Supply point configuration scheme determination method, system, device, medium, and product

Abstract

The application discloses a kind of supply point configuration scheme determination method, system, equipment, medium and product, belong to electric power system field, method is: based on the distance matrix between multiple first supply points and multiple first demand points, respectively determine the matching relationship in first preset radius and second preset radius, obtain first matching matrix and second matching matrix;Based on first matching matrix and second matching matrix, hierarchical distribution is carried out to each first supply point under preset supply quantity, and an initial configuration scheme including multiple second supply points is obtained;Obtain objective function and target constraint condition, based on objective function and target constraint condition, the initial configuration scheme is iteratively optimized using discrete iteration strategy, until the preset stop condition is met, to obtain the target configuration scheme, therefore, by implementing the present application, it can be realized that all demand points are matched by supply point at the same time, to realize the fast, reliable and preferential supply of second demand point.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method, system, equipment, medium, and product for determining a supply point configuration scheme. Background Technology

[0002] With the continuous expansion of the power system and the continuous improvement of its intelligence level, how to configure the supply nodes for storing power materials in order to achieve precise service and rapid response to widely distributed demand nodes has become a key link in ensuring equipment operation and maintenance efficiency and improving emergency response capabilities.

[0003] Traditional supply point configuration methods mainly use a static matching model based on a single service radius. This method usually obtains the straight-line distance between supply points and demand points, sets a unified service matching radius, and then generates a supply point configuration scheme by using a greedy algorithm or simple weight evaluation with the goal of maximizing the number of matching demand points or minimizing the total distance.

[0004] However, due to the varying importance of different demand points in actual power operations, existing allocation methods may result in demand points where critical power equipment is located not receiving rapid and reliable resource supply, leading to problems such as weak grid emergency response capabilities, imbalanced resource allocation strategies, and low overall operational efficiency. Summary of the Invention

[0005] This invention provides a method, system, equipment, medium, and product for determining supply point configuration schemes, which can solve the problems of existing methods being unable to simultaneously consider global matching and priority guarantee of secondary demand points, resulting in slow emergency response, unbalanced resource allocation, and low operational efficiency.

[0006] This invention provides a method for determining a supply point configuration scheme, comprising: Obtain a distance matrix between multiple first supply points and multiple first demand points. Based on the distance matrix, determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix. The first preset radius is greater than the second preset radius. The first demand point includes the multiple second demand points and multiple third demand points. The second demand point is the demand point among the first demand points whose importance is greater than a preset threshold. Based on the first matching matrix and the second matching matrix, each of the first supply points is hierarchically allocated under a preset supply quantity to obtain an initial configuration scheme including multiple second supply points. Obtain the objective function and objective constraints. Based on the objective function and objective constraints, use a discrete iterative strategy to iteratively optimize the initial configuration scheme until a preset stopping condition is met to obtain the objective configuration scheme. Configure the supply points according to the objective configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined according to a first redundancy, and the second matching coefficient is determined according to a second redundancy corresponding to the first preset radius.

[0007] This invention employs a dual-radius mechanism to stratify demand points according to their importance, forming a two-tiered service gradient of general matching and core rapid response, laying a quantitative foundation for subsequent differentiated site selection. Under a preset supply quantity, each first supply point is hierarchically allocated based on two matching matrices to obtain an initial configuration scheme. The quantity limit and hierarchical matching are simultaneously incorporated into the initial solution, avoiding redundancy caused by traditional greedy algorithms. An objective function is constructed using the first redundancy as a penalty component and the second redundancy as a reward component, and objective constraints are applied. A discrete iterative strategy is used to optimize the initial scheme until a stopping condition is met, outputting the target configuration scheme. A composite objective-guided algorithm of redundancy penalty + response reward automatically reduces redundant matching and enhances the protection of critical nodes, achieving a win-win situation of cost savings and time efficiency. Overall, this embodiment ensures that all demand points are matched by supply points while achieving rapid, reliable, and prioritized supply to critical demand points, thereby improving the power grid's emergency response capability, resource allocation utilization, and overall operational efficiency.

[0008] Furthermore, obtaining the distance matrix between the multiple first supply points and the multiple first demand points specifically involves: Obtain the first coordinate data of multiple first supply points and the second coordinate data of multiple first demand points; Based on the first coordinate data and the second coordinate data, the spherical distance between each of the first supply points and each of the first demand points is calculated using the spherical distance formula, and the distance matrix is ​​determined based on all the spherical distances.

[0009] By replacing the planar Euclidean distance with a spherical cosine model, the Earth's curvature error is automatically corrected, improving the accuracy of distance measurement and providing high-fidelity input for subsequent radius determination.

[0010] Furthermore, before the hierarchical allocation of each of the first supply points under the preset supply quantity, the method further includes: Based on the first matching matrix, determine whether any first demand point is not matched with any of the first supply points; Alternatively, based on the second matching matrix, it can be determined whether the second demand point does not match any of the first supply points within the second preset radius; If either of these conditions exists, the first supply point is re-determined; if neither exists, hierarchical allocation is performed.

[0011] By making feasibility verification a prerequisite for configuration schemes, the algorithm avoids ineffective iterations in infeasible spaces and reduces wasted computation time.

[0012] Furthermore, the hierarchical allocation of each first supply point under a preset supply quantity to obtain an initial configuration scheme including multiple second supply points is specifically as follows: Obtain an initial hierarchical set, a second demand set including all second demand points, a third demand set including all third demand points, and a first supply set including all first supply points, wherein the initial hierarchical set is an empty set; In each iteration of the first preset level, if the number of supply points in the initial level set of the previous round is less than the preset supply quantity, then a first target supply point is determined based on the maximum number of matching relationships between the current first supply set and the current second demand set. The current first supply set and the current initial level set are updated according to the first target supply point, and the current second demand set is updated according to the second demand points that have a matching relationship with the first target supply point. The iteration stops when the current second demand set is empty, and the updated first level set is obtained. If the number of supply points in the initial level set of the previous round is greater than or equal to the preset supply quantity, then the level allocation is completed, and the initial configuration scheme is obtained. In each iteration of the second preset level, if the number of supply points in the first level set of the previous round is less than the preset supply quantity, then a second target supply point is determined based on the maximum number of matching relationships between the current first supply set and the current third demand set. The current first supply set and the current first level set are updated according to the second target supply point, and the current third demand set is updated according to the third demand points that have a matching relationship with the second target supply point. The iteration stops when the current third demand set is empty, and the second level set after the first level set is updated is obtained. If the number of supply points in the first level set of the previous round is greater than or equal to the preset supply quantity, then the level allocation is completed, and the initial configuration scheme is obtained. In each iteration of the third preset level, if the number of supply points in the second level set of the previous round is less than the preset supply quantity, the current second level set is updated based on the redundancy of the current first supply set and all first demand points until the number of supply points in the current second level set is greater than or equal to the preset supply quantity, the iteration stops and the level allocation is completed to obtain the initial configuration scheme.

[0013] This three-tiered progressive allocation is structured as follows: The first preset level prioritizes matching the second demand point (high importance), selecting points round by round based on maximizing the number of matching relationships, until the set of high-importance demands is empty; the second preset level continues to supplement the remaining third demand points according to maximizing the number of matching relationships; if the third preset level still does not reach the preset supply quantity, it supplements to the upper limit with minimum redundancy. This ensures that key demand points are supplied first, then the global matching degree of all demand points is satisfied, and finally redundancy is controlled, so that the initial solution has the characteristics of high matching, low repetition, and fast response. Furthermore, the selected supply point and demand point sets are updated in each round to achieve dynamic set reduction, reducing the search space in subsequent iterations, which can reduce the pressure of subsequent iterations and significantly shorten the iteration time.

[0014] Furthermore, the step of iteratively optimizing the initial configuration scheme using a discrete iterative strategy based on the objective function and the objective constraints until a preset stopping condition is met, thereby obtaining the target configuration scheme, specifically involves: Get pseudo-random number pairs; In each iteration of the initial configuration scheme, the supply point to be replaced is determined in the previous configuration scheme based on the pseudo-random number pair, and the replacement supply point is determined among the supply points of each first supply point that do not belong to the previous configuration scheme. The supply point to be replaced is replaced based on the replacement supply point to obtain the current configuration scheme. It is determined whether the current configuration scheme meets the target constraint condition. If it does not meet the constraint condition, the replacement supply point and the supply point to be replaced are re-determined. If it meets the constraint condition, the first function value of the initial configuration scheme is calculated according to the target function, and the second function value of the current configuration scheme is calculated according to the target function. If the second function value is greater than the first function value, the current configuration scheme is retained. If the second function value is less than or equal to the first function value, the configuration scheme of the previous round is retained. This process continues until the preset iteration stop condition is met, at which point the target configuration scheme is obtained.

[0015] This approach uses pseudo-random number pairs to randomly determine the supply point to be replaced in the previous round of solutions, and randomly selects a replacement point from the unselected points. After a one-to-one replacement, it first checks whether the objective constraint (hard constraint) is met. If it is, the objective function value is calculated. If the new function value is greater than the old value (due to a larger reward component or a smaller penalty component), the new solution is accepted; otherwise, the original solution is maintained. This process is repeated until the iteration stopping condition is met. This ensures global search capability while avoiding crossing constraint boundaries, significantly reducing computational complexity. Overall, this embodiment improves the speed of discrete location optimization and has a smooth convergence curve.

[0016] Furthermore, the first matching coefficient is determined based on the first redundancy, and the second matching coefficient is determined based on the second redundancy corresponding to the first preset radius, specifically as follows: For each of the first demand points, the average value of the first redundancy between the first demand point and all the second supply points is calculated to obtain the first matching coefficient; For each first demand point, calculate the second redundancy between the first demand point and all second supply points within the first preset radius, and determine the response degree corresponding to each second redundancy according to the preset response function. Process each response degree according to the weight coefficient of all second supply points within the first preset radius to obtain the second matching coefficient.

[0017] This approach quantifies redundancy and response to the same order of magnitude, allowing for dynamic updates during iterations. It avoids the drawback of fixed weights being unable to adapt to regions with varying densities. The two coefficients, serving as penalty and reward components respectively, directly determine the increase or decrease of the objective function, thereby guiding the algorithm to automatically reduce excessive matching and enhance the response depth in key areas. Overall, this embodiment achieves self-balancing optimization with a fixed number of supply points through a dual-coefficient approach of local response reward and global redundancy penalty, reducing the redundancy of the final solution and increasing the response depth of core nodes.

[0018] Another embodiment of the present invention provides a supply point configuration scheme determination system, including: a data processing module, a hierarchical allocation module, and an iterative optimization module; The data processing module is used to obtain a distance matrix between multiple first supply points and multiple first demand points, and based on the distance matrix, to determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix, wherein the first preset radius is greater than the second preset radius, the first demand point includes the multiple second demand points and multiple third demand points, and the second demand point is the demand point among the first demand points whose importance is greater than a preset threshold; The hierarchical allocation module is used to hierarchically allocate each of the first supply points based on the first matching matrix and the second matching matrix, under a preset supply quantity, to obtain an initial configuration scheme including multiple second supply points. The iterative optimization module is used to obtain the objective function and the objective constraints, and to iteratively optimize the initial configuration scheme using a discrete iterative strategy based on the objective function and the objective constraints until a preset stopping condition is met, thereby obtaining the target configuration scheme. The supply points are then configured according to the target configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined based on a first redundancy, and the second matching coefficient is determined based on a second redundancy corresponding to the first preset radius.

[0019] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the supply point configuration scheme determination method of the present invention.

[0020] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the supply point configuration scheme determination method of the present invention.

[0021] Another embodiment of the present invention also provides a computer program product stored in a storage medium, the computer program product being executed by at least one processor to implement the steps of the supply point configuration scheme determination method as described in any one of the first aspects. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for determining a supply point configuration scheme according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a supply point configuration scheme determination system provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to match non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0031] See Figure 1 To address the problem that existing methods cannot simultaneously consider global matching and prioritize the second demand point, leading to slow emergency response, unbalanced resource allocation, and low operational efficiency, an embodiment of the present invention provides a method for determining a supply point configuration scheme, comprising: Step 101: Obtain the distance matrix between multiple first supply points and multiple first demand points. Based on the distance matrix, determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix. The first preset radius is greater than the second preset radius. The first demand point includes the multiple second demand points and multiple third demand points. The second demand point is the demand point among the first demand points whose importance is greater than a preset threshold.

[0032] In this embodiment, a spatial relationship model between supply points and demand points is established. A distance matrix reflecting the spatial relationship between all point groups is obtained through distance calculation. Based on this distance matrix, two different service radius thresholds are set. By comparing each distance value in the distance matrix with a first preset radius and a second preset radius, a first matching matrix reflecting the supply capacity within the first preset radius and a second matching matrix reflecting the supply capacity within the second preset radius are obtained. The generation rule for the first matching matrix is: for any first supply point and a first demand point, as long as the distance between them is less than or equal to the first preset radius, their corresponding positions in the first matching matrix satisfy the matching condition and are marked as valid (e.g., represented by the value 1); otherwise, they are marked as invalid (e.g., represented by the value 0). The generation rule for the second matching matrix is: for any supply point and a demand point, as long as the distance between them is less than or equal to the second preset radius, their corresponding positions in this matrix satisfy the matching condition and are marked as valid; otherwise, they are marked as invalid.

[0033] In this system, the first demand point is the complete set of all objects that need to be supplied, including the second and third demand points required in subsequent steps. The first demand point can be understood as the entire set of maintenance points. The second demand point is a subset selected from all maintenance points, with the selection criterion being that the importance of the second demand point is higher than a preset threshold. Importance might represent a very large number of users served by the point (e.g., a core substation) or extremely critical users served by the point (e.g., government agencies, hospitals, data centers, important factories). The third demand point consists of the remaining demand points whose importance does not reach the preset threshold; these are ordinary maintenance points other than critical nodes. The importance can be comprehensively evaluated and quantified based on factors such as the power load level served by the demand point or its criticality in the power grid topology. In this embodiment, the content of importance is not limited; it is a standard pre-set before the computer executes the method.

[0034] As an example of an embodiment of the present invention, obtaining the distance matrix between multiple first supply points and multiple first demand points specifically involves: obtaining first coordinate data of multiple first supply points and second coordinate data of multiple first demand points; calculating the spherical distance between each first supply point and each first demand point using the spherical distance formula based on the first coordinate data and the second coordinate data; and determining the distance matrix based on all the spherical distances.

[0035] In this embodiment, geographic coordinate information of all candidate first supply points and all first demand points is collected, with the location of each point represented in latitude and longitude coordinates. A basic database containing the coordinates of all points is established, and supply points and demand points are uniformly numbered and managed. Subsequently, spatial distance calculations are performed based on the geographic coordinate system. The latitude and longitude coordinates of each point are converted from degrees to radians, and the actual surface distance between each pair of supply and demand points is calculated using a spherical geometry algorithm. Specifically, accurate calculations are achieved through a spherical distance formula that includes trigonometric function operations. This calculation process considers the influence of the Earth's curvature on the actual distance. Finally, a systematic distance matrix is ​​constructed. The distance values ​​between all calculated supply-demand point pairs are filled into a two-dimensional matrix structure according to the rule that the supply point number is the row index and the demand point number is the column index, forming a complete distance matrix as the basic data for subsequent processing. Specifically, a geographic coordinate system is established in the area to be planned, using latitude and longitude coordinates, and a constant Earth radius is set. ; The total number of candidate first supply points is denoted as . The coordinates of each first supply point are marked as , ,in, For the first The geographical coordinates of the first supply point This is the number of the first supply point. For the first The longitude of the first supply point For the first The latitude of the first supply point; The total number of primary demand points for electrical equipment is denoted as . The coordinates of the first requirement point are set as follows: , ,in, For the first The geographical coordinates of the first demand point Number the first requirement point. For the first The longitude of the first point of demand For the first The dimension of the first demand point; The first matching matrix is ​​obtained by calculating the geographical spherical distance between the supply point and the demand point pairwise. .

[0036] Specifically: ; ; in, supply point With demand points spherical distance; function Used to convert real numbers Crop to ; This is the core calculation part of the formula for the spherical cosine theorem, and its result is the intermediate value necessary for calculating the spherical distance; For the conversion from degrees to radians, the formal parameters For the degree.

[0037] Set the first preset radius as The second preset radius is The first preset radius is set to be larger than the second preset radius. The significance of this step is that the first preset radius indicates a low supply capacity of the supply points within that radius, while the second preset radius indicates a high supply capacity. For each pair of first supply points and first demand points... Execution decision: , ; in, Indicates supply point First preset radius Does it match the first requirement? 1 indicates a match, and 0 indicates no match; Indicates supply point Second preset radius Does it match the first requirement? 1 indicates a match, and 0 indicates no match; this yields the first matching matrix. With the second matching matrix .

[0038] Step 102: Based on the first matching matrix and the second matching matrix, perform hierarchical allocation of each of the first supply points under the preset supply quantity to obtain an initial configuration scheme including multiple second supply points.

[0039] In this embodiment, given a predetermined upper limit on the number of supply points, a hierarchical strategy is adopted to generate an initial configuration scheme. For example, the first layer prioritizes supply points that can match the most unmatched critical demand points; the second layer prioritizes supply points that can match ordinary demand points that are not matched within a first preset radius; if the upper limit is still not reached, the third layer aims to improve resource utilization efficiency by selecting supply points that will result in the least increase in redundancy after introduction to supplement the supply points, ultimately obtaining an initial set of supply points that meets the configuration quantity requirements. This set is the initial configuration scheme.

[0040] Step 103: Obtain the objective function and objective constraints. Based on the objective function and objective constraints, use a discrete iterative strategy to iteratively optimize the initial configuration scheme until a preset stopping condition is met to obtain the target configuration scheme. Configure the supply points according to the target configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined according to a first redundancy, and the second matching coefficient is determined according to a second redundancy corresponding to the first preset radius.

[0041] In this embodiment, a comprehensive evaluation function containing penalty and reward terms is constructed as the optimization objective function. The penalty term is used to suppress redundant configuration of supply points, and the reward term is used to improve the supply guarantee for key demand points. Constraints are set, including a fixed configuration quantity, all demand points must be matched by supply points within a first preset radius, and all key demand points must be matched by supply points within a second preset radius. Based on this objective function and constraints, a neighborhood search mechanism is used to iteratively improve the initial configuration scheme. By replacing each supply point in the initial scheme and calculating the objective function value of the new scheme, optimization continues until the convergence condition is met, and finally, the optimal configuration scheme is output.

[0042] As an example of an embodiment of the present invention, before performing hierarchical allocation of each of the first supply points under the preset supply quantity, the method further includes: determining, based on the first matching matrix, whether any of the first demand points does not match with any of the first supply points; or, based on the second matching matrix, determining whether the second demand point does not match with any of the first supply points within a second preset radius; if either case exists, the first supply points are re-determined; if neither case exists, hierarchical allocation is performed.

[0043] In this embodiment, based on the first matching matrix The process iterates through all first demand points, verifying whether each first demand point is matched by at least one first supply point, i.e., whether each first demand point satisfies the matching condition at least once with all first supply points. Based on the second matching matrix, it verifies whether each second demand point is matched by at least one first supply point, i.e., whether each second demand point satisfies the matching condition at least once with all first supply points. It also verifies whether each second demand point is matched by at least one first supply point within a second preset radius. If any of the above verifications fails, the current supply point set is deemed unsatisfactory, triggering a supply point re-determination process. Only when both types of verifications pass does the subsequent hierarchical allocation stage proceed. Specifically, the number of successfully matched demand points for each first supply point is calculated: , ; in, First supply point In radius The number of successfully matched demand points. First supply point In radius The number of demand points that were successfully matched; If any first demand point exists make If any first requirement point is not met, it is determined to be globally infeasible; and That is, any second demand point makes If any condition is deemed globally infeasible, then the first supply point is re-determined; otherwise, the next supply point selection process continues. Used to identify the first If the first requirement is the same as the second requirement, then it is a core requirement if the value is 1, and a non-core requirement if the value is 0.

[0044] As an example of an embodiment of the present invention, the step of hierarchically allocating each first supply point under a preset supply quantity to obtain an initial configuration scheme including multiple second supply points specifically involves: obtaining an initial hierarchical set, a second demand set including all second demand points, a third demand set including all third demand points, and a first supply set including all first supply points, wherein the initial hierarchical set is an empty set; in each iteration of the first preset hierarchical set, if the number of supply points in the previous round's initial hierarchical set is less than the preset supply quantity, then based on the maximum value of the number of matching relationships between the current first supply set and the current second demand set, a first target supply point is determined; the current first supply set and the current initial hierarchical set are updated according to the first target supply point; and the current second demand set is updated according to the second demand points that have a matching relationship with the first target supply point, until the current second demand set is an empty set, at which point the iteration stops, and the updated first hierarchical set is obtained; if the number of supply points in the previous round's initial hierarchical set is greater than or equal to the preset supply quantity, then the hierarchical allocation is completed, and the initial hierarchical set is obtained. The initial configuration scheme is as follows: In each iteration of the second preset level, if the number of supply points in the first level set of the previous round is less than the preset supply quantity, then a second target supply point is determined based on the maximum number of matching relationships between the current first supply set and the current third demand set. The current first supply set and the current first level set are updated according to the second target supply point, and the current third demand set is updated according to the third demand points that have a matching relationship with the second target supply point. The iteration stops when the current third demand set is empty, and the updated second level set of the first level set is obtained. If the number of supply points in the first level set of the previous round is greater than or equal to the preset supply quantity, the level allocation is completed, and the initial configuration scheme is obtained. In each iteration of the third preset level, if the number of supply points in the second level set of the previous round is less than the preset supply quantity, then the current second level set is updated based on the redundancy of the current first supply set and all first demand points. The iteration stops and the level allocation is completed when the number of supply points in the current second level set is greater than or equal to the preset supply quantity, and the initial configuration scheme is obtained.

[0045] In this embodiment, four core data sets are established: an initial hierarchical set (initially empty), a second demand set (containing all critical operation and maintenance points), a third demand set (containing all ordinary operation and maintenance points), and a first supply set (containing all candidate supply points). These four sets constitute the basic data framework of the hierarchical allocation algorithm, providing structured data support for subsequent hierarchical optimization. In the first level, an iterative optimization mechanism is adopted. Under the premise of meeting the preset supply quantity, priority is given to selecting the second demand point with the highest number of successful matches. Specifically, in each iteration, the optimal supply point that can match the most second demand points is selected from the current first supply set. This supply point is added to the initial hierarchical set and simultaneously removed from the first supply set. The second demand set is updated synchronously, removing all second demand points matched by this supply point. The iteration continues until all second demand points are matched or the preset supply quantity limit is reached. The second level, building upon the first level, completes the comprehensive matching of the third demand points. Specifically, for the current set of third demand points, those that could not be matched by the first supply point after the first level matching, the same iterative mechanism is used to select the optimal supply point that can match the most third demand points, update the relevant set, and iterate until all third demand points are matched or the preset supply quantity is reached. The third level performs redundancy optimization control. If the preset supply quantity is still not reached after the first two levels, a redundancy control mechanism is activated: the impact of the remaining first supply points on the overall redundancy is evaluated, and based on the redundancy index, the supply point with the smallest redundancy increment after introduction is selected, and the redundancy is replenished cyclically until the preset supply quantity requirement is reached.

[0046] Specifically, obtain the preset supply quantity, denoted as... Construct a binary addressing variable: ; in, For binary decision variables, 1 indicates that the supply point is selected. 0 indicates that it is not selected; Construct the initial location set Specifically, this includes: constructing a second set of requirements. and set the initial hierarchy set. ; When the second demand set When that happens, select the first supply point that matches the largest number of second demand points, i.e. ; in, For the current loop, not yet affected by the second preset radius The matching second requirement point index set, Index of supply points selected for the first level; If they are tied, take the smallest index. ;make and will ; in, Indicates the first supply point Second demand point not matched by the second preset radius It remains in the current second set of requirements; The construction has not yet been pre-set to the first radius. Matching third requirement set ; when and When, choose ; in, The index of the supply point selected to match the gain according to the first preset radius; If they are tied, take the smallest index. ;make and will ; like Then according to ; in, The index of the supply point selected according to the minimum redundancy criterion. For supply index; If they are tied, take the smallest index. ,make until ,right ,make The rest of the orders Initialize the iteration count to ,in, For discrete iterative indexes.

[0047] As an example of an embodiment of the present invention, the step of iteratively optimizing the initial configuration scheme based on the objective function and the objective constraints using a discrete iteration strategy until a preset stopping condition is met, thereby obtaining the target configuration scheme, specifically involves: obtaining pseudo-random number pairs; in each iteration of the initial configuration scheme, determining the supply point to be replaced in the previous configuration scheme based on the pseudo-random number pairs, and determining the replacement supply point among the supply points of each first supply point that do not belong to the previous configuration scheme; replacing the supply point to be replaced based on the replacement supply point to obtain the current configuration scheme; determining whether the current configuration scheme meets the objective constraints; if not, re-determining the replacement supply point and the supply point to be replaced; if so, calculating the first function value of the initial configuration scheme according to the objective function, and calculating the second function value of the current configuration scheme according to the objective function; if the second function value is greater than the first function value, retaining the current configuration scheme; if the second function value is less than or equal to the first function value, retaining the configuration scheme of the previous round, until a preset iteration stopping condition is met, thereby obtaining the target configuration scheme.

[0048] Wherein, the first matching coefficient (O) is the average number of redundant coverage times of all first demand points under the first preset radius (penalty item), and the second matching coefficient (L) is the average response rate of all second demand points under the second preset radius after being transformed by the response function (reward item).

[0049] In this embodiment, an initial configuration scheme is recorded, and its corresponding first matching coefficient, second matching coefficient, and objective function value are calculated. A continuous unimproved counter is set and initialized to zero. Simultaneously, the maximum number of iterations and a convergence threshold are determined as stopping conditions. A pseudo-random number generation method is used to construct a random sequence, providing a source of randomness for subsequent replacement operations. In each iteration, a supplier to be replaced is selected from the current configuration scheme based on the random sequence, and a replacement supplier is selected from the remaining candidate suppliers, constructing a candidate solution. The candidate solution is subjected to constraint verification, including supplier number consistency verification, global matching integrity verification, and core matching sufficiency verification. If a candidate solution violates any constraint, a search mechanism is initiated, traversing other possible replacement combinations to find a feasible solution. For candidate solutions that pass the feasibility verification, their first matching coefficient, second matching coefficient, and objective function value are recalculated. When the objective function value of the new solution is better than the current solution, an update is accepted, and the unimproved counter is reset; otherwise, the current solution is maintained, and the unimproved counter is incremented. This mechanism effectively avoids getting trapped in local optima while pursuing objective improvement. The iterative process continues until the preset maximum number of iterations is reached or the number of consecutive unimproved iterations exceeds the convergence threshold. The algorithm then automatically terminates and outputs the historical best solution as the final target configuration scheme.

[0050] Specifically, construct a unified comprehensive objective function. ; Set the first constraint. , , , and the second constraint, , ; The optimal objective function is constructed as follows: ; in, To satisfy all constraints and make Find the set of supply points with the smallest global minimum. make ,Pick ,calculate , , ,in, , , These are the first matching coefficient, the second matching coefficient, and the objective function value of the initial solution, respectively. Set up a continuous unimproved counter ,in, This represents the number of consecutive times no improvement has been made. Given an integer seed Construct linear congruent sequences: in, , , , , The parameters are for linear congruent sequences; For the sequence number Items used for index selection; It is a set of positive integers; Modulo operation; like Then directly order Output the target supply point set; proceed to the next step. Next iteration: In the ascending index list, the length is Selecting the position Take the corresponding index as ;in, For the first The current set of supply points in the next iteration; Based on The list position index refers to the specific position number in the list of the supply point that is to be replaced or removed in the currently selected supply point index list; For supply point index, referring to the The index of the actual supply point located, i.e., the supply point to be removed in the current solution; in the unselected set In the ascending index list, its length is Selecting the position Take the corresponding index as ;in, For relative to The complement of represents the set of unselected supply point indices; For location indexing, the specific location number is calculated from deterministic sequence values ​​within an ordered sequence of the unselected supply point index list; Number the supply point, corresponding to the location. The actual index pointing to the unselected supply point; construct a new set: ;in, For the candidate replacement set; if If any general matching hard constraint or core matching hard constraint is violated, a complete trial strategy is employed, specifically: all commutative pairs are iterated in turn. If no action is found after the entire traversal is completed Then let , , ; If feasible Recalculate: ; ; ; in, , , Candidate sets The first matching coefficient, the second matching coefficient, and the objective function value are given below. like Accept updates to make , , ; Otherwise keep , , and order ; Set the maximum number of iterations threshold to... The shutdown threshold is ,when or If the iteration terminates, proceed to the next iteration; otherwise, proceed to the next iteration.

[0051] As an example of an embodiment of the present invention, the first matching coefficient is determined based on the first redundancy, and the second matching coefficient is determined based on the second redundancy corresponding to the first preset radius. Specifically, for each first demand point, the average value of the first redundancy between the first demand point and all second supply points is calculated to obtain the first matching coefficient; for each first demand point, the second redundancy between the first demand point and all second supply points within the first preset radius is calculated, and the response degree corresponding to each second redundancy is determined according to a preset response function; the response degree is processed according to the weight coefficient of all second supply points within the first preset radius to obtain the second matching coefficient.

[0052] The first redundancy is the redundancy corresponding to each first demand point when calculating the first matching coefficient O, which is the value "max(0, coverage count - 1)". The second redundancy is the redundancy corresponding to each second demand point when calculating the second matching coefficient L, which is the coverage count. .

[0053] In this embodiment, for each first demand point, the total number of successful matches with the second supply point under the current configuration is counted. Based on this total number of matches, the redundancy of a single first demand point is calculated using a redundancy transformation function, specifically employing a truncation method where the number of matches is reduced by one and the result is a non-negative value. Finally, the arithmetic mean of the redundancy of all first demand points is calculated to obtain a first matching coefficient characterizing the overall redundancy level of the system. This coefficient serves as a penalty term in the objective function to suppress over-matching. For the second demand point, the number of times each second demand point is covered by the second supply point within a second preset radius is counted. A nonlinear response function maps the matching counts to a responsiveness within the (0,1) interval. An exponential decay function is used to achieve the diminishing marginal effect of the transition. Finally, the arithmetic mean of all responsivenesses on the set of second demand points is calculated to obtain the second matching coefficient, which characterizes the core responsiveness of the system. This coefficient serves as a reward term in the objective function to improve the guarantee level of key nodes. In the responsiveness calculation, a weighted coefficient based on the importance of the second demand point is introduced, and the responsivenesses of different second demand points are weighted and averaged to ensure that the second demand point receives a higher weight allocation. Simultaneously, normalization is used to eliminate the cardinality effect, making the final matching coefficient comparable and having a clear physical meaning.

[0054] Specifically, for each second demand point, calculate the second redundancy: ; in, For the first The number of times the selected supply point for the second demand point is covered within the second preset radius, when When it is the third demand point The count automatically resets to 0. Get the preset response function: ; in, For the first The responsiveness of the demand point; the second matching coefficient is calculated. .

[0055] As an example of an embodiment of the present invention, the final optimal configuration scheme is subjected to multi-dimensional performance evaluation. The evaluation results include: supply point location results, statistical distance indicators from demand points to the nearest supply points, matching uniformity indicators, supply capacity indicators for the second demand point, and an overall performance index that integrates multiple indicators. Simultaneously, demand points whose service distance exceeds the standard threshold are identified, and a targeted list of improvement suggestions is generated to provide decision support for the implementation and subsequent optimization of the scheme.

[0056] Specifically, output the set of optimal addresses. ; Calculate the nearest supply distance for each demand point: ; in, For the first The distance from the demand point to the nearest selected supplier; Calculate the average minimum response distance: ; The matching uniformity rate and response rate are calculated separately as follows: , ; , ; in, For the first The number of matches between the demand point and all candidate supply points. This is the normalized upper bound for redundancy. To match the uniformity, For a fast response rate; Build a comprehensive performance index: ; in, Used to evaluate collaborative performance A larger value indicates better synergistic performance; Get the maximum response distance: ; like If the response requirements are met, the rectification list will be left empty; if... When that happens, identify all points that do not meet the requirements. As a rectification list; The final output includes a set of supply IDs. Average minimum response distance Matching uniformity Response rate Comprehensive performance index Maximum response distance And a list of rectification measures.

[0057] like Figure 2 As shown, based on the above-mentioned method embodiments, an embodiment of the present invention provides a supply point configuration scheme determination system 200, including: a data processing module 201, a hierarchical allocation module 202, and an iterative optimization module 203; The data processing module is used to obtain a distance matrix between multiple first supply points and multiple first demand points, and based on the distance matrix, to determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix, wherein the first preset radius is greater than the second preset radius, the first demand point includes the multiple second demand points and multiple third demand points, and the second demand point is the demand point among the first demand points whose importance is greater than a preset threshold; The hierarchical allocation module is used to hierarchically allocate each of the first supply points based on the first matching matrix and the second matching matrix, under a preset supply quantity, to obtain an initial configuration scheme including multiple second supply points. The iterative optimization module is used to obtain the objective function and the objective constraints, and to iteratively optimize the initial configuration scheme using a discrete iterative strategy based on the objective function and the objective constraints until a preset stopping condition is met, thereby obtaining the target configuration scheme. The supply points are then configured according to the target configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined based on a first redundancy, and the second matching coefficient is determined based on a second redundancy corresponding to the first preset radius.

[0058] It is understood that the above system item embodiments correspond to the method item embodiments of the present invention, and can implement the supply point configuration scheme determination method provided by any of the above method item embodiments of the present invention.

[0059] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0060] For ease of description and brevity, the system embodiments of the present invention include all the implementation methods described in the above-described supply point configuration scheme determination method embodiments, and will not be repeated here.

[0061] Based on the above embodiments of the supply point configuration scheme determination method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the supply point configuration scheme determination method of any embodiment of the present invention.

[0062] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0063] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0064] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0065] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the supply point configuration scheme determination method described in any of the above-described method embodiments of the present invention.

[0066] Based on the above-described method embodiments, this invention also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of any of the above-described method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0067] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0068] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for determining a supply point configuration scheme, characterized in that, include: Obtain a distance matrix between multiple first supply points and multiple first demand points. Based on the distance matrix, determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix. The first preset radius is greater than the second preset radius. The first demand point includes the multiple second demand points and multiple third demand points. The second demand point is the demand point among the first demand points whose importance is greater than a preset threshold. Based on the first matching matrix and the second matching matrix, each of the first supply points is hierarchically allocated under a preset supply quantity to obtain an initial configuration scheme including multiple second supply points. Obtain the objective function and objective constraints. Based on the objective function and objective constraints, use a discrete iterative strategy to iteratively optimize the initial configuration scheme until a preset stopping condition is met to obtain the objective configuration scheme. Configure the supply points according to the objective configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined according to a first redundancy, and the second matching coefficient is determined according to a second redundancy corresponding to the first preset radius.

2. The method for determining the supply point configuration scheme as described in claim 1, characterized in that, The specific steps for obtaining the distance matrix between multiple first supply points and multiple first demand points are as follows: Obtain the first coordinate data of multiple first supply points and the second coordinate data of multiple first demand points; Based on the first coordinate data and the second coordinate data, the spherical distance between each of the first supply points and each of the first demand points is calculated using the spherical distance formula, and the distance matrix is ​​determined based on all the spherical distances.

3. The method for determining the supply point configuration scheme as described in claim 1, characterized in that, Before the hierarchical allocation of each of the first supply points under the preset supply quantity, the method further includes: Based on the first matching matrix, determine whether any first demand point is not matched with any of the first supply points; Alternatively, based on the second matching matrix, it can be determined whether the second demand point does not match any of the first supply points within the second preset radius; If either of these conditions exists, the first supply point is re-determined; if neither exists, a hierarchical allocation is performed.

4. The method for determining the supply point configuration scheme as described in claim 1, characterized in that, The step of hierarchically allocating each of the first supply points under a preset supply quantity to obtain an initial configuration scheme including multiple second supply points is as follows: Obtain an initial hierarchical set, a second demand set including all second demand points, a third demand set including all third demand points, and a first supply set including all first supply points, wherein the initial hierarchical set is an empty set; In each iteration of the first preset level, if the number of supply points in the initial level set of the previous round is less than the preset supply quantity, then a first target supply point is determined based on the maximum number of matching relationships between the current first supply set and the current second demand set. The current first supply set and the current initial level set are updated according to the first target supply point, and the current second demand set is updated according to the second demand points that have a matching relationship with the first target supply point. The iteration stops when the current second demand set is empty, and the updated first level set is obtained. If the number of supply points in the initial level set of the previous round is greater than or equal to the preset supply quantity, then the level allocation is completed, and the initial configuration scheme is obtained. In each iteration of the second preset level, if the number of supply points in the first level set of the previous round is less than the preset supply quantity, then a second target supply point is determined based on the maximum number of matching relationships between the current first supply set and the current third demand set. The current first supply set and the current first level set are updated according to the second target supply point, and the current third demand set is updated according to the third demand points that have a matching relationship with the second target supply point. The iteration stops when the current third demand set is empty, and the second level set after the first level set is updated is obtained. If the number of supply points in the first level set of the previous round is greater than or equal to the preset supply quantity, then the level allocation is completed, and the initial configuration scheme is obtained. In each iteration of the third preset level, if the number of supply points in the second level set of the previous round is less than the preset supply quantity, the current second level set is updated based on the redundancy of the current first supply set and all first demand points until the number of supply points in the current second level set is greater than or equal to the preset supply quantity, the iteration stops and the level allocation is completed to obtain the initial configuration scheme.

5. The method for determining the supply point configuration scheme as described in claim 1, characterized in that, Based on the objective function and the objective constraints, a discrete iterative strategy is used to iteratively optimize the initial configuration scheme until a preset stopping condition is met, thus obtaining the target configuration scheme. Specifically: Get pseudo-random number pairs; In each iteration of the initial configuration scheme, the supply point to be replaced is determined in the previous configuration scheme based on the pseudo-random number pair, and the replacement supply point is determined among the supply points of each first supply point that do not belong to the previous configuration scheme. The supply point to be replaced is replaced based on the replacement supply point to obtain the current configuration scheme. It is determined whether the current configuration scheme meets the target constraint condition. If it does not meet the constraint condition, the replacement supply point and the supply point to be replaced are re-determined. If it meets the constraint condition, the first function value of the initial configuration scheme is calculated according to the target function, and the second function value of the current configuration scheme is calculated according to the target function. If the second function value is greater than the first function value, the current configuration scheme is retained. If the second function value is less than or equal to the first function value, the configuration scheme of the previous round is retained. This process continues until the preset iteration stop condition is met, at which point the target configuration scheme is obtained.

6. The method for determining the supply point configuration scheme as described in claim 1, characterized in that, The first matching coefficient is determined based on the first redundancy, and the second matching coefficient is determined based on the second redundancy corresponding to the first preset radius, specifically as follows: For each of the first demand points, the average value of the first redundancy between the first demand point and all the second supply points is calculated to obtain the first matching coefficient; For each first demand point, calculate the second redundancy between the first demand point and all second supply points within the first preset radius, and determine the response degree corresponding to each second redundancy according to the preset response function. Process each response degree according to the weight coefficient of all second supply points within the first preset radius to obtain the second matching coefficient.

7. A system for determining supply point configuration schemes, characterized in that, include: Data processing module, hierarchical allocation module, and iterative optimization module; The data processing module is used to obtain a distance matrix between multiple first supply points and multiple first demand points, and based on the distance matrix, to determine the matching relationship between each first supply point and each first demand point within a first preset radius and a second preset radius, respectively, to obtain a first matching matrix and a second matching matrix, wherein the first preset radius is greater than the second preset radius, the first demand point includes the multiple second demand points and multiple third demand points, and the second demand point is the demand point among the first demand points whose importance is greater than a preset threshold; The hierarchical allocation module is used to hierarchically allocate each of the first supply points based on the first matching matrix and the second matching matrix, under a preset supply quantity, to obtain an initial configuration scheme including multiple second supply points. The iterative optimization module is used to obtain the objective function and the objective constraints, and to iteratively optimize the initial configuration scheme using a discrete iterative strategy based on the objective function and the objective constraints until a preset stopping condition is met, thereby obtaining the target configuration scheme. The supply points are then configured according to the target configuration scheme. The objective function includes a first matching coefficient as a penalty component and a second matching coefficient as a reward component. The first matching coefficient is determined based on a first redundancy, and the second matching coefficient is determined based on a second redundancy corresponding to the first preset radius.

8. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the supply point configuration scheme determination method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the supply point configuration scheme determination method as described in any one of claims 1-6.

10. A computer program product, characterized in that, include: Computer instructions, when executed by a processor, implement the steps in the supply point configuration scheme determination method as described in any one of claims 1 to 6.

Images