Post-disaster power resource allocation method and device, electronic equipment and storage medium

Abstract

The invention discloses a post-disaster power resource distribution method and device, electronic equipment and a storage medium, and the method comprises the steps: dividing a target disaster area into n target areas and distributable power resources, n being an integer greater than 1, then determining a disaster influence degree value corresponding to each target area in the n target areas, and determining a disaster influence degree value corresponding to each target area in the n target areas; n disaster influence degree values are obtained, then priorities of n target areas are determined based on the n disaster influence degree values, a target priority sequence is determined according to the priorities of the n target areas, and finally power resource allocation is performed on the n target areas and the allocatable power resources based on the target priority sequence. And the target disaster-affected area recovers the electric power response. By adoption of the embodiment of the invention, the post-disaster power resource recovery efficiency of the affected area is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to a post-disaster power resource allocation method, device, electronic equipment and storage medium. Background Art

[0002] After a natural disaster or sudden incident damages the power system, rapid power restoration in the affected area is crucial to ensuring the supply of critical loads and preventing a chain reaction of disasters. However, the limited availability of allocable power resources conflicts with the dynamic nature of power demand in the affected area. Traditional static allocation models are unable to optimize resource scheduling in real time, which can easily lead to power delays in critical areas such as medical facilities and communication base stations, or idle power resources in non-critical areas. Therefore, improving the efficiency of post-disaster power resource restoration in the affected area is an urgent issue. Summary of the Invention

[0003] The embodiments of the present application provide a post-disaster power resource allocation method, device, electronic device and storage medium, which improve the post-disaster power resource recovery efficiency in the disaster-stricken area.

[0004] In a first aspect, an embodiment of the present application provides a method for allocating power resources after a disaster, the method comprising:

[0005] Acquisition of distributable power resources;

[0006] Divide the target disaster-stricken area into n target areas; n is an integer greater than 1;

[0007] Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values;

[0008] Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas;

[0009] Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

[0010] In a second aspect, an embodiment of the present application provides a post-disaster power resource allocation device, the device comprising: an acquisition unit and a processing unit;

[0011] The acquisition unit is used to divide the target disaster-stricken area into n target areas and allocable power resources; n is an integer greater than 1;

[0012] The processing unit is used to

[0013] Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values;

[0014] Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas;

[0015] Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device performs the method of the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0018] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0019] The implementation of the present invention has the following beneficial effects:

[0020] It can be seen that the post-disaster power resource allocation method described in the embodiment of the present invention first divides the target disaster-stricken area into n target areas and allocable power resources, where n is an integer greater than 1, and then determines the disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values, and then determines the priority of the n target areas based on the n disaster impact degree values, and determines the target priority order according to the priority of the n target areas, and finally allocates power resources to the n target areas and the allocable power resources based on the target priority order, so that the target disaster-stricken area restores power response, thereby improving the post-disaster power resource recovery efficiency of the disaster-stricken area. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the implementation methods or background technologies of the present application, the drawings required for use in the implementation methods or background technologies of the present application will be described below.

[0022] Figure 1 This is a flow chart of a post-disaster power resource allocation method provided by an embodiment of the present application;

[0023] Figure 2 This is a flow chart for determining a disaster impact value provided by an embodiment of the present application;

[0024] Figure 3 This is a flow chart for determining reliability risk indicators provided by an embodiment of the present application;

[0025] Figure 4 This is a flow chart for determining a security risk indicator provided by an embodiment of the present application;

[0026] Figure 5 This is a flow chart of determining a disaster impact degree value corresponding to a first target area provided by an embodiment of the present application;

[0027] Figure 6 This is a flow chart of determining a disaster impact value based on population density provided by an embodiment of the present application;

[0028] Figure 7 This is a structural diagram of a post-disaster power resource allocation device provided by an embodiment of the present application;

[0029] Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present invention, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0032] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] See also Figure 1 , Figure 1 This is a flowchart of a post-disaster power resource allocation method provided by an embodiment of the present application, including but not limited to the following steps:

[0034] S101: Acquire allocatable power resources.

[0035] In this embodiment, allocable power resources are all sources of electricity that can be used to restore power supply after a disaster, including undamaged power generation facilities in the disaster-stricken area, externally allocated emergency power supplies, temporary power generation equipment, etc., which are physically accessible and technically feasible.

[0036] The allocable power resources can be determined by counting the power generation equipment available after the disaster (such as diesel generators, emergency power generation vehicles), the remaining capacity of the power grid, external support power, etc., and then recording parameters such as the type of power resources (AC or DC), power, continuous power supply time, access location (such as substation, temporary power supply point), and checking whether the resources can be transported to the disaster-stricken area and whether they meet the grid access standards (such as voltage level matching). The power resources that can be transported to the disaster-stricken area and meet the grid access standards are determined as allocable power resources.

[0037] S102: Divide the target disaster-stricken area into n target areas.

[0038] In this embodiment, n is an integer greater than 1. It is necessary to break down the large disaster-stricken area into several independent sub-areas (i.e., target areas) in order to accurately assess the disaster situation in each area and allocate power resources. The administrative areas (such as streets, towns), natural geographical boundaries (rivers, mountains) or physical divisions of the power grid can be used as a benchmark to ensure that the boundaries of the sub-areas are clear; they can also be divided according to the distribution of power nodes and the direction of the lines, so that the power facilities (substations, distribution lines) in each sub-area form relatively independent power supply units; because the degree of disaster damage and the type of damaged power facilities in the same sub-area need to be similar, they can also be divided according to the nature of the disaster to facilitate unified assessment.

[0039] Specifically, first collect geographic information (satellite maps, terrain data), power grid topology (power node locations, line connections), and administrative division files for the target disaster area. If a city is affected by an earthquake, obtain the city's administrative division map and a schematic diagram of the power grid substation distribution. Then, divide the area based on the substation's power supply range and the distribution line's radiation area. For example, a substation and its subordinate distribution substations constitute a target area. Remote sensing imagery, drone surveys, and other means can also be used to identify severely affected areas, moderately affected areas, and lightly affected areas, and divide them into different target areas. Geographic information system tools can be used to polygonize the affected area, ensuring that each sub-area has a sufficient number of power nodes and independent power incoming or interconnecting line interfaces to facilitate subsequent resource allocation.

[0040] It's important to note that the more power nodes and more complex the wiring within the affected area, the larger the n value (for example, a large city can be divided into dozens of target areas). A target disaster area refers to the entire geographic area where power supply needs to be restored after a disaster (such as an earthquake, typhoon, or flood). For example, if heavy rain causes power outages in multiple cities and counties in a province, that province would be considered a target disaster area. The target disaster area encompasses multiple power facilities, densely populated areas, and different types of electricity loads, requiring a comprehensive power restoration strategy.

[0041] N subregions are divided from the target disaster area, each serving as an independent power resource allocation unit. This breaks down the complex, large-scale disaster problem into multiple subproblems, facilitating individual priority assessment and resource allocation, thereby improving power restoration efficiency. Disaster impacts often vary across different areas within a disaster area (for example, during a flood, low-lying areas may experience more severe power outages, while high-rise buildings may still have some power). Dividing a large area into multiple subregions allows for independent assessments of power facility damage and load demand characteristics (e.g., load loss, voltage stability, and economic losses) in each subregion. This avoids overshadowing local differences through a "holistic assessment" and provides a more accurate basis for subsequent prioritization. Without regional division, power resources may be allocated evenly or according to crude criteria, resulting in resource waste or insufficient power in critical areas. Subdivision allows for priority determination based on each subregion's disaster impact (e.g., reliability risk, safety risk, and economic loss indicators). This allows for "demand-based allocation," prioritizing limited allocable power resources to meet the needs of high-priority areas and accelerating overall power restoration efficiency. Post-disaster power restoration is typically a phased process. By dividing a large area into multiple sub-regions, a phased power supply plan can be developed based on priority (e.g., restoring high-priority areas first, then gradually covering medium- and low-priority areas). This facilitates orderly execution by the dispatch team and avoids confusion in resource allocation. Furthermore, the progress of power restoration in each sub-region can be independently monitored, facilitating real-time policy adjustments.

[0042] S103: Determine the disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values.

[0043] In this embodiment, when determining the disaster impact level value of each target area, it is necessary to first identify the m power nodes contained in any target area (set as the first target area), and then collect the load loss and voltage data of these nodes in the first historical time period. The target load loss is obtained by calculating the sum of the m load losses, combining it with the corresponding reference reliability risk index, and using the first adjustment parameter calculated by the standard deviation of the load loss to correct the reference index, thereby obtaining the reliability risk index of the area. At the same time, the difference between the voltage of each power node and the preset value is calculated, and the safety risk index of each node is obtained based on the mapping relationship between the voltage difference and the safety risk index. The maximum value is taken as the reference safety risk index, and then the reference index is adjusted according to the second adjustment parameter corresponding to the backup power capacity in the area to obtain the safety risk index. In addition, the economic loss value of the area in the first historical time period is obtained to determine the economic risk index. Finally, the three risk indicators of reliability, safety, and economy are comprehensively considered. By optimizing the corresponding weights of each indicator (for example, optimizing the reference weights of reliability and safety indicators according to the demand for important loads and the frequency of voltage sags, obtaining the target weights, and determining the third target weight of the economic indicator), the numerical values ​​of each indicator are combined with the corresponding weight calculations, and the target fine-tuning factor adjustment results corresponding to population density are introduced to finally obtain the disaster impact degree value of the target area. The calculation of all n target areas is completed according to this process.

[0044] S104: Determine the priorities of the n target areas based on the n disaster impact degree values, and determine a target priority order according to the priorities of the n target areas.

[0045] In this embodiment, when determining the priority and order of target areas based on n disaster impact values, the n disaster impact values ​​are regarded as quantitative indicators to measure the severity of the disaster in each area. The larger the value, the more severe the disaster and the more urgent the need for power restoration. By sorting these values ​​from large to small, the priority of each target area can be directly obtained. The area with the highest value has the highest priority and is the first to obtain power resource allocation, and the area with the lowest value has the lowest priority. After the sorting is completed, the target areas are arranged in order from high to low priority, that is, the target priority order is formed, which provides a clear execution basis for the subsequent allocation of allocable power resources according to the principle of "urgent first, then slow", ensuring that the power needs of high-priority areas are met first, and improving the overall efficiency of post-disaster power restoration and the rationality of resource utilization.

[0046] S105: Allocate power resources to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

[0047] In this embodiment, power resources are allocated based on target priority, with the core principle of "urgent first, less urgent later" being adopted. Starting with the highest-priority target areas, available power resources are allocated sequentially according to their ranking. Resources are prioritized to areas most severely affected by the disaster and with the greatest impact on people's livelihoods and social operations. For example, emergency generators and restoration of main power lines are prioritized for high-priority areas with densely populated hospitals and communication base stations, ensuring rapid power recovery for critical facilities. During the allocation process, resource types and quantities are precisely matched based on the scale of each region's power demand (e.g., the amount of lost load and the proportion of critical loads) and resource supply capacity. For example, high-power generators are allocated to large industrial areas, while adapted power distribution equipment is provided to residential areas. For lower-priority areas, remaining resources are allocated on demand after the needs of high-priority areas are met. Simultaneously, the progress of power restoration in each region is dynamically monitored. If resource shortages or emergencies occur, allocation strategies are adjusted promptly, and resources are flexibly dispatched until all n target areas have restored basic power response, ultimately achieving the gradual recovery and stable operation of the overall power supply system in the target disaster-stricken areas.

[0048] It can be seen that dividing the target disaster-affected areas and allocating power resources according to the process can achieve efficient resource utilization and scientific disaster relief through precise quantification, hierarchical response, and dynamic allocation. Specifically, dividing the disaster-affected area into n target zones allows disaster relief units to focus on the characteristics of different areas, such as residential areas and medical areas, laying the foundation for subsequent precise assessment. Determining the disaster impact value for each area and comprehensively considering indicators such as reliability, safety, and economic efficiency, such as calculated load loss, voltage deviation, and economic losses, can quantify the differences in disaster impact and avoid blind resource allocation. Prioritizing and sorting based on the impact value, adhering to the principle of "first urgent, then less urgent", prioritizes power supply to key areas such as hospitals and communication hubs, improving the targeted nature of disaster relief. When allocating resources according to priority, dynamic adjustments are made based on the scale of demand in each area, such as allocating high-power power generation equipment to high-priority areas. Simultaneously, progress monitoring and flexible scheduling ensure that limited resources are used for critical living facilities while accelerating overall recovery efficiency through regionalized progress. Ultimately, the orderly restoration of power response in the affected areas is achieved, minimizing the impact of the disaster and safeguarding people's livelihoods and social operations.

[0049] See also Figure 2 , Figure 2 This is a flowchart of determining a disaster impact value provided by an embodiment of the present application, including but not limited to the following steps:

[0050] S201: Determine m power nodes corresponding to a first target area.

[0051] In this embodiment, the first target area is any one of the n target areas, where m is an integer greater than 1. After the disaster-stricken area is divided into n target areas, one of the areas is selected, namely the first target area, and the number (m) of power nodes contained in the area and their specific locations are determined. The power nodes here refer to key equipment in the power system (such as substations, distribution stations, etc.). These power nodes can define specific scopes for subsequent data collection and risk indicator calculations, focusing on the operating status of micro power units.

[0052] S202: Obtain the load loss amount and the power node voltage of each power node in the m power nodes in a first historical time period to obtain m load loss amounts and m power node voltages.

[0053] In this embodiment, the load loss is the amount of power loss at a node due to a disaster, reflecting the scale of the power supply interruption; the power node voltage is the actual operating voltage value of the node, which is used to determine whether the voltage deviates from the normal range. By quantifying basic data at the node level, it provides a basis for evaluating the reliability and security of the regional power system.

[0054] Specifically, two key data (load loss and power node voltage) are collected for each power node (m power nodes) in the first target area in the first historical time period (such as 72 hours before and after the disaster), and m load loss amounts and m power node voltages are obtained.

[0055] S203: Determine a reliability risk index of the first target area based on the m load loss amounts, and determine a safety risk index of the first target area based on the m power node voltages.

[0056] In this embodiment, the reliability risk index of the first target area is determined based on the m load loss amounts. The load loss data of the m nodes are used to quantify the stability of the power supply in the area through statistical analysis (such as summation, calculation of standard deviation, etc.), and the total load loss (the sum of the load loss amounts of each node) is calculated. The overall scale of power outage is measured, and the risk index is adjusted in combination with the fluctuation degree of the load loss (standard deviation). The greater the fluctuation, the worse the power supply reliability. The node-level data can be summarized into regional-level indicators to reflect the ability of the power system to maintain normal power supply under disasters.

[0057] The safety risk index of the first target area is determined based on the voltages of the m power nodes. The voltage data of the m nodes are analyzed to determine whether the voltage threatens the equipment safety or the power supply quality. The deviation value of the voltage of each node from the rated voltage (such as voltage sag, overvoltage, etc.) is first calculated. Then, the maximum deviation value is taken as the reference indicator of regional voltage safety. The risk level is adjusted in combination with the backup power supply capacity (such as emergency generators) in the area. The more backup power supplies there are, the lower the safety risk may be. The operational safety of the power system under disasters can be evaluated to avoid equipment damage or secondary failures due to voltage anomalies.

[0058] S204: Obtain an economic loss value of the first target area in the first historical time period, and determine an economic risk index of the first target area based on the economic loss value.

[0059] In this embodiment, the economic loss value of the first target area in the first historical time period is obtained by statistically analyzing the direct economic losses (such as enterprise production suspension losses, resident property losses, etc.) and indirect losses (such as industrial chain interruption, emergency rescue costs, etc.) caused by power outages or anomalies in the first target area in the same historical time period, quantifying the impact of disasters from an economic dimension, and providing a reference for economic benefits for resource allocation.

[0060] Determining the economic risk index of the first target area based on the economic loss value is to convert the economic loss value into a standardized risk index. For example, a loss threshold is set (such as 1 million yuan corresponds to a risk index of 10), and the index value is calculated according to the ratio of the actual loss to the threshold. The greater the loss, the higher the economic risk. The abstract economic impact is converted into a quantifiable and comparable indicator, which is convenient for comprehensive evaluation with reliability and safety indicators.

[0061] S205: Determine a disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, and the economic risk index.

[0062] In this implementation, the three risk indicators are combined and weighted (e.g., reliability accounts for 40%, safety accounts for 30%, and economic efficiency accounts for 30%) to arrive at a final disaster impact value. These weights can be adjusted based on regional characteristics (e.g., medical areas prioritize reliability, while industrial areas prioritize economic efficiency), and can be fine-tuned by factors such as population density (the denser the population, the higher the impact value). By integrating these multi-dimensional indicators, a comprehensive, quantitative value is generated that fully reflects the severity of a regional disaster, providing a core basis for subsequent prioritization and resource allocation.

[0063] It can be seen that by determining the disaster impact value of the target area through multi-dimensional data collection and indicator construction, it is possible to achieve accurate mapping from micro data to macro assessment, and provide scientific quantitative support for disaster response. Specifically, first locking the m power nodes in the first target area, the assessment can be focused on the basic units of the power system to ensure the pertinence and granularity of data collection. For example, nodes in different functional areas such as hospitals and residential areas can be monitored separately; the load loss and voltage data of each node in the historical period can be obtained, and the abnormal state of the power system can be captured from the two dimensions of power supply capacity (load loss reflects the scale of interruption) and operational safety (voltage deviation reflects equipment risk). For example, areas with large load loss directly reflect power supply reliability defects, and voltage exceeding the limit indicates hidden dangers of equipment damage; reliability and safety risk indicators are generated based on load loss and voltage data respectively, and the scattered node data are converted into regional risk quantification values ​​through statistical modeling, such as using the total load loss combined with the fluctuation coefficient to represent reliability, and using voltage deviation to represent reliability. The maximum difference value is combined with the backup power supply capacity to adjust safety, making abstract risks measurable and comparable; the introduction of economic loss values ​​and their conversion into economic risk indicators can monetize the social impact of power outages, such as the loss of production suspension of enterprises and the cost of emergency repairs, which are all included in the assessment, avoiding focusing only on technical indicators and ignoring the actual economic consequences; finally, a comprehensive disaster impact value is generated through the weighted integration of the three types of indicators, which not only covers the technical characteristics of the power system (reliability and safety), but also is related to the economic and social impact (economy). The weights can also be dynamically adjusted according to regional attributes (such as medical areas and industrial areas), so that the assessment results are more in line with actual disaster relief needs, and provide a scientific and flexible decision-making basis for subsequent priority sorting and resource allocation, ensuring that limited resources are invested first in areas with the highest comprehensive risks and the most significant impacts.

[0064] See also Figure 3 , Figure 3 This is a flowchart for determining a reliability risk indicator provided by an embodiment of the present application, including but not limited to the following steps:

[0065] S301: Determine the sum of the m load loss amounts to obtain a target load loss amount.

[0066] In this embodiment, the load loss of m power nodes within the first target area is summed to obtain the total load loss of the area (unit: kW or kWh). For example, if m = 3, and the load loss of the three nodes is 100kW, 200kW, and 150kW, respectively, then the target load loss is 450kW.

[0067] Through total statistics, the overall scale of power supply interruptions in the region is quantified from a macro perspective, providing basic values ​​for reliability risk assessment.

[0068] S302: Determine a reference reliability risk indicator corresponding to the target load loss amount.

[0069] In this embodiment, it may be a preset mapping relationship between the load loss amount and the reliability risk index, and based on the mapping relationship, a reference reliability risk index corresponding to the target load loss amount may be determined.

[0070] Establish a quantitative relationship between load loss and reliability risk to form an initial assessment benchmark to facilitate horizontal comparison between different regions.

[0071] S303: Determine the standard deviations corresponding to the m load loss amounts.

[0072] In this embodiment, the standard deviation corresponding to the m load loss amounts is calculated, and the distribution balance of the load loss amounts in the region can be measured by the standard deviation. The larger the standard deviation, the greater the difference in the degree of interruption of each node, and the higher the volatility of system reliability.

[0073] S304: Determine a first adjustment parameter corresponding to the standard deviation.

[0074] In this embodiment, it may be a mapping relationship between a preset standard deviation and an adjustment parameter, and the first adjustment parameter corresponding to the standard deviation may be determined based on the mapping relationship.

[0075] According to the size of the standard deviation, the coefficient used to adjust the reference risk indicator (i.e., the first adjustment parameter) is determined through preset rules (such as piecewise functions and mapping tables), thereby converting the distribution characteristics of the load loss amount into a quantifiable adjustment factor, making up for the limitations of using only the total amount for evaluation, and making the risk indicator more in line with the actual power supply fluctuations.

[0076] S305: Adjust the reference reliability risk indicator based on the first adjustment parameter to obtain the reliability risk indicator of the first target area.

[0077] In this embodiment, the reliability risk index is calculated specifically according to the following formula:

[0078] Reliability risk index = reference reliability risk index × (1 + first adjustment parameter);

[0079] According to the above formula, the reference reliability risk index can be adjusted based on the first adjustment parameter to obtain the reliability risk index of the first target area.

[0080] It can be seen that by quantifying the total amount of load loss and its distribution characteristics to construct a reliability risk index, a dual verification from "scale assessment" to "fluctuation assessment" can be achieved, providing a more comprehensive decision-making basis for power system reliability analysis. By matching the standard deviation with the first adjustment parameter, the degree of dispersion can be converted into a risk correction factor. For example, the larger the standard deviation, the larger the adjustment parameter, which upwardly adjusts the reference index to avoid ignoring the extreme impact of key nodes due to focusing only on the total amount. The final adjusted reliability risk index not only includes the "interruption scale risk" represented by the total amount of load loss, but also incorporates the "load imbalance risk" through the standard deviation and adjustment parameter. It is particularly suitable for areas with important loads (such as medical and transportation). When the amount of load loss at key nodes is large and the degree of dispersion is high, the index will be significantly improved, thereby prioritizing such high-risk areas in subsequent resource allocation. This deepens the reliability assessment from "numerical statistics" to "risk mechanism analysis" and improves the accuracy of disaster response.

[0081] See also Figure 4 , Figure 4 This is a flowchart for determining a security risk indicator provided by an embodiment of the present application, including but not limited to the following steps:

[0082] S401: Determine a difference between each of the m power node voltages and a preset power node voltage to obtain m power node voltage differences.

[0083] In this embodiment, the preset power node voltage is the standard voltage value during normal operation of the power system (such as a rated voltage of 220V or 10kV). It serves as a reference benchmark. For each of the m power nodes in the first target area, the difference between the actual voltage and the preset voltage (i.e., the "voltage deviation") is calculated. For example, if the actual voltage of a node is 200V and the preset voltage is 220V, the difference is -20V, resulting in m voltage differences, each corresponding to the voltage deviation of a node.

[0084] S402: Obtain a mapping relationship between a power node voltage difference and a power node safety risk index.

[0085] In this embodiment, the mapping relationship is usually formulated based on power system safety standards, historical operating data or industry specifications. For example, a voltage deviation within ±5% corresponds to a low risk indicator, a deviation between ±5% and ±10% corresponds to a medium risk indicator, and a deviation exceeding ±10% corresponds to a high risk indicator. Alternatively, the power node voltage difference may correspond to a specific power node safety risk indicator value. The mapping relationship can be represented by a table, a function formula (such as a piecewise function) or an empirical model, thereby facilitating the conversion of the voltage difference into a quantifiable risk indicator.

[0086] S403: Determine the power node safety risk index corresponding to each power node voltage difference among the m power node voltage differences based on the mapping relationship to obtain m power node safety risk indexes.

[0087] In this embodiment, the voltage difference of each power node is substituted into the established mapping relationship, and the voltage differences of m nodes are processed one by one, and finally the safety risk indicators of the m nodes are obtained to reflect the voltage safety status of each node.

[0088] S404: Determine the safety risk index of the first target area based on the m power node safety risk indicators.

[0089] Exemplarily, the maximum value among the safety risk indicators of the m power nodes is determined to obtain a reference safety risk indicator. Specifically, the indicator with the largest value is screened out from the safety risk indicators of the m power nodes in the target area, and it is used as the preliminary reference value for the regional safety assessment. In the power system, the maximum value of the safety risk indicator of a node often represents the most serious safety hazard in the area (for example, the voltage deviation of a node exceeds the threshold, which may cause a chain reaction). Taking the maximum value as a reference, the "short board effect" principle is followed to ensure that the assessment results reflect the most pressing safety issues in the area.

[0090] For example, the backup power capacity within the first target area is obtained. Backup power capacity is a key parameter for measuring the risk resistance of a regional power grid. When the main power supply fails, the backup power supply can temporarily provide power, mitigating the risk of voltage fluctuations or power outages. Its capacity directly affects the actual level of regional safety. The core function of a backup power supply (such as energy storage batteries, diesel generators, and uninterruptible power supplies) is to quickly supplement power supply when the main power supply fails, voltage drops suddenly, or the load increases suddenly. This ensures node voltage stability and load power supply, preventing safety incidents such as equipment damage and system disconnection caused by power shortages. The backup power capacity directly reflects the risk resistance redundancy of the regional power grid. The larger the capacity, the greater the system's adjustment space and fault tolerance under abnormal operating conditions. Initial reference risk indicators (such as the maximum node risk) only reflect the "level of danger of problems such as voltage deviations themselves" and do not consider whether the system has the ability to cope with them. The introduction of backup power capacity is precisely to incorporate "response capability" into risk assessments, making the indicator more closely aligned with the actual safety level. Therefore, the backup power capacity within the first target area is obtained.

[0091] Exemplarily, determining a second adjustment parameter corresponding to the backup power supply capacity may specifically be a mapping relationship between a preset backup power supply capacity and an adjustment parameter, and based on the mapping relationship, determining the second adjustment parameter corresponding to the backup power supply capacity may be performed.

[0092] Exemplarily, the reference safety risk index is adjusted based on the second fine-tuning parameter to obtain the safety risk index of the first target area. Specifically, the safety risk index is calculated according to the following formula:

[0093] Security risk index = reference security risk index × (1 + second fine-tuning parameter);

[0094] According to the above formula, the reference safety risk index can be adjusted based on the second fine-tuning parameter to obtain the safety risk index of the first target area.

[0095] As can be seen, the dual mechanism of "maximuming the risk baseline and dynamically adjusting the backup capacity" enables precise quantification and scientific assessment of regional power security risks. First, the maximum value of the security risk indicators of m power nodes is used as a reference to ensure that the assessment benchmark is anchored to the most stringent single-point risk, preventing the average of local high risks from masking real hidden dangers. Second, the backup power capacity is introduced as a key buffer factor, and the risk resistance of the backup power source is converted into a quantitative correction through a second adjustment parameter. When the backup capacity is sufficient, the reference risk indicator can be adjusted downward based on its corresponding adjustment parameter to reflect the system's actual response to extreme risks. Conversely, the parameter is adjusted upward to amplify risk warnings and avoid risk misjudgments caused by insufficient backup resources. This approach not only captures the "worst-case scenario" bottom-line thinking in risk assessment, but also incorporates the dynamic adjustment of system redundancy capacity. This ensures that the final security risk indicator reflects both the severity of potential risks and the actual risk resistance. This provides a more valuable decision-making basis for power dispatching, backup resource allocation, and risk prevention and control, achieving an upgrade from "static risk identification" to "dynamic risk management."

[0096] It should be explained that, in this embodiment, the voltage safety risk of the entire region can also be comprehensively evaluated based on the m power node safety risk indicators. The safety risk indicators of the m nodes can be summarized. Specifically, the arithmetic average or weighted average of the risk indicators of all nodes can be calculated (such as giving higher weights to key nodes) to reflect the overall risk level of the region. The indicator with the highest risk level among all nodes can also be taken as the regional indicator (applicable to scenarios where key nodes are prioritized, such as areas where hospitals and data centers are located). The distribution of risk indicators can also be analyzed (such as the proportion of high-risk nodes). Combined with the comprehensive judgment of regional importance, the first target regional safety risk indicator is finally obtained, which reflects the overall risk level of the voltage stability of the power system in the region and provides a basis for subsequent resource allocation or fault handling.

[0097] As can be seen, first, calculating the difference based on a preset voltage as a benchmark converts the voltage status of each power node into a quantifiable deviation value, providing a unified mathematical foundation for risk assessment, avoiding the ambiguity of subjective judgment, and laying the data foundation for subsequent risk stratification. Second, establishing a mapping relationship between voltage differences and risk indicators, converting physical quantities into risk indicators, aligns with the engineering practice of power system security assessment. Third, calculating risk indicators node by node and comprehensively assessing regional risks balances single-point accuracy with regional integrity. On the one hand, the risk indicator for each node can pinpoint specific voltage anomalies (e.g., persistently low voltage at a node in an industrial park), facilitating fault tracing. On the other hand, integrating the risk indicators of all nodes in a region through methods such as mean, extreme value, or weighted aggregation can provide a comprehensive picture of the voltage stability of the regional power grid (e.g., voltage exceeding the limit at multiple nodes in a commercial district indicates transformer overload risk). Finally, the modular design of this method facilitates flexible adjustments: the preset voltage can be dynamically adjusted according to different voltage levels, the mapping relationship can be iteratively optimized based on new operating data, and the regional comprehensive assessment weights can be flexibly set based on the importance of the nodes (for example, hospitals and transportation hubs are given higher weights), thereby adapting to the power grid security assessment needs in different scenarios and providing a scientific basis for scheduling decisions, equipment maintenance, and risk warnings.

[0098] See also Figure 5 , Figure 5 The flowchart of determining the disaster impact value corresponding to the first target area provided by the embodiment of the present application includes but is not limited to the following steps:

[0099] S501: Determine a first reference weight corresponding to the reliability risk indicator and a second reference weight corresponding to the safety risk indicator.

[0100] In this embodiment, the sum of the first reference weight and the second reference weight is less than 1. The reference weight is a preliminary set weight, and the dynamic adjustment of the actual operation scenario has not yet been considered. The sum of the two is less than 1, which reflects the scalability of the index system. The initial weight coefficients (i.e., the "first reference weight" and the "second reference weight") are set for the "reliability risk index" and the "safety risk index" respectively. The sum of these two weights needs to be less than 1 to reserve space for the subsequent introduction of weights of other indicators (such as economic indicators) to ensure that the sum of all indicator weights is 1 (in line with the basic logic of weight distribution).

[0101] S502: Determine the important load demand of the first target area in the second historical time period.

[0102] In this embodiment, the end time of the second historical time period is earlier than the start time of the first historical time period. Critical load demand refers to load demand in the power system that would have a serious impact on social security, economic operations, or public services if a power outage were to occur. Critical load demand specifically covers several key areas: public service loads such as hospital operating rooms and fire command centers; transportation hub loads such as airport signaling systems and subway traction power supply; communication and data loads such as communication base stations and data centers; critical industrial loads such as key petrochemical equipment and semiconductor manufacturing plants; financial and government loads such as bank transaction systems and government emergency command platforms; and public welfare loads such as large supermarket refrigeration equipment and heating and cooling centers for extreme weather conditions. These loads can be categorized by importance as Level 1 (e.g., nuclear power plant safety systems, where power outages result in casualties or significant losses); Level 2 (e.g., provincial government agencies, where power outages result in significant economic losses); and Level 3 (e.g., critical equipment in small and medium-sized enterprises, where power restoration is prioritized). These loads have demand characteristics such as continuous power supply, sensitivity to voltage sags, or seasonal fluctuations, and are the core electricity users prioritized in grid planning and emergency dispatch.

[0103] The demand for important loads directly affects the weight setting corresponding to the reliability risk indicator by reflecting the priority of key power equipment in the region: when the demand for important loads is large, it means that the region has higher reliability requirements such as power supply continuity and voltage stability. At this time, the corresponding first optimization factor will be generated according to its demand characteristics (such as the proportion of primary loads, the allowable power outage time, etc.), and the first reference weight of the reliability risk indicator will be adjusted upward to highlight its importance in the comprehensive risk assessment; conversely, if the demand for important loads is small, the optimization factor will reduce the weight proportion accordingly, making the assessment weight tilt toward other risk indicators. This dynamic adjustment mechanism ensures that the weight allocation matches the reliability urgency of actual power demand, thereby more accurately quantifying the risk level of the power grid in protecting critical loads and providing a scientific basis for resource scheduling and planning. Therefore, it is necessary to determine the important load demand of the first target area in the second historical time period.

[0104] S503: Determine a first optimization factor corresponding to the important load demand, and optimize the first reference weight based on the first optimization factor to obtain a first target weight.

[0105] In this embodiment, it may be a preset mapping relationship between important load demand and optimization factor, and the first optimization factor corresponding to the important load demand may be determined based on the mapping relationship.

[0106] Exemplarily, the first reference weight is optimized based on the first optimization factor to obtain a first target weight. Specifically, the first target weight is calculated according to the following formula:

[0107] First target weight = first reference weight × (1 + first optimization factor);

[0108] According to the above formula, the first reference weight can be optimized based on the first optimization factor to obtain a first target weight.

[0109] It can be seen that by introducing important load demand data from historical time periods, a dynamic and precise weight optimization mechanism is established: by using the load data from the second historical time period (which ends earlier than the start of the first historical time period), the interference of data fluctuations in the current period can be avoided, and the first optimization factor is determined based on the actual historical demand characteristics, and then the first reference weight is scientifically adjusted so that the first target weight can more objectively reflect the reliability guarantee priority of the target area in the power supply and demand relationship. This method not only improves the credibility of the weight setting by leveraging the stability of historical data, but also realizes the real-time response of the weight to the importance of actual electricity consumption through the correspondence between load demand and optimization factors, ensuring that the weight of the reliability indicator in the risk assessment can accurately match the regional load characteristics, providing a quantitative basis that is more in line with actual needs for subsequent power grid planning, risk prevention and control, and other decisions.

[0110] S504: Determine the voltage sag frequency in the first target area during the second historical time period.

[0111] In this embodiment, the voltage sag frequency directly affects the weight adjustment corresponding to the safety risk index by reflecting the stability of the power supply quality of the power grid: when the voltage sag frequency in a certain area is high, it indicates that the power grid voltage fluctuates frequently, which may pose a threat to the safe operation of sensitive equipment (such as medical precision instruments and industrial automation devices). At this time, a corresponding second optimization factor will be generated according to the level of the sag frequency, and the second reference weight of the safety risk index will be increased to strengthen the weight of this indicator in the comprehensive assessment; on the contrary, if the voltage sag frequency is low, it means that the power supply quality is stable, then the optimization factor will reduce the weight ratio, and the assessment focus will shift to other risk indicators. This association mechanism can quantify the voltage sag, a factor that directly affects the safety of equipment, into a weight change, thereby more accurately reflecting the degree of threat to equipment safety from the operation of the power grid in the risk assessment, and providing quantitative support for the formulation of voltage management measures and safety prevention and control strategies. Therefore, it is necessary to determine the voltage sag frequency of the first target area in the second historical time period.

[0112] S505: Determine a second optimization factor corresponding to the voltage sag frequency, and optimize the second reference weight based on the second optimization factor to obtain a second target weight.

[0113] In this embodiment, it may be a preset mapping relationship between the voltage sag frequency and the optimization factor, and the second optimization factor corresponding to the voltage sag frequency may be determined based on the mapping relationship.

[0114] Exemplarily, the second reference weight is optimized based on the second optimization factor to obtain a second target weight. Specifically, the second target weight is calculated according to the following formula:

[0115] Second target weight = second reference weight × (1 + second optimization factor);

[0116] According to the above formula, the second reference weight can be optimized based on the second optimization factor to obtain the second target weight.

[0117] It can be seen that by introducing voltage sag frequency data from historical time periods, a dynamic optimization system for weights based on power supply quality characteristics is constructed: using the voltage sag frequency from the second historical time period (which objectively reflects the historical power supply stability), the interference of accidental factors in the current period can be avoided, and the second optimization factor can be determined based on long-term statistical laws. Then, the second reference weight is adjusted in a targeted manner, so that the second target weight can accurately reflect the actual impact of the voltage sag risk in the safety assessment. This approach not only improves the reliability of weight setting by relying on the continuity of historical data, but also realizes the real-time mapping of weights to power supply quality safety hazards through the quantitative correspondence between sag frequency and optimization factor. When the historical sag frequency is high, the weight automatically strengthens the assessment priority of the safety indicator, and vice versa, it is reasonably weakened, ensuring that the risk assessment system can dynamically adapt to the actual operating conditions of the power grid, providing a more timely and scientific quantitative basis for voltage management, equipment protection strategy formulation, etc., thereby more accurately preventing and controlling safety risks caused by voltage fluctuations.

[0118] S506: Determine a third target weight based on the first target weight and the second target weight.

[0119] In this embodiment, the sum of the first target weight, the second target weight, and the third target weight is 1. Since the first target weight and the second target weight have been determined, the sum of the first target weight, the second target weight, and the third target weight is 1, and the third target weight can be directly determined based on the first target weight and the second target weight.

[0120] S507: Determine the disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight.

[0121] In this embodiment, based on the reliability risk index, safety risk index, economic risk index and their corresponding first, second and third target weights (the sum of the three weights is 1), the disaster impact degree value of the first target area is calculated by weighted summation, that is, each risk index is multiplied by the corresponding weight and then added. The three core risk indicators of power supply continuity (reliability), power supply quality threat (safety), and economic loss expectation (economicity) in the power system are weighted by weights and converted into a unified quantitative assessment result to avoid the one-sidedness of a single indicator. The weight can be dynamically adjusted according to historical data (such as voltage sag frequency and load demand). For example, the weight of the safety indicator will be increased in areas with frequent voltage sags, so that the assessment is more in line with the actual risk characteristics of the region. The weight allocation clearly reflects the priority of different risks (such as the reliability weight is emphasized in important load areas), ensuring that technical risks (reliability, safety) and economic loss assessment (economicity) are balanced within the same framework, avoiding excessive emphasis on one dimension and ignoring the comprehensive impact.

[0122] It can be seen that by combining actual operating data such as historical important load demand and voltage sag frequency, the reference weights of reliability and safety indicators are dynamically optimized, and then the weights of economic indicators are determined based on the constraint that the sum of the three weights is 1. Finally, the disaster impact value is obtained through weighted calculation. The core advantage of this method is that it deeply combines data-driven dynamic weight optimization with multi-dimensional risk comprehensive assessment. According to the important load demand (such as the proportion of hospital and industrial loads) and voltage sag frequency (the frequency of power supply quality problems) in the second historical time period, the reference weights are dynamically adjusted through the first and second optimization factors, so that the weights of reliability and safety indicators can reflect the real risk pain points of the region, and the three types of indicators, reliability (power supply continuity), safety (voltage quality), and economy (loss estimation), are included in a unified calculation framework, and different risks are clarified through weight allocation. At the same time, the constraint of "the sum of weights is 1" is used to ensure that the evaluation dimension of economic indicators is not neglected, forming a comprehensive quantitative result covering technical risks and economic impacts. The second historical data earlier than the first historical time period is selected as the basis for weight optimization, which not only avoids the interference of real-time data fluctuations, but also dynamically adjusts the weights based on long-term operating rules (such as seasonal load changes and temporary frequency drops caused by equipment aging), so that the evaluation model has the "memory ability" of historical risk characteristics, improving the accuracy of long-term evaluation. The disaster impact degree value obtained by weighted calculation of the dynamically optimized weights and risk indicators can be directly used in power grid disaster prevention planning (such as prioritizing the upgrading of weak links corresponding to high-weight indicators) and disaster emergency response (dispatching resources in a hierarchical manner according to quantitative results), realizing a closed-loop connection from risk assessment to actual decision-making, and enhancing the practical value of the evaluation model.

[0123] See also Figure 6 , Figure 6This is a flowchart of determining a disaster impact value based on population density provided by an embodiment of the present application, including but not limited to the following steps:

[0124] S601: Calculate based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight to obtain a reference disaster impact degree value.

[0125] In this embodiment, the reference disaster impact value is calculated specifically according to the following formula:

[0126] Reference disaster impact value = reliability risk index × first target weight + safety risk index × second target weight + economic risk index × third target weight;

[0127] According to the above formula, a reference disaster impact value can be obtained by calculation based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight.

[0128] S602: Obtain the population density in the first target area.

[0129] In this embodiment, the population size per unit area of ​​the target region is obtained through census data, satellite remote sensing statistics, or real-time population monitoring systems. Population density is a core indicator that is directly related to the difficulty of evacuating people and rescue during disasters, as well as the scope of social impact.

[0130] S603: Determine a target fine-tuning factor corresponding to the population density.

[0131] In this embodiment, a mapping relationship between a preset population density and a fine-tuning factor may be used, and a target fine-tuning factor corresponding to the population density may be determined based on the mapping relationship.

[0132] S604: Adjust the reference disaster impact degree value according to the target fine-tuning factor to obtain the disaster impact degree value corresponding to the first target area.

[0133] In this embodiment, the disaster impact degree value corresponding to the first target area is calculated specifically according to the following formula:

[0134] Disaster impact value = reference disaster impact value × (1 + target fine-tuning factor);

[0135] According to the above formula, the reference disaster impact degree value can be adjusted according to the target fine-tuning factor to obtain the disaster impact degree value corresponding to the first target area.

[0136] It can be seen that incorporating population density into the calculation of disaster impact values ​​can further highlight the vulnerability characteristics of the social dimension on the basis of technical and economic risk assessment. Specifically, by calculating reference values ​​based on reliability, safety, and economic indicators and corresponding weights, a basic assessment framework can be constructed from objective aspects such as infrastructure, life and property, and economic losses. After introducing population density and target fine-tuning factors, the risk differences between high-population areas and sparsely populated areas can be dynamically amplified or reduced. For example, in high-density urban areas, due to the difficulty of evacuating people and the high potential risk of casualties, the fine-tuning factors will significantly improve the comprehensive assessment value, thereby making the assessment results more consistent with the actual impact of the disaster on people. In low-density areas, the factors weaken the weight of social impact and avoid assessment bias.

[0137] In summary, the implementation of the present invention has the following beneficial effects:

[0138] It can be seen that the post-disaster power resource allocation method described in the embodiment of the present invention first divides the target disaster-stricken area into n target areas and allocable power resources, where n is an integer greater than 1, and then determines the disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values, and then determines the priority of the n target areas based on the n disaster impact degree values, and determines the target priority order according to the priority of the n target areas, and finally allocates power resources to the n target areas and the allocable power resources based on the target priority order, so that the target disaster-stricken area restores power response, thereby improving the post-disaster power resource recovery efficiency of the disaster-stricken area.

[0139] See also Figure 7 , Figure 7 is a structural diagram of a post-disaster power resource allocation device provided in an embodiment of the present application. The post-disaster power resource allocation device 700 includes: an acquisition unit 701 and a processing unit 702;

[0140] The acquisition unit 701 is used to acquire allocatable power resources;

[0141] The processing unit 702 is configured to divide the target disaster-stricken area into n target areas, where n is an integer greater than 1;

[0142] Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values;

[0143] Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas;

[0144] Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

[0145] In some possible implementations, in determining the disaster impact level value corresponding to each of the n target areas to obtain the n disaster impact level values, the processing unit 702 is specifically configured to:

[0146] Determine m power nodes corresponding to a first target area; the first target area is any one of the n target areas, and m is an integer greater than 1;

[0147] Obtaining a load loss amount and a power node voltage of each of the m power nodes in a first historical time period to obtain m load loss amounts and m power node voltages;

[0148] determining a reliability risk index of the first target area based on the m load loss amounts;

[0149] determining a safety risk index of the first target area based on the m power node voltages;

[0150] Obtaining the economic loss value of the first target area during the first historical time period;

[0151] determining an economic risk index of the first target area based on the economic loss value;

[0152] A disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, and the economic risk index.

[0153] In some possible implementations, in determining the reliability risk index of the first target area based on the m load loss amounts, the processing unit 702 is specifically configured to:

[0154] Determine the sum of the m load loss amounts to obtain a target load loss amount;

[0155] determining a reference reliability risk indicator corresponding to the target load loss amount;

[0156] Determining the standard deviations corresponding to the m unloaded loads;

[0157] determining a first adjustment parameter corresponding to the standard deviation;

[0158] The reference reliability risk indicator is adjusted based on the first adjustment parameter to obtain the reliability risk indicator of the first target area.

[0159] In some possible implementations, in determining the safety risk indicator of the first target area based on the m power node voltages, the processing unit 702 is specifically configured to:

[0160] determining a difference between each of the m power node voltages and a preset power node voltage to obtain m power node voltage differences;

[0161] Obtaining a mapping relationship between a power node voltage difference and a power node safety risk indicator;

[0162] Determine, based on the mapping relationship, a power node safety risk index corresponding to each of the m power node voltage differences, to obtain m power node safety risk indicators;

[0163] The safety risk index of the first target area is determined based on the m power node safety risk indicators.

[0164] In some possible implementations, in determining the security risk indicator of the first target area based on the m power node security risk indicators, the processing unit 702 is specifically configured to:

[0165] Determining a maximum value among the m power node safety risk indicators to obtain a reference safety risk indicator;

[0166] Obtaining backup power capacity within the first target area;

[0167] determining a second adjustment parameter corresponding to the backup power supply capacity;

[0168] The reference safety risk indicator is adjusted based on the second fine-tuning parameter to obtain the safety risk indicator of the first target area.

[0169] In some possible implementations, in determining the disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, and the economic risk index, the processing unit 702 is specifically configured to:

[0170] Determining a first reference weight corresponding to the reliability risk indicator and a second reference weight corresponding to the safety risk indicator; the sum of the first reference weight and the second reference weight is less than 1;

[0171] Determining an important load demand of the first target area in a second historical time period; the end time of the second historical time period is earlier than the start time of the first historical time period;

[0172] determining a first optimization factor corresponding to the important load demand;

[0173] Optimizing the first reference weight based on the first optimization factor to obtain a first target weight;

[0174] determining a voltage sag frequency in the first target area during the second historical time period;

[0175] determining a second optimization factor corresponding to the voltage sag frequency;

[0176] Optimizing the second reference weight based on the second optimization factor to obtain a second target weight;

[0177] Determine a third target weight based on the first target weight and the second target weight; the sum of the first target weight, the second target weight, and the third target weight is 1;

[0178] The disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight.

[0179] In some possible implementations, in determining the disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight, the processing unit 702 is specifically configured to:

[0180] Calculating based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight to obtain a reference disaster impact degree value;

[0181] Obtaining the population density within the first target area;

[0182] determining a target fine-tuning factor corresponding to the population density;

[0183] The reference disaster impact degree value is adjusted according to the target fine-tuning factor to obtain the disaster impact degree value corresponding to the first target area.

[0184] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided by the embodiment of this application. Figure 8 As shown, electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for executing the following steps:

[0185] Acquisition of distributable power resources;

[0186] Divide the target disaster-stricken area into n target areas; n is an integer greater than 1;

[0187] Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values;

[0188] Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas;

[0189] Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

[0190] In some possible implementations, in determining the disaster impact level value corresponding to each of the n target areas to obtain the n disaster impact level values, the program includes instructions for executing the following steps:

[0191] Determine m power nodes corresponding to a first target area; the first target area is any one of the n target areas, and m is an integer greater than 1;

[0192] Obtaining a load loss amount and a power node voltage of each of the m power nodes in a first historical time period to obtain m load loss amounts and m power node voltages;

[0193] determining a reliability risk index of the first target area based on the m load loss amounts;

[0194] determining a safety risk index of the first target area based on the m power node voltages;

[0195] Obtaining the economic loss value of the first target area during the first historical time period;

[0196] determining an economic risk index of the first target area based on the economic loss value;

[0197] A disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, and the economic risk index.

[0198] In some possible implementations, in determining the reliability risk indicator of the first target area based on the m load loss amounts, the program includes instructions for performing the following steps:

[0199] Determine the sum of the m load loss amounts to obtain a target load loss amount;

[0200] determining a reference reliability risk indicator corresponding to the target load loss amount;

[0201] Determining the standard deviations corresponding to the m unloaded loads;

[0202] determining a first adjustment parameter corresponding to the standard deviation;

[0203] The reference reliability risk indicator is adjusted based on the first adjustment parameter to obtain the reliability risk indicator of the first target area.

[0204] In some possible implementations, in determining the safety risk indicator of the first target area based on the m power node voltages, the program includes instructions for performing the following steps:

[0205] determining a difference between each of the m power node voltages and a preset power node voltage to obtain m power node voltage differences;

[0206] Obtaining a mapping relationship between a power node voltage difference and a power node safety risk indicator;

[0207] Determine, based on the mapping relationship, a power node safety risk index corresponding to each of the m power node voltage differences, to obtain m power node safety risk indicators;

[0208] The safety risk index of the first target area is determined based on the m power node safety risk indicators.

[0209] In some possible implementations, in determining the security risk indicator of the first target area based on the m power node security risk indicators, the program includes instructions for performing the following steps:

[0210] Determining a maximum value among the m power node safety risk indicators to obtain a reference safety risk indicator;

[0211] Obtaining backup power capacity within the first target area;

[0212] determining a second adjustment parameter corresponding to the backup power supply capacity;

[0213] The reference safety risk indicator is adjusted based on the second fine-tuning parameter to obtain the safety risk indicator of the first target area.

[0214] In some possible implementations, in determining the disaster impact level value corresponding to the first target area based on the reliability risk index, the safety risk index, and the economic risk index, the program includes instructions for executing the following steps:

[0215] Determining a first reference weight corresponding to the reliability risk indicator and a second reference weight corresponding to the safety risk indicator; the sum of the first reference weight and the second reference weight is less than 1;

[0216] Determining an important load demand of the first target area in a second historical time period; the end time of the second historical time period is earlier than the start time of the first historical time period;

[0217] determining a first optimization factor corresponding to the important load demand;

[0218] Optimizing the first reference weight based on the first optimization factor to obtain a first target weight;

[0219] determining a voltage sag frequency in the first target area during the second historical time period;

[0220] determining a second optimization factor corresponding to the voltage sag frequency;

[0221] Optimizing the second reference weight based on the second optimization factor to obtain a second target weight;

[0222] Determine a third target weight based on the first target weight and the second target weight; the sum of the first target weight, the second target weight, and the third target weight is 1;

[0223] The disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight.

[0224] In some possible implementations, in determining the disaster impact value corresponding to the first target area based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight, the program includes instructions for performing the following steps:

[0225] Calculating based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight to obtain a reference disaster impact degree value;

[0226] Obtaining the population density within the first target area;

[0227] determining a target fine-tuning factor corresponding to the population density;

[0228] The reference disaster impact degree value is adjusted according to the target fine-tuning factor to obtain the disaster impact degree value corresponding to the first target area.

[0229] It should be understood that the electronic devices in this application may include smartphones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices (MIDs) or wearable devices, or servers, edge computing power nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0230] The embodiments of the present application further provide a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0231] The embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.

[0232] It should be noted that for the aforementioned method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0233] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0234] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0235] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0236] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0237] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various implementation methods of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0238] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0239] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A post-disaster power resource allocation method, characterized in that: include: Acquisition of distributable power resources; Divide the target disaster-stricken area into n target areas; n is an integer greater than 1; Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values; Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas; Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

2. The method according to claim 1, wherein The determining of the disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values ​​includes: Determine m power nodes corresponding to a first target area; the first target area is any one of the n target areas, and m is an integer greater than 1; Obtaining a load loss amount and a power node voltage of each of the m power nodes in a first historical time period to obtain m load loss amounts and m power node voltages; determining a reliability risk index of the first target area based on the m load loss amounts; determining a safety risk index of the first target area based on the m power node voltages; Obtaining the economic loss value of the first target area during the first historical time period; determining an economic risk index of the first target area based on the economic loss value; A disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, and the economic risk index.

3. The method according to claim 2, wherein The determining of the reliability risk index of the first target area based on the m load loss amounts includes: Determine the sum of the m load loss amounts to obtain a target load loss amount; determining a reference reliability risk indicator corresponding to the target load loss amount; Determining the standard deviations corresponding to the m unloaded loads; determining a first adjustment parameter corresponding to the standard deviation; The reference reliability risk indicator is adjusted based on the first adjustment parameter to obtain the reliability risk indicator of the first target area.

4. The method according to claim 3, wherein The determining the safety risk index of the first target area based on the m power node voltages includes: determining a difference between each of the m power node voltages and a preset power node voltage to obtain m power node voltage differences; Obtaining a mapping relationship between a power node voltage difference and a power node safety risk indicator; Determine, based on the mapping relationship, a power node safety risk index corresponding to each of the m power node voltage differences, to obtain m power node safety risk indicators; The safety risk index of the first target area is determined based on the m power node safety risk indicators.

5. The method according to claim 4, wherein The determining the safety risk index of the first target area based on the m power node safety risk indicators includes: Determining a maximum value among the m power node safety risk indicators to obtain a reference safety risk indicator; Obtaining backup power capacity within the first target area; determining a second adjustment parameter corresponding to the backup power supply capacity; The reference safety risk indicator is adjusted based on the second fine-tuning parameter to obtain the safety risk indicator of the first target area.

6. The method according to any one of claims 2 to 5, characterized in that The determining of the disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, and the economic risk index includes: Determining a first reference weight corresponding to the reliability risk indicator and a second reference weight corresponding to the safety risk indicator; the sum of the first reference weight and the second reference weight is less than 1; Determining an important load demand of the first target area in a second historical time period; the end time of the second historical time period is earlier than the start time of the first historical time period; determining a first optimization factor corresponding to the important load demand; Optimizing the first reference weight based on the first optimization factor to obtain a first target weight; determining a voltage sag frequency in the first target area during the second historical time period; determining a second optimization factor corresponding to the voltage sag frequency; Optimizing the second reference weight based on the second optimization factor to obtain a second target weight; Determine a third target weight based on the first target weight and the second target weight; the sum of the first target weight, the second target weight, and the third target weight is 1; The disaster impact degree value corresponding to the first target area is determined based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight.

7. The method according to claim 6, wherein The determining the disaster impact degree value corresponding to the first target area based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight includes: Calculating based on the reliability risk index, the safety risk index, the economic risk index, the first target weight, the second target weight, and the third target weight to obtain a reference disaster impact degree value; Obtaining the population density within the first target area; determining a target fine-tuning factor corresponding to the population density; The reference disaster impact degree value is adjusted according to the target fine-tuning factor to obtain the disaster impact degree value corresponding to the first target area.

8. A post-disaster power resource allocation device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire allocatable power resources; The processing unit is used to divide the target disaster-stricken area into n target areas; n is an integer greater than 1; Determining a disaster impact degree value corresponding to each of the n target areas to obtain n disaster impact degree values; Determining the priorities of the n target areas based on the n disaster impact degree values, and determining a target priority order according to the priorities of the n target areas; Power resources are allocated to the n target areas and the allocatable power resources based on the target priority order, so that the target disaster-stricken area recovers power response.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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