling strategy is adjusted based on the newly collected data.

[0072] 4. Graph neural networks are used to model node relationships in complex networks:

[0073] Core formula:

[0074]

[0075] in is the feature vector of node v in the k+1th layer (updated inventory or demand); is the feature vector of the neighbor node u (such as the inventory of the warehouse directly connected to v); is the set of neighbor nodes of node v; d v is the degree of node v, indicating the number of nodes connected to it; σ is the activation function, used for nonlinear transformation; W( k ) is the learnable weight matrix of the kth layer.

[0076] Combined with the scene:

[0077] Logistics network modeling: use nodes to represent warehouses or demand points, and use edge weights to represent transportation costs; dynamic allocation: explore the graph neural network message passing mechanism to optimize node characteristics (inventory or demand) and resource flow.

[0078] 5. Ant Colony Optimization Algorithm for Multi-Objective Path Optimization:

[0079] Core formula:

[0080] where τ ij (t) is the pheromone concentration of edge (i, j) at time t, reflecting the degree of preference for the path; ρ is the pheromone volatility coefficient, which controls the decay rate of pheromone over time; is the increment of pheromone left by ant k; Q is a constant proportional to the pheromone intensity; L k is the total length of the path selected by ant k, reflecting the quality of the path.

[0081] Combined with the scene:

[0082] Path selection: Paths with high pheromone concentrations are more likely to be selected to find the optimal transportation path; multi-objective optimization: through multiple iterations, balance transportation time, cost and resource fairness.

[0083] 6. Dynamic programming + simulated annealing + graph embedding algorithm for adaptive resource allocation and path optimization in complex logistics networks:

[0084] Core dynamic programming formula: Where V t (s) is the minimum cumulative cost function in state s at time t; A(s): is the set of actions that can be taken in state s (such as allocating a certain amount of resources from a warehouse to the demand point); C(s,a) is the immediate cost of taking action a in state s, including transportation and inventory adjustment; γ is the discount factor, which indicates the degree of concern for future benefits, γ∈[0,1]; s ′ It is the new state after action a is executed.

[0085] Simulated annealing formula:

[0086] Where P(ΔE) is the distance from the current solution x to the neighboring solution x ′ The acceptance probability of ΔE=E(x ′ )-E(x) is the new solution x ′ The performance difference with the current solution x (such as the change in total path cost); T: is the current temperature parameter, which has a large initial value and gradually decreases to reduce randomness; E(x) is the objective function value of the current solution (such as total transportation cost).

[0087] Graph embedding formula:

[0088]

[0089] in is the embedding vector of node v at the k+1th layer, indicating the feature update of the warehouse or demand node; f is the node update function, which is fitted by the neural network; AGG is the aggregation function, which summarizes the neighbor node information of node v; is the feature of node u and edge weight e u Combination function of v; is the set of neighbor nodes of node v.

[0090] Combined with the scene

[0091] Dynamic programming: Find the optimal resource allocation and scheduling path based on current inventory and demand. Simulated annealing: Used to solve large-scale multi-objective optimization problems in logistics networks to ensure high-quality solutions within an acceptable time. Graph embedding: In logistics networks, complex node and edge features are mapped to low-dimensional vector space for efficient modeling and analysis of bottleneck nodes.

[0092] 7. Multi-objective optimization + Bayesian reasoning + analytic hierarchy process are used to build a moral priority scheduling model:

[0093] Multi-objective optimization formula:

[0094] minF(x)=[f1(x),f2(x),…,fm (x)]

[0095] Where F(x) is a set of multi-objective optimization functions, including m objective functions; f1(x) is the total transportation cost; f2(x) is an indicator that satisfies the fairness of demand points; f3(x) is moral priority scheduling (such as priority support for high-demand areas); x is a set of decision variables, representing various resource scheduling plans.

[0096] Bayesian inference formula:

[0097]

[0098] Where P(H|D) is the posterior probability that hypothesis H is true given known data D; P(D|H) is the likelihood function of observing data D under hypothesis H; P(H) is the prior probability of hypothesis H; and P(D) is the marginal distribution of data D.

[0099] AHP formula:

[0100]

[0101] where w i is the weight value of target i, which is used for priority evaluation during decision making; a ij is the priority ratio of target i relative to target j; n is the total number of targets.

[0102] Combined with the scene

[0103] Multi-objective optimization: balance cost, efficiency and ethical needs, and find the optimal scheduling solution that meets multiple goals through Pareto optimization; Bayesian reasoning: in a dynamic demand environment, combine historical data and real-time information to predict demand distribution and optimize decision-making; hierarchical analysis method: comprehensively consider the weight of each goal, set scheduling priorities through expert experience or statistical data, and realize an ethical scheduling model. Specific embodiment four:

[0105] like Figure 1-4 As shown, the following is a description of the specific application logic steps of each module and algorithm in the logistics support dynamic scheduling and emergency response system based on intelligent data fusion:

[0106] 1. Data acquisition module

[0107] Application logic:

[0108] The data collection module is mainly responsible for collecting various real-time logistics resource data, including material inventory, demand intensity, transportation status, warehouse location, traffic conditions and other information. These data come from different sensors, GPS positioning devices, inventory management systems, road monitoring systems, etc. The module supports high-frequency updates to ensure the real-time and accuracy of the data.

[0109] Steps:

[0110] Deploy multi-source sensors and equipment to conduct real-time monitoring at key nodes in the logistics network (such as warehouses, transport vehicles, and demand points). Collect and preliminarily process data through sensors (such as denoising and data format standardization), and transmit data to the central data processing center through distributed nodes to ensure real-time updating and high-frequency transmission of data.

[0111] 2. Data fusion and processing module

[0112] Application logic: The data fusion and processing module is responsible for integrating, cleaning, and deduplicating the collected multi-source heterogeneous data, and performing in-depth analysis through advanced algorithms to convert them into data inputs that can be used for decision-making. The core of the module is to use combinatorial algorithms such as variational quantum optimization, quantum annealing, and the fusion of genetic algorithms to perform global optimization of logistics resource scheduling.

[0113] Step description: Through data cleaning and preprocessing, outliers are removed, missing data is filled, and data quality is ensured; combinatorial optimization algorithms, such as the fusion of variational quantum optimization algorithms, quantum annealing, and genetic algorithms, are used to globally optimize the scheduling problem of logistics resources and obtain the optimal solution for resource allocation; a combination of deep reinforcement learning, Transformer, and genetic algorithms is used to intelligently analyze real-time data, automatically learn, and optimize scheduling strategies.

[0114] 3. Scenario prediction and simulation module application logic:

[0115] Application logic: The scenario prediction and simulation module is mainly responsible for simulating and predicting possible emergency scenarios in the future, such as the impact of natural disasters, traffic jams, etc. on the allocation of logistical resources. This module integrates generative adversarial networks, graph neural networks, and long short-term memory network algorithms for dynamic scenario prediction and supports multi-scenario deduction.

[0116] Step description: Generate simulated data under different emergency scenarios using a generative adversarial network for subsequent decision-making; use graph neural networks to process spatial and temporal relationships in emergency resource scheduling, analyze resource flows between multiple regions, use LSTM networks to model historical data, predict future demand and resource supply, assist in emergency scheduling decisions, simulate resource allocation plans under different emergency scenarios, and optimize scheduling strategies.

[0117] 4. Privacy-preserving collaborative scheduling module

[0118] Application logic: The privacy protection collaborative scheduling module ensures data privacy security among multiple regions and institutions, and dynamically generates the optimal scheduling plan through the integration of federated learning, deep reinforcement learning and Kalman filtering. Information is shared in a decentralized manner without exposing each other's sensitive data.

[0119] Steps:

[0120] Using federated learning technology, various regions and institutions jointly participate in model training, but the data is always retained locally to ensure privacy protection; collaborative scheduling is intelligently optimized through deep reinforcement learning, and strategies are continuously adjusted based on historical and real-time data; Kalman filtering is used to estimate the resource status of each institution in real time to ensure efficient scheduling in a dynamic environment.

[0121] 5. Game optimization scheduling module

[0122] Application logic: The game optimization scheduling module solves the multi-party resource conflict and scheduling path optimization problems through a hybrid algorithm of ant colony optimization, game theory and Monte Carlo tree search. This module optimizes the dynamic collaborative decision-making of multiple participants to ensure that the allocation of logistics resources is both efficient and fair.

[0123] Steps:

[0124] Use game theory models to simulate conflicts of interest and cooperation among multiple participants and determine the strategies of each party; combine with ant colony optimization algorithm to find the optimal path for resource allocation by simulating the process of ant foraging; use Monte Carlo tree search to search for the optimal scheduling path within a limited time, and adaptively adjust the strategy in multiple rounds of games; output the optimal scheduling plan and make real-time adjustments to meet new needs.

[0125] 6. Transparent resource tracking module

[0126] Application logic: The transparent resource tracking module uses blockchain technology and smart contracts, combined with conditional random field algorithms, to achieve full traceability of logistics resources. Through the immutability of blockchain, the scheduling process of logistics resources is ensured to be transparent and the trust of management is improved.

[0127] Steps:

[0128] Blockchain technology is used to record every resource allocation and scheduling operation to ensure that the data cannot be tampered with. Resource scheduling is managed through smart contracts, and resource allocation decisions are automatically executed to ensure fairness. The conditional random field (CRF) algorithm is used to predict future resource demand and allocation, combined with the transparency of blockchain to provide auditing and traceability of real-time scheduling solutions.

[0129] 7. Adaptive network optimization module

[0130] Application logic: The adaptive network optimization module combines dynamic programming, simulated annealing and graph embedding algorithms to optimize resource allocation and path selection in logistics networks, thereby improving network performance and emergency response speed.

[0131] Steps:

[0132] The dynamic programming algorithm is used to calculate the optimal resource allocation path based on the current resource status and demand. The simulated annealing algorithm is used to perform global optimization in the logistics network to reduce costs and improve resource allocation efficiency. The graph embedding algorithm embeds the nodes and edges in the logistics network into a low-dimensional vector space to facilitate the processing of large-scale data sets, identify bottlenecks and optimize resource allocation.

[0133] 8. Ethical Priority Scheduling Module

[0134] Application logic: The ethical priority scheduling module uses multi-objective optimization, Bayesian reasoning and analytic hierarchy process (AHP) to build a resource scheduling model to ensure that the scheduling process complies with the ethical priority principle, such as giving priority to protecting the vulnerable groups in society and economy.

[0135] Steps:

[0136] Multi-objective optimization methods are used to balance cost, efficiency and ethical priorities in the scheduling process. Bayesian reasoning is used to predict the impact of different resource scheduling schemes based on historical data and real-time information and evaluate their ethical effects. The analytic hierarchy process (AHP) is used to set the priority and ethical standards of each resource to ensure that resource allocation meets social justice and ethical principles.

[0137] Things that need to be explained include Figure 4 As shown, the first chart is a graph of network performance over time:

[0138] Title: The network performance over time graph represents the changing trend of network performance (such as the efficiency of resource allocation or network optimization results) over multiple time steps (from 1 to 100).

[0139] Horizontal axis (time step): represents the change in time from step 1 to step 100.

[0140] Vertical axis (network performance index): represents the network performance value at each time step.

[0141] The data shows fluctuations, indicating that network performance has large dynamic changes at different time points.

[0142] The performance range is lower (approximately between 0 and 0.5), which may reflect unstable resource utilization or optimization effects.

[0143] The second chart shows the change of morality score over time

[0144] Title: Morality score over time graph shows the changing trend of morality scores (such as fairness of resource allocation or ethical priority) at different time steps.

[0145] Horizontal axis (time step): also represents the time series from step 1 to step 100.

[0146] Vertical axis (morality score): represents the numerical value of the morality score (ranging from approximately 4 to 6.5).

[0147] The data fluctuates within a certain time step, but the fluctuation range is relatively small, showing a certain stability.

[0148] A high morality score indicates that the allocation scheme may focus more on fairness or ethical priorities. Specific embodiment five:

[0150] like Figure 1-4 As shown in the figure, the data acquisition module includes multi-source sensors, embedded acquisition terminals, communication modules and edge computing devices. Multi-source sensors such as RFID sensors, temperature and humidity sensors, acceleration sensors, cameras and GPS modules are used to collect material location, environmental conditions, transportation status and real-time image information respectively; these sensors have high precision and low power consumption characteristics, such as sensors produced by Honeywell or Bosch. The embedded acquisition terminal is designed based on an ARM processor (such as RaspberryPi 4 or STM32), with multiple interfaces to support sensor data access and local computing capabilities, and integrates an efficient power management module to meet long-term working requirements. The communication module supports LoRa, NB-IoT and 5G communication protocols, among which the LoRa module (such as Semtech SX1276) realizes long-distance low-power communication, the NB-IoT module (such as Quectel BC95) ensures wide-area coverage, and the 5G module (such as Qualcomm X55) provides high-speed transmission capabilities. Edge computing devices such as NVIDIA Jetson XavierNX are equipped with GPU accelerators and storage expansion interfaces, and have powerful real-time data preprocessing and analysis capabilities.

[0151] The data fusion and processing module consists of a central server, distributed computing nodes, and data storage devices. The central server uses high-performance processors (such as Intel Xeon Gold 6258R) and professional graphics cards (such as NVIDIA A100) to provide powerful computing capabilities, with a memory configuration of no less than 64GB to support large-scale parallel computing. Storage devices use NVMe SSDs (such as Samsung PM1735) to ensure high-speed data reading and writing. Distributed computing nodes are based on cloud computing platforms (such as AWS EC2) or high-performance computing clusters. FPGA (such as Xilinx Alveo U250) or TPU (such as Google TPU v4) acceleration units are integrated in the nodes to optimize the reasoning efficiency of deep learning tasks. Data storage devices include NAS (such as Synology DS1621xs+) and HDFS distributed file systems. The former is suitable for fast file access in the local area, and the latter is used for fault-tolerant storage of large-scale data.

[0152] The hardware of the scenario prediction and simulation module includes high-performance workstations, virtual reality devices, and simulation servers. The workstation uses multi-core CPUs (such as AMD Ryzen Threadripper 3990X) and high-end GPUs (such as NVIDIA RTX 4090) to run complex scenario prediction algorithms, and is equipped with large-capacity memory (128GB) and high-speed storage (NVMe SSD). Virtual reality devices (such as Oculus Quest 2 or HTC Vive Pro 2) support high-definition display and motion capture functions, providing interactive scenario demonstrations. The simulation server uses high-performance processors and multi-GPU architectures, such as the NVIDIA DGX system, to support parallel deduction of complex scenarios and integrate large-scale simulation platforms (such as AnyLogic or MATLAB Simulink).

[0153] The hardware of the privacy protection collaborative scheduling module includes edge computing nodes, blockchain node devices, quantum communication devices and distributed storage devices. Edge computing nodes use processors that support hardware encryption functions (such as Intel SGX or AMD EPYC SEV) to ensure the security of data processing. Blockchain node devices use dedicated hardware (such as Bitmain Antminer series) to run smart contracts to achieve security and immutability of resource scheduling and data protection. Quantum communication equipment (such as ID Quantique QKD system) provides quantum key distribution function to enhance security during communication. Distributed storage devices such as IPFS network nodes, combined with high-speed hard drives (such as Seagate Exos X series), achieve efficient data storage and access.

[0154] The hardware of the game optimization scheduling module mainly includes a CPU cluster, a GPU accelerator, and high-bandwidth network devices. The CPU cluster is equipped with multi-core processors (such as AMD EPYC 7742) to support the efficient operation of game theory and optimization algorithms. The GPU accelerator uses NVIDIA Tesla V100 or A100 to accelerate complex algorithms such as ant colony optimization and Monte Carlo tree search through parallelism. High-bandwidth network devices include 10 Gigabit Ethernet switches (such as Cisco Nexus 9300 series) to ensure low latency and high throughput of data transmission.

[0155] The hardware of the transparent resource tracking module includes IoT tags, blockchain devices, and conditional random field (CRF) acceleration hardware. IoT tags are designed based on RFID technology (such as Impinj Speedway R420) to support real-time tracking and high-frequency communication. Blockchain devices use high-performance node servers (such as Dell PowerEdge R740) to run distributed ledgers and smart contracts, and support hardware encryption modules (such as HSM). CRF acceleration hardware uses FPGA (such as Intel Stratix 10) to achieve efficient conditional random field calculations and optimize the processing speed of resource tracking tasks.

[0156] The hardware of the adaptive network optimization module consists of routers that support SDN (Software Defined Network), load balancing devices, and dynamic network monitoring devices. Routers (such as Cisco Catalyst 9500 series) support SDN functions to achieve dynamic network optimization and traffic scheduling. Load balancing devices (such as F5 BIG-IP system) optimize multi-node data traffic distribution and improve network performance. Dynamic network monitoring devices (such as Nagios XI or Palo Alto Networks) provide real-time network status monitoring and fault location functions.

[0157] The hardware of the ethical priority scheduling module includes decision support system (DSS) hardware, natural language processing equipment and data visualization hardware. DSS hardware uses high-performance computing devices (such as IBM Power Systems AC922) to support multi-objective optimization and ethical decision modeling. The natural language processing equipment uses NVIDIA DGX Station or Google TPU Pods to run NLP algorithms to generate ethical assessment reports. The data visualization hardware is equipped with a 4K interactive display (such as Dell UltraSharp 32 series) to provide an intuitive presentation of the visual scheduling solution.

[0158] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0159] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Dynamic logistics dispatch and emergency response system based on intelligent data fusion, characterized by: It includes a data acquisition module, a data fusion and processing module, a scenario prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, an adaptive network optimization module and a moral priority scheduling module. The data acquisition module is responsible for collecting relevant data, the data fusion and processing module is used to integrate and process data, the scenario prediction and simulation module predicts and simulates different scenarios, the privacy protection collaborative scheduling module ensures data privacy security, the game optimization scheduling module optimizes the scheduling process, the transparent resource tracking module tracks resource usage, the adaptive network optimization module optimizes network performance, and the moral priority scheduling module regulates the morality of decision-making.

2. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claim 1 is characterized by: The data acquisition module is configured with multi-source sensors to collect dynamic information of logistics resources, wherein the dynamic information of logistics resources includes material inventory, geographical location, demand intensity and traffic status, and transmits the collected data to the central data processing center through distributed nodes, supporting real-time updates and high-frequency data transmission.

3. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-4 is characterized in that: The data fusion and processing module constructs an optimization algorithm framework by integrating variational quantum optimization algorithm, quantum annealing and genetic algorithm, optimizes the global dynamic scheduling of logistics resources, uses a combination of deep reinforcement learning, Transformer and genetic algorithm to perform intelligent analysis and strategy optimization on the collected real-time data, and uses knowledge graph embedding, random forest and multi-scale deep neural network to perform real-time evaluation and optimization of bottleneck nodes in complex logistics networks. The core formula of the variable component quantum optimization algorithm is as follows: Where E(θ) is the objective function, which represents the total cost of the current resource allocation and path design scheme; |ψ(θ) is a parameterized quantum state, which is a candidate solution for logistic resource allocation and is generated by adjusting the parameter θ; is the Hamiltonian, which represents the cost function of the logistics scheduling problem, and its structure is: where c ij is the transportation cost between two nodes i and j; x i represents the distribution status of resources at node i (binary variable, 0 means no resources, 1 means there are resources); b i is the self-demand cost of node i. The core formula of the deep reinforcement learning algorithm is as follows: where π * (s) is the optimal strategy function, which indicates the optimal action a selected in state s; Q(s,a) is the Q-value, which indicates the value function of selecting action a in state s; r is the immediate reward function, which indicates the immediate benefit brought by the current action a; γ is the discount factor, which is used to balance short-term and long-term benefits γ∈[0,1]; s is the system state, a is the action, which indicates the operation of allocating a certain amount of resources from a warehouse to a demand node; s′ is the new state after the execution of action a; a′ is all possible actions that can be taken in the new state s′.

4. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-3 is characterized by: The scenario prediction and simulation module will integrate generative adversarial networks, graph neural networks and long short-term memory networks to construct dynamic scenario predictions for complex emergency events, and realize multi-scenario deduction and dynamic scheduling simulation, providing multi-dimensional strategy references for emergency response. The privacy-preserving collaborative scheduling module will integrate federated learning, deep reinforcement learning and Kalman filtering algorithms to achieve collaborative resource scheduling among multiple regions and institutions, while ensuring data privacy and security, and dynamically generating optimal scheduling plans in a distributed architecture.

5. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-4 is characterized in that: The game optimization scheduling module uses a hybrid algorithm of ant colony optimization, game theory and Monte Carlo tree search to solve multi-party resource conflicts and scheduling path optimization problems, and supports dynamic collaborative decision-making of multiple participants to optimize the allocation efficiency and fairness of logistics resources. The transparent resource tracking module is based on blockchain technology, combined with smart contracts and conditional random field algorithms, to achieve full traceability of logistics resources and the immutability of dynamic scheduling plans, and provide a transparent audit function for the scheduling process.

6. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-5, characterized in that: The adaptive network optimization module integrates dynamic programming, simulated annealing and graph embedding algorithms to build a self-organizing network of logistics resources and realize efficient resource allocation and dynamic path optimization in complex networks.

7. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-6, characterized in that: The ethical priority scheduling module integrates the algorithm framework of multi-objective optimization, Bayesian reasoning and hierarchical analysis method, constructs a resource allocation model based on ethical priorities, and implements it in the resource allocation model.

8. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-7 is characterized in that: The data fusion and processing module further includes an abnormal data detection and repair function. By combining the generative adversarial network, support vector machine and conditional random field algorithm, the abnormal values ​​in the collected data are identified with high precision, and the abnormal data is dynamically repaired through an adaptive algorithm based on Bayesian reasoning to ensure the integrity and accuracy of the subsequent processed data.

9. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-8, characterized in that: The scenario prediction and simulation module further achieves real-time prediction of complex emergency scenarios and iterative optimization of optimal scheduling plans through the integration of quantum computing-based dynamic programming algorithms, variational autoencoders and adaptive neural fuzzy inference systems, and supports the self-learning and scenario adaptation capabilities of the prediction plan.

10. The logistics support dynamic scheduling and emergency response system based on intelligent data fusion according to claims 1-9, characterized in that: The privacy-preserving collaborative scheduling module enhances the federated learning mechanism and combines the blockchain consensus mechanism with quantum key distribution technology to achieve full-link encryption and privacy protection of data during transmission, storage and calculation, and dynamically optimizes the computing efficiency and security level of each regional resource scheduling plan.

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