Concentrator site selection method and device based on wireless communication scheme, and storage medium
Through the adaptive density feedback clustering algorithm and Voronoi diagram optimization, the concentrator location is automatically selected, which solves the problems of low efficiency, insufficient rationality and poor adaptability in traditional methods and achieves high efficiency and accuracy in concentrator site selection.
Patent Information
- Application Number
- CN202511133882.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-14
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional concentrator site selection methods are inefficient, lack rationality, and have poor adaptability. They cannot adapt to large-scale meter distribution and complex geographical environments, resulting in poor communication performance.
An adaptive density feedback clustering algorithm based on wireless communication scheme is adopted, combined with K-Means++ clustering and density-aware Voronoi diagram optimization. The location of the concentrator is automatically selected through distance-weighted initial cluster center selection, dynamic adjustment of coverage range by density factor and feedback iterative optimization.
It improves the efficiency and accuracy of concentrator site selection, solves the problems of low efficiency, insufficient rationality and poor adaptability of traditional manual site selection, and realizes concentrator load balancing and communication distance optimization.
Smart Images
Figure CN120639641A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of concentrator site selection based on computer data processing, and in particular relates to a concentrator site selection method, system, device and storage medium based on a wireless communication solution. Background Art
[0002] In the electricity meter communication industry, the widespread adoption of smart meters and the development of IoT technology have made the efficient collection and transmission of meter data a critical step. As the core node for data transmission, the location of the concentrator directly impacts communication efficiency and system performance, requiring comprehensive consideration of multiple factors, including the number of mounted meters, communication distance, and geographic environment. Traditional concentrator site selection relies on manual experience, with meter locations determined by hand. This approach suffers from significant drawbacks: First, inefficiency: With the surge in the number of meters and increasing complexity, manual operations are unable to cope with large-scale data processing; second, site selection is insufficiently rational: human factors can easily lead to problems such as unbalanced concentrator load and excessive communication distances, impacting data transmission quality; and third, poor adaptability: the system cannot flexibly adapt to diverse geographic environments (e.g., dense urban areas versus remote mountainous areas) and meter distribution patterns (e.g., clustered versus dispersed). Therefore, an automated, intelligent concentrator site selection system is urgently needed to address the limitations of traditional methods and promote the digital upgrade of the electricity meter communication industry.
[0003] In the existing electricity meter communication field, while GIS systems have been applied to concentrator site selection, leveraging their geographic information display capabilities to assist industry professionals in decision-making, their powerful capabilities in spatial analysis and intelligent computing have yet to be fully utilized. Consequently, in actual concentrator site selection, industry professionals often rely on their own experience to manually delineate meter coverage areas and determine the specific locations of concentrators using GIS systems. However, this approach has significant drawbacks. Firstly, with the rapid increase in the number of meters and their expanded distribution, manual area delineation and location determination are extremely inefficient, making them difficult to adapt to large-scale and complex application scenarios. Secondly, relying solely on empirical judgment makes it difficult to accurately control the number of meters mounted on a concentrator, potentially leading to overloading of some concentrators while underloading others. Furthermore, it is impossible to ensure that the communication distance between all meters and the concentrator meets the required limits, resulting in poor communication performance and suboptimal system operation. Summary of the Invention
[0004] In response to the above problems, the first aspect of the present invention proposes a concentrator site selection method based on a wireless communication solution, comprising the following steps: S1, obtain the geographic location information of all electricity meters in the target area, extract the longitude and latitude coordinates, and select the initial concentrator location set based on the K-Means++ clustering algorithm; S2: Construct a Voronoi diagram using the initial concentrator location as the generating point, dividing the target area into multiple sub-areas. Dynamically adjust the coverage range based on the density factor, and the threshold of the density factor is adaptively adjusted according to the actual scenario. Each meter is assigned to the Voronoi diagram sub-area corresponding to the nearest concentrator. S3, after the Voronoi diagram is divided, the capacity and distance limits are checked, and the areas exceeding the capacity or distance are marked as "to be adjusted", resulting in several "to be adjusted" areas; S4, based on the number and coordinates of the electric meters in the "to be adjusted" area, calculate the distribution center of gravity, and use the center of gravity coordinates as the location coordinates of the concentrator in the "to be adjusted" area; S5, iterate S2 to S4 until the proportion of the "to be adjusted" area is lower than the threshold or the maximum number of iterations is reached.
[0005] Preferably, after the S5 iteration is completed, if there is an electricity meter that is not included in the effective coverage area of any concentrator, the electricity meter is determined to be an isolated point. By calculating the proximity between the isolated point and the surrounding concentrators, it is preferentially assigned to the concentrator with the highest proximity; if it still cannot be assigned, it is marked as "need to configure a separate concentrator".
[0006] Preferably, the specific process of S1 is: S11, collect the geographical location information of all electricity meters in the target area and extract the longitude and latitude coordinates; suppose there are N electricity meters in the target area, and the longitude and latitude data of each meter are represented as a two-dimensional vector ,in, For the i The longitude value of the meter, in degrees, For the i The latitude value of the meter, in degrees; dataset ; S22, normalize the longitude and latitude data, and normalize the longitude and latitude values. The processed data is still recorded as ; S23, according to the meter distribution density and communication coverage requirements of the target area, the number of initial concentrators is preset to k, where k ≥ 1 and is a positive integer; S34, select the first initial concentrator, randomly select the longitude and latitude coordinates of an electric meter from the data set X as the location of the first initial concentrator, denoted as ; S35, select a subsequent concentrator; for each meter in the data set X that has not been selected as the initial concentrator , calculate the distance between it and the nearest initial concentrator that has been selected , the distance calculation formula uses Euclidean distance:
[0007] Where m is the number of initial concentrators selected , is the longitude and latitude coordinates of the jth initial concentrator; the distance The square of is used as the probability weight to construct the probability distribution P of selecting the next initial concentrator, that is, i The probability that a meter is selected as the next initial concentrator is, according to the probability distribution P, the longitude and latitude coordinates of a meter are randomly selected as the m+1th initial concentrator ; Repeat S35 until k initial concentrators are selected, and finally the initial concentrator position set is obtained. .
[0008] Preferably, the specific process of S2 is: S21, define the boundary conditions of the Voronoi region: For the initial concentrator location set , No. j concentrator , the corresponding Voronoi region The boundary is determined by the distance condition: any meter in the area arrive The distance is less than that to all other concentrators distance; S22, determine the region boundary equation: Any two adjacent concentrators and The corresponding Voronoi region boundary is the perpendicular bisector of the line segment, let , , then any point (x, y) on the boundary satisfies the boundary equation ; Determine the complete boundary of each Voronoi region by solving the boundary equations of all adjacent concentrators; S23, divide the Voronoi region: Traverse all meter data X, and assign each meter to the Voronoi region corresponding to the nearest concentrator according to the distance condition, and finally obtain k non-overlapping sub-regions that cover all meters. ; S24: Calculate the electricity meter density in each area: Defining regional density Voronoi region The ratio of the number of electric meters within the area to the area of the area; S25: Set density threshold and dynamically adjust coverage; Preset high density threshold and low density threshold ( > ), for high-density areas > , when dividing the Voronoi diagram, the coverage of a single concentrator is reduced to avoid concentrator overload; for low-density areas ( < ), expand coverage and reduce the number of concentrators; S26, the capacity and distance of each area are verified to ensure the stability of the concentrator operation and the communication quality, and the areas with excess capacity or excess distance are marked as to be adjusted, thereby obtaining a set of areas "to be adjusted".
[0009] Preferably, in S25, the specific formula is as follows: For high-density areas , the adjusted maximum communication distance threshold:
[0010] For low-density areas , the adjusted maximum communication distance threshold:
[0011] Normal density area , keep the default maximum communication distance threshold:
[0012] The core variables are defined as follows: D : The adjusted maximum communication distance of the concentrator; : Default basic maximum communication distance; : Actual node density in the area; : high density threshold; : low density threshold; : High-density area adjustment coefficient; : low-density area adjustment coefficient; : Minimum communication distance lower limit; : Maximum communication distance limit.
[0013] Preferably, the specific process of S4 is: For the set of areas to be adjusted … Any area in , the set of electric meters contained in this area is … , where n is the area The total number of meters in the area, the coordinates of the kth meter are ; area Internal meter distribution center The calculation formula is as follows:
[0014] in, The coordinates are expressed as , using the coordinates as the region The corresponding location of the new concentrator.
[0015] Preferably, the specific processing process of isolated points is: Assume that the set of isolated points … , where the coordinates of the pth isolated point are ,p is the total number of isolated points; for the isolated point set , combined with all current concentrator locations ,The isolated points are assigned based on the following steps: Calculate isolated points With each concentrator The proximity is calculated as follows:
[0016] The proximity calculation formula takes into account both distance and the remaining capacity of the concentrator, where:
[0017] Isolated point With each concentrator The Euclidean distance of is the weight coefficient, ; For each isolated point , confirm the concentrator corresponding to its maximum proximity , if the concentrator The remaining capacity satisfies:
[0018] Then Assigned to the concentrator, if the isolated point If the concentrator with the highest proximity has no remaining capacity, it traverses other concentrators and selects the concentrator with the second highest proximity and remaining capacity for allocation. If all related concentrators have no remaining capacity, the isolated point is marked as requiring separate concentrator configuration.
[0019] A second aspect of the present invention provides a concentrator site selection device based on a wireless communication scheme, the device comprising at least one processor and at least one memory, the processor and the memory being coupled; a computer execution program being stored in the memory; when the processor executes the computer execution program stored in the memory, the processor executes a concentrator site selection method based on a wireless communication scheme as described in the first aspect.
[0020] A third aspect of the present invention provides a computer-readable storage medium, in which a computer program or instruction is stored. When the program or instruction is executed by a processor, the processor executes a concentrator site selection method based on a wireless communication scheme as described in the first aspect.
[0021] Compared with the prior art, the present invention has the following beneficial effects: The present invention adopts an adaptive density feedback clustering hybrid algorithm that integrates the iterative mechanism of K-Means++ initial clustering and density-aware Voronoi diagram optimization. Through the process of "distance-weighted initial cluster center selection → dynamic adjustment of coverage range by density factor → feedback-based local iterative optimization", it solves the problems of local optimal traps and insufficient adaptability of traditional algorithms in non-uniform distribution scenarios. It is an original algorithm design.
[0022] The fully automatic algorithm provided by the present invention improves the efficiency and accuracy of concentrator site selection, and comprehensively solves the problems of low efficiency, insufficient rationality and poor adaptability of traditional manual site selection. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 This is a flow chart of the overall site selection method of the present invention.
[0024] Figure 2 This is a flow chart of the system operation in Example 2 of the present invention.
[0025] Figure 3 This is a diagram of the interface for setting the parameters of the site selection algorithm in Example 2 of the present invention.
[0026] Figure 4 This is a schematic diagram of the concentrator site selection results in Example 2 of the present invention.
[0027] Figure 5 This is a list of electricity meters covered by the concentrator DCU_0010 in Example 2 of the present invention. DETAILED DESCRIPTION
[0028] The present invention proposes a concentrator site selection method based on a wireless communication solution, comprising the following steps: S1, obtain the geographic location information of all electricity meters in the target area, extract the longitude and latitude coordinates, and select the initial concentrator location set based on the K-Means++ clustering algorithm; S2: Construct a Voronoi diagram using the initial concentrator location as the generating point, dividing the target area into multiple sub-areas. Dynamically adjust the coverage range based on the density factor, and the threshold of the density factor is adaptively adjusted according to the actual scenario. Each meter is assigned to the Voronoi diagram sub-area corresponding to the nearest concentrator. S3, after the Voronoi diagram is divided, the capacity and distance limits are checked, and the areas exceeding the capacity or distance are marked as "to be adjusted", resulting in several "to be adjusted" areas; S4, based on the number and coordinates of the electric meters in the "to be adjusted" area, calculate the distribution center of gravity, and use the center of gravity coordinates as the location coordinates of the concentrator in the "to be adjusted" area; S5, iterate S2 to S4 until the proportion of the "to be adjusted" area is lower than the threshold or the maximum number of iterations is reached.
[0029] The specific implementation process of the present invention is further described below in conjunction with specific embodiments.
[0030] Example 1: 1. K-Means++ clustering to select the initial concentrator position The specific process is as follows: S11: Collect the geographic location information of all electricity meters in the target area and extract the longitude and latitude coordinates. Assume that there are N electricity meters in the target area, and the longitude and latitude data of each meter are represented as a two-dimensional vector , in, For the Longitude value of the meter only (unit: degree), For the Latitude value of the meter only (unit: degrees), dataset ; S12: Standardize the longitude and latitude data to normalize the longitude and latitude values to avoid the influence of unit differences on clustering effect. The processed data is still recorded as .
[0031] S13: Based on the requirements of the target area such as the meter distribution density and the communication coverage, the number of initial concentrators is preset to k (k ≥ 1, which is a positive integer).
[0032] S14: Select the first initial concentrator; randomly select the longitude and latitude coordinates of an electric meter from the data set X as the location of the first initial concentrator, denoted as ; S15: Select subsequent concentrators; for each meter in the data set X that has not been selected as the initial concentrator , calculate the distance between it and the nearest initial concentrator that has been selected , the distance calculation formula uses Euclidean distance:
[0033] Where m is the number of initial concentrators selected , For the The latitude and longitude coordinates of the initial concentrator. The square of is used as the probability weight to construct the probability distribution P of selecting the next initial concentrator, that is, The probability that a meter is selected as the next initial concentrator is:
[0034] According to the probability distribution P, randomly select the longitude and latitude coordinates of an electric meter as the m+1th initial concentrator ; Repeat step S15 until k initial concentrators are selected, and finally the initial concentrator position set is obtained. .
[0035] 2. Voronoi diagram (Voronoi diagram) partitioning with density factor The Voronoi diagram uses the initial concentrator position as the generating point to divide the target area into multiple sub-areas. The specific steps are as follows: S21: Define the boundary conditions of the Voronoi region; For the initial concentrator location set , the jth concentrator Corresponding Voronoi region The boundary is determined by the distance condition: any meter in the area arrive The distance is less than that to all other concentrators The distance, that is:
[0036] in,
[0037] S22: Determine the boundary equation of the region; Any two adjacent concentrators and The corresponding Voronoi region boundary is the perpendicular bisector of the line segment, let , , then any point (x, y) on the boundary satisfies the boundary equation By solving the boundary equations for all adjacent concentrators, the complete boundary of each Voronoi region can be determined.
[0038] S23: Divide the Voronoi region; Traverse all meter data X, and assign each meter to the Voronoi region corresponding to the nearest concentrator according to the distance condition in step 1, and finally obtain k non-overlapping sub-regions that cover all meters. ; S24: Calculate the density of electric meters in each area; Defining regional density Voronoi region The ratio of the number of electric meters in a region to the area of the region.
[0039] in, For the region The total number of electric meters in For the region Area (unit: square meters).
[0040] S25: Set density threshold and dynamically adjust coverage; Preset high density threshold and low density threshold ( > ), for high-density areas ( > ), when dividing the Voronoi diagram, the coverage of a single concentrator is reduced (by dynamically reducing the maximum communication distance threshold) to avoid concentrator overload; for low-density areas ( < ), expand coverage (increase the maximum communication distance threshold), reduce the number of concentrators, and reduce costs. The specific formula is as follows: (1) For high-density areas ( ), the adjusted maximum communication distance threshold:
[0041] (2) For low-density areas ( ), the adjusted maximum communication distance threshold:
[0042] (3) Normal density area ( ), keep the default maximum communication distance threshold:
[0043] The core variables are defined as follows: D : The adjusted maximum communication distance (i.e. coverage range) of the concentrator; : Default basic maximum communication distance (initial value under normal density, which can be set according to the scenario); : Actual node density in the area (e.g., number of terminal devices per unit area); : High density threshold (ρ> is determined as a high-density area); : Low density threshold (ρ< is determined as a low-density area); : High-density area adjustment coefficient; : low-density area adjustment coefficient; : Minimum communication distance lower limit; : Maximum communication distance limit.
[0044] After completing the Voronoi diagram division combined with density adjustment, it is necessary to verify the capacity and distance of each area to ensure the stability of the concentrator operation and the communication quality. The over-capacity / over-distance areas are marked as pending adjustment, and the set of "pending adjustment" areas is obtained. … .
[0045] Feedback iterative adjustment to improve convergence efficiency: For the set of areas to be adjusted … Any area in , the set of electric meters contained in this area is … , where n is the area The total number of meters in the area, the coordinates of the kth meter are . area Internal meter distribution center The calculation formula is as follows:
[0046] in, The coordinates are expressed as , using the coordinates as the region The corresponding location of the new concentrator.
[0047] After obtaining the new concentrator position, repeat steps S23-S25 until the proportion of the “to be adjusted” area is less than 3% or the number of iterations exceeds 5.
[0048] 3. Isolated Point Processing After the optimization iteration is completed, if there is an electric meter that is not included in the effective coverage area of any concentrator, the electric meter is determined to be an isolated point. Let the isolated point set … , where the coordinates of the pth isolated point are ,p is the total number of isolated points. , combined with all current concentrator locations ,The isolated points are assigned based on the following steps: S31: Calculate isolated points With each concentrator The proximity is calculated as follows:
[0049] The proximity calculation formula takes into account both distance and the remaining capacity of the concentrator, where:
[0050] Isolated point With each concentrator The Euclidean distance of is the weight coefficient .
[0051] S32: For each isolated point , confirm the concentrator corresponding to its maximum proximity , if the concentrator The remaining capacity satisfies:
[0052] Then Assigned to the concentrator. If the isolated point If the concentrator with the highest proximity has no remaining capacity, the concentrator is traversed to select the concentrator with the next highest proximity and remaining capacity for allocation. If all related concentrators have no remaining capacity, the isolated point is marked as requiring a separate concentrator configuration.
[0053] The present invention also provides a concentrator site selection device based on a wireless communication solution. The device includes at least one processor and at least one memory, as well as a communication interface and an internal bus. The memory stores a computer executable program. The memory stores the computer executable program. When the processor executes the computer executable program stored in the memory, the processor can execute the concentrator site selection method based on the wireless communication solution described above. The internal bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of this application are not limited to only one bus or a single type of bus. The memory may include high-speed RAM memory and may also include non-volatile storage (NVM), such as at least one disk storage. It can also be a USB flash drive, a mobile hard drive, a read-only memory, a magnetic disk, or an optical disk.
[0054] The device may be provided as a terminal, a server, or other form of device. In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.
[0055] The present invention also provides a computer-readable storage medium, in which a computer execution program is stored. When the computer execution program is executed by a processor, the processor can execute the above-mentioned concentrator site selection method based on a wireless communication solution.
[0056] Specifically, a system, device, or apparatus equipped with a machine-readable storage medium may be provided, wherein the machine-readable storage medium stores software program code that implements the functions of any of the above-described embodiments, and the system, device, or apparatus is configured to read and execute the instructions stored in the machine-readable storage medium. In this case, the program code read from the machine-readable storage medium itself can implement the functions of any of the above-described embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of the present invention.
[0057] Example 2: Based on the site selection algorithm in Example 1, deployed in the system, this embodiment provides a specific implementation system framework.
[0058] 1. Front-end module (1) Import and verify the longitude and latitude of electric meters The system provides a standardized import template (including mandatory fields for "meter number", "longitude" and "latitude"). Users can download the template and fill in the data according to the format. During import, the front-end uses JavaScript scripts to verify data integrity (such as whether mandatory fields are missing), format validity (such as whether the longitude range is between -180° and 180°, whether the latitude range is between -90° and 90°), and uniqueness (such as whether the meter number is repeated) in real time. If there is an error, the system will highlight the error line on the interface and prompt the specific reason (such as "meter number 000046002787 is repeated" and "longitude -190° is out of range"). Users can edit and correct it online and then re-upload it to ensure the accuracy of the imported data.
[0059] (2) Parameter setting interaction The front-end interface presents the core parameter input boxes in the form of a form, and each input box is equipped with real-time verification and prompt functions, such as Figure 3 As shown: Concentrator load capacity: Only positive integers are allowed. If 0 or a negative number is entered, the prompt "Load capacity must be greater than 0" will be displayed immediately. Maximum communication distance: supports input of decimals (unit: kilometers) and limits the maximum value (such as 5 kilometers). When the maximum distance is exceeded, it will prompt "the maximum distance does not exceed 5km"; Number of concentrators: Only positive integers are allowed, and the default value is intelligently recommended based on the total number of meters (for example, 1 concentrator for every 200 meters).
[0060] (3) Enhanced visualization of site selection results Based on map visualization tools (Google Maps API), the front-end implements multi-level display functions: Basic layer: Displays the concentrator site selection results. Different concentrator coverage areas are distinguished by polygons of different colors (such as red, blue, and yellow blocks). Figure 4 As shown; Interactive layer: supports mouse dragging of maps and scroll wheel zooming (zoom levels 1-18). Clicking a concentrator will pop up an information window displaying its serial number, number of loaded meters, latitude and longitude coordinates, and other core data. Statistics layer: The right side of the map is equipped with a data panel, such as Figure 5 Click Figure 4 When selecting a concentrator in the network, the panel displays the number of meters loaded by the concentrator, meter numbers, and distances from the meters to the concentrator in real time, helping users quickly evaluate the rationality of site selection.
[0061] (4) Support manual adjustment of site selection results The front end provides an intuitive drag-and-drop adjustment interface: Meter allocation adjustment: Users can select meters within the coverage area of a concentrator (supports batch selection by selecting a box) and drag them to the area of another concentrator. The system automatically verifies whether the target concentrator meets the capacity and distance restrictions. If not, a prompt will pop up (such as "The target concentrator is overloaded, currently loaded 200 / 200"); Concentrator position modification: Click the blue dot of the concentrator and drag it to a new location. After releasing the mouse, the system instantly calculates the new distance between the concentrator and the originally covered meter, automatically updates the mark of the out-of-distance meter (for example, changing the red dot of the out-of-distance meter to an orange dot), and synchronously adjusts the area polygon range.
[0062] 2. Backend module (1) Computational optimization: The back-end algorithm module uses multi-threaded processing. When users import large-scale electricity meter data, the thread pool parallel computing is automatically started.
[0063] (2) Data storage and management: The backend adopts a MySQL+Redis hybrid storage architecture. MySQL is used to persistently store all historical site selection results (including concentrator locations, meter allocation relationships, and parameter configurations), supporting queries by time, region, and other dimensions. Redis is used to cache real-time data of the current session (such as the site selection plan that the user is adjusting), improving the response speed of high-frequency access.
[0064] (3) Site selection result export: The exported Excel file adopts a multi-worksheet structured design. Each concentrator corresponds to a worksheet named "DCU_xxxx", which contains the meter number, longitude and latitude, and distance from the concentrator.
[0065] The system presented in this paper is highly user-friendly and practical. The front-end visual interface supports user operation even with zero prior knowledge. Real-time parameter verification and intelligent recommendations reduce manual decision-making costs, and the drag-and-drop interactive interface for manual adjustments aligns with user experience. It also supports manual adjustments based on actual geographic conditions (such as building obstruction and signal interference), balancing the objectivity of the algorithm with the subjectivity of field experience, avoiding the "theoretical optimization but implementation difficulties" problem often associated with purely automated algorithms.
[0066] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.
[0067] Although the above describes the specific implementation methods of the present invention, it does not limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.
Claims
1. A concentrator site selection method based on a wireless communication solution, characterized in that: The following steps are involved: S1, obtain the geographic location information of all electricity meters in the target area, extract the longitude and latitude coordinates, and select the initial concentrator location set based on the K-Means++ clustering algorithm; S2: Construct a Voronoi diagram using the initial concentrator location as the generating point, dividing the target area into multiple sub-areas. Dynamically adjust the coverage range based on the density factor, and the threshold of the density factor is adaptively adjusted according to the actual scenario. Each meter is assigned to the Voronoi diagram sub-area corresponding to the nearest concentrator. S3: After the Voronoi diagram is divided, the capacity and distance limits are checked, and the areas exceeding the capacity or distance are marked as "to be adjusted", resulting in several "to be adjusted" areas. S4, based on the number and coordinates of the electricity meters in the "to be adjusted" area, calculate the distribution center of gravity, and use the center of gravity coordinates as the location coordinates of the concentrator in the "to be adjusted" area; S5, iterate S2 to S4 until the proportion of the "to be adjusted" area is lower than the threshold or the maximum number of iterations is reached.
2. The method for selecting a concentrator site based on a wireless communication solution according to claim 1, wherein: After iteration S5, if any meter is not covered by any concentrator, it is considered an outlier. The proximity between the outlier and surrounding concentrators is calculated, and the meter is preferentially assigned to the concentrator with the highest proximity. If no concentrator is available, the meter is marked as requiring separate concentrator configuration.
3. The method for concentrator site selection based on a wireless communication solution according to claim 1, wherein: The specific process of S1 is: S11, collect the geographical location information of all electricity meters in the target area and extract the longitude and latitude coordinates; suppose there are N electricity meters in the target area, and the longitude and latitude data of each meter are represented as a two-dimensional vector ,in, For the i The longitude value of the meter, in degrees, For the i The latitude value of the meter, in degrees; dataset ; S22, normalize the longitude and latitude data, and normalize the longitude and latitude values. The processed data is still recorded as ; S23, according to the meter distribution density and communication coverage requirements of the target area, the number of initial concentrators is preset to k, where k ≥ 1 and is a positive integer; S34, select the first initial concentrator, randomly select the longitude and latitude coordinates of an electric meter from the data set X as the location of the first initial concentrator, denoted as ; S35, select a subsequent concentrator; for each meter in the data set X that has not been selected as the initial concentrator , calculate the distance between it and the nearest initial concentrator that has been selected , the distance calculation formula uses Euclidean distance: Where m is the number of initial concentrators selected , is the longitude and latitude coordinates of the jth initial concentrator; the distance The square of is used as the probability weight to construct the probability distribution P of selecting the next initial concentrator, that is, i The probability that a meter is selected as the next initial concentrator is, according to the probability distribution P, the longitude and latitude coordinates of a meter are randomly selected as the m+1th initial concentrator ; Repeat S35 until k initial concentrators are selected, and finally the initial concentrator position set is obtained. .
4. The method for selecting a concentrator site based on a wireless communication solution according to claim 1, wherein: The specific process of S2 is: S21, define the boundary conditions of the Voronoi region: For the initial concentrator location set , No. j concentrator , the corresponding Voronoi region The boundary is determined by the distance condition: any meter in the area arrive The distance is less than that to all other concentrators distance; S22, determine the region boundary equation: Any two adjacent concentrators and The corresponding Voronoi region boundary is the perpendicular bisector of the line segment, let , , then any point (x, y) on the boundary satisfies the boundary equation ; Determine the complete boundary of each Voronoi region by solving the boundary equations of all adjacent concentrators; S23, divide the Voronoi region: Traverse all meter data X, and assign each meter to the Voronoi region corresponding to the nearest concentrator according to the distance condition, and finally obtain k non-overlapping sub-regions that cover all meters. ; S24: Calculate the electricity meter density in each area: Defining regional density Voronoi region The ratio of the number of electric meters within the area to the area of the area; S25: Set density threshold and dynamically adjust coverage; Preset high density threshold and low density threshold ( > ), for high-density areas > , when dividing the Voronoi diagram, the coverage of a single concentrator is reduced to avoid concentrator overload; for low-density areas ( < ), expand coverage and reduce the number of concentrators; S26, the capacity and distance of each area are verified to ensure the stability of the concentrator operation and the communication quality. The areas with excessive capacity or excessive distance are marked as pending adjustment, and a set of "pending adjustment" areas is obtained.
5. The method for concentrator site selection based on a wireless communication solution according to claim 4, wherein: In the S25, the specific formula is as follows: For high-density areas , the adjusted maximum communication distance threshold: For low-density areas , the adjusted maximum communication distance threshold: Normal density area , keep the default maximum communication distance threshold: The core variables are defined as follows: D : The adjusted maximum communication distance of the concentrator; : Default basic maximum communication distance; : Actual node density in the area; : high density threshold; : low density threshold; : High-density area adjustment coefficient; : low-density area adjustment coefficient; : Minimum communication distance lower limit; : Maximum communication distance limit.
6. The method for concentrator site selection based on a wireless communication solution according to claim 1, wherein: The specific process of S4 is: For the set of areas to be adjusted … Any area in , the set of electric meters contained in this area is … , where n is the area The total number of meters in the area, the coordinates of the kth meter are ; area Internal meter distribution center The calculation formula is as follows: in, The coordinates are expressed as , using the coordinates as the region The corresponding location of the new concentrator.
7. The method for selecting a concentrator site based on a wireless communication solution according to claim 2, wherein: The specific process of processing isolated points is as follows: Assume that the set of isolated points … , where the coordinates of the pth isolated point are ,p is the total number of isolated points; for the isolated point set , combined with all current concentrator locations ,The isolated points are assigned based on the following steps: Calculate isolated points With each concentrator The proximity is calculated as follows: The proximity calculation formula takes into account both distance and the remaining capacity of the concentrator, where: Isolated point With each concentrator The Euclidean distance of is the weight coefficient, ; For each isolated point , confirm the concentrator corresponding to its maximum proximity , if the concentrator The remaining capacity satisfies: Then Assigned to the concentrator, if the isolated point If the concentrator with the highest proximity has no remaining capacity, it traverses other concentrators and selects the concentrator with the second highest proximity and remaining capacity for allocation. If all related concentrators have no remaining capacity, the isolated point is marked as requiring a separate concentrator configuration.
8. A concentrator site selection device based on a wireless communication solution, characterized in that: The device includes at least one processor and at least one memory, the processor and the memory are coupled; a computer execution program is stored in the memory; when the processor executes the computer execution program stored in the memory, the processor executes a concentrator site selection method based on a wireless communication scheme as described in any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, and when the program or instruction is executed by the processor, the processor executes the concentrator site selection method based on a wireless communication solution according to any one of claims 1 to 7.