Device reinforcement and emergency resource preset optimization method based on disaster prediction

Abstract

This invention relates to the field of emergency resource optimization and allocation technology, and in particular provides a method for optimizing equipment reinforcement and emergency resource pre-positioning based on disaster prediction. The method includes: reverse-engineering the critical damage threshold with the impact intensity parameters of the corresponding location in disaster prediction to obtain the critical response time window for each facility point; combining the critical response time window of each facility point with the number of registered mobile devices and the location of fixed equipment anchor points; arranging the facility points in ascending order of critical response time windows, and successively adding the number of mobile devices and the location of fixed equipment anchor points to the reinforcement material consumption coefficient of the corresponding facility point during the arrangement process to form an equipment reinforcement sequence; and generating the optimal resupply point location to which each resource storage node needs to be migrated and the total amount of resources that each optimal resupply point should store based on the impact resistance improvement value calculated after reinforcement of each facility point in the equipment reinforcement sequence. This invention improves the systematicness and adaptability of emergency resource pre-positioning.

Description

Technical Field

[0001] This invention relates to the field of technology, and in particular to a method for equipment reinforcement and emergency resource pre-positioning optimization based on disaster prediction. Background Technology

[0002] In the field of emergency management for natural disasters such as typhoons, floods, earthquakes, and industrial explosions, how to scientifically and efficiently deploy limited reinforcement resources and emergency supplies to minimize the risk of damage to critical infrastructure, such as production facilities, storage containers, and supporting structures, remains a long-standing technical challenge. Traditional disaster prevention practices typically rely on static risk zoning and fixed emergency plans. For example, based on historical disaster statistics, certain areas are designated as high-risk zones, and supplies are stockpiled in these areas in advance. With the improvement of numerical simulation technology and computing power, disaster prediction technology can now provide dynamic prediction results with high spatiotemporal resolution; for example, predicting the intensity, path, start time, and duration of disasters such as storm surges and shock waves over a future period, forming a spatiotemporal probability distribution of the disaster; thus providing a data foundation for shifting from passive response to proactive pre-positioning.

[0003] However, a significant technological gap exists between acquiring high-precision disaster prediction data and developing executable and optimizable reinforcement and resource pre-positioning plans. How to couple abstract, continuously changing disaster impact intensity parameters with the complex engineering realities of specific facility locations, such as material physical properties, movable equipment resources, and fixed anchor point layouts, and ultimately transform this into an action plan with clear spatiotemporal priorities and resource consumption, is a pressing technical challenge. Simple static planning or decision-making methods relying on human experience are no longer adequate for the dynamic and nonlinear development of disasters; a systematic technical solution capable of integrating multi-source information and performing collaborative optimization is urgently needed.

[0004] Currently, existing technologies related to this patent technology field are mainly divided into the following categories: 1. Facility reinforcement prioritization technology based on risk thresholds: This type of technology typically first obtains the intensity distribution of disaster prediction, then sets a fixed risk threshold, such as critical damage intensity, for each facility point; by comparing the predicted intensity with the preset threshold, the risk level of each facility point is assessed, such as high, medium, and low risk; then, based on this risk level, a rough reinforcement priority list is manually or semi-automatically formulated. For example, all high-risk facility points are listed as priority reinforcement targets. 2. Emergency resource pre-positioning technology based on coverage models: This type of technology mainly solves the problem of resource warehouse, i.e., the location of resource storage nodes; it usually aims to minimize response time or maximize the coverage population / facilities by establishing a mathematical model. Input parameters include candidate warehouse locations, road network distances, service radii, and the estimated demand of various demand points, such as communities and facility points. By solving classic operations research problems such as the maximum coverage model or the P-center model, the location of existing warehouses or the location of new warehouses is optimized.

[0005] While the aforementioned existing technologies have achieved certain results in their respective fields, they are fragmented and fail to form a closed-loop optimization system that links disaster dynamic prediction, facility vulnerability assessment, and resource coordinating pre-positioning. This leads to a core deficiency: the lack of a method to dynamically couple the temporal urgency of facility reinforcement operations with the spatial demand distribution of emergency resource pre-positioning for joint optimization. Specifically, this manifests in the following ways: 1. Disconnect between reinforcement priority and resource allocation: The first type of technology prioritizes based solely on fixed risk thresholds, ignoring the crucial dimension of time. For example, two facility sites may eventually be destroyed, but one might reach the critical point 10 minutes after a disaster, while the other might reach it an hour later. Existing technologies cannot accurately calculate this critical response time window, resulting in a coarse prioritization of reinforcement. More importantly, it fails to incorporate valuable dynamic resources such as mobile equipment into the prioritization and allocation process. 2. Resource pre-positioning based on static demand, lacking spatial coupling: When selecting resource nodes and calculating reserves, the second type of technology typically uses static demand data, based on historical experience or simple population / facility density estimates. It fails to reflect a fundamental physical fact: the increased impact resistance of a facility after reinforcement will have a radiating effect on the overall risk of the surrounding area through the spatial attenuation law of disaster impact intensity. In other words, reinforcing point A not only protects point A but may also indirectly alleviate resource pressure on point B and the surrounding area by reducing the propagation effect of disaster impact. Current technology has failed to establish this spatial convolution relationship between point-based reinforcement and area-based demand, leading to distorted estimations of emergency resource demand density, and consequently, a lack of scientific basis for calculating the optimal location and reserve volume of resource nodes.

[0006] In summary, existing technologies treat facility reinforcement and resource pre-positioning as two independent processes. The former lacks fine-grained scheduling of dynamic resources and time, while the latter, based on static needs, fails to perceive changes in spatial risks brought about by reinforcement actions. This results in a fundamental loss of efficiency in the spatiotemporal coordination of the final emergency response plan, failing to achieve the core objective of using the right resources at the right time and in the right place in disaster response. This invention aims to solve this technical problem by providing a method that enables dynamic and coordinated optimization of facility reinforcement and resource pre-positioning. Summary of the Invention

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] One aspect of the present invention provides a method for equipment reinforcement and emergency resource pre-positioning optimization based on disaster prediction, comprising the following steps:

[0009] Obtain the spatiotemporal probability distribution of disaster prediction, extract the critical damage threshold of the material of each facility point within the coverage area of ​​the distribution, and inversely extrapolate the critical damage threshold with the impact intensity parameter of the corresponding location in the disaster prediction to obtain the critical response time window of each facility point.

[0010] The critical response time window for each facility point is combined with the number of registered mobile devices and the location of fixed equipment anchor points. The facilities points are arranged in order of increasing critical response time window. During the arrangement process, the number of mobile devices and the location of fixed equipment anchor points are successively added to the reinforcement material consumption coefficient of the corresponding facility point to form an equipment reinforcement sequence. The equipment reinforcement sequence records the reinforcement operation sequence number of each facility point and the type and amount of reinforcement materials required.

[0011] Based on the impact resistance enhancement value calculated after each facility point in the equipment reinforcement sequence is reinforced, the impact resistance enhancement value is convolved point by point with the impact intensity decay curve with distance in disaster prediction to obtain an emergency resource demand density field covering the entire area. Then, the emergency resource demand density field is spatially superimposed and matched with the effective supply radius of each existing resource storage node that has been pre-mapped to generate the optimal supply point location that each resource storage node needs to migrate to and the total amount of resources that each optimal supply point should store.

[0012] This invention achieves dynamic coordination of time window, reinforcement operation, and resource layout in disaster response by linking a three-layer technical architecture of temporal deduction, sequence optimization, and spatial convolution, thereby improving the systematicness and adaptability of emergency resource pre-positioning. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0014] Figure 1 This is a flowchart of the equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction provided in Embodiment 1 of the present invention;

[0015] Figure 2 This is a schematic diagram of the equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction provided in Embodiment 1 of the present invention;

[0016] Figure 3 This is a diagram illustrating the process of reverse-engineering the critical failure threshold with the impact intensity parameter at the corresponding location in disaster prediction, as provided in Embodiment 2 of the present invention.

[0017] Figure 4 This is a process diagram of forming the device hardening sequence provided in Embodiment 4 of the present invention;

[0018] Figure 5 This is a diagram illustrating the process of spatially superimposing and matching the emergency resource demand density field with the effective supply radius of each existing resource storage node as provided in Embodiment 6 of the present invention.

[0019] Figure 6 A block diagram of the electronic device provided by the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "multiple" means two or more.

[0022] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, a connection can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, a connection can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, coupling can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, coupling can be an indirect electrical connection between two components through an intermediate medium; or, coupling can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0023] In this embodiment of the invention, directional terms such as up, down, left, and right may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0024] Example 1: As Figure 1 As shown, this embodiment of the invention provides a method for equipment reinforcement and emergency resource pre-positioning optimization based on disaster prediction, comprising the following steps:

[0025] Step S100: Obtain the spatiotemporal probability distribution of disaster prediction, extract the critical damage threshold of the material of each facility point within the spatiotemporal probability distribution coverage area, and back-derive the critical damage threshold with the impact intensity parameter of the corresponding location in the disaster prediction to obtain the critical response time window of each facility point.

[0026] Step S200: Based on the critical response time window of each facility point, combined with the number of registered movable equipment and the location of fixed equipment anchor points at the facility point, arrange the facility points in ascending order of critical response time window. During the arrangement process, the number of movable equipment and the location of fixed equipment anchor points are successively added to the reinforcement material consumption coefficient of the corresponding facility point to form an equipment reinforcement sequence. The equipment reinforcement sequence records the reinforcement operation sequence number of each facility point and the type and amount of reinforcement material required.

[0027] Step S300: Based on the impact resistance improvement value calculated after each facility point in the equipment reinforcement sequence is reinforced, the impact resistance improvement value is convolved point by point with the curve of impact intensity parameter decay with distance in disaster prediction to obtain an emergency resource demand density field covering the entire region in terms of spatiotemporal probability distribution; then the emergency resource demand density field is spatially superimposed and matched with the effective supply radius of each existing resource storage node pre-mapped to generate the optimal supply point location to which each resource storage node needs to be migrated and the total amount of resources that each optimal supply point should store.

[0028] The overall mathematical formula for pointwise convolution is as follows:

[0029] Let the entire space be Any spatial location can be represented by a vector. This indicates that the first step in the equipment hardening sequence... The location of the facility is as follows: The calculated improvement in impact resistance after reinforcement of the facility site is ΔI_i. The curve of impact intensity parameter decreasing with distance in disaster prediction is denoted as the function. ,in The straight-line distance between two points in space. This represents the impact intensity parameter originating from a certain point over a propagation distance. The remaining proportion value of the function is monotonically decreasing and its range is between 0 and 1.

[0030] Then any location in the entire region Emergency resource demand density field Given by the following formula:

[0031]

[0032] Where N is the total number of facility sites, Indicates position With the Location of each facility The Euclidean distance between them. The principle of this formula is as follows: the increase in impact resistance obtained after reinforcement of each facility point. This represents the additional impact strength that the point itself can withstand. However, as the impact propagates outward from this point, its intensity decreases with distance. To compensate for any potential gaps in impact resistance that may still exist throughout the reinforced area, the enhancement value of each point needs to be diffused to all surrounding locations according to the law of impact intensity decreasing with distance. At any location... At this point, the contribution values ​​from each facility point are decayed through a decay function. Weighted summation, i.e., performing pointwise convolution operations, yields the required emergency resource unit density for that location; this density reflects the amount of resources that must be pre-stocked to balance the remaining risks after hardening.

[0033] Disaster prediction refers to the spatial and temporal estimation of the intensity, range, start time, and duration of potential disasters in a specified area over a future period, based on numerical simulation results or historical statistical patterns obtained before the disaster occurs. This estimation is used to determine the spatiotemporal probability distribution and impact intensity parameters. The spatiotemporal probability distribution refers to the distribution of the probability and intensity of a disaster occurrence given by the disaster prediction on spatial coordinates and a time axis. Each spatial location corresponds to a time series, and the value at each moment in the time series represents the probability or expected value of that location experiencing a specific impact intensity at that moment, serving as the input boundary conditions for the back-calculation of each facility point. A facility point refers to the fixed artificial structure or equipment installation location within the disaster prediction coverage area, including but not limited to production devices, storage containers, and supporting structures. Each facility point has unique spatial coordinates, geometric dimensions, and the type of equipment installed therein; the critical failure threshold of this point is determined by its own material. The facility point's own material refers to the solid material constituting the main load-bearing structure or shell of the facility point, including metals, concrete, and composite materials. Each material has a repeatable critical failure threshold in laboratory or field tests; this material property is used for comparison with the impact intensity parameters. The critical failure threshold refers to the minimum impact energy or force required for the material of a facility to transition from elastic deformation to permanent deformation or fracture failure. This threshold is stored in physical units and is used to determine whether the predicted impact intensity of the disaster has reached the level of damage. The impact intensity parameter refers to the quantified value of the instantaneous or cumulative impact force given by the disaster prediction at a specific spatial location and time point, such as peak pressure, shock wave overpressure, vibration acceleration, or wind pressure. This parameter is used in reverse engineering along with the critical failure threshold. The critical response time window refers to the time elapsed from the start of the disaster prediction until the critical failure threshold of the facility is exactly exceeded by the impact intensity parameter. Reverse engineering is performed by substituting the critical failure threshold into the curve of the impact intensity parameter over time to solve for this time length, obtaining the upper limit of the time before which reinforcement operations must be completed at each facility. The number of movable equipment refers to the number of non-fixed devices that can be moved from other locations to or near the facility before the disaster occurs to enhance its impact resistance. Each movable equipment has a known contribution value to reinforcement capacity, which is added to the reinforcement material consumption coefficient. The location of fixed equipment anchor points refers to the three-dimensional coordinates of the permanent anchoring structures that have been pre-buried or installed at the facility point and its surroundings. Anchor points are used to connect reinforcement materials or fix mobile equipment. The load-bearing capacity and spatial distribution of each anchor point affect the usage and quantity of reinforcement materials. This location information is superimposed sequentially after being sorted together with the number of mobile equipment.The reinforcement material consumption coefficient refers to the mass or volume of reinforcement materials (such as steel cables, support rods, and fillers) required to improve the unit impact resistance of a facility point. This coefficient is successively adjusted based on the number of mobile devices and the location of fixed equipment anchor points; that is, the more equipment and the denser the anchor points, the lower or higher the coefficient value, ultimately yielding the type and amount of reinforcement materials required for each facility point. The equipment reinforcement sequence refers to a sequential operation list generated by sorting all facility points from shortest to longest critical response time window, combined with the reinforcement material consumption coefficients obtained from the adjustments based on the number of mobile devices and the location of fixed equipment anchor points. Each entry in this sequence contains the reinforcement operation sequence number for a facility point and the specific type and amount of reinforcement materials required for that point. The impact resistance improvement value refers to the additional impact strength that a facility point can withstand compared to its unreinforced state after completing reinforcement operations with the specified material type and amount in the equipment reinforcement sequence. This value is obtained by converting the equivalent constraint force formed by the combined action of reinforcement materials and anchor points into impact resistance, and is used for point-by-point convolution operations. The emergency resource demand density field refers to the scalar field obtained at each spatial location in the entire region after point-by-point convolution of the impact resistance enhancement value of all facility points in the equipment reinforcement sequence with the impact intensity decay curve with distance in disaster prediction. This field represents the unit density value of emergency resources, such as spare equipment, repair materials, and rescue tools, that need to be pre-stocked near the location to compensate for potential impact resistance gaps after reinforcement. Existing resource storage nodes refer to fixed resource warehouses, material storage yards, or distribution centers that existed before the disaster. Each node has known spatial coordinates and an effective supply radius. The effective supply radius refers to the maximum straight-line distance or road network distance from the node that can deliver resources within the time allowed by the disaster response. The optimal supply point location refers to the new spatial coordinates obtained by spatially superimposing and matching the emergency resource demand density field with the effective supply radius of each existing resource storage node, and then relocating and adjusting the original location of the node. This location maximizes the total resource demand density covered by the node while keeping the relocation distance within a feasible range. Total resources refer to the sum of the quantity or quality of various emergency resources that need to be stored at each optimal supply point location. It is determined by the integral value of the emergency resource demand density field within the effective supply radius coverage area of ​​that point, ensuring that the replenishment needs of all facilities in the area can be met after a disaster occurs.

[0034] For details on the principles described in the above embodiments, please refer to the appendix. Figure 2This embodiment integrates disaster prediction, facility vulnerability assessment, and dynamic resource allocation mechanisms to achieve a closed-loop technology chain from disaster risk identification to optimized spatial allocation of emergency resources. Its core effects are as follows: First, based on the inverse extrapolation of the spatiotemporal probability distribution of disasters and the damage threshold of facility materials, abstract disaster intensity parameters are transformed into concrete critical response time windows. This transformation quantifies the urgency of facility protection, providing a time dimension basis for differentiated reinforcement strategies. Second, by integrating the number of mobile devices and fixed anchor points, the time window sequence is coupled with the dynamic characteristics of the equipment, forming an equipment reinforcement sequence that balances timeliness and resource constraints. This equipment reinforcement sequence not only clarifies the priority of reinforcement operations but also achieves refined prediction of reinforcement resource requirements through the dynamic superposition of material consumption coefficients. Finally, by using the spatial convolution operation of the impact resistance enhancement value and the disaster intensity decay curve, an emergency resource demand density field is constructed. The emergency resource demand density field breaks through the limitations of traditional static demand assessment and realizes the spatial coupling analysis of disaster impact propagation effect and facility protection effectiveness. Then, by spatial matching with existing resource storage nodes, a dual optimization scheme of resource migration path and reserve quantity is generated, thereby maximizing the spatial efficiency of resource allocation while ensuring the timeliness of critical facility protection.

[0035] In summary, this embodiment achieves dynamic coordination of time window, hardening operation, and resource layout in disaster response by linking a three-layer technical architecture of temporal deduction, sequence optimization, and spatial convolution, thereby improving the systematicness and adaptability of emergency resource pre-positioning.

[0036] Example 2: As Figure 3 As shown, based on Example 1, the process of reverse-engineering the critical damage threshold with the impact intensity parameter at the corresponding location in disaster prediction in step S100 of this embodiment of the invention specifically includes the following steps:

[0037] Step S101: For each facility point, extract the continuous curve of the impact intensity parameter of the facility point location changing with time from the spatiotemporal probability distribution of disaster prediction; divide the continuous curve into multiple adjacent straight line segments according to the time axis, each straight line segment consisting of its start time, end time and the rate of change of impact intensity within the time period.

[0038] Step S102: Use the critical failure threshold of each facility point as a fixed reference value and compare its position with each straight line segment. Determine the moment when the impact intensity parameter is exactly equal to the critical failure threshold between the starting impact intensity parameter value and the ending impact intensity parameter value of the straight line segment. If the moment is between the starting moment and the ending moment of the straight line segment, it is recorded as the effective passage moment of the straight line segment.

[0039] Step S103: Take the minimum value from the effective passage times of all straight segments, measure the time interval between the minimum value and the start time of disaster prediction, and the measured time length is the critical response time window of the facility point.

[0040] The time period refers to the time interval covered by each straight line segment after dividing the continuous curve of impact intensity parameter change over time along the time axis. This interval is defined by the start and end times of the straight line segment and is used to limit the calculation range of the impact intensity change rate and the search interval of the effective passing time in subsequent steps. The impact intensity change rate refers to the change of the impact intensity parameter per unit time within the time period. It is calculated by subtracting the impact intensity parameter value at the start time from the impact intensity parameter value at the end time of the time period, and then dividing by the length of the time period. This rate is used to determine the impact intensity parameter value corresponding to any time on the straight line segment, and then compare it with the critical failure threshold. The critical failure threshold refers to the minimum impact energy or minimum impact force corresponding to the material of the facility point changing from elastic deformation to permanent deformation or fracture failure. This threshold is stored in the form of physical units and serves as a fixed reference value, which is compared with the position of the changing impact intensity parameter on each straight line segment. The start impact intensity parameter value and the end impact intensity parameter value refer to the specific values ​​of the impact intensity parameter corresponding to the start time and the end time of the straight line segment, respectively. These two values ​​together constitute the range of impact intensity parameter variation on the straight line segment, used to determine whether the critical failure threshold falls within this range. The effective passage time refers to the specific moment when the impact intensity parameter, calculated based on the start and end times of the straight line segment and the rate of change of impact intensity, is exactly equal to the critical failure threshold, provided the critical failure threshold lies between the initial and final impact intensity parameter values ​​of that segment. This moment must be between the start and end times of the straight line segment to be recorded. The effective passage times of all straight line segments are collected and used to filter for the minimum value to determine the critical response time window.

[0041] In the above embodiments, this embodiment reduces the complexity of continuous curve analysis and improves computational efficiency and operability by using piecewise linearization; it accurately determines the threshold arrival time by using interpolation methods, enhancing the accuracy of time positioning; by filtering effective passage times and selecting the minimum value, it ensures a conservative estimate of the critical response time window, providing a reliable time reference for disaster emergency response; the overall simulation process realizes the quantitative conversion from disaster impact intensity to facility critical response time, providing key time parameter support for disaster risk assessment and emergency decision-making.

[0042] Example 3: Based on Example 2, the process of dividing a continuous curve into multiple adjacent straight line segments according to the time axis in step S101 of this embodiment of the invention specifically includes the following steps:

[0043] Step S1011: Extract the ratio of the change in impact intensity parameter to the change in time between all two adjacent time points from the continuous curve to obtain a series of local change ratios; calculate the difference between every two adjacent local change ratios to obtain the change amplitude of the ratio at each intermediate time point.

[0044] Step S1012: Mark the points where the change in ratio exceeds the statistical center value of all the change in ratio on the continuous curve as the dividing points where the curve shape changes significantly, and collect the time corresponding to the dividing points.

[0045] Step S1013: According to the collected boundary point times in ascending order, the time axis of the continuous curve is sequentially cut into multiple continuous time intervals; within each time interval, the original connecting curve is replaced by a straight line connecting the start and end points of the time interval to form multiple adjacent straight line segments; the start and end times of each straight line segment are the interval endpoints, and the rate of change of impact intensity within the time period of the interval endpoints is calculated from the impact intensity parameter values ​​and time length of the two endpoints of the interval.

[0046] In the above embodiments, this embodiment identifies the boundary point by using an adaptive threshold based on the local change amplitude, which can objectively reflect the turning characteristics of the curve itself and reduce the bias introduced by subjective division; using the statistical center value as the judgment basis enhances the robustness of boundary point identification and avoids over-segmentation caused by noise or small fluctuations; using the endpoints to form straight line segments simplifies the expression while ensuring the overall approximation of the change trend within the interval; the final piecewise linear model is more convenient to calculate, provides a structured input for comparison and analysis with fixed thresholds, and maintains the key information of the original curve's dynamic characteristics by retaining the main turning points.

[0047] Example 4: Figure 4 As shown, based on Example 1, the process of forming the device hardening sequence in step S200 of this embodiment of the invention specifically includes the following steps:

[0048] Step S201: Take the reciprocal of the critical response time window value of each facility point and fuse it with the number of mobile devices at the facility point in a spatially independent manner to obtain the time-efficiency resource composite index of each facility point; arrange the time-efficiency resource composite indices of all facility points in descending order to form a preliminary priority queue.

[0049] Step S202: For the first facility point in the priority queue, read the three-dimensional coordinate set of its fixed equipment anchor point positions, calculate the inverse sum of the spatial distances between all fixed equipment anchor points in the three-dimensional coordinate set, and obtain the spatial clustering degree of all fixed equipment anchor points; numerically couple the spatial clustering degree of fixed equipment anchor points with the initial reinforcement material consumption coefficient of the facility point to obtain the anchoring correction consumption coefficient of the first facility point; and record the anchoring correction consumption coefficient as the final reinforcement material consumption coefficient of the facility point.

[0050] Step S203: Take the next facility point from the priority queue, add the number of mobile devices at the next facility point to the unused remaining amount of mobile devices in the final reinforcement material consumption coefficient of the previous processed facility point, and obtain the total number of available devices for the facility point; then perform spatial constraint matching between the total number of available devices and the spatial distribution range of the fixed equipment anchoring points of the facility point to obtain the dynamic anchoring adjustment amount of the facility point; multiply the dynamic anchoring adjustment amount by its initial reinforcement material consumption coefficient to obtain the final reinforcement material consumption coefficient of the facility point; repeat the operation until all facility points in the priority queue are processed, and record the reinforcement operation sequence number of each facility point and the type and amount of reinforcement material required in sequence to form an equipment reinforcement sequence.

[0051] In the above embodiments, the process of forming the equipment reinforcement sequence aims to comprehensively consider time urgency, resource availability, and spatial layout constraints to generate an orderly and executable facility reinforcement plan. By numerically fusing the reciprocal of the critical response time window with the number of movable equipment, a time-sensitive resource composite index that simultaneously reflects time pressure and resource availability is constructed, and a preliminary priority queue is established to ensure that facilities with short time windows and relatively abundant resources receive priority attention. For the facility at the top of the queue, the spatial clustering degree of the anchoring points is quantified by calculating the sum of the reciprocals of the spatial distances between its fixed equipment anchoring points. This spatial clustering degree is coupled with the initial reinforcement material consumption coefficient to obtain the anchoring correction consumption coefficient, thereby incorporating the compactness of the spatial layout into the material consumption assessment, making the material demand estimation more consistent with the actual physical layout. For subsequent facility sites, the number of mobile devices at this facility site is superimposed with the number of mobile devices remaining after reinforcement at the previous facility site, enabling dynamic flow and cumulative utilization of equipment resources. The total number of available equipment is constrained and matched with the spatial distribution range of anchoring points within the facility site to calculate the actual number of equipment that can be deployed under spatial constraints, the dynamic anchoring adjustment amount, and then the final reinforcement material consumption coefficient is determined in combination with the initial consumption coefficient. This achieves serial scheduling and allocation of equipment resources among facility sites and ensures that the material consumption calculation fully considers the real-time available equipment resources and their spatial deployment feasibility.

[0052] Spatial location refers to the planar or spatial coordinates of the facility point within the disaster prediction coverage area, as well as the three-dimensional coordinates of the fixed equipment anchor points registered at the facility point. It is used to calculate the spatial distance between fixed equipment anchor points and to perform spatial constraint matching between the total number of available equipment and the spatial distribution range of fixed equipment anchor points. Spatial location itself does not participate in numerical fusion, but serves as the basic input for spatial distance calculation and spatial distribution range determination. The timeliness resource composite index refers to a single value obtained by taking the reciprocal of the critical response time window value for each facility point and fusing it with the number of registered mobile devices at the facility point. This index is constructed as follows: the shorter the critical response time window, the larger its reciprocal, indicating a higher urgency for reinforcement of the facility point; the larger the number of mobile devices, the richer the available mobile reinforcement resources for the facility point. These two values ​​are fused spatially independent by multiplication or weighted summation to obtain a composite value that reflects both time urgency and the number of available equipment. This index is used to arrange all facility points in descending order, forming a preliminary priority queue. Numerical coupling refers to the process of combining two numerical values ​​with different physical meanings into a new value according to preset operational rules. Numerical coupling involves multiplying or performing other deterministic operations on the spatial aggregation degree of fixed equipment anchor points (a dimensionless value or a value with spatial density dimensions) with the initial reinforcement material consumption coefficient of the facility point (a value with mass or volume dimensions) to obtain the anchorage correction consumption coefficient. Numerical superposition and matching also involve similar coupling operations. The purpose of numerical coupling is to embed spatial aggregation information or equipment remaining quantity information into the correction of the reinforcement material consumption coefficient, so that the final reinforcement material consumption coefficient simultaneously reflects the combined effects of time window, number of movable equipment, and distribution of fixed anchor points.

[0053] In summary, this embodiment's initial priority ranking incorporates both time and static resource factors; the handling of the first facility point introduces a correction for material consumption based on spatial clustering; and the iterative processing of subsequent facility points achieves dynamic inheritance of equipment resources and adaptive adjustments under spatial constraints. The final generated equipment reinforcement sequence is not only a priority-ranked list but also a comprehensive action plan integrating time urgency, dynamic resource allocation, and spatial layout constraints, thus improving the overall feasibility and resource utilization efficiency of the reinforcement scheme.

[0054] Example 5: Based on Example 4, the process of obtaining the dynamic anchorage adjustment amount of the facility point in step S203 provided in this embodiment of the invention specifically includes the following steps:

[0055] Step S2031: Extract the extreme coordinates of all anchor points in the horizontal direction from the three-dimensional coordinate set of the fixed equipment anchor points of the facility point, that is, the maximum and minimum values ​​of the horizontal coordinate and the maximum and minimum values ​​of the vertical coordinate; subtract the minimum value from the maximum value of the horizontal coordinate to obtain the horizontal span, subtract the minimum value from the maximum value of the vertical coordinate to obtain the vertical span, and multiply the horizontal span and the vertical span to obtain the horizontal area of ​​the anchor point distribution.

[0056] Step S2032: Divide the total number of available devices by the horizontal area of ​​the anchor point distribution to obtain the density of deployable devices per unit area; then compare the density of deployable devices per unit area with the average spatial distance between any two adjacent anchor points in the set of fixed device anchor point locations; if the density of deployable devices per unit area is greater than the reciprocal of the average spatial distance, then take the reciprocal of the average spatial distance as the benchmark matching coefficient; otherwise, take the density of deployable devices per unit area as the benchmark matching coefficient.

[0057] Step S2033: Multiply the benchmark matching coefficient by the proportion of material consumption related to the bearing capacity of the anchorage point in the initial reinforcement material consumption coefficient of the facility point to obtain the dynamic anchorage adjustment amount of the facility point.

[0058] In the above embodiments, this embodiment quantifies the spatial distribution range by calculating the horizontal area; then, by calculating and constraining the equipment density per unit area, it jointly considers the total number of available equipment and the spatial interval of anchoring points to determine the feasible equipment deployment density; finally, it converts this density into specific material usage adjustments; this ensures that when calculating the final material consumption of facility points, the actual deployment limit of equipment resources under a specific spatial layout is fully considered, avoiding the possible disconnect between the plan and the actual situation due to simply estimating materials based on the number of equipment, and improving the rationality and operability of material demand planning in the reinforcement scheme.

[0059] Example 6: As Figure 5 As shown, based on Example 1, the process of spatially superimposing and matching the emergency resource demand density field with the effective supply radius of each existing resource storage node in step S300 of this embodiment of the invention specifically includes the following steps:

[0060] Step S301: For each existing resource storage node, taking the current spatial coordinates of the existing resource storage node as the center, within the circular area covered by its effective supply radius, the value of the emergency resource demand density field is spatially overlapped and integrated with the reciprocal of the radial distance of each point in the circular area relative to the center point to obtain the total coverage efficiency value of the existing resource storage node at its current position; keeping the effective supply radius unchanged, the spatial coordinates of the existing resource storage node are shifted by one unit step in eight directions on the horizontal plane according to a fixed step size. Each shift is used to calculate a new total coverage efficiency value. The spatial coordinates with the largest total coverage efficiency value are selected from the nine positions, including the original position, as the single-step optimal migration position of the existing resource storage node;

[0061] Step S302: Using the single-step optimal migration position as the new center, repeat the offset and total coverage performance calculation process until the increase in total coverage performance after two consecutive migrations is lower than the preset convergence threshold; stop the migration, record the final position as the initial optimal replenishment point position of the node, and simultaneously record the total coverage performance corresponding to the single-step optimal migration position.

[0062] Step S303: Sort the preliminary optimal resupply point locations of all existing resource storage nodes in descending order of total coverage effectiveness, and select the existing resource storage node with the largest total coverage effectiveness; take the preliminary optimal resupply point location as the final optimal resupply point location of the existing resource storage node, and calculate the integral value of the emergency resource demand density field within the effective resupply radius of the preliminary optimal resupply point location to obtain the total amount of resources that the existing resource storage node should store; delete all other nodes located within the coverage area of ​​the effective resupply radius of the determined node from the remaining existing resource storage nodes, and repeat the operation on the remaining existing existing resource storage nodes that have not been deleted until all existing resource storage nodes have been processed, generating the final optimal resupply point location and the corresponding total amount of resources for each resource storage node.

[0063] In the above embodiments, this embodiment achieves the systematic deployment and resource allocation of emergency resource storage nodes through spatial optimization and resource matching mechanisms. Specifically, it is manifested in the following ways: by calculating coverage efficiency based on the spatial overlap integral of density field and distance reciprocal, the responsiveness of nodes to surrounding emergency needs at specific locations is quantified; a multi-directional step offset and iterative convergence method is adopted to gradually migrate each node to a locally optimal location, improving the demand matching efficiency within its coverage area; through sorting and filtering and regional deduplication operations, the final location and resource reserve of high-performance nodes are determined first, while eliminating the spatial overlap of redundant nodes, ensuring that the overall layout maximizes the satisfaction of emergency resource demand density distribution while avoiding duplicate coverage; finally, a solution combining spatial location optimization and quantitative resource allocation is formed, providing data support for emergency resource network planning.

[0064] Example 7: Based on Example 6, the process of obtaining the total amount of resources that the existing resource storage node should reserve in step S303 of this embodiment of the invention specifically includes the following steps:

[0065] Step S3031: Using the initial optimal supply point location as the center, divide the effective supply radius value of the existing resource storage node into multiple continuous radius intervals with equal lengths, starting from zero; extract all values ​​of the emergency resource demand density field on the annular area covered by the radius interval within each radius interval, and take the arithmetic mean of all values ​​as the representative density value of that radius interval.

[0066] Step S3032: Multiply the representative density value of each radius interval by the area of ​​the annulus corresponding to the radius interval to obtain the resource demand component within the radius interval; accumulate each resource demand component in order of increasing radius interval to form an accumulation sequence; record the last value in the accumulation sequence as the estimated total resource demand within the coverage area of ​​the node;

[0067] Step S3033: The estimated total resource demand is numerically fused with the total coverage performance recorded by the existing resource storage nodes; the estimated total resource demand is divided by the total coverage performance and then multiplied by the ratio of the effective supply radius of the existing resource storage nodes to the median of the effective supply radii of all nodes to obtain the total amount of resources that the existing resource storage nodes should store.

[0068] In the above embodiments, this embodiment achieves refined calculation of resource reserves through hierarchical density aggregation and performance-weighted correction. Specifically, it is manifested as follows: by dividing the supply radius into continuous intervals and extracting the arithmetic mean of the demand density in each annular region, the continuously spatially distributed demand density field is transformed into discrete hierarchical representative values, thereby reducing computational complexity while preserving spatial distribution characteristics; by multiplying the representative density of each layer by the corresponding annular area and summing them sequentially, an asymptotic integral estimation of the total resource demand within the coverage area is achieved, forming a demand accumulation profile from the center to the edge; by fusing the total demand estimate with the total node coverage performance value and introducing a relative proportional factor of the effective supply radius, the final reserve not only reflects the absolute demand scale but also incorporates the median benchmark of the node location performance advantage and the overall network supply capacity, achieving a weighted synthesis of demand basis, local performance, and global equilibrium, thereby outputting a more reasonable resource allocation scheme that adapts to the overall system configuration.

[0069] Example 8: Based on Example 7, the process of obtaining the total amount of resources that the existing resource storage node should reserve in step S3033 of this embodiment of the invention specifically includes the following steps:

[0070] Step S30331: Extract the value in the middle position after sorting in ascending order from the effective supply radius values ​​of all existing resource storage nodes, and use it as the center radius reference; after converting the effective supply radius of the current node and the center radius reference into binary representations respectively, calculate the scaling factor of the effective supply radius of the current existing resource storage node relative to the center radius reference through bit-by-bit comparison and cyclic shift operation. The scaling factor is stored in binary floating-point format.

[0071] Step S30332: Repeatedly perform multiplication and addition operations on the total coverage performance value of the current node, and update an intermediate approximation value in each iteration until the difference between the product of the intermediate approximation value and the total coverage performance value and the value 1 is less than the preset error threshold; output the final approximation value as the multiplicative inverse of the total coverage performance value.

[0072] Step S30333: Perform binary multiplication on the estimated total resource requirement of the current node and the multiplicative inverse to obtain an intermediate product; then perform binary multiplication on the intermediate product and the obtained scaling factor to obtain the total amount of resources that the current existing resource storage node should store.

[0073] In the above embodiments, this embodiment achieves adaptive adjustment of the reserve amount of resource storage nodes; by radius benchmarking and scaling factor calculation, the geographical coverage characteristics of nodes are transformed into quantifiable adjustment parameters; the multiplicative inverse of coverage performance is obtained using an iterative approximation method, and the performance index is transformed into the inverse weight of resource allocation; finally, through two-level multiplication operations, the resource reserve amount responds simultaneously to the node's own needs and the relative position characteristics in the network topology; it can dynamically generate differentiated reserve strategies based on the spatial distribution and performance differences of nodes in the network, thereby optimizing the overall resource allocation efficiency while ensuring coverage requirements.

[0074] Example 9: Based on Example 8, the process of calculating the scaling factor of the effective supply radius of the current existing resource storage node relative to the center radius reference in step S30331 of this embodiment of the invention specifically includes the following steps:

[0075] Step S303311: Using the effective supply radius value and the center radius benchmark value of the current existing resource storage node as the side lengths, construct a square coverage area on the emergency resource demand density field for each; extract the density values ​​of all grid points within each square coverage area, calculate the statistical center value of the density values ​​in the two areas respectively, and obtain the statistical center value of the density value corresponding to the effective supply radius of the current existing resource storage node and the statistical center value of the density value corresponding to the center radius; process the two statistical center values ​​to obtain the density-weighted radius ratio as the initial scaling factor;

[0076] The two regions refer to: The first region is a square coverage area constructed on the emergency resource demand density field, using the effective supply radius of the current existing resource storage nodes as its side length; the second region is another square coverage area constructed on the emergency resource demand density field, using the center radius benchmark as its side length. Both square coverage areas share the same geometric center point, which is the initial optimal supply point location for the current existing resource storage nodes. The two regions have different side lengths, corresponding to the effective supply radius of the current node and the median of the effective supply radii of all nodes, respectively, i.e., the center radius benchmark.

[0077] Step S303312: Couple the initial scaling factor with the total coverage performance recorded by the current existing resource storage node. Multiply the total coverage performance by the initial scaling factor to obtain the coupled scaling factor. Use the value of the coupled scaling factor as the number of bits for cyclic shifting. Perform a cyclic shift operation on the binary representation of the effective supply radius of the current existing resource storage node. Each shift yields a shifted binary number. Perform a bitwise logical AND operation between the shifted binary number and the binary representation of the center radius reference value. Count the number of bits with a value of 1 in the logical AND result.

[0078] Step S303313: Select the maximum value from the number of bits corresponding to all cyclic shift counts and record the shift count corresponding to the maximum value; take the product of the shift count and the coupling scaling factor as the integer part of the scaling factor, and then fuse the integer part with the total number of bits with a value of 1 in the binary representation of the center radius reference. After adding the total number of bits to the integer part, convert it to binary floating-point format to obtain the final scaling factor.

[0079] In the above embodiments, this embodiment realizes cross-level information fusion from spatial density field to node performance data and then to binary numerical structure, so that the scaling factor can simultaneously reflect the spatial heterogeneity of emergency resource distribution, the historical service performance of nodes and the stability requirements of numerical representation, and provide multi-dimensional calibration basis for the dynamic replenishment radius adjustment of resource storage nodes.

[0080] Example 10: Based on Example 9, the process of calculating the statistical center value of the density values ​​within the two square coverage areas in step S303311 of this embodiment of the invention specifically includes the following steps:

[0081] Step S3033111: For the first square coverage area, its side length is equal to the effective supply radius of the existing resource storage nodes; starting from the geometric center, identify all facility points located in the equipment hardening sequence, and extract the critical response time window value and impact resistance improvement value of each facility point; calculate the spatial straight-line distance from each facility point to the geometric center, substitute the spatial straight-line distance into the curve of impact intensity parameter decay with distance in disaster prediction, and obtain the decay weight of each facility point; divide the impact resistance improvement value of each facility point by the critical response time window value of the facility point, and then multiply it by the decay weight to obtain the center contribution intensity of each facility point. Arrange the emergency resource demand density values ​​of all grid points in ascending order of their spatial straight-line distance from the geometric center to form a density distance sequence. Replace the original density value of the grid point closest to the facility point in the density distance sequence with the central contribution intensity of each facility point. For grid points that are not replaced, set their density value to the cumulative average of the density values ​​of the two adjacent replaced points to obtain a corrected density distance sequence. Extract the central contribution intensity values ​​of all replaced points from the density distance sequence, arrange them in ascending order of the critical response time window values ​​of the corresponding facility points, and take the value in the middle of the arranged sequence as the density statistical center value.

[0082] Step S3033112: For the second square coverage area, its side length is equal to the center radius reference; starting from the same geometric center, perform the same operation as the first step: identify facility points within the area, extract the critical response time window and the impact resistance improvement value, calculate the attenuation weight, obtain the center contribution intensity of each facility point, and generate a density distance sequence; replace the density value of the closest grid point with the center contribution intensity, fill the unreplaced points with the cumulative average value, extract the center contribution intensity of the replaced points and sort them according to the critical response time window, and take the value of the middle position as the density statistical center value;

[0083] Step S3033113: Take the density statistical center value obtained from the first region as the numerator and the density statistical center value obtained from the second region as the denominator, and perform a binary division operation to obtain the center value ratio; then take the effective supply radius value of the first region as the numerator and the center radius reference as the denominator, and perform a binary division operation to obtain the radius ratio; perform a binary multiplication operation on the center value ratio and the radius ratio to obtain the product result; output the product result as the density weighted radius ratio of the initial scaling factor.

[0084] In the above embodiments, this embodiment completes a full calculation chain from facility point attributes to contribution intensity, from contribution intensity to sequence reconstruction, from sequence reconstruction to robust statistics, and from dual-region statistical values ​​to composite ratios; so that the final output density-weighted radius ratio not only reflects the proportion of the two regions on a spatial scale, but also, more importantly, incorporates the disaster resistance capability, response urgency, and impact attenuation effect of the facility points within the region, giving the initial scaling factor physical meaning and operational adaptability under disaster response scenarios.

[0085] Example 11: Based on Example 10, the process of performing binary multiplication of the center value ratio and the radius ratio in step S3033113 of this embodiment of the invention specifically includes the following steps:

[0086] Step S30331131: Convert the central value ratio and the radius ratio into exponential forms with the disaster prediction start time as the origin, that is, extract the natural logarithm of the central value ratio as the first exponent and extract the natural logarithm of the radius ratio as the second exponent; perform binary addition on the first exponent and the second exponent to obtain the sum exponent; then, using the attenuation coefficient in the curve of the impact intensity parameter attenuating with distance in the disaster prediction as the base, perform exponentiation on the sum exponent to obtain the initial intermediate value of the product;

[0087] Step S30331132: Divide the binary representation of the initial product intermediate value into a high-order segment and a low-order segment according to its numerical value. The high-order segment corresponds to the area where the first grid points are located after the density values ​​in the emergency resource demand density field are sorted from largest to smallest. The number of first grid points is equal to the total number of facility points in the equipment hardening sequence. The low-order segment corresponds to the area of ​​the remaining grid points. The high-order segment is corrected bit by bit using the total coverage performance value of the current existing resource storage nodes. The correction method is as follows: add each bit of the total coverage performance value in binary representation with the corresponding bit of the high-order segment without carrying over to obtain the corrected high-order segment. Reassemble the corrected high-order segment and the low-order segment to obtain the corrected product intermediate value.

[0088] Step S30331133: Using the corrected intermediate value of the product as the operand and the total number of bits with a value of 1 in the binary representation of the center radius as the operand, perform the inverse binary division operation based on the facility point critical response time window sorting: Starting from the highest bit of the operand, take out each bit in turn and compare it with the operand. If the bit is greater than or equal to the operand, set the current result bit to 1 and subtract the operand from the current bit of the operand to obtain the new remaining part of the operand; if the bit is less than the operand, set the current result bit to 0 and retain the current bit of the operand; after each comparison, merge the next bit of the operand with the current remaining part, and repeat this process until all bits have been processed; the final result is the product of the binary multiplication of the center value ratio and the radius ratio.

[0089] In the above embodiments, this embodiment constructs a multi-level, multi-factor coupled binary multiplication framework. First, it integrates attribute ratios and physical attenuation laws through exponential transformation and power operation. Then, it introduces resource coverage efficiency to adjust key areas through segmented position correction. Finally, it embeds the discrete features of geometric radius into the result generation process through iterative inverse operation based on fixed operands. The final product result is an integrated index that combines multiple information such as disaster resistance contribution ratio, spatial scale ratio, impact attenuation characteristics, resource coverage efficiency, and radius binary structure, providing a highly scenario-adaptable and structurally robust numerical foundation for the application of scaling factors.

[0090] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0091] Electronic devices may include a central processing unit / microprocessor / main control chip; and a storage medium coupled to the central processing unit / microprocessor / main control chip, wherein computer-executable instructions are stored for performing the steps of various methods of embodiments of the present invention when executed by a processor.

[0092] The central processing unit / microprocessor / main control chip may include, but is not limited to, one or more processors or microprocessors.

[0093] Storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0094] In addition, the electronic device may include (but is not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (e.g., keyboard, mouse, speaker, etc.).

[0095] The central processing unit / microprocessor / main control chip can communicate with external devices via wired or wireless networks (not shown) through input / output buses / external buses / device buses.

[0096] The storage medium may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip is running.

[0097] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0098] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0102] Emergency reinforcement and material allocation for factories during a typhoon: A coastal industrial park received a typhoon warning and was expected to be hit by strong winds and heavy rain in the next few hours. The park has three key facilities: a high-pressure storage tank, a central control room, a backup generator room, and an existing emergency material warehouse.

[0103] Known information:

[0104] Facility locations: Storage tank, location A: Constructed of steel, with a high critical failure threshold, but the typhoon impact intensity will increase rapidly over time; Control room, location B: Constructed of concrete, with a medium critical failure threshold; Generator room, location C: Constructed of ordinary steel plates, with a low critical failure threshold. Each facility location has several fixed anchor points nearby for securing reinforcement materials, and the number of movable reinforcement equipment, such as mobile support frames, is recorded. Existing warehouse, location D: Stores emergency supplies such as sandbags and steel cables, with an effective supply radius of 200 meters.

[0105] Application process:

[0106] Step 1: Calculate the emergency time window for each facility; based on the typhoon's predicted impact intensity over time, and combined with the damage threshold of each facility's materials, reverse-engineer how long each facility can withstand before being damaged; Results: Storage tanks can withstand 30 minutes, control rooms can withstand 60 minutes, and generator rooms can withstand 90 minutes; the shorter the time, the more priority is given to reinforcement.

[0107] Step 2: Determine the equipment reinforcement sequence, prioritizing the storage tank with the shortest time window. Simultaneously consider the number of movable equipment and the distribution of anchor points: the storage tank has 2 movable equipment units, and the dense anchor points require less reinforcement material, necessitating only a small amount of steel cable; the control room has 1 movable equipment unit, but the sparse anchor points require more material; the generator room has no movable equipment unit, but a long time window, so it's placed last. The final result is an ordered reinforcement task list: 1. Storage tank (requires 100kg steel cable); 2. Control room (requires 150kg steel cable); 3. Generator room (requires 200 sandbags).

[0108] Step 3: Generate an emergency supplies demand map and optimize warehouse locations. Reinforcing each facility improves its impact resistance, but it also affects surrounding areas because the intensity of a typhoon's impact decreases with distance. Through mathematical convolution calculations, determine the additional emergency supplies needed for each location within the entire park (density field). Move the existing warehouse (location D) gradually along this density field to find the optimal new location that covers the largest demand hotspots. Simultaneously, calculate the total amount of supplies the warehouse should store based on the total demand within the coverage area. Result: Moving the warehouse 80 meters closer to the storage tanks and storing 500 sandbags and 300kg of steel cables effectively supports all facilities.

[0109] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing equipment reinforcement and emergency resource pre-positioning based on disaster prediction, characterized in that, Includes the following steps: The equipment reinforcement sequence records the reinforcement operation sequence number of each facility point, as well as the type and amount of reinforcement materials required. Based on the impact resistance improvement value calculated after each facility point in the equipment reinforcement sequence is completed, the impact resistance improvement value is convolved point by point with the curve of impact intensity parameter decay with distance in disaster prediction to obtain an emergency resource demand density field covering the entire region in terms of spatiotemporal probability distribution. Then, the emergency resource demand density field is spatially superimposed and matched with the effective supply radius of each existing resource storage node that has been pre-mapped to generate the optimal supply point location that each resource storage node needs to migrate to and the total amount of resources that each optimal supply point should store.

2. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 1, characterized in that, The process of spatially superimposing and matching the emergency resource demand density field with the effective supply radius of each existing resource storage node as pre-mapped includes the following steps: For each existing resource storage node, taking the current spatial coordinates of the existing resource storage node as the center, within the circular area covered by its effective supply radius, the value of the emergency resource demand density field is spatially overlapped and integrated with the reciprocal of the radial distance of each point in the circular area relative to the center point to obtain the total coverage efficiency value of the existing resource storage node at its current position. Keeping the effective supply radius unchanged, the spatial coordinates of the existing resource storage node are shifted by one unit step in each of the eight directions on the horizontal plane at a fixed step size. Each shift calculates a new total coverage efficiency value. The spatial coordinates with the largest total coverage efficiency value are selected from the nine positions, including the original position, as the single-step optimal migration position of the existing resource storage node. Using the optimal migration position in a single step as the new center, repeat the process of offset and total coverage performance calculation until the increase in total coverage performance after two consecutive migrations is lower than the preset convergence threshold; stop the migration, record the final position as the initial optimal replenishment point position of the node, and simultaneously record the total coverage performance corresponding to the optimal migration position in a single step. Sort the initial optimal replenishment point locations of all existing resource storage nodes in descending order of total coverage performance, and select the existing resource storage node with the largest total coverage performance. The initial optimal supply point location is taken as the final optimal supply point location of the existing resource storage nodes, and the integral value of the emergency resource demand density field within the effective supply radius of the initial optimal supply point location is calculated to obtain the total amount of resources that the existing resource storage nodes should store. Remove all other nodes within the effective supply radius of the determined nodes from the remaining existing resource storage nodes. Repeat the operation on the remaining existing resource storage nodes that have not been deleted until all existing resource storage nodes have been processed, and generate the final optimal supply point location and the corresponding total amount of resources for each resource storage node.

3. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 2, characterized in that, The process of determining the total amount of resources that existing resource storage nodes should store includes the following steps: Using the initial optimal supply point location as the center, the effective supply radius of the existing resource storage nodes is divided into multiple continuous radius intervals of equal length, starting from zero. Within each radius interval, all values ​​of the emergency resource demand density field on the annular area covered by the radius interval are extracted, and the arithmetic mean of all values ​​is taken as the representative density value of the radius interval. Multiply the representative density value of each radius interval by the area of ​​the annulus corresponding to the radius interval to obtain the resource demand component within the radius interval; accumulate each resource demand component in order of increasing radius interval to form an accumulation sequence; record the last value in the accumulation sequence as the estimated total resource demand within the coverage area of ​​the existing resource storage node; The total resource demand estimate is numerically fused with the total coverage performance value recorded by the existing resource storage nodes. The total resource demand estimate is divided by the total coverage performance value and then multiplied by the ratio of the effective supply radius of the existing resource storage nodes to the median of the effective supply radii of all nodes to obtain the total amount of resources that the existing resource storage nodes should store.

4. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 3, characterized in that, The process of determining the total amount of resources that existing resource storage nodes should store includes the following steps: Extract the effective supply radius value in the middle position after sorting in ascending order from the effective supply radius values ​​of all existing resource storage nodes, and use it as the center radius reference; after converting the effective supply radius of the current node and the center radius reference into binary representations respectively, calculate the scaling factor of the effective supply radius of the current existing resource storage node relative to the center radius reference through bit-by-bit comparison and cyclic shift operation. The scaling factor is stored in binary floating-point format. Repeatedly perform multiplication and addition operations on the total coverage performance value of the current node, updating an intermediate approximation value in each iteration, until the difference between the product of the intermediate approximation value and the total coverage performance value and the value 1 is less than the preset error threshold; output the final approximation value as the multiplicative inverse of the total coverage performance value. Perform binary multiplication on the estimated total resource requirement of the current node and the multiplicative inverse to obtain an intermediate product; then perform binary multiplication on the intermediate product and the obtained scaling factor to obtain the total amount of resources that the current existing resource storage node should store.

5. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 4, characterized in that, The process of calculating the scaling factor of the effective supply radius of the current existing resource storage node relative to the center radius reference includes the following steps: Using the effective supply radius and the baseline center radius of the current existing resource storage node as side lengths, construct a square coverage area on the emergency resource demand density field. Extract the density values ​​of all grid points within each square coverage area, and calculate the statistical center values ​​of the density values ​​within the two square coverage areas to obtain the statistical center values ​​of the density values ​​corresponding to the effective supply radius and the center radius of the current existing resource storage node. Process the two statistical center values ​​to obtain the density-weighted radius ratio, which serves as the initial scaling factor. The initial scaling factor is numerically coupled with the total coverage performance value recorded by the current existing resource storage nodes. The coupled scaling factor is obtained by multiplying the total coverage performance value by the initial scaling factor. Using the value of the coupling scaling factor as the number of bits for the cyclic shift, perform a cyclic shift operation on the binary representation of the effective supply radius of the current existing resource storage node. Each shift yields a shifted binary number. Perform a bitwise logical AND operation between the shifted binary number and the binary representation of the center radius reference value, and count the number of bits with a value of 1 in the logical AND result. Select the maximum value from the number of bits corresponding to all cyclic shifts and record the shift count corresponding to the maximum value; multiply the shift count by the coupling scaling factor as the integer part of the scaling factor, then fuse the integer part with the total number of bits with a value of 1 in the binary representation of the center radius reference, add the integer part to the total number of bits and convert it to binary floating-point format to obtain the final scaling factor.

6. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 5, characterized in that, The process of calculating the statistical center value of the density values ​​within the two square coverage areas includes the following steps: For the first square coverage area, its side length is equal to the effective supply radius of the existing resource storage nodes; starting from the geometric center, identify all facility points located in the equipment hardening sequence, and extract the critical response time window value and shock resistance improvement value of each facility point; Calculate the straight-line spatial distance from each facility point to the geometric center. Substitute this straight-line spatial distance into the curve of impact intensity parameter decay with distance in disaster prediction to obtain the decay weight of each facility point. Divide the impact resistance improvement value of each facility point by the critical response time window value of the facility point, and then multiply by the decay weight to obtain the central contribution intensity of each facility point. Arrange the emergency resource demand density values ​​of all grid points in ascending order of their straight-line spatial distance from the geometric center to form a density distance sequence. Replace the original density value of the grid point closest to the facility point in the density distance sequence with the central contribution intensity of each facility point. For grid points that are not replaced, set their density value to the cumulative average of the density values ​​of the two adjacent replaced points to obtain the corrected density distance sequence. Extract the central contribution intensity values ​​of all replaced points from the density distance sequence, arrange them in ascending order of the critical response time window value of the corresponding facility point, and take the value in the middle position of the arranged sequence as the density statistical center value. For the second square coverage area, its side length is equal to the central radius reference; starting from the same geometric center, perform the same operation as for the first square coverage area: obtain the corresponding density statistical center value; The density statistical center value obtained from the first region is used as the numerator, and the density statistical center value obtained from the second region is used as the denominator. A binary division operation is performed to obtain the center value ratio. Then, the effective supply radius value of the first region is used as the numerator, and the center radius reference is used as the denominator. A binary division operation is performed to obtain the radius ratio. The center value ratio and the radius ratio are then multiplied by binary to obtain the product. The product is output as the density-weighted radius ratio of the initial scaling factor.

7. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 6, characterized in that, The process of performing binary multiplication of the ratio of the center value to the ratio of the radius includes the following steps: The ratio of central values ​​and the ratio of radius are converted into exponential forms with the disaster prediction start time as the origin to obtain the initial intermediate value of the product; The binary representation of the initial product intermediate value is divided into a high-order segment and a low-order segment according to its numerical value. The value of each bit in the binary representation covering the total performance value is added to the corresponding bit in the high-order segment without carrying over, to obtain the corrected high-order segment. The corrected high-order segment and the low-order segment are then concatenated to obtain the corrected product intermediate value. Using the intermediate value of the corrected product as the operand and the total number of bits with a value of 1 in the binary representation of the center radius as the operand, perform the inverse binary division operation based on the facility point critical response time window sorting: if the bit is less than the operand, the current result bit is set to 0, and the current bit of the operand is retained; after each comparison, the next bit of the operand is merged with the current remaining part, and this process is repeated until all bits have been processed; the final result is the product of the binary multiplication of the center value ratio and the radius ratio.

8. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 1, characterized in that, The spatiotemporal probability distribution of disaster prediction is obtained. The critical damage threshold of the material of each facility point within the spatiotemporal probability distribution coverage area is extracted. The critical damage threshold is then reverse-engineered with the impact intensity parameter of the corresponding location in the disaster prediction to obtain the critical response time window of each facility point.

9. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 8, characterized in that, The process of inversely extrapolating the critical failure threshold against the impact intensity parameters at the corresponding locations in disaster prediction includes the following steps: For each facility site, a continuous curve of the impact intensity parameter of the facility site location changing with time is extracted from the spatiotemporal probability distribution of disaster prediction; the continuous curve is divided into multiple adjacent straight line segments along the time axis, and each straight line segment consists of its start time, end time, and the rate of change of impact intensity within the time period. The critical failure threshold of each facility point is used as a fixed reference value. The position is compared with each straight line segment. The moment when the impact intensity parameter equals the critical failure threshold is determined between the starting impact intensity parameter value and the ending impact intensity parameter value of the straight line segment. If the moment is between the starting moment and the ending moment of the straight line segment, it is recorded as the effective passage moment of the straight line segment. The minimum value is taken from the effective passage times of all straight segments, and the time interval between the minimum value and the start time of disaster prediction is measured. The measured time length is the critical response time window of the facility point.

10. The equipment reinforcement and emergency resource pre-positioning optimization method based on disaster prediction as described in claim 8, characterized in that, The critical response time window for each facility point is combined with the number of registered mobile devices and the location of fixed equipment anchor points. The facilities points are arranged in order of increasing critical response time window. During the arrangement process, the number of mobile devices and the location of fixed equipment anchor points are successively added to the reinforcement material consumption coefficient of the corresponding facility point to form an equipment reinforcement sequence. The equipment reinforcement sequence records the reinforcement operation sequence number of each facility point and the type and amount of reinforcement materials required.

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