Cross-regional rescue force allocation method based on dynamic resource game

Abstract

The present invention discloses a method for allocating cross-regional rescue forces based on dynamic resource game theory, comprising the following steps: S1, collecting disaster data, standardizing it, and extracting a feature set; S2, constructing a disaster scenario graph based on the feature set to generate a preliminary disaster scenario model; S3, defining a game model to generate a preliminary resource allocation model; S4, introducing a counterfactual reasoning algorithm to optimize the resource allocation model; S5, constructing a resource incentive module to optimize the incentive strategy; S6, based on the incentive strategy, predicting resource demand and generating a scheduling plan; S7, combining feedback information to generate a cross-regional rescue allocation plan. Through an optimization algorithm based on dynamic resource game theory, the present invention achieves efficient, fair, and dynamic resource allocation for cross-regional rescue forces, significantly improving disaster rescue response speed and resource utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of game theory application, and in particular to a cross-regional rescue force allocation method based on dynamic resource game. Background Art

[0002] With the frequent occurrence of natural disasters, cross-regional deployment of rescue forces and resource allocation have become particularly important. Post-disaster emergency rescue tasks involve multi-party coordination, and rescue resources (such as medical teams, supplies, equipment, etc.) need to be accurately and quickly deployed to the most severely affected areas. However, due to the diversity and complexity of disasters, traditional cross-regional resource scheduling methods have obvious shortcomings in efficiency, response time and fairness. Existing technologies generally rely on static scheduling strategies and resource allocation based on predetermined rules. These methods are difficult to cope with the complex needs brought about by sudden disasters, and usually ignore the real-time deployment and dynamic changes of resources, resulting in limited optimization and fairness of resource allocation.

[0003] Traditional methods for allocating rescue resources rely primarily on manual instructions and experience to deploy resources in disaster-stricken areas. Especially during large-scale disasters, resource scarcity and cross-regional coordination become even more complex, often requiring reliance on national or local governments for dispatch. While these methods can cope with disasters of a certain scale, they often lack flexibility and adaptability when faced with more sudden, rapidly changing, and complex disaster situations. Furthermore, due to information lags, decisions are often based on pre-disaster resource planning and standardized processes, resulting in inefficient and inaccurate resource dispatch. Furthermore, since the allocation of rescue resources is typically handled by a few key stakeholders, the resource allocation process is affected by resource type, fairness, and the needs of the disaster area, potentially resulting in excess resources in some areas and shortages in others.

[0004] Another notable shortcoming is that existing methods generally ignore the spatiotemporal correlation of resource demand across disaster-affected regions. Disaster relief requires not only the timely delivery of rescue resources but also an effective assessment of the diffusion and propagation paths of resource demand across disaster-affected regions. However, current resource allocation methods often use a single metric to determine demand, lacking real-time tracking and prediction of disaster dynamics. This makes it difficult for resource scheduling systems to respond quickly to sudden disasters and unable to adapt to the complexity and variability of disaster development, leading to delayed and uneven resource allocation.

[0005] Furthermore, current technologies lack effective incentive mechanisms and game theory models in resource allocation. In traditional rescue resource allocation methods, the interests of various participants are often not effectively regulated, leading to unclear priorities or conflicts of interest in the resource allocation process. Especially in cross-regional scheduling, due to the differences in resource demands, disaster severity, and response speed across regions, how to promote the joint participation of all participants in resource allocation decisions through reasonable incentive mechanisms becomes the key to solving the problems of fairness and efficiency in resource allocation. However, existing technologies have not fully introduced game theory optimization models and dynamic incentive strategies, resulting in resource allocation decisions failing to achieve an optimal balance between the various participants. This often leads to serious imbalances in resource allocation in individual regions, affecting the efficiency of the overall rescue work.

[0006] Furthermore, traditional resource allocation methods are mostly based on static algorithms or manual calculations, neglecting the real-time data feedback and adjustments required in dynamic environments. Due to the ever-changing situation at a disaster site, the relationship between resource demand and supply can fluctuate at any time, placing higher demands on the real-time nature of resource scheduling. However, traditional methods often remain at the disaster warning or post-disaster stages, lacking scheduling models based on real-time dynamic information. This static approach is unable to make accurate resource allocation decisions quickly in the immediate aftermath of a disaster, resulting in inefficient resource scheduling and potentially missing the optimal rescue opportunity.

[0007] Finally, another major shortcoming of existing technologies is the lack of a risk assessment mechanism for resource allocation. During the resource allocation process, oversupply or undersupply can occur, hindering the smooth execution of rescue missions. Especially when dispatching across regions, demand and transportation conditions in the disaster area can fluctuate dramatically. Most existing technologies lack dynamic risk control modules. In this context, how to dynamically optimize resource allocation strategies based on feedback from the disaster site, timely adjust resource supply and demand, and ensure the effective utilization of rescue resources and risk mitigation has become a pressing issue.

[0008] Therefore, how to provide a cross-regional rescue force allocation method based on dynamic resource game is a problem that technicians in this field urgently need to solve. Summary of the Invention

[0009] One purpose of the present invention is to propose a cross-regional rescue force allocation method based on dynamic resource game. The present invention makes full use of advanced algorithms such as game theory, dynamic resource scheduling, counterfactual reasoning, and reinforcement learning, and describes in detail how to dynamically optimize the cross-regional rescue resource allocation plan based on disaster site data and resource needs of each region after a disaster occurs. By establishing a multi-level disaster scenario model and combining game optimization and incentive mechanisms, the present invention can effectively deploy and allocate rescue forces, improve resource utilization, rescue efficiency, and allocation fairness. Compared with the existing technology, the present invention has the advantages of real-time dynamic adjustment, fair cross-regional allocation, and risk control, ensuring that rescue resources can be optimally allocated and efficiently used in complex and changing disaster environments.

[0010] The cross-regional rescue force allocation method based on dynamic resource game according to an embodiment of the present invention includes the following steps:

[0011] S1. Collect disaster data from multiple sources, perform standardization processing and multi-dimensional feature extraction to generate a disaster scene feature set;

[0012] S2. Based on the disaster scenario feature set, a dynamic multi-scale disaster scenario map is constructed to characterize the resource demand diffusion pattern and transportation path between disaster areas, and generate a preliminary disaster scenario model;

[0013] S3. Based on the disaster scenario model, define the dynamic resource game model in cross-regional rescue and generate a preliminary resource allocation model;

[0014] S4. Combining the disaster scenario feature set, disaster scenario model, and preliminary resource allocation model, a multi-level counterfactual reasoning algorithm is introduced to conduct supply and demand analysis and generate an optimized resource allocation model;

[0015] S5. Utilize the optimized resource allocation model and supply and demand analysis results to build a resource adaptive incentive module and optimize the incentive strategy using a dynamic game algorithm.

[0016] S6. Based on the optimized incentive strategy, combined with the dynamic response prediction network and multimodal sequence modeling algorithm, the time series changes of disaster propagation trends and resource demand are predicted to generate resource scheduling plans;

[0017] S7. Based on the resource scheduling plan, a dynamic risk control module is constructed. Combined with the feedback information from the disaster site, the resource allocation model is iteratively optimized to generate a cross-regional rescue allocation plan.

[0018] Optionally, the S3 specifically includes:

[0019] S31. Set the set of participants in the game model P = {p1, p2, ..., p n}, where n represents the number of disaster areas, and each participant pi The corresponding resource demand vector is D i ={d i1 ,d i2 ,…,d im}, where m is the number of resource types, d ij represents the demand of region i for the jth type of resource;

[0020] S32, define the policy set S = {s1, s2, ..., s n}, where s i ={s i1 ,s i2 ,…,s im} is the resource allocation strategy for region i, which satisfies the following constraints:

[0021]

[0022] Among them, R j is the total supply of the jth type of resource, s ij Assigned to p i The amount of resources of category j;

[0023] S33, for each participant p i Define the payment function U i (s), used to quantify benefits, taking into account rescue efficiency, time cost, resource consumption and distribution fairness:

[0024]

[0025] Among them, α, β, γ are weight coefficients, η ij is the response efficiency, τ i is the rescue time of the i-th area, c ij is the unit cost of the jth type of resource, and m is the number of resource types;

[0026] S34, based on the payment function U i (s), define the global optimization objective function Φ(s), which is expressed as:

[0027]

[0028] in, is the variance of the distribution ratio, n represents the number of disaster areas, and λ is the weight parameter of distribution fairness;

[0029] S35. Use a method combining reinforcement learning and game optimization to solve the objective function Φ(s):

[0030]

[0031] Among them, s* is the final optimized strategy solution, s is the current strategy, argmax s∈S The corresponding strategy solution s when solving the function Φ(s) to maximize * , ρ is the policy learning rate of reinforcement learning, is the gradient of the global optimization objective function Φ(s) with respect to the parameter θ, is the weight parameter optimized for the game, is the global average return;

[0032] S36, Strategy solution based on final optimization * and resource constraints to generate a preliminary resource allocation model.

[0033] Optionally, the S4 specifically includes:

[0034] S41, based on the disaster scene feature set D = {D1, D2, ..., D n}, the preliminary resource allocation model S, combined with the dynamic adjustment function, calculates the supply and demand deviation matrix G = [δ ij ]:

[0035]

[0036] Among them, δ ij is the supply and demand deviation of the i-th region for the j-th type of resource, d ij is the demand in region i, s ij is the current allocation amount, ∈ is the regularization parameter;

[0037] S42. Define the counterfactual loss function L cf (h,G) quantifies the supply and demand balance of different resource allocation strategies:

[0038]

[0039] Where n is the number of disaster areas, m is the number of resource types, λ1 is the time smoothing regularization weight, and h is the resource allocation strategy at the current iteration moment;

[0040] S43. In the high-dimensional strategy space H, set the high-dimensional projection function P(h) to constrain the search range:

[0041]

[0042] Among them, h k ′ is the candidate strategy obtained by the k-th search, K is the number of searches for strategy optimization, η1 is the learning rate, is the gradient of the counterfactual loss function;

[0043] S44, the strategy set H'={h1',h2',...,hK '}, dynamic constraint game optimization is used to solve the optimal strategy:

[0044]

[0045] Among them, h * is the optimized resource allocation strategy, h' is the candidate strategy in the current optimization process, Φ(h') is the joint objective function, h ij is the number of resources of type j allocated to the i-th region under the current policy h, is the global average return, U i (h') is the payoff function of the i-th region, argmax h'∈H' To find the optimal resource allocation strategy h in the high-dimensional strategy space H' * , so that the objective function achieves the maximum value;

[0046] S45. Resource allocation strategy based on optimization * , calculate the supply and demand equilibrium G(h * ):

[0047]

[0048] If G(h * ) exceeds the threshold τ, then adjust the strategy:

[0049]

[0050] Among them, ρ o is the equilibrium regulation rate, δ ij (h * ) is the supply and demand deviation term, is the gradient;

[0051] S46, based on the optimized resource allocation strategy h * and supply and demand equilibrium G(h * ), generate an optimized resource allocation model.

[0052] Optionally, the S5 specifically includes:

[0053] S51, based on the optimization of resource allocation model z={z1,z2,…,z n} and the supply and demand analysis result G(z), define the resource incentive function Ω(z):

[0054]

[0055] Among them, μ1 is the incentive weight coefficient, μ2 is the balance adjustment coefficient, ε is the regularization parameter, n is the number of disaster areas, m is the number of resource categories, z ijThe amount of resources of type j allocated to the ith disaster area, d ij is the demand for the jth type of resources in the i-th disaster area;

[0056] S52. Based on the resource incentive function, calculate the revenue adjustment amount Θ(z) of the resource supplier:

[0057]

[0058] Among them, λ k is the dynamic incentive adjustment coefficient;

[0059] S53. Based on the dynamic game optimization goal, a model for strengthening incentive strategy optimization is constructed. By utilizing the supply and demand analysis results and the game incentive mechanism, the objective function is optimized by comprehensively considering the benefits of resource suppliers, equilibrium constraints, and distribution stability:

[0060]

[0061] Among them, Φ * (z) is the objective function for optimizing the incentive strategy, U i (z) is the revenue function of the i-th resource supplier, ξ is the smooth incentive weight, is the amount of resources of type j allocated to the i-th region after the previous round of optimization, and ln is a logarithmic function;

[0062] S54. Use variational adaptive game optimization method to solve the equilibrium solution:

[0063]

[0064] Among them, z * is the resource allocation scheme, argmax z∈H To find a strategy that can make the objective function Φ * (z) maximizes the optimal strategy z * , α o is the time series smoothing weight;

[0065] S55. Calculate the adaptability of the equilibrium solution:

[0066]

[0067] If Ψ(z * )>α p , then perform balance adjustment:

[0068]

[0069] Among them, α p To adjust the threshold for balance, Ψ(z * ) is the equilibrium adaptive loss function, is the optimized resource allocation, α d is the regularization parameter, is the gradient of the equilibrium loss function;

[0070] S56, based on resource allocation scheme z * and the impact of supply and demand incentives to generate optimized incentive strategies.

[0071] Optionally, the S6 specifically includes:

[0072] S61, based on resource allocation scheme z * and optimized excitation strategy, defining the input data matrix X t :

[0073]

[0074] Among them, G(z * ) is the supply and demand equilibrium function, is the disaster situation data matrix, ξ k is the disaster propagation impact factor, Φ k (t) is the value of disaster characteristic k at time t;

[0075] S62, using dynamic response prediction network f DRP , combined with the time dependency, predict the resource demand at the future time t+Δt:

[0076]

[0077] in, is the resource demand matrix, w j is the dynamic weight of resource category j under different disaster scenarios, θ k is the deep reinforcement learning parameter, m is the number of resource types;

[0078] S63, based on multimodal sequence modeling algorithm g MSM , calculate the resource scheduling weight at future time:

[0079]

[0080] in, is the resource demand matrix, δ1 is the weight adjustment coefficient, T ij is the resource scheduling optimization matrix, ψ ij is the dynamic impact factor, ω t+Δt is the resource scheduling weight matrix, n represents the number of disaster areas, and m is the number of resource types;

[0081] S64. Combined with resource demand forecast results and resource scheduling weight ω t+Δt, define the resource scheduling optimization goal:

[0082]

[0083] Among them, Φ * (r) is the resource scheduling optimization objective function, r ij is the resource scheduling scheme, ε1 is the scheduling stability weight parameter, is the resource allocation scheme, ε2 is the scheduling time smoothness weight parameter, and T is the time step of resource scheduling;

[0084] S65. Using variational reinforcement learning algorithm to solve resource scheduling optimization objectives:

[0085]

[0086] Among them, r * is the resource scheduling scheme, argmax r Φ * (r) is to find the optimal resource scheduling solution r * , is the resource scheduling plan after the previous round of optimization, ε3 is the adjustment coefficient, and ln is the logarithmic function;

[0087] S66, based on resource scheduling scheme r * , and input it into the dynamic resource scheduling system.

[0088] Optionally, the S7 specifically includes:

[0089] S71, based on resource scheduling scheme r * , define the dynamic risk metric for resource allocation:

[0090]

[0091] in, is the resource scheduling risk metric, α ij is the dynamic risk weight, is the allocation amount in the current resource scheduling scheme, d ij is the demand of disaster area i for resource j, ∈ t To prevent small positive numbers with a denominator of zero, n is the number of disaster areas and m is the number of resource types;

[0092] S72. Define the standard deviation of distribution fairness σ f , used to measure the balance of the current distribution:

[0093]

[0094] When σ f >τ f, adjust the allocation scheme based on the fairness standard deviation:

[0095]

[0096] in, is the average resource allocation ratio of all disaster areas, τ f Threshold parameters optimized for fairness, is the adjusted resource allocation, ρ f To optimize the step size;

[0097] S73. Combine the disaster site feedback information F(t) to adjust the resource allocation plan after risk adjustment. Perform optimization and correction to generate the final optimized resource allocation plan

[0098]

[0099] Among them, O f is the feedback optimization weight, Q1 and Q2 are the demand modification weight and feedback response weight respectively;

[0100] S74. Introducing risk aversion function Redistribute and optimize high-risk allocation areas:

[0101]

[0102] Among them, ξ3 is the risk control weight, ξ4 is the time smoothing weight, is the resource allocation after the previous round of optimization, ln is the logarithmic function, and t is the time dimension;

[0103] S75. Integrate dynamic risk control, on-site feedback optimization and risk avoidance strategies to output cross-regional rescue allocation plans.

[0104] The beneficial effects of the present invention are:

[0105] This invention overcomes the existing problems of delayed resource allocation, low scheduling efficiency, and poor allocation fairness by introducing a cross-regional rescue force allocation method based on dynamic resource game theory. First, by leveraging game theory models, the present invention can ensure fairer and more reasonable resource allocation across regions through dynamic optimization strategies in a complex environment with multiple disaster areas and multiple resource demands. Unlike traditional resource scheduling methods that rely on static rules and manual experience, the present invention can obtain multi-source data from disaster sites in real time. Through multi-level disaster scenario modeling and dynamic game optimization, it achieves a precise match between resource demand and supply, thereby improving the efficiency of rescue resource utilization.

[0106] Furthermore, this invention leverages counterfactual reasoning algorithms and reinforcement learning techniques to optimize the resource allocation decision-making process. Faced with the suddenness and complexity of disasters, this invention can dynamically adjust resource scheduling plans based on real-time data, promptly responding to changes in the disaster area and avoiding the resource mismatch or under-allocation problems often associated with information lags in traditional approaches. By thoroughly analyzing the needs of all parties involved in a rescue mission and employing incentive mechanisms to motivate all participants, resource allocation becomes more efficient and timely.

[0107] This invention also incorporates a dynamic risk control module that continuously optimizes resource allocation strategies through real-time feedback from disaster sites, reducing the risks associated with over- or under-allocation of resources. Especially during cross-regional scheduling, taking into account the varying demands and complexities of resource supply across different regions, this invention ensures the security and stability of resource allocation through precise risk assessment and dynamic adjustments. Ultimately, this invention not only improves the efficiency of rescue resource utilization but also reduces post-disaster rescue response time, ensuring that rescue efforts can be effectively carried out in the shortest possible time.

[0108] Therefore, the present invention has high real-time, accuracy and fairness. It can provide scientific decision-making support for cross-regional rescue force allocation through precise data analysis and intelligent optimization models within the first time after a disaster occurs, significantly improving the efficiency and safety of rescue work. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0110] Figure 1 This is a flow chart of the cross-regional rescue force allocation method based on dynamic resource game proposed by the present invention;

[0111] Figure 2 This is a structural diagram of the cross-regional rescue force allocation method based on dynamic resource game proposed by the present invention. DETAILED DESCRIPTION

[0112] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0113] refer to Figure 1-2 ,The cross-regional rescue force allocation method based on dynamic resource game, includes the following steps:

[0114] S1. Collect disaster data from multiple sources, perform standardization processing and multi-dimensional feature extraction to generate a disaster scene feature set;

[0115] S2. Based on the disaster scenario feature set, a dynamic multi-scale disaster scenario map is constructed to characterize the resource demand diffusion pattern and transportation path between disaster areas, and generate a preliminary disaster scenario model;

[0116] S3. Based on the disaster scenario model, define the dynamic resource game model in cross-regional rescue and generate a preliminary resource allocation model;

[0117] S4. Combining the disaster scenario feature set, disaster scenario model, and preliminary resource allocation model, a multi-level counterfactual reasoning algorithm is introduced to conduct supply and demand analysis and generate an optimized resource allocation model;

[0118] S5. Utilize the optimized resource allocation model and supply and demand analysis results to build a resource adaptive incentive module and optimize the incentive strategy using a dynamic game algorithm.

[0119] S6. Based on the optimized incentive strategy, combined with the dynamic response prediction network and multimodal sequence modeling algorithm, the time series changes of disaster propagation trends and resource demand are predicted to generate resource scheduling plans;

[0120] S7. Based on the resource scheduling plan, a dynamic risk control module is constructed. Combined with the feedback information from the disaster site, the resource allocation model is iteratively optimized to generate a cross-regional rescue allocation plan.

[0121] In this embodiment, S3 specifically includes:

[0122] S31. Set the set of participants in the game model P = {p1, p2, ..., p n}, where n represents the number of disaster areas, and each participant p i The corresponding resource demand vector is D i ={d i1 ,d i2 ,…,d im}, where m is the number of resource types, d ij represents the demand of region i for the jth type of resource;

[0123] S32, define the policy set S = {s1, s2, ..., s n}, where s i ={s i1 ,s i2 ,…,s im} is the resource allocation strategy for region i, which satisfies the following constraints:

[0124]

[0125] Among them, R j is the total supply of the jth type of resource, sij Assigned to p i The amount of resources of category j;

[0126] S33, for each participant p i Define the payment function U i (s), used to quantify benefits, taking into account rescue efficiency, time cost, resource consumption and distribution fairness:

[0127]

[0128] Among them, α, β, γ are weight coefficients, η ij is the response efficiency, τ i is the rescue time of the i-th area, c ij is the unit cost of the jth type of resource, and m is the number of resource types;

[0129] S34, based on the payment function U i (s), define the global optimization objective function Φ(s), which is expressed as:

[0130]

[0131] in, is the variance of the distribution ratio, n represents the number of disaster areas, and λ is the weight parameter of distribution fairness;

[0132] S35. Use a method combining reinforcement learning and game optimization to solve the objective function Φ(s):

[0133]

[0134] Among them, s * is the final optimized strategy solution, s is the current strategy, argmax s∈S The corresponding strategy solution s when solving the function Φ(s) to maximize * , ρ is the policy learning rate of reinforcement learning, is the gradient of the global optimization objective function Φ(s) with respect to the parameter θ, is the weight parameter optimized for the game, is the global average return;

[0135] S36, Strategy solution based on final optimization * and resource constraints to generate a preliminary resource allocation model.

[0136] In this embodiment, the S4 specifically includes:

[0137] S41, based on the disaster scene feature set D = {D1, D2, ..., D n}, the preliminary resource allocation model S, combined with the dynamic adjustment function, calculates the supply and demand deviation matrix G = [δ ij ]:

[0138]

[0139] Among them, δ ij is the supply and demand deviation of the i-th region for the j-th type of resource, d ij is the demand in region i, s ij is the current allocation amount, ∈ is the regularization parameter;

[0140] S42. Define the counterfactual loss function L cf (h,G) quantifies the supply and demand balance of different resource allocation strategies:

[0141]

[0142] Where n is the number of disaster areas, m is the number of resource types, λ1 is the time smoothing regularization weight, and h is the resource allocation strategy at the current iteration moment;

[0143] S43. In the high-dimensional strategy space H, set the high-dimensional projection function P(h) to constrain the search range:

[0144]

[0145] Among them, h k ′ is the candidate strategy obtained by the k-th search, K is the number of searches for strategy optimization, η1 is the learning rate, is the gradient of the counterfactual loss function;

[0146] S44, the strategy set H'={h1',h2',...,h K '}, dynamic constraint game optimization is used to solve the optimal strategy:

[0147]

[0148] Among them, h * is the optimized resource allocation strategy, h' is the candidate strategy in the current optimization process, Φ(h') is the joint objective function, h ij is the number of resources of type j allocated to the i-th region under the current policy h, is the global average return, U i (h') is the payoff function of the i-th region, argmax h'∈H' To find the optimal resource allocation strategy h in the high-dimensional strategy space H' * , so that the objective function achieves the maximum value;

[0149] S45. Resource allocation strategy based on optimization* , calculate the supply and demand equilibrium G(h * ):

[0150]

[0151] If G(h * ) exceeds the threshold τ, then adjust the strategy:

[0152]

[0153] Among them, ρ o is the equilibrium regulation rate, δ ij (h * ) is the supply and demand deviation term, is the gradient;

[0154] S46, based on the optimized resource allocation strategy h * and supply and demand equilibrium G(h * ), generate an optimized resource allocation model.

[0155] In this embodiment, the S5 specifically includes:

[0156] S51, based on the optimization of resource allocation model z={z1,z2,…,z n} and the supply and demand analysis result G(z), define the resource incentive function Ω(z):

[0157]

[0158] Among them, μ1 is the incentive weight coefficient, μ2 is the balance adjustment coefficient, ε is the regularization parameter, n is the number of disaster areas, m is the number of resource categories, z ij The amount of resources of type j allocated to the ith disaster area, d ij is the demand for the jth type of resources in the i-th disaster area;

[0159] S52. Based on the resource incentive function, calculate the revenue adjustment amount Θ(z) of the resource supplier:

[0160]

[0161] Among them, λ k is the dynamic incentive adjustment coefficient;

[0162] S53. Based on the dynamic game optimization goal, a model for strengthening incentive strategy optimization is constructed. By utilizing the supply and demand analysis results and the game incentive mechanism, the objective function is optimized by comprehensively considering the benefits of resource suppliers, equilibrium constraints, and distribution stability:

[0163]

[0164] Among them, Φ * (z) is the objective function for optimizing the incentive strategy, U i (z) is the revenue function of the i-th resource supplier, ξ is the smooth incentive weight, is the amount of resources of type j allocated to the i-th region after the previous round of optimization, and ln is a logarithmic function;

[0165] S54. Use variational adaptive game optimization method to solve the equilibrium solution:

[0166]

[0167] Among them, z * is the resource allocation scheme, argmax z∈H To find a strategy that can make the objective function Φ * (z) maximizes the optimal strategy z * , α o is the time series smoothing weight;

[0168] S55. Calculate the adaptability of the equilibrium solution:

[0169]

[0170] If Ψ(z * )>α p , then perform balance adjustment:

[0171]

[0172] Among them, α p To adjust the threshold for balance, Ψ(z * ) is the equilibrium adaptive loss function, is the optimized resource allocation, α d is the regularization parameter, is the gradient of the equilibrium loss function;

[0173] S56, based on resource allocation scheme z * and the impact of supply and demand incentives to generate optimized incentive strategies.

[0174] In this embodiment, S6 specifically includes:

[0175] S61, based on resource allocation scheme z * and optimized excitation strategy, defining the input data matrix X t :

[0176]

[0177] Among them, G(z * ) is the supply and demand equilibrium function, is the disaster situation data matrix, ξ k is the disaster propagation impact factor, Φ k (t) is the value of disaster characteristic k at time t;

[0178] S62, using dynamic response prediction network f DRP , combined with the time dependency, predict the resource demand at the future time t+Δt:

[0179]

[0180] in, is the resource demand matrix, w j is the dynamic weight of resource category j under different disaster scenarios, θ k is the deep reinforcement learning parameter, m is the number of resource types;

[0181] S63, based on multimodal sequence modeling algorithm g MSM , calculate the resource scheduling weight at future time:

[0182]

[0183] in, is the resource demand matrix, δ1 is the weight adjustment coefficient, T ij is the resource scheduling optimization matrix, ψ ij is the dynamic impact factor, ω t+Δt is the resource scheduling weight matrix, n represents the number of disaster areas, and m is the number of resource types;

[0184] S64. Combined with resource demand forecast results and resource scheduling weight ω t+Δt , define the resource scheduling optimization goal:

[0185]

[0186] Among them, Φ * (r) is the resource scheduling optimization objective function, r ij is the resource scheduling scheme, ε1 is the scheduling stability weight parameter, is the resource allocation scheme, ε2 is the scheduling time smoothness weight parameter, and T is the time step of resource scheduling;

[0187] S65. Using variational reinforcement learning algorithm to solve resource scheduling optimization objectives:

[0188]

[0189] Among them, r * is the resource scheduling scheme, argmax r Φ *(r) is to find the optimal resource scheduling solution r * , is the resource scheduling plan after the previous round of optimization, ε3 is the adjustment coefficient, and ln is the logarithmic function;

[0190] S66, based on resource scheduling scheme r * , and input it into the dynamic resource scheduling system.

[0191] In this embodiment, the S7 specifically includes:

[0192] S71, based on resource scheduling scheme r * , define the dynamic risk metric for resource allocation:

[0193]

[0194] in, is the resource scheduling risk metric, α ij is the dynamic risk weight, is the allocation amount in the current resource scheduling scheme, d ij is the demand of disaster area i for resource j, ∈ t To prevent small positive numbers with a denominator of zero, n is the number of disaster areas and m is the number of resource types;

[0195] S72. Define the standard deviation of distribution fairness σ f , used to measure the balance of the current distribution:

[0196]

[0197] When σ f >τ f , adjust the allocation scheme based on the fairness standard deviation:

[0198]

[0199] in, is the average resource allocation ratio of all disaster areas, τ f Threshold parameters optimized for fairness, is the adjusted resource allocation, ρ f To optimize the step size;

[0200] S73. Combine the disaster site feedback information F(t) to adjust the resource allocation plan after risk adjustment. Perform optimization and correction to generate the final optimized resource allocation plan

[0201]

[0202] Among them, O fis the feedback optimization weight, Q1 and Q2 are the demand modification weight and feedback response weight respectively;

[0203] S74. Introducing risk aversion function Redistribute and optimize high-risk allocation areas:

[0204]

[0205] Among them, ξ3 is the risk control weight, ξ4 is the time smoothing weight, is the resource allocation after the previous round of optimization, ln is the logarithmic function, and t is the time dimension;

[0206] S75. Integrate dynamic risk control, on-site feedback optimization and risk avoidance strategies to output cross-regional rescue allocation plans.

[0207] Example 1:

[0208] In order to verify the feasibility of the present invention in practice, the present invention was applied to a flood disaster in a certain province. In this flood disaster, the affected area involved multiple counties and cities, the disaster situation was serious, the lives and safety of tens of thousands of people were threatened, and the infrastructure was severely damaged. After the disaster, the demand for resources such as medical care, daily necessities, and rescue teams increased sharply, and rescue operations urgently needed to be coordinated. However, due to the different resource demands of each disaster area and the great difficulties in mutual allocation, the traditional resource allocation method failed to fully play its role, resulting in insufficient resource supply in some areas and an oversupply of resources in other areas. In order to solve this problem, a cross-regional rescue force allocation method based on dynamic resource game was adopted.

[0209] During the emergency rescue efforts for this flood disaster, the system first collected multi-source data from multiple disaster areas, including information on disaster type, area, population density, road conditions, material needs, and medical resources. It then standardized and extracted features from this data to form a disaster scenario feature set. This feature set reflects the basic conditions in the disaster area and provides foundational data for subsequent resource scheduling decisions.

[0210] Then, based on the disaster scenario feature set, the system constructed a dynamic, multi-scale disaster scenario graph. This graph not only depicts the geographical relationships between disaster areas but also connects the resource flows between them through resource demand diffusion patterns and transportation routes. Through this graph model, the system can accurately determine the resource needs of each disaster area and the optimal cross-regional transportation routes, generating a preliminary disaster scenario model.

[0211] Next, the system constructed a dynamic resource game model, simulating the competitive and cooperative relationships among disaster-stricken areas (as participants in the game) in resource allocation. Each disaster area's resource demand and the required rescue resource equation were input into the game model. To ensure fairness in the distribution of rescue resources, the system employed a payoff function to quantify the benefits to each area in resource allocation. Through game optimization calculations, a preliminary resource allocation model was developed.

[0212] The system then employed a multi-level counterfactual reasoning algorithm to analyze the supply and demand relationships in each disaster area and optimize resource allocation. This optimization enabled the system to maximize the fairness and efficiency of resource allocation while taking into account the needs of all parties. Furthermore, based on the optimized resource allocation model, the system constructed a resource adaptive incentive module to further optimize the incentive strategy and ensure that all stakeholders actively responded to resource allocation decisions.

[0213] On this basis, the system combines a dynamic response prediction network with a multimodal sequence modeling algorithm to predict the spread of disasters and changes in resource demand, generating a resource scheduling plan. This prediction allows the system to predict which areas will experience a sharp increase in resource demand within the next few hours and promptly adjust resource scheduling strategies to ensure that resources are accurately and efficiently allocated to where they are most needed.

[0214] Finally, the system iteratively optimized the resource allocation model based on feedback from the disaster site, generating a final cross-regional rescue allocation plan. This plan ensured that resource allocation across disaster areas was balanced in terms of efficiency and fairness, and responded promptly to changes in the disaster situation.

[0215] Table 1 Comparison of rescue resource allocation based on traditional method and the method of the present invention

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[0217] Table 2 Comparison of resource arrival time based on traditional method and the method of the present invention

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[0220] As can be seen from Table 1, the differences between the traditional method and the present invention in terms of resource arrival time and resource gap. Under the traditional method, the resource arrival time for disaster area A is 12 hours, the total amount of resources is 3,000, and there is a resource gap of 1,000 items; the resource arrival time for disaster area B is 14 hours, and the resource gap is 500 items; the resource arrival time for disaster area C is 16 hours, and there is a resource gap of 1,500 items; the resource arrival time for disaster area D is 10 hours, but the resources have already met the demand. Using the method of the present invention, the resource arrival time for all disaster areas is greatly shortened, 4 hours for disaster area A, 5 hours for disaster area B, 6 hours for disaster area C, and 3 hours for disaster area D. In addition, the resource needs of all disaster areas are fully met, and the resource gap is 0. This shows that the present invention can accurately allocate rescue resources to where they are needed in a shorter time, eliminating the resource gap problem in the traditional method.

[0221] The data in Table 2 demonstrates a significant difference in time savings. In disaster area A, the traditional method reduced resource arrival time to 12 hours, while the method of the present invention reduced it to 4 hours, saving 8 hours. Disaster area B saw a 9-hour reduction; disaster area C saw a 10-hour reduction; and disaster area D saw a 7-hour reduction. These data demonstrate that the present invention significantly improves the timeliness of resource allocation and significantly reduces rescue response time.

[0222] Overall, this invention, through advanced algorithms such as dynamic resource game optimization, counterfactual reasoning, and reinforcement learning, effectively improves the efficiency of dispatching rescue resources, shortens resource arrival time, ensures that the needs of each disaster area are fully met, and avoids imbalances in resource allocation. Compared with traditional static, empirical resource scheduling methods, this invention can allocate resources more scientifically and accurately, improve rescue efficiency, and minimize the plight of people in disaster-stricken areas, demonstrating significant social benefits and practical application value.

[0223] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A cross-regional rescue force allocation method based on dynamic resource game, characterized by: The steps include: S1. Collect disaster data from multiple sources, perform standardization processing and multi-dimensional feature extraction to generate a disaster scene feature set; S2. Based on the disaster scenario feature set, a dynamic multi-scale disaster scenario map is constructed to characterize the resource demand diffusion pattern and transportation path between disaster areas, and generate a preliminary disaster scenario model; S3. Based on the disaster scenario model, define the dynamic resource game model in cross-regional rescue and generate a preliminary resource allocation model; S4. Combining the disaster scenario feature set, disaster scenario model, and preliminary resource allocation model, a multi-level counterfactual reasoning algorithm is introduced to conduct supply and demand analysis and generate an optimized resource allocation model; S5. Utilize the optimized resource allocation model and supply and demand analysis results to build a resource adaptive incentive module and optimize the incentive strategy using a dynamic game algorithm. S6. Based on the optimized incentive strategy, combined with the dynamic response prediction network and multimodal sequence modeling algorithm, the time series changes of disaster propagation trends and resource demand are predicted to generate resource scheduling plans; S7. Based on the resource scheduling plan, a dynamic risk control module is constructed. Combined with the feedback information from the disaster site, the resource allocation model is iteratively optimized to generate a cross-regional rescue allocation plan.

2. The cross-regional rescue force allocation method based on dynamic resource game according to claim 1 is characterized in that: The S3 specifically includes: S31. Set the set of participants in the game model P = {p1, p2, ..., p n }, where n represents the number of disaster areas, and each participant p i The corresponding resource demand vector is D i ={d i1 ,d i2 ,…,d im }, where m is the number of resource types, d ij represents the demand of region i for the jth type of resource; S32, define the policy set S = {s1, s2, ..., s n }, where s i ={s i1 ,s i2 ,…,s im } is the resource allocation strategy for region i, which satisfies the following constraints: Among them, R j is the total supply of the jth type of resource, s ij Assigned to p i The amount of resources of category j; S33, for each participant p i Define the payment function U i (s), used to quantify benefits, taking into account rescue efficiency, time cost, resource consumption and distribution fairness: Among them, α, β, γ are weight coefficients, η ij is the response efficiency, τ i is the rescue time of the i-th area, c ij is the unit cost of the jth type of resource, and m is the number of resource types; S34, based on the payment function U i (s), define the global optimization objective function Φ(s), which is expressed as: in, is the variance of the distribution ratio, n represents the number of disaster areas, and λ is the weight parameter of distribution fairness; S35. Use a method combining reinforcement learning and game optimization to solve the objective function Φ(s): Among them, s * is the final optimized strategy solution, s is the current strategy, argmax s∈S The corresponding strategy solution s when solving the function Φ(s) to maximize * , ρ is the policy learning rate of reinforcement learning, is the gradient of the global optimization objective function Φ(s) with respect to the parameter θ, is the weight parameter optimized for the game, is the global average return; S36, Strategy solution based on final optimization * and resource constraints to generate a preliminary resource allocation model.

3. The cross-regional rescue force allocation method based on dynamic resource game according to claim 1 is characterized in that: The S4 specifically includes: S41, based on the disaster scene feature set D={D1,D2,…,D n }, the preliminary resource allocation model S, combined with the dynamic adjustment function, calculates the supply and demand deviation matrix G = [δ ij ]: Among them, δ ij is the supply and demand deviation of the i-th region for the j-th type of resource, d ij is the demand in region i, s ij is the current allocation, ε is the regularization parameter; S42. Define the counterfactual loss function L cf (h,G) quantifies the supply and demand balance of different resource allocation strategies: Where n is the number of disaster areas, m is the number of resource types, λ1 is the time smoothing regularization weight, and h is the resource allocation strategy at the current iteration moment; S43. In the high-dimensional strategy space H, set the high-dimensional projection function P(h) to constrain the search range: Among them, h k ' is the candidate strategy obtained by the k-th search, K is the number of searches for strategy optimization, η1 is the learning rate, is the gradient of the counterfactual loss function; S44, the strategy set H'={h1',h2',...,h K '}, dynamic constraint game optimization is used to solve the optimal strategy: Among them, h * is the optimized resource allocation strategy, h' is the candidate strategy in the current optimization process, Φ(h') is the joint objective function, h ij is the number of resources of type j allocated to the i-th region under the current policy h, is the global average return, U i (h') is the payoff function of the i-th region, argmax h'∈H' To find the optimal resource allocation strategy h in the high-dimensional strategy space H' * , so that the objective function achieves the maximum value; S45. Resource allocation strategy based on optimization * , calculate the supply and demand equilibrium G(h * ): If G(h * ) exceeds the threshold τ, then adjust the strategy: Among them, ρ o is the equilibrium regulation rate, δ ij (h * ) is the supply and demand deviation term, is the gradient; S46, based on the optimized resource allocation strategy h * and supply and demand equilibrium G(h * ), generate an optimized resource allocation model.

4. The cross-regional rescue force allocation method based on dynamic resource game according to claim 1 is characterized in that: The S5 specifically includes: S51, based on the optimization of resource allocation model z={z1,z2,…,z n } and the supply and demand analysis result G(z), define the resource incentive function Ω(z): Among them, μ1 is the incentive weight coefficient, μ2 is the balance adjustment coefficient, ε is the regularization parameter, n is the number of disaster areas, m is the number of resource categories, z ij The amount of resources of type j allocated to the ith disaster area, d ij is the demand for the jth type of resources in the i-th disaster area; S52. Based on the resource incentive function, calculate the revenue adjustment amount Θ(z) of the resource supplier: Among them, λ k is the dynamic incentive adjustment coefficient; S53. Based on the dynamic game optimization goal, a model for strengthening incentive strategy optimization is constructed. By utilizing the supply and demand analysis results and the game incentive mechanism, the objective function is optimized by comprehensively considering the benefits of resource suppliers, equilibrium constraints, and distribution stability: Among them, Φ * (z) is the objective function for optimizing the incentive strategy, U i (z) is the revenue function of the i-th resource supplier, ξ is the smooth incentive weight, is the amount of resources of type j allocated to the i-th region after the previous round of optimization, and ln is a logarithmic function; S54. Use variational adaptive game optimization method to solve the equilibrium solution: Among them, z * is the resource allocation scheme, argmax z∈H To find a strategy that can make the objective function Φ * (z) maximizes the optimal strategy z * , α o is the time series smoothing weight; S55. Calculate the adaptability of the equilibrium solution: If Ψ(z * )>α p , then perform balance adjustment: Among them, α p To adjust the threshold for balance, Ψ(z * ) is the equilibrium adaptive loss function, is the optimized resource allocation, α d is the regularization parameter, is the gradient of the equilibrium loss function; S56, based on resource allocation scheme z * and the impact of supply and demand incentives to generate optimized incentive strategies.

5. The cross-regional rescue force allocation method based on dynamic resource game according to claim 1 is characterized in that: The S6 specifically includes: S61, based on resource allocation scheme z * and optimized excitation strategy, defining the input data matrix X t : Among them, G(z * ) is the supply and demand equilibrium function, is the disaster situation data matrix, ξ k is the disaster propagation impact factor, Φ k (t) is the value of disaster characteristic k at time t; S62, using dynamic response prediction network f DRP , combined with the time dependency, predict the resource demand at the future time t+Δt: in, is the resource demand matrix, w j is the dynamic weight of resource category j under different disaster scenarios, θ k is the deep reinforcement learning parameter, m is the number of resource types; S63, based on multimodal sequence modeling algorithm g MSM , calculate the resource scheduling weight at future time: in, is the resource demand matrix, δ1 is the weight adjustment coefficient, T ij is the resource scheduling optimization matrix, ψ ij is the dynamic impact factor, ω t+Δt is the resource scheduling weight matrix, n represents the number of disaster areas, and m is the number of resource types; S64. Combined with resource demand forecast results and resource scheduling weight ω t+Δt , define the resource scheduling optimization goal: Among them, Φ * (r) is the resource scheduling optimization objective function, r ij is the resource scheduling scheme, ε1 is the scheduling stability weight parameter, is the resource allocation scheme, ε2 is the scheduling time smoothness weight parameter, and T is the time step of resource scheduling; S65. Using variational reinforcement learning algorithm to solve resource scheduling optimization objectives: Among them, r * is the resource scheduling scheme, argmax r Φ * (r) is to find the optimal resource scheduling solution r * , is the resource scheduling plan after the previous round of optimization, ε3 is the adjustment coefficient, and ln is the logarithmic function; S66, based on resource scheduling scheme r * , and input it into the dynamic resource scheduling system.

6. The cross-regional rescue force allocation method based on dynamic resource game according to claim 1 is characterized in that: The S7 specifically includes: S71, based on resource scheduling scheme r * , define the dynamic risk metric for resource allocation: in, is the resource scheduling risk metric, α ij is the dynamic risk weight, is the allocation amount in the current resource scheduling scheme, d ij is the demand of disaster area i for resource j, ∈ t To prevent small positive numbers with a denominator of zero, n is the number of disaster areas and m is the number of resource types; S72. Define the standard deviation of distribution fairness σ f , used to measure the balance of the current distribution: When σ f >τ f , adjust the allocation scheme based on the fairness standard deviation: in, is the average resource allocation ratio of all disaster areas, τ f Threshold parameters optimized for fairness, is the adjusted resource allocation, ρ f To optimize the step size; S73. Combine the disaster site feedback information F(t) to adjust the resource allocation plan after risk adjustment. Perform optimization and correction to generate the final optimized resource allocation plan Among them, O f is the feedback optimization weight, Q1 and Q2 are the demand modification weight and feedback response weight respectively; S74. Introducing risk aversion function Redistribute and optimize high-risk allocation areas: Among them, ξ3 is the risk control weight, ξ4 is the time smoothing weight, is the resource allocation after the previous round of optimization, ln is the logarithmic function, and t is the time dimension; S75. Integrate dynamic risk control, on-site feedback optimization and risk avoidance strategies to output cross-regional rescue allocation plans.

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