Business-oriented microgrid planning method, device, equipment and storage medium

Abstract

The application discloses a service-oriented micro-grid planning method, device and equipment and a storage medium. Service distribution information and original fiber distribution point information of a micro-grid are acquired. All service uncovered areas in the micro-grid are determined according to the service distribution information and the original fiber distribution point information. The optimal positions of newly-built fiber distribution points for all service uncovered areas are calculated based on a shortest distance principle and a distance constraint. The shortest distance principle is that the sum of distances from the newly-built fiber distribution point to associated service uncovered areas is the shortest. The distance constraint is that the distance from the newly-built fiber distribution point to each associated service uncovered area is less than a set coverage radius. The optimal positions of newly-built fiber distribution points for all service uncovered areas are calculated based on the shortest distance principle and the distance constraint, so that the micro-grid is reasonably planned, and the micro-grid planning efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a service-oriented micro-grid planning method, device, equipment and storage medium. BACKGROUND

[0002] The micro-grid refers to a smaller grid unit divided in a comprehensive service access area according to service demand, municipal roads and other factors, and an effective connection point between the network and the service demand is established, which plays a crucial role in service access. However, the micro-grid has the problem of mismatch between resources and services, and how to optimize the resource allocation of the micro-grid and carry out efficient construction is the pain point of micro-grid planning.

[0003] The traditional micro-grid planning method requires the designer to open the micro-grid and the layer of the distribution point after receiving the task, to scatter the location of the service distribution on the map, to judge whether a new distribution point needs to be built in the micro-grid based on the understanding and experience of the specification, and when the number of services is large, a lot of time is needed to plan, and the output scheme result is often different for different people, lacking a unified standard, and once the on-site situation occurs, the scheme cannot be flexibly adjusted, affecting the efficiency of planning. SUMMARY

[0004] Therefore, the present application provides a service-oriented micro-grid planning method, device, equipment and storage medium, which can calculate the optimal position of a new distribution point for all service uncovered areas based on the shortest distance principle and distance constraint, realize the reasonable planning of the micro-grid, and improve the micro-grid planning efficiency.

[0005] To achieve the above-mentioned purpose, the embodiment of the present application provides a service-oriented micro-grid planning method, comprising:

[0006] Obtain the service distribution information and the original distribution point information of the micro-grid;

[0007] Determine the service uncovered area in the micro-grid according to the service distribution information and the original distribution point information;

[0008] Calculate the optimal position of a new distribution point for the service uncovered area based on the shortest distance principle and distance constraint; wherein the shortest distance principle is that the sum of the distances from the new distribution point to the service uncovered areas associated with it is the shortest, and the distance constraint is that the distance from the new distribution point to each service uncovered area associated with it is less than the set coverage radius.

[0009] As an improvement of the above scheme, the shortest distance principle is that the sum of the distances from the new distribution point to the mass centers of the service uncovered areas associated with it is the shortest, and the distance constraint is that the distance from the new distribution point to each of the mass centers of the service uncovered areas associated with it is less than the set coverage radius.

[0010] As an improvement of the above scheme, the shortest distance principle and the distance constraint are used to calculate the optimal position of the new distribution point for the service uncovered areas, including:

[0011] In the current round of search, for each pending cluster of the micro-grid, a point with the smallest sum of the distances from the point to the mass centers of each of the service uncovered areas in the cluster is found as a candidate distribution point; when the current round of search is the first round of search, the pending cluster is the micro-grid.

[0012] When the distance from the candidate distribution point to each of the mass centers of the service uncovered areas in the cluster is less than the set coverage radius, the candidate distribution point is set as the new distribution point, and the pending cluster is set as the determined cluster.

[0013] When there is at least one distance from the candidate distribution point to each of the mass centers of the service uncovered areas in the cluster that is greater than or equal to the coverage radius, the cluster where the candidate distribution point is located is divided into new pending clusters, and the candidate distribution point is deleted, and the next round of search is entered until the distance from the candidate distribution point to each of the mass centers of the service uncovered areas in the cluster is less than the set coverage radius.

[0014] As an improvement of the above scheme, in the current round of search, for each pending cluster of the micro-grid, a point with the smallest sum of the distances from the point to the mass centers of each of the service uncovered areas in the cluster is found as a candidate distribution point, including:

[0015] A target function is set:

[0016]

[0017] Wherein, n represents the total number of service uncovered areas in the cluster, x i represents the horizontal coordinate of the mass center of the i-th service uncovered area in the cluster, y i represents the vertical coordinate of the mass center of the i-th service uncovered area in the cluster, x represents the horizontal coordinate of the candidate distribution point in the cluster, and y represents the vertical coordinate of the candidate distribution point in the cluster.

[0018] First-order partial derivatives of x and y are solved respectively, the barycenter of all the service uncovered areas in the cluster is taken as the initial distribution point, and an iterative method is used to solve the value of the first-order partial derivatives being 0 to obtain the candidate distribution point.

[0019] As an improvement of the above scheme, the region division on the cluster where the candidate fiber splitting point is located to obtain a new pending cluster comprises:

[0020] The neighborhood radius is set as a preset radius, the minimum cluster number is set as 1, and the DBSCAN algorithm based on density clustering is used to cluster the service non-coverage area in the cluster where the candidate fiber splitting point is located, to obtain a plurality of new pending clusters.

[0021] As an improvement of the above scheme, the service non-coverage area in the micro-grid is determined according to the service distribution information and the original fiber splitting point information, comprising:

[0022] A to-be-evaluated resource map of the micro-grid is generated according to the service distribution information and the original fiber splitting point information; wherein the pixel RGB value of each pixel point in the to-be-evaluated resource map is associated with the coverage times;

[0023] The to-be-evaluated resource map is subjected to gray scale conversion and binaryzation processing to obtain a binary map;

[0024] The binary map is subjected to an open operation to obtain a denoising image;

[0025] All service non-coverage areas in the denoising image are identified.

[0026] As an improvement of the above scheme, before the optimal position of the new fiber splitting point for the service non-coverage area is calculated based on the shortest distance principle and distance constraint, comprising:

[0027] The number of each color step pixel point in the to-be-evaluated resource map is calculated, and the proportion of each color step pixel is counted;

[0028] The service non-coverage area with a pixel proportion less than a preset minimum threshold is eliminated.

[0029] As an improvement of the above scheme, further comprising: generating a visual GIS map of the micro-grid according to the optimal position of the new fiber splitting point, the original fiber splitting point information and the service distribution information.

[0030] To achieve the above object, the embodiment of the present application further provides a micro-grid planning device for service, comprising:

[0031] An information acquisition module is configured to acquire service distribution information and original fiber splitting point information of a micro-grid;

[0032] A non-coverage area determination module is configured to determine a service non-coverage area in the micro-grid according to the service distribution information and the original fiber splitting point information;

[0033] The fiber distribution point planning module is configured to calculate optimal positions of new fiber distribution points for the service uncovered areas based on a shortest distance principle and a distance constraint. The shortest distance principle is that the sum of distances from the new fiber distribution point to the service uncovered areas associated with the new fiber distribution point is the shortest. The distance constraint is that the distance from the new fiber distribution point to each of the service uncovered areas associated with the new fiber distribution point is less than a set coverage radius.

[0034] To achieve the above object, the embodiment of the present application further provides a service-oriented micro-grid planning device, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the service-oriented micro-grid planning method according to any one of the above embodiments when executing the computer program.

[0035] To achieve the above object, the embodiment of the present application further provides a computer readable storage medium, comprising a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the service-oriented micro-grid planning method according to any one of the above embodiments when the computer program runs.

[0036] Compared with the prior art, the service-oriented micro-grid planning method, device, equipment and storage medium disclosed by the embodiment of the present application firstly acquire service distribution information and original fiber distribution point information of a micro-grid, then determine all service uncovered areas in the micro-grid according to the service distribution information and the original fiber distribution point information, and finally calculate optimal positions of new fiber distribution points for all the service uncovered areas based on a shortest distance principle and a distance constraint. The shortest distance principle is that the sum of distances from the new fiber distribution point to the service uncovered areas associated with the new fiber distribution point is the shortest. The distance constraint is that the distance from the new fiber distribution point to each of the service uncovered areas associated with the new fiber distribution point is less than a set coverage radius. It can be seen that the embodiment of the present application calculates optimal positions of new fiber distribution points for all the service uncovered areas based on the shortest distance principle and the distance constraint, realizes reasonable planning of the micro-grid, and improves the micro-grid planning efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0038] Figure 1 is a flowchart of a service-oriented micro-grid planning method provided by an embodiment of the present application;

[0039] Figure 2 is a micro-grid schematic diagram provided by an embodiment of the present application;

[0040] Figure 3 is a new fiber distribution point solving process schematic diagram provided by an embodiment of the present application;

[0041] Figure 4 is a process schematic diagram of another business-oriented micro-grid planning method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0043] Referring to Figure 1 is a process schematic diagram of a business-oriented micro-grid planning method provided by an embodiment of the present application. Specifically, the business-oriented micro-grid planning method comprises steps S1-S3:

[0044] S1, obtaining business distribution information and original fiber distribution point information of a micro-grid;

[0045] S2, determining a business uncovered area in the micro-grid according to the business distribution information and the original fiber distribution point information;

[0046] S3, calculating an optimal position of a newly-built fiber distribution point for the business uncovered area based on a shortest distance principle and a distance constraint. The shortest distance principle is that the sum of distances from the newly-built fiber distribution point to the business uncovered areas associated with the newly-built fiber distribution point is the shortest. The distance constraint is that the distance from the newly-built fiber distribution point to each of the business uncovered areas associated with the newly-built fiber distribution point is less than a set coverage radius.

[0047] It is worth noting that the micro-grid refers to dividing smaller grid units in a comprehensive business access area according to business demand, municipal roads and other factors, and establishing effective connection points between the network and business demand. Each micro-grid should contain 1-6 secondary fiber distribution points and distribution optical cables. The micro-grid is mainly to achieve the goals of network deep coverage and resource fine management and control, further shorten the business end access distance, improve the business access efficiency and reduce the rate of repeated construction. The fiber distribution point refers to an optical fiber branching point set on the optical cable route to realize fast and convenient access of customer business. The outdoor mainly refers to an optical distribution frame, and the indoor mainly refers to an ODF frame.

[0048] For example, step S1 is a resource importing step, and the imported information includes service distribution information of the micro-grid and original fiber distribution point information: importing basic resource data from the resource management system, including micro-grid name, area, contour, and longitude and latitude of the fiber distribution points in the micro-grid; at the same time, the service distribution in the micro-grid needs to be imported, and the imported service can be a point or a range area; step S2 is an identification of a service non-coverage area: based on the information imported in step S1, all service non-coverage areas in the micro-grid are determined; step S3 is a fiber distribution point planning step: considering the distribution of all service non-coverage areas, a new fiber distribution point is set for the service non-coverage area, so that the sum of distances from the new fiber distribution point to the corresponding service non-coverage areas is minimum, and the distance from the new fiber distribution point to each corresponding service non-coverage area is less than a set coverage radius. In subsequent network planning, the fiber distribution points are arranged according to the coordinates of the new fiber distribution points, and the network performance is optimized.

[0049] It can be understood that the coverage radius and the coverage range of the fiber distribution point are in a positive correlation, and the coverage radius can be set according to actual conditions.

[0050] Compared with the prior art, the embodiment of the application calculates the optimal position of the new fiber distribution point for all service non-coverage areas based on the shortest distance principle and distance constraint, realizes reasonable planning of the micro-grid, and improves the micro-grid planning efficiency.

[0051] In one embodiment, the shortest distance principle is that the sum of distances from the new fiber distribution point to the centroids of the service non-coverage areas associated with the new fiber distribution point is minimum, and the distance constraint is that the distance from the new fiber distribution point to the centroid of each service non-coverage area associated with the new fiber distribution point is less than a set coverage radius.

[0052] For example, one service non-coverage area refers to a connected area in which a service is not covered, and the shape of the area can be regular or irregular. The distance from the centroid of the service non-coverage area to the new fiber distribution point represents the distance from the service non-coverage area to the new fiber distribution point.

[0053] In one embodiment, the optimal position of the new fiber distribution point for the service non-coverage area is calculated based on the shortest distance principle and distance constraint, including:

[0054] In the current round search, for each pending cluster of the micro-grid, a point with the minimum sum of centroid distances of each service non-coverage area in the cluster is found as a candidate fiber distribution point; when the current round search is the first round search, the pending cluster is the micro-grid;

[0055] When the centroid distance from the candidate fiber distribution point to each service non-coverage area in the cluster is less than a set coverage radius, the candidate fiber distribution point is set as a new fiber distribution point, and the pending cluster is set as a determined cluster.

[0056] When there is at least one distance greater than or equal to the coverage radius from the candidate fiber splitting point to the centroid of each of the service uncovered areas in the cluster, the cluster where the candidate fiber splitting point is located is divided into regions to obtain new pending clusters, and the candidate fiber splitting point is deleted, and the next round of search is entered, until the distance from the candidate fiber splitting point to the centroid of each of the service uncovered areas in the cluster is less than the set coverage radius.

[0057] Specifically, the calculation of the optimal position of the newly built fiber splitting point is the core algorithm part of the entire model. In order to obtain the optimal deployment position of the fiber splitting point, two problems need to be solved: (1) a point whose sum of distances to the centroids of each service uncovered area is the shortest needs to be found, and (2) the distance from this point to the centroid of any service uncovered area in the micro-grid needs to be less than the set coverage radius. For example, in the first round of search, the entire micro-grid is taken as the search range, a point whose sum of distances to the centroids of each service uncovered area in the micro-grid is the smallest is found as the candidate fiber splitting point, then the distance from the centroid of each service uncovered area to the candidate fiber splitting point is calculated, and when there is a distance greater than or equal to the set coverage radius, it indicates that there is a service uncovered area that cannot be covered by the newly built fiber splitting point. Therefore, the service uncovered areas in the micro-grid are clustered to obtain a plurality of clusters, and the second round of search is entered. In the second round of search, each cluster is searched respectively, and the like. After a plurality of rounds of search, the optimal position of the newly built fiber splitting point is obtained.

[0058] In an embodiment, in the current round of search, for each pending cluster of the micro-grid, a point whose sum of distances to the centroids of each service uncovered area in the cluster is the smallest is found as the candidate fiber splitting point, comprising:

[0059] A target function is set:

[0060]

[0061] wherein n represents the total number of service uncovered areas in the cluster, x i represents the horizontal coordinate of the centroid of the i-th service uncovered area in the cluster, y i represents the vertical coordinate of the centroid of the i-th service uncovered area in the cluster, x represents the horizontal coordinate of the candidate fiber splitting point in the cluster, and y represents the vertical coordinate of the candidate fiber splitting point in the cluster.

[0062] First-order partial derivatives of x and y are solved respectively, the centroid of all the service uncovered areas in the cluster is taken as the initial fiber splitting point, and an iterative method is used to solve the value where the first-order partial derivatives are both 0, to obtain the candidate fiber splitting point.

[0063] In an embodiment, the division of the cluster where the candidate fiber splitting point is located into regions to obtain new pending clusters comprises:

[0064] The neighborhood radius is set as a preset radius, the minimum cluster number is set as 1, and the DBSCAN algorithm based on density clustering is used to cluster the service uncovered areas in the cluster where the candidate fiber splitting point is located, to obtain a plurality of new pending clusters.

[0065] Specifically, step S3 needs to solve two problems: (1) a point needs to be found whose sum of distances to the centroids of each service uncovered area is the shortest; and (2) the distance from the point to any service uncovered area in the micro-grid is less than the set coverage radius.

[0066] For the first problem, it is actually a Fermat problem of n points. When n = 3, it is a traditional triangle Fermat point problem, and the position of the point with the shortest sum of distances to the three vertices of the triangle can be obtained by plane geometry. Here, a fiber splitting point needs to be found whose sum of distances to the service uncovered areas is the shortest. This problem can be transformed into finding a point Q(x, y) in a plane with n (n ≥ 3) points P1(x1, y1), P2(x2, y2), …, Pn(xn, y

[0067] n m n The objective function is as follows:

[0068]

[0069] In order to find the minimum value of the objective function, the partial derivatives of x and y are needed.

[0070]

[0071]

[0072] When the two first-order partial derivatives are both 0, it is a stationary point of the function. In order to determine whether the point whose first-order partial derivative is 0 is a minimum value point, the second-order partial derivative of the above two partial derivatives is needed, and the result is as follows:

[0073]

[0074]

[0075] Through the second-order partial derivative, it can be found that it is always greater than 0, that is, the original function is a convex function, and the point whose first-order partial derivative is 0 is the minimum value point. In order to solve the value when the first-order partial derivative is 0, an iterative method is used here, and the first-order partial derivatives of x and y can be expressed as:

[0076] ​​​

[0077]

[0078] Using the iterative method requires setting the initial value of the calculation, here the center of gravity of the n points is selected as the initial value to start iteration, since the second derivative is greater than 0 constant, so the first derivative is monotonic, so the above equation has only one root. Through Python, the above function is programmed, and after several iterations, the Fermat point of the sum of the distances of the n business uncovered areas can be calculated.

[0079] Here the first problem has been solved, but the position of the calculated fiber point may not meet the requirements of the second problem, that is, the distance to each uncovered area is less than the set coverage radius. In order to solve this problem, the uncovered areas need to be classified, and then the number of fiber point construction is increased to meet the business coverage requirements.

[0080] Referring to Figure 2 The actual business distribution in the micro-grid can be found that the business that needs to be covered often appears in patches, such as contiguous residential areas, professional markets, and business districts. Therefore, the idea of clustering can be used to divide business points with similar distances into a cluster, and then the business coverage is performed. Combined with the actual use of the scene, the DBSCAN algorithm based on density clustering is selected here. The clustering process of the DBSCAN algorithm is to derive the maximum density connected sample set according to the core weak coverage point, and the density connected points are clustered according to Minpts (minimum cluster number) and Eps (neighborhood radius), and the iteration is stopped until all core sample points have corresponding categories. For the business coverage scene in the micro-grid, when the coverage radius of the fiber point is 200 meters, the Euclidean distance Eps between the weak coverage points can be set to 0.004, and the minimum cluster number Minpts is set to 1. After clustering by the DBSCAN algorithm, the Fermat point of each cluster can be recalculated.

[0081] The first round of coverage distance of the Fermat point is too long, although it is improved after clustering using the DBSCAN algorithm, there may still be a problem that the Fermat point after clustering is still too long, so secondary, tertiary or multiple clustering is required to solve the problem here. Because the number of clustering cannot be determined, simple logical judgment statements cannot be used here. Therefore, the recursive programming idea is selected here, which can adjust the number of times of using the DBSCAN algorithm according to the actual situation, and the termination condition of recursion is set to only one business uncovered area. In the process of recursive solution, the optimal fiber point obtained after clustering is added to the construction list until all uncovered areas can be covered by the fiber point, and the recursive solution process is ended; for details, see Figure 3The new building fiber distribution point solving process diagram shown.

[0082] In an embodiment, the determining the service uncovered area in the micro-grid according to the service distribution information and the original fiber distribution point information comprises:

[0083] generating an evaluation resource map of the micro-grid according to the service distribution information and the original fiber distribution point information; wherein the pixel RGB value of each pixel point in the evaluation resource map is associated with the coverage times;

[0084] performing gray scale conversion and binaryzation processing on the evaluation resource map to obtain a binary image;

[0085] performing open operation on the binary image to obtain a denoising image;

[0086] identifying all service uncovered areas in the denoising image.

[0087] For example, the specific implementation process of step S2 is as follows: according to the imported basic resources and service demand, using matplotlib library and combining with multi-process technology to generate an evaluation resource map; performing gray scale conversion on the service uncovered area to convert the picture into a gray scale image with equal RGB color components, performing binaryzation on the image according to the value of different regions of the gray scale image, performing "denoising" processing on the binary image through open operation (first erosion, then expansion) of the image; using openCV findContours to identify all service uncovered areas of the denoising image, and using the moments method in openCV to obtain the centroid coordinates of each region.

[0088] In an embodiment, before the calculating the optimal position of the new building fiber distribution point for the service uncovered area based on the shortest distance principle and distance constraint, comprising:

[0089] calculating the number of each color step pixel point in the evaluation resource map, and counting the proportion of each color step pixel;

[0090] eliminating the service uncovered area with a pixel proportion less than a preset minimum threshold.

[0091] For example, for the resource map to be evaluated, the proportion of each pixel is counted: due to the irregularity of the coverage area of each micro-grid, in order to calculate the proportion of covered and uncovered areas, the Monte Carlo method is used to solve the problem by force. First, a mapping table of the relationship between the pixel RGB value and the number of coverages is established, and then the hist method provided in openCV is used to count the number of different color step pixel points in each micro-grid. Since the pixel mapping process of the micro-grid is biased towards the computing performance of the computer, multi-threading technology is used to count the pixel proportion. It is worth noting that using the pixel proportion to screen the final business uncovered area, for the business uncovered area with small pixel proportion, considering that the resource investment cannot bring the expected benefit, therefore, the area is excluded from the business uncovered area, which can realize the effective utilization of resources.

[0092] In an implementation, further comprising: generating a visual GIS map of the micro-grid according to the optimal position of the new distribution point, the original distribution point information and the business distribution information.

[0093] Specifically, outputting the list and generating a GIS map: importing the data of the micro-grid, the distribution point (current situation), the new distribution point and the business demand using Folium, generating a visual GIS map, and enabling network planners to intuitively see the planning effect.

[0094] Specifically, the specific process of the micro-grid planning method for business can also be referred to Figure 4 .

[0095] Compared with the prior art, the embodiment of the application establishes a rigorous business resource evaluation model, and finely evaluates the micro-grid combined with business demand. Through DBSCAN, Fermat point, recursive analysis and other means, an accurate distribution point construction list and a visual GIS map are output, the problems of pseudo coverage, non-quantitative evaluation and disconnection from business in the traditional micro-grid are solved, the demand for planning and construction and rapid business opening is met, the contradiction between business access coverage and micro-grid resource matching can be well solved, the optical access planning in the micro-grid is more scientific and accurate, the problem of different optical access point selection standards for network designers is solved, and the quality and efficiency of network planning are greatly improved, the applicability is wide, the business income is promoted to a certain extent, and the repeated and invalid network construction investment is reduced.

[0096] The embodiment of the application also provides a micro-grid planning device for business, comprising:

[0097] An information acquisition module is configured to acquire business distribution information and original distribution point information of a micro-grid.

[0098] an uncovered area determining module configured to determine a service uncovered area in the micro-grid according to the service distribution information and the original fiber distribution point information;

[0099] a fiber distribution point planning module configured to calculate an optimal location of a new fiber distribution point for the service uncovered area based on a shortest distance principle and a distance constraint, wherein the shortest distance principle is that a sum of distances from the new fiber distribution point to service uncovered areas associated with the new fiber distribution point is the shortest, and the distance constraint is that a distance from the new fiber distribution point to each of the service uncovered areas associated with the new fiber distribution point is less than a set coverage radius.

[0100] In an embodiment, the shortest distance principle is that a sum of distances from the new fiber distribution point to centers of mass of the service uncovered areas associated with the new fiber distribution point is the shortest, and the distance constraint is that a distance from the new fiber distribution point to each of the centers of mass of the service uncovered areas associated with the new fiber distribution point is less than the set coverage radius.

[0101] In an embodiment, the fiber distribution point planning module is specifically configured to:

[0102] In a current round of searching, for each pending cluster of the micro-grid, a point with a minimum sum of distances to centers of mass of each of the service uncovered areas in the cluster is found as a candidate fiber distribution point, wherein the pending cluster is the micro-grid when the current round of searching is a first round of searching.

[0103] When the distance from the candidate fiber distribution point to each of the centers of mass of the service uncovered areas in the cluster is less than the set coverage radius, the candidate fiber distribution point is set as the new fiber distribution point, and the pending cluster is set as a determined cluster.

[0104] When the distance from the candidate fiber distribution point to each of the centers of mass of the service uncovered areas in the cluster has at least one distance greater than or equal to the coverage radius, the cluster where the candidate fiber distribution point is located is divided into new pending clusters, and the candidate fiber distribution point is deleted, and a next round of searching is entered until the distance from the candidate fiber distribution point to each of the centers of mass of the service uncovered areas in the cluster is less than the set coverage radius.

[0105] In an embodiment, in a current round of searching, for each pending cluster of the micro-grid, a point with a minimum sum of distances to centers of mass of each of the service uncovered areas in the cluster is found as a candidate fiber distribution point, including:

[0106] a target function is set as:

[0107]

[0108] wherein n represents a total number of service uncovered areas in the cluster, x i represents an abscissa of a center of mass of an i-th service uncovered area in the cluster, y ia center of mass longitudinal coordinate of an i-th service uncovered area in a cluster, x represents a candidate fiber distribution point horizontal coordinate in the cluster, and y represents a candidate fiber distribution point longitudinal coordinate in the cluster;

[0109] first-order partial derivatives are calculated for x and y respectively, gravity centers of all the service uncovered areas in the cluster are taken as initial fiber distribution points, and an iterative method is used to solve values of the first-order partial derivatives being 0 to obtain candidate fiber distribution points.

[0110] In an embodiment, the region division on the cluster where the candidate fiber distribution points are located to obtain new pending clusters comprises:

[0111] A neighborhood radius is set as a preset radius, a minimum cluster number is set as 1, and a DBSCAN algorithm based on density clustering is used to cluster service uncovered areas in the cluster where the candidate fiber distribution points are located to obtain a plurality of new pending clusters.

[0112] In an embodiment, the determination of the service uncovered areas in the micro-grid according to the service distribution information and the original fiber distribution point information comprises:

[0113] A to-be-evaluated resource map of the micro-grid is generated according to the service distribution information and the original fiber distribution point information; wherein a pixel RGB value of each pixel point in the to-be-evaluated resource map is associated with a coverage frequency.

[0114] The to-be-evaluated resource map is subjected to grayscale conversion and binarization processing to obtain a binary image.

[0115] The binary image is subjected to an opening operation to obtain a denoising image.

[0116] All service uncovered areas in the denoising image are identified.

[0117] In an embodiment, the uncovered area determination module is further configured to:

[0118] The number of each color step pixel point in the to-be-evaluated resource map is calculated, and a proportion of each color step pixel is counted.

[0119] Service uncovered areas with a pixel proportion less than a preset minimum threshold value are removed.

[0120] In an embodiment, a display module is further included, configured to generate a visual GIS map of the micro-grid according to the optimal position of the newly-built fiber distribution point, the original fiber distribution point information and the service distribution information.

[0121] It is worth noting that the working process of the specific business-oriented micro-grid planning device can refer to the working process of the business-oriented micro-grid planning method described in the above embodiments, which will not be described here again.

[0122] Compared with the prior art, the service-oriented micro-grid planning device disclosed by the embodiment of the application realizes reasonable planning of the micro-grid and improves the micro-grid planning efficiency by calculating the optimal position of a newly-built distribution point for all service-uncovered areas based on the shortest distance principle and distance constraints.

[0123] The embodiment of the application further provides a service-oriented micro-grid planning device, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the steps in the above-mentioned service-oriented micro-grid planning method embodiments, such as steps S1-S3 in the above-mentioned service-oriented micro-grid planning method embodiment, when executing the computer program. Figure 1

[0124] For example, 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 application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the service-oriented micro-grid planning device. For example, the computer program can be divided into multiple modules, and the specific functions of the modules are as follows:

[0125] An information acquisition module is configured to acquire service distribution information and original distribution point information of a micro-grid;

[0126] An uncovered area determination module is configured to determine service-uncovered areas in the micro-grid according to the service distribution information and the original distribution point information;

[0127] A distribution point planning module is configured to calculate optimal positions of newly-built distribution points for the service-uncovered areas based on the shortest distance principle and distance constraints; wherein the shortest distance principle is that the sum of distances from the newly-built distribution points to the service-uncovered areas associated with the newly-built distribution points is the shortest, and the distance constraint is that the distance from the newly-built distribution point to each service-uncovered area associated with the newly-built distribution point is less than a set coverage radius.

[0128] The specific working processes of the modules can refer to the working processes of the service-oriented micro-grid planning device described in the above-mentioned embodiments, which will not be described here again.

[0129] The service-oriented micro-grid planning device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices. The service-oriented micro-grid planning device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the service-oriented micro-grid planning device can also include an input / output device, a network access device, a bus, and the like. ​

[0130] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the business-oriented micro-grid planning device, and connects various parts of the business-oriented micro-grid planning device through various interfaces and lines.

[0131] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the business-oriented micro-grid planning device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0132] The modules of the service-oriented micro-grid planning device integration can be stored in a computer-readable storage medium if they are implemented in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0133] The above is the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application. These improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A business-oriented microgrid planning method, characterized in that, include: Obtain the service distribution information and original fiber distribution point information of the microgrid; The service-uncovered areas in the microgrid are determined based on the service distribution information and the original fiber distribution point information; Based on the principle of shortest distance and distance constraints, the optimal location of a new fiber distribution point is calculated for the service-uncovered area; wherein, the principle of shortest distance is that the sum of the distances from the new fiber distribution point to its associated service-uncovered area is the shortest, and the distance constraint is that the distance from the new fiber distribution point to each associated service-uncovered area is less than the set coverage radius.

2. The business-oriented microgrid planning method as described in claim 1, characterized in that, The shortest distance principle is that the sum of the centroid distances from the newly established fiber distribution point to the uncovered service area associated with it is the shortest, and the distance constraint is that the centroid distance from the newly established fiber distribution point to each uncovered service area associated with it is less than the set coverage radius.

3. The business-oriented microgrid planning method as described in claim 2, characterized in that, The calculation of the optimal location for new fiber distribution points in the service-uncovered area, based on the principle of shortest distance and distance constraints, includes: In the current round of search, for each undetermined cluster of the microgrid, the point with the smallest sum of centroid distances for each of the service-uncovered areas within the cluster is found as a candidate fiber splitting point; wherein, when the current round of search is the first round of search, the undetermined cluster is the microgrid; When the centroid distance from the candidate fiber splitter to each of the service-uncovered areas within the cluster is less than the set coverage radius, the candidate fiber splitter is set as a new fiber splitter, and the undetermined cluster is set as a predetermined cluster. When the centroid distance from the candidate fiber splitter to each of the service-uncovered areas within the cluster is at least greater than or equal to the coverage radius, the cluster containing the candidate fiber splitter is divided into regions to obtain a new undetermined cluster, and the candidate fiber splitter is deleted. The next round of search continues until the centroid distance from the candidate fiber splitter to each of the service-uncovered areas within the cluster is less than the set coverage radius.

4. The business-oriented microgrid planning method as described in claim 3, characterized in that, In the current round of search, for each undetermined cluster of the microgrid, the point with the smallest sum of centroid distances for each of the service-uncovered areas within the cluster is found as a candidate fiber splitter, including: Set the target function: Where n represents the total number of areas within the cluster that are not covered by services, x i y represents the x-coordinate of the centroid of the i-th uncovered service area within the cluster. i x represents the centroid ordinate of the i-th uncovered service area within the cluster, x represents the x-coordinate of the candidate fiber splitter within the cluster, and y represents the ordinate of the candidate fiber splitter within the cluster. Calculate the first-order partial derivatives with respect to x and y respectively, take the centroid of all the service-uncovered areas in the cluster as the initial fiber splitting point, and use the iterative method to solve for the value of the first-order partial derivatives being 0, to obtain the candidate fiber splitting point.

5. The business-oriented microgrid planning method as described in claim 3, characterized in that, The step of dividing the cluster containing the candidate fiber splitting point into regions to obtain new undetermined clusters includes: The neighborhood radius is set to a preset radius, and the minimum number of clusters is set to 1. The DBSCAN algorithm based on density clustering clusters the service-uncovered areas in the clusters where the candidate fiber splitting points are located, and obtains several new undetermined clusters.

6. The business-oriented microgrid planning method as described in claim 2, characterized in that, The step of determining the service-uncovered area in the microgrid based on the service distribution information and the original fiber distribution point information includes: The microgrid resource map to be evaluated is generated based on the service distribution information and the original fiber distribution point information; wherein, the pixel RGB value of each pixel in the resource map to be evaluated is associated with the number of coverages; The resource map to be evaluated is subjected to grayscale conversion and binarization to obtain a binary map; Perform an opening operation on the binary image to obtain a denoised image; Identify all service-uncovered areas in the denoised image.

7. The business-oriented microgrid planning method as described in claim 6, characterized in that, Before calculating the optimal location for new fiber distribution points in the service-uncovered area based on the shortest distance principle and distance constraints, the following steps are included: Calculate the number of pixels at each color level in the resource image to be evaluated, and count the percentage of pixels at each color level; Remove service-uncovered areas where the pixel percentage is less than the preset minimum threshold.

8. The business-oriented microgrid planning method as described in any one of claims 1 to 6, characterized in that, Also includes: A visualized GIS map of the microgrid is generated based on the optimal location of the newly established fiber distribution point, the information of the existing fiber distribution points, and the business distribution information.

9. A business-oriented microgrid planning device, characterized in that, include: The information acquisition module is used to acquire the service distribution information and original fiber distribution point information of the microgrid; The uncovered area determination module is used to determine the uncovered service areas in the microgrid based on the service distribution information and the original fiber distribution point information; The fiber distribution point planning module is used to calculate the optimal location of a new fiber distribution point for the service-uncovered area based on the shortest distance principle and distance constraints. The shortest distance principle means that the sum of the distances from the new fiber distribution point to its associated service-uncovered area is the shortest, and the distance constraints mean that the distance from the new fiber distribution point to each associated service-uncovered area is less than the set coverage radius.

10. A business-oriented microgrid planning device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the business-oriented microgrid planning method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium resides to perform the business-oriented microgrid planning method as described in any one of claims 1 to 8.

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