Low-cost station area mass electric energy meter geographic position automatic positioning method, system and device and medium

Abstract

The invention relates to the technical field of intelligent power grid distribution automation and Internet of Things positioning. The invention discloses a low-cost station area mass electric energy meter geographic position automatic positioning method, system and device and a medium. The method comprises the following steps: selecting an electric energy meter with a known geographic position from a low-voltage transformer area meter reading communication network as a beacon node; coordinating the whole transformer area to enter a distance measurement mode, and obtaining a distance measurement result through bidirectional message exchange and timestamp recording; constructing a distance matrix, removing abnormal values and taking an average value; based on the distance matrix and the beacon node coordinates, calculating the coordinates of all unknown electric energy meters by adopting a two-stage hybrid algorithm, and optimizing a rectangular plane coordinate system; converting the optimized plane coordinates into geodetic latitude and longitude coordinates, and uploading the geodetic latitude and longitude coordinates to a master station system; and positioning is executed regularly, the precision is evaluated, and automatic repositioning and parameter adjustment are performed when errors exceed the limit. According to the invention, automatic and accurate positioning of geographic positions of massive electric energy meters is realized, and the informatization and intelligence level of operation and maintenance management of a transformer area is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid power distribution automation and Internet of Things positioning, and particularly relates to a low-cost geographic position automatic positioning method, system, device and medium for a large number of electric energy meters in a transformer area. BACKGROUND

[0002] At present, a low-voltage transformer area communication network has been widely applied and can realize the collection of electric energy meter power consumption information, but the specific geographic position information of the electric energy meter is still missing. This information gap leads to low work efficiency in daily operation and maintenance, fault handling, line loss analysis, and the like, which is particularly prominent in rural areas and the like where users are scattered, and the operation and maintenance work is time-consuming and labor-intensive and highly dependent on manual experience, thereby restricting the improvement of the lean management level of the power grid.

[0003] There are mainly two existing positioning schemes: one is to install a satellite positioning module for each electric energy meter, but the hardware cost is too high, and the signal is poor in a serious shielding environment such as indoors, and it is difficult to be popularized on a large scale; the other is to rely on an operation and maintenance personnel to measure on site and manually input by using a handheld device, and when facing a large number of electric energy meters, the work is heavy, the efficiency is low, and errors are easy to occur, and it is difficult to perform dynamic maintenance. In addition, the existing technical solutions are mostly concentrated in transformer area identification or topological relationship judgment, and cannot realize the accurate acquisition of the latitude and longitude coordinates of the electric energy meter, and cannot meet the demand for visual operation and maintenance based on a map.

[0004] Therefore, the existing technologies cannot realize the automatic and accurate positioning of the geographic position of a large number of electric energy meters in a low-voltage transformer area on the premise of controlling the cost, and an innovative solution is needed that can reuse existing facilities, has a low cost, and automatically generates accurate position information. SUMMARY

[0005] In view of the above existing problems, the present application provides a low-cost geographic position automatic positioning method, system, device and medium for a large number of electric energy meters in a transformer area.

[0006] Therefore, the technical problem solved by the present application is: how to automatically and accurately generate and dynamically update the latitude and longitude coordinate information of the electric energy meter by reusing the existing low-voltage power consumption information collection system without increasing the cost of additional hardware, to realize the low-cost, automatic and high-precision positioning of the geographic position of a large number of electric energy meters, so as to meet the demand for visual operation and maintenance based on a map and lean management.

[0007] To solve the above technical problems, the present application provides the following technical scheme: a low-cost geographic position automatic positioning method for a large number of electric energy meters in a transformer area, comprising, selecting an electric energy meter with known geographic position information as a beacon node coordinate from an automatic meter reading communication network of a low-voltage transformer area; The communication network issues a positioning start command, coordinates the network of the whole area group to enter the ranging mode, carries out the two-way message exchange, records the time stamp, and obtains the two-way ranging result; All successful two-way ranging results are collected to construct a symmetric distance matrix, and abnormal values are eliminated and the average value is measured; Based on the symmetric distance matrix and the known beacon node coordinates, the coordinates of all unknown positions of the beacon nodes are obtained, and an optimized plane rectangular coordinate system is obtained; The optimized plane rectangular coordinate system is projected onto the geodetic latitude and longitude coordinates to obtain the final geographic position coordinates of all electric energy meters and the corresponding device identifiers, and is uploaded to the master station system; Periodically perform automatic positioning, evaluate the overall accuracy of this positioning, and automatically trigger repositioning and adjust parameters when the error exceeds the set threshold.

[0008] As a preferred scheme of the low-cost automatic positioning method of the geographic position of the mass electric energy meter in the area, wherein the known geographic position information of the electric energy meter in the automatic meter reading communication network of the low-voltage area is selected as the beacon node coordinates, including, The low-voltage area has inherent automatic meter reading communication network, the communication network adopts mesh topology, contains a terminal and an electric energy meter communication module, and the electric energy meter with known geographic position information is selected as the beacon node.

[0009] As a preferred scheme of the low-cost automatic positioning method of the geographic position of the mass electric energy meter in the area, wherein the communication network issues a positioning start command, coordinates the network of the whole area group to enter the ranging mode, carries out the two-way message exchange, records the time stamp, and obtains the two-way ranging result, including, The terminal issues a unified positioning start command, coordinates the network of the whole area group to enter the ranging mode, and each electric energy meter module communicates with all neighbor modules within the communication range of the corresponding electric energy meter module to exchange two-way messages and accurately record the time stamp.

[0010] As a preferred scheme of the low-cost automatic positioning method of the geographic position of the mass electric energy meter in the area, wherein the known geographic position information of the electric energy meter in the automatic meter reading communication network of the low-voltage area is selected as the beacon node coordinates, including, The terminal is used as a data aggregation point to collect all successful two-way ranging results of the whole network to construct an N*N symmetric distance matrix D, wherein N is the total number of electric energy meters in the area, and the matrix element Indicates the measured distance between node i and node j; When the measured distance is greater than the physical size of the area, it is considered as a measurement error and is eliminated, and the two-way ranging between the same modules is carried out to take the average value as the final .

[0011] As a preferred embodiment of the low-cost automatic location method for a large number of electricity meters in a distribution area as described in this invention, the method involves obtaining the coordinates of all unknown beacon nodes based on a symmetric distance matrix and known beacon node coordinates, and then obtaining an optimized Cartesian coordinate system, including... Based on the distance matrix D and the known coordinates of the beacon nodes, the two-dimensional geographic coordinates of all unknown location energy meter modules are solved. When the diameter of the transformer area is smaller than the set value, the latitude and longitude coordinates are projected into the local plane rectangular coordinate system, and a two-stage hybrid algorithm is used for calculation. Phase one involves generating a global relative map; Calculate the bicentric inner product matrix using the distance matrix. D Construct the squared distance matrix , element is The inner product matrix is ​​calculated through a double centering operation. B , in, , N This represents the total number of electricity meters in the distribution area. I It is an N×N identity matrix, and 1 is an N×1 column vector consisting entirely of 1s. It is an N×N matrix where each element is , J It is an N×N matrix, also known as a doubly centered matrix; inner product matrix B Perform eigenvalue decomposition. in, It is a diagonal matrix composed of eigenvalues. It is the corresponding eigenvector matrix; Take the two largest eigenvalues and and the corresponding feature vectors and The relative coordinate matrix of all nodes for, ; Phase two involves absolute coordinate transformation and precise optimization; Using the known true coordinates of the beacon nodes and the relative coordinates obtained in stage one Find an optimal similarity transformation, which is expressed as: in, s It is a scaling factor. R It is a rotation matrix. t It is a translation vector; solving for it yields the optimal solution. , This is the optimal solution for the scaling factor. The optimal solution for the rotation matrix. The optimal solution for the translation vector; The relative coordinate matrix of all nodes obtained in stage one When applied to similarity transformations, preliminary absolute coordinate estimates are obtained. , is represented as , Preliminary absolute coordinate estimation Using the initial values, we construct a weighted nonlinear least squares problem to optimize the coordinates and fit the actual distance measurement data. The objective function is: in, = ) are the coordinates to be optimized for node i. = ) are the coordinates to be optimized for node j. Coordinates to be optimized X Jacobian matrix, It is the set of neighbors that can successfully measure distances with node i. This represents the measured distance between node i and node j. It is a weighting factor, set according to the signal-to-noise ratio of the measured distance; measurements with a higher signal-to-noise ratio are assigned greater weight; G is the set of beacon nodes. It is a regularization parameter. The true coordinates of the energy meter k at the beacon node. The temporary coordinates for the beacon node currently being fine-tuned; The LM algorithm is used to solve the nonlinear least squares problem, and the iterative update formula is used to solve for the coordinate update in the current iteration step. Update coordinates X Until convergence, the formula is: in, H It is the objective function. W It is determined by the weighting factor The diagonal matrix formed r It is the residual vector. μ It is the damping factor. I It is an identity matrix.

[0012] As a preferred embodiment of the low-cost automatic location method for a large number of electricity meters in a distribution area as described in this invention, the step of projecting the optimized Cartesian coordinate system onto the Earth's latitude and longitude coordinates to obtain the final geographical location coordinates and corresponding device identifiers of all electricity meters, and uploading them to the main station system, includes: The optimized Cartesian coordinates are projected onto the geodetic latitude and longitude coordinates. The data acquisition terminal in the distribution area packages the final geographical coordinates of all electricity meters and their corresponding equipment identifiers, and uploads them to the main station system. The main station system updates this information to the power grid geographic information system and displays it on the electronic map.

[0013] As a preferred embodiment of the low-cost automatic location method for a large number of electricity meters in a distribution area according to the present invention, the step of periodically performing automatic location, evaluating the overall accuracy of the current location, and automatically triggering relocation and parameter adjustment when the error exceeds a set threshold includes: The positioning process is executed periodically. The overall accuracy of the positioning is evaluated by comparing the error between the calculated coordinates of the beacon node and its actual coordinates. When the average error exceeds the set threshold, repositioning is automatically triggered and the algorithm parameters are adjusted.

[0014] This invention utilizes a low-voltage distribution area automated meter reading communication network for two-way ranging and coordinate optimization, enabling automatic location of a massive number of electricity meters and achieving low-cost, high-precision distribution area electricity meter management.

[0015] This invention provides a low-cost automatic location system for a large number of electricity meters in a distribution area, comprising: The beacon node selection module selects energy meters with known geographical location information from the automated meter reading communication network of the low-voltage distribution area as beacon node coordinates, providing a reference benchmark for subsequent positioning. The ranging control module issues a positioning start command, coordinates the entire network to enter ranging mode, controls the energy meter module to perform bidirectional message exchange, accurately records the timestamp, and obtains the bidirectional ranging results. The distance matrix processing module collects all successful two-way ranging results, constructs a symmetric distance matrix, removes outliers, and averages the ranging results. The coordinate calculation module, based on the symmetric distance matrix and the known beacon node coordinates, calculates the coordinates of all unknown location energy meter modules through a two-stage hybrid algorithm and optimizes the Cartesian coordinate system; The coordinate projection module projects the optimized Cartesian coordinate system onto the latitude and longitude coordinates of the earth to obtain the final geographical coordinates of all electricity meters and their corresponding device identifiers, and packages and uploads them to the main station system for visualization on the electronic map. The positioning evaluation module periodically executes the automatic positioning process. It evaluates the overall accuracy by comparing the calculated coordinates of the beacon nodes with the actual coordinates. When the average error exceeds a set threshold, it automatically triggers repositioning and adjusts the algorithm parameters.

[0016] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a low-cost automatic location method for a large number of electricity meters in a distribution area.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a low-cost method for automatically locating the geographic location of a large number of electricity meters in a distribution area are implemented.

[0018] Compared with existing technologies, the advantages of this invention are as follows: This invention selects electricity meters with known geographical locations as beacon nodes to construct a positioning benchmark; commands are issued from the distribution area acquisition terminal to coordinate the entire distribution area into ranging mode, achieving accurate distance measurement through bidirectional message exchange and timestamp recording; after collecting the ranging results, a distance matrix is ​​constructed, and outlier removal and data smoothing are performed to ensure data reliability; based on the distance matrix and beacon node coordinates, a two-stage hybrid positioning algorithm is adopted. First, a relative coordinate layout is generated through multidimensional scaling, then the absolute coordinates are solved through similarity transformation and nonlinear optimization, and finally, the coordinates are back-projected into latitude and longitude coordinates and uploaded to the main station system, completing the automatic updating and visualization of geographical location information; the system also has a positioning performance evaluation and adaptive relocation mechanism, ensuring long-term positioning accuracy through error monitoring and parameter adjustment. This invention achieves automatic and accurate positioning of the geographical locations of massive electricity meters with zero hardware modification cost, effectively overcoming the problems of high cost, low efficiency, and insufficient accuracy caused by relying on manual inspection or external positioning modules in traditional solutions, significantly improving the informatization and intelligentization level of distribution area operation and maintenance management. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The above is a flowchart illustrating a low-cost automatic location method for a large number of electricity meters in a distribution area, as provided in one embodiment of the present invention.

[0021] Figure 2This invention provides a two-way message exchange ranging map for a low-cost automatic location method for a large number of electricity meters in a distribution area, as an embodiment of the present invention.

[0022] Figure 3 The system network topology diagram is provided for a low-cost automatic location method for a large number of electricity meters in a distribution area, according to an embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 The first embodiment of the present invention provides a low-cost method for automatic location positioning of a large number of electricity meters in a distribution area, comprising: S1: Select the energy meters with known geographical location information from the automated meter reading communication network of the low-voltage distribution area as the coordinates of the beacon node.

[0025] S2: The communication network issues a positioning start command, coordinates the entire network to enter ranging mode, performs bidirectional message exchange, records timestamps, and obtains bidirectional ranging results.

[0026] S3: Collect all successful two-way distance measurement results, construct a symmetric distance matrix, remove outliers, and measure the average value.

[0027] S4: Based on the symmetric distance matrix and the known beacon node coordinates, obtain the coordinates of all unknown beacon nodes and obtain the optimized Cartesian coordinate system.

[0028] S5: Project the optimized Cartesian coordinate system onto the geodetic latitude and longitude coordinates to obtain the final geographical location coordinates and corresponding device identifiers of all electricity meters, and upload them to the main station system.

[0029] S6: Perform automatic positioning periodically, evaluate the overall accuracy of the positioning, and automatically trigger repositioning and adjust parameters when the error exceeds the set threshold.

[0030] It should be noted that traditional methods for locating electricity meters rely on expensive satellite positioning modules or inefficient manual data entry, resulting in high hardware costs, low operational efficiency, and a high risk of errors. Furthermore, existing technologies are often limited to identifying transformer substations or providing rough location data, failing to provide precise geographic coordinates and lacking robustness and automation capabilities in scenarios with severe signal obstruction or a large number of meters. Therefore, developing a low-cost, automated location method based on existing low-voltage transformer substation communication networks, addressing issues such as accurate two-way ranging, distance matrix construction and preprocessing, multi-stage coordinate solution optimization, and positioning accuracy assessment and adaptive readjustment, is also crucial.

[0031] Therefore, addressing the aforementioned issues of high hardware costs, low efficiency of manual operation, inability to utilize existing communication networks, and insufficient positioning accuracy, steps S1-S6 are implemented. By selecting beacon nodes and utilizing existing low-voltage distribution area communication networks for automatic ranging and data collection, additional hardware modifications are avoided, improving cost-effectiveness and network resource utilization. The automatic issuance of positioning commands, coordination of bidirectional message exchange, and recording of timestamps by the distribution area acquisition terminal, along with the construction of a distance matrix for data preprocessing, enhances automation and measurement efficiency. The elimination of clock deviations through bidirectional ranging, the removal of outliers through data preprocessing, and the use of a hybrid algorithm combining MDS and weighted least squares for coordinate solving and optimization, combined with periodic evaluation and relocation mechanisms, improve positioning accuracy and system robustness. Simultaneously, the entire automated process achieves the goal of low-cost, high-precision, and adaptively adjustable automatic location positioning for a massive number of electricity meters.

[0032] Example 2, refer to Figure 1 - Figure 3 As an embodiment of the present invention, based on the above embodiment, a low-cost method for automatic location of a large number of electricity meters in a distribution area is provided.

[0033] In this embodiment of the application, step S1, selecting electricity meters with known geographical location information as beacon node coordinates from the automated meter reading communication network of the low-voltage distribution area, includes: The existing automated meter reading communication network of the low-voltage distribution area is utilized. The communication network adopts a mesh topology and includes a distribution area acquisition terminal and an electricity meter communication module. Electricity meters with known geographical location information are selected as beacon nodes.

[0034] Specifically, the inherent automated meter reading communication network of the low-voltage distribution area is utilized. This network typically adopts a tree or mesh topology, containing one distribution area acquisition terminal (as the root node) and a large number of electricity meter communication modules (as leaf nodes and routing nodes). Within the network, at least three (preferably more) electricity meters with known geographical location information are selected as beacon nodes.

[0035] The latitude and longitude coordinates of these beacon nodes are obtained in advance with precision through the following methods: Select electricity meters that are fixed and easy to measure, such as those installed at transformer substations, distribution boxes, village committees, or entrances to residential areas; manually determine their coordinates using high-precision BeiDou or GPS measuring equipment and then enter them into the system; or obtain them from existing, high-precision power grid GIS (Geographic Information System) maps.

[0036] The selection of beacon nodes should be as geographically even as possible, surrounding the entire area to be located, in order to avoid the "geometric dilution of accuracy (GDOP)" problem in positioning.

[0037] In an alternative implementation, beacon node coordinates can also be obtained based on a low-precision positioning device.

[0038] Specifically, in the automated meter reading communication network of low-voltage distribution areas, when selecting electricity meters with known geographical location information as beacon nodes, a common commercial-grade positioning module is used to determine the latitude and longitude coordinates of the electricity meters. The selection range of beacon nodes includes electricity meters installed in any location. The coordinate data is entered into the system after manual on-site measurement, and the distribution of beacon nodes is selected by on-site personnel.

[0039] In another alternative implementation, beacon nodes can be selected based on non-uniform distribution and existing low-precision data sources.

[0040] Specifically, when selecting electricity meters with known geographical location information as beacon nodes from the low-voltage distribution area automated meter reading communication network, existing low-precision geographical data in the power grid system is directly utilized. Priority is given to selecting electricity meters that are close to the distribution area's data acquisition terminal or easily accessible, and the number of nodes must meet a minimum requirement of three. Coordinate data is derived from historical records or manual estimation.

[0041] In this embodiment of the invention, step S2 involves the communication network issuing a positioning start command to coordinate the entire network to enter ranging mode, perform bidirectional message exchange, record timestamps, and obtain bidirectional ranging results, including: The data acquisition terminal in the distribution area issues a unified positioning start command to coordinate all networked energy meter modules in the distribution area to enter the ranging mode. Each energy meter module exchanges bidirectional messages with all neighboring modules within its communication range and accurately records the timestamp.

[0042] Specifically, neighbor modules are typically modules that can communicate directly via dual-mode communication (power line carrier + wireless). The ranging principle and process are detailed below: The electricity meter module A sends a ranging request message to module B and records the precise local transmission timestamp. ; When module B receives the message, it records its local arrival timestamp. ; Module B immediately constructs a response message, which contains... and the local timestamp of the response message it sent. Then, the response message is sent back to module A; When module A receives a response message, it records its local arrival timestamp. .

[0043] Assume that the local clocks of modules A and B have a fixed relative clock offset. However, during a brief message exchange, clock drift is negligible. Therefore, the four timestamps satisfy the following relationship: ; ; in, Let be the actual one-way travel time of the signal between A and B. Combining the two equations, clock skew can be eliminated. Calculate the propagation delay : Then based on the signal propagation speed (Speed ​​of light) Calculate the estimated distance between A and B. : ; This bidirectional ranging method does not require high-precision clock synchronization between the communicating parties. It only requires that each party's local clock has short-term stability, thus eliminating ranging errors caused by clock deviations between different energy meters. It is very suitable for low-cost energy meter clock systems.

[0044] In one optional implementation, the communication network can also issue a positioning start command to coordinate the entire station network to enter the ranging mode, perform one-way message exchange, record the timestamp, and obtain the one-way ranging result.

[0045] Specifically, after the data acquisition terminal in the distribution area issues a positioning start command, the electricity meter module A sends a ranging request message to module B and records the local transmission timestamp; after receiving the message, module B directly constructs and sends a response message to module A; when module A receives the response message, it records the local arrival timestamp; module A uses the local transmission timestamp and arrival timestamp to calculate the propagation delay.

[0046] In another optional implementation, a positioning start command can be issued from the communication network to coordinate the entire network to enter ranging mode and perform bidirectional message exchange without timestamp transmission.

[0047] Specifically, after the data acquisition terminal in the distribution area issues a positioning start command, the electricity meter module A sends a ranging request message to module B and records the local sending timestamp; after receiving the message, module B records the local arrival timestamp, and the response message it sends does not contain any timestamp information; when module A receives the response message, it records the local arrival timestamp; module A calculates the propagation delay based on the local sending timestamp and arrival timestamp.

[0048] In this embodiment of the invention, step S3 involves collecting all successful two-way ranging results, constructing a symmetric distance matrix, removing outliers, and measuring the average value, including the following steps A1-A2: A1: The data acquisition terminal in the distribution area acts as a data aggregation point, collecting all successful two-way ranging results from the entire network and constructing an N×N symmetric distance matrix D, where N is the total number of electricity meters in the distribution area, and the matrix elements... This represents the measured distance between node i and node j.

[0049] A2: When the measured distance exceeds the physical dimensions of the transformer area, it is considered a measurement error and discarded. Bidirectional distance measurements are then performed between the same pair of modules, and the average value is taken as the final measurement. .

[0050] Specifically, for module pairs that cannot communicate directly, their corresponding This is recorded as an invalid value (e.g., NaN). To improve data quality, the following preprocessing is required: Outlier removal: If the measured distance value is significantly larger than the physical size of the station area (e.g., greater than 2 kilometers), it is considered a communication jump or measurement error and is removed. Multiple measurement averaging: To improve accuracy, multiple (e.g., 3-5) bidirectional distance measurements can be performed between the same pair of modules, and the average value is taken as the final value. To smooth out random noise.

[0051] In an alternative implementation, all successful one-way ranging results can be collected to construct an asymmetric distance matrix.

[0052] Specifically, the data acquisition terminal in the transformer substation acts as a data aggregation point, collecting all successful one-way ranging results from the entire network to construct an N×N asymmetric distance matrix D, where N is the total number of electricity meters in the transformer substation, and the matrix elements... This represents the unidirectional measured distance from module i to module j.

[0053] In another alternative implementation, missing data can be filled with fixed values ​​when constructing the matrix.

[0054] Specifically, the data acquisition terminal in the distribution area acts as a data aggregation point, collecting all successful two-way ranging results from the entire network to construct an N×N symmetric distance matrix D, where N is the total number of electricity meters in the distribution area, and the matrix elements... This represents the measured distance between module i and module j. For module pairs that cannot communicate directly, their corresponding... It can be directly recorded as 0 or a preset fixed value.

[0055] In this embodiment of the application, step S4 obtains the coordinates of all unknown beacon nodes based on the symmetric distance matrix and the known beacon node coordinates, and obtains the optimized Cartesian coordinate system, including the following steps B1-B3: B1: Based on the obtained distance matrix D and the known coordinates of the beacon nodes, the two-dimensional geographic coordinates of all unknown location energy meter modules are calculated. When the diameter of the distribution area is smaller than a set value, the latitude and longitude coordinates are projected onto a local Cartesian coordinate system, and a two-stage hybrid algorithm is used for calculation. Considering the curvature of the Earth, when the distribution area is small (e.g., diameter < 3km), the latitude and longitude coordinates can be projected onto a local Cartesian coordinate system (e.g., UTM projection) for calculation to simplify the operation.

[0056] B2: Phase 1 is the generation of a global relative map; Calculate the bicentric inner product matrix using the distance matrix. D Construct the squared distance matrix , element is The inner product matrix is ​​calculated through a double centering operation. B , in, , N This represents the total number of electricity meters in the distribution area. I It is an N×N identity matrix, and 1 is an N×1 column vector consisting entirely of 1s. It is an N×N matrix where each element is , J It is an N×N matrix, also known as a double-centering matrix. Its function is to perform a "centering" operation on the data. The purpose of this step is to eliminate the influence of absolute position and obtain a matrix that reflects the relative positional relationship between nodes. inner product matrix B Perform eigenvalue decomposition. in, It is a diagonal matrix composed of eigenvalues. It is the corresponding eigenvector matrix; Take the two largest eigenvalues and and the corresponding feature vectors and The relative coordinate matrix of all nodes for, The coordinates obtained at this point are a "relative map" that differs from the true coordinates by a rotation, translation, and possibly mirroring.

[0057] B3: Phase Two involves absolute coordinate transformation and precise optimization; Using the known true coordinates of the beacon nodes and the relative coordinates obtained in stage one Find an optimal similarity transformation (including scaling, rotation, and translation) that minimizes the error between the transformed relative coordinates and the true coordinates. The similarity transformation is expressed as: in, s It is a scaling factor. R It is a rotation matrix. t It is a translation vector; solving for it yields the optimal solution. , This is the optimal solution for the scaling factor. The optimal solution for the rotation matrix. The optimal solution for the translation vector; The relative coordinate matrix of all nodes obtained in stage one When applied to similarity transformations, preliminary absolute coordinate estimates are obtained. , is represented as , Preliminary absolute coordinate estimation Using the initial values, a weighted nonlinear least squares problem is constructed to optimize the coordinates, fit the actual ranging data, and consider the reliability of different measurement links. The objective function is: in, = ) are the coordinates to be optimized for node i. = ) are the coordinates to be optimized for node j. Coordinates to be optimized X Jacobian matrix, It is the set of neighbors that can successfully measure distances with node i. This represents the measured distance between node i and node j. It is a weighting factor, set according to the signal-to-noise ratio of the measured distance; measurements with a higher signal-to-noise ratio are assigned greater weight; G is the set of beacon nodes. It is a regularization parameter. The true coordinates of the energy meter k at the beacon node. The temporary coordinates for the beacon node currently being fine-tuned; The Levenberg-Marquardt (LM) algorithm is used to solve nonlinear least squares problems. The LM algorithm combines the Gauss-Newton method with gradient descent, exhibiting good convergence and robustness. The iterative update formula is used to calculate the coordinate updates in the current iteration step. Update the coordinate X until convergence, using the formula: in, H It is the objective function. W It is determined by the weighting factor The diagonal matrix formed r It is the residual vector. μ It is the damping factor. I It is an identity matrix.

[0058] In an alternative implementation, direct triangulation can also be used to obtain the coordinates of all unknown beacon nodes.

[0059] Specifically, the coordinates of unknown nodes are calculated using a small number of beacon nodes and a distance matrix through local triangulation. Three nodes with relatively close positions are selected from the known beacon nodes as reference points. For each unknown node, the two-dimensional coordinates are directly calculated using trilateration based on the measured distances between it and the three reference beacon nodes. If the unknown node has distance data with multiple beacon nodes, multiple coordinate values ​​are calculated separately, and a simple arithmetic mean is taken as the final coordinates.

[0060] In another alternative implementation, a simple linear transformation method can be used to obtain the coordinates of all unknown beacon nodes.

[0061] Specifically, based on the coordinates and distance matrix of the beacon nodes, a simple linear scaling and translation transformation is performed to convert the relative coordinates into absolute coordinates. The measured distances between all beacon nodes are extracted from the distance matrix, and the average ratio to the true distance is calculated as a scaling factor. Using the true and relative coordinates of the beacon nodes, a translation vector is calculated to align the center of the relative coordinates with the center of the true coordinates. The scaling and translation transformations are applied to the relative coordinates of all nodes to obtain absolute coordinate estimates. The transformed coordinates are used as the final result.

[0062] In this embodiment of the application, step S5 projects the optimized Cartesian coordinate system onto the geodetic latitude and longitude coordinates to obtain the final geographical location coordinates and corresponding device identifiers of all electricity meters, and uploads them to the main station system, including: The optimized Cartesian coordinates are projected onto the geodetic latitude and longitude coordinates. The data acquisition terminal in the distribution area packages the final geographical coordinates of all electricity meters and their corresponding equipment identifiers, and uploads them to the main station system. The main station system updates this information to the power grid geographic information system and displays it on the electronic map.

[0063] Specifically, the optimized Cartesian coordinates ( Back projection back to Earth's latitude and longitude coordinates ( The data acquisition terminal in the distribution area packages and uploads the final geographical coordinates (latitude and longitude) of all electricity meters and their corresponding device identifiers (such as meter barcodes) to the main station system. The main station system updates this information in the power grid geographic information system (GIS) and displays it on an electronic map, providing data support for subsequent operation and maintenance applications.

[0064] In an alternative implementation, a simple linear proportional conversion can also be used to obtain the final geographical coordinates of all electricity meters and their corresponding device identifiers.

[0065] Specifically, the optimized Cartesian coordinates are directly converted into geodetic latitude and longitude coordinates using a fixed linear scaling factor. Based on a preset simple scaling factor, the Cartesian coordinates are converted into approximate geodetic latitude and longitude coordinates. The data acquisition terminals in the distribution areas package and upload the converted latitude and longitude coordinates along with the equipment identification to the main station system. The main station system then directly updates this information in the power grid geographic information system for electronic map display.

[0066] In another alternative implementation, the final geographical coordinates of all electricity meters and their corresponding device identifiers can be obtained based on the offset transformation of a fixed reference point.

[0067] Specifically, using the coordinate transformation relationship of a single fixed reference point, all optimized Cartesian coordinates are converted into geodetic latitude and longitude coordinates through a unified offset. A fixed reference point is selected, and its Cartesian coordinates and geodetic latitude and longitude coordinates are used as a baseline. The Cartesian coordinates of all other electricity meters are offset relative to this reference point through simple offset calculations to obtain approximate geodetic latitude and longitude coordinates. The data acquisition terminals in the distribution areas package and upload the converted latitude and longitude coordinates and equipment identification to the main station system. The main station system updates this information in the power grid geographic information system and displays it on the electronic map.

[0068] In this embodiment of the application, step S6 involves periodically performing automatic positioning to evaluate the overall accuracy of the current positioning. If the error exceeds a set threshold, automatic repositioning and parameter adjustment are triggered, including: The positioning process is executed periodically. The overall accuracy of the positioning is evaluated by comparing the error between the calculated coordinates of the beacon node and its actual coordinates. When the average error exceeds the set threshold, repositioning is automatically triggered and the algorithm parameters are adjusted.

[0069] Specifically, the positioning process is executed periodically (e.g., monthly) or triggered by events (e.g., when there are significant changes in network topology). The overall accuracy of the positioning is evaluated by comparing the calculated coordinates of the beacon node with its actual coordinates. If the average error exceeds a threshold of 5 meters, repositioning can be automatically triggered, or algorithm parameters (e.g., weights) can be adjusted. ).

[0070] In an alternative implementation, an evaluation scheme based on network topology changes can also be used.

[0071] Specifically, the system triggers a positioning accuracy assessment by monitoring changes in network topology, using a subset of beacon nodes for coordinate comparison. The system continuously monitors the network topology and records changes in node connection status. When the number of nodes experiencing topology changes exceeds a preset proportion of the total number of nodes, a positioning process is executed. During this process, a subset of beacon nodes are randomly selected, and the error between their coordinates and the actual coordinates is calculated.

[0072] In another alternative implementation, a simplified evaluation scheme based on relative distance error can also be used.

[0073] Specifically, the positioning process is executed periodically, and the relative distance error between beacon nodes is used as the evaluation indicator when assessing accuracy. The positioning process is executed at a fixed cycle (e.g., monthly); the relative distance error between beacon nodes is calculated, which is the difference between the measured distance and the calculated distance; and the average error is calculated based on the relative distance errors of all beacon node pairs.

[0074] In summary, this invention establishes a positioning benchmark by selecting electricity meters with known locations from the existing meter reading communication network as beacon nodes. The distribution area acquisition terminal issues commands to coordinate the entire distribution area into ranging mode, achieving accurate distance measurement through bidirectional message exchange and timestamp recording. After collecting the ranging results, a distance matrix is ​​constructed, and outlier removal and data smoothing ensure data reliability. Based on the distance matrix and beacon node coordinates, a two-stage hybrid positioning algorithm is employed. First, a relative coordinate layout is generated using a multidimensional scaling method, and then the absolute coordinates are solved through similarity transformation and nonlinear optimization. The optimized planar coordinates are projected into latitude and longitude coordinates and uploaded to the main station system, completing the automatic updating and visualization of geographical location information. The system also features a positioning performance evaluation and adaptive relocation mechanism, ensuring long-term positioning accuracy through error monitoring and parameter adjustment. This invention enables automatic and accurate positioning of massive numbers of electricity meters with zero hardware modification cost, effectively overcoming the high cost and low efficiency problems caused by relying on manual inspections or external positioning modules in traditional solutions, and significantly improving the informatization and intelligence level of distribution area operation and maintenance management.

[0075] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a low-cost automatic location system for a large number of electricity meters in a distribution area, including: The beacon node selection module selects energy meters with known geographical location information from the automated meter reading communication network of the low-voltage distribution area as beacon node coordinates, providing a reference benchmark for subsequent positioning. The ranging control module issues a positioning start command, coordinates the entire network to enter ranging mode, controls the energy meter module to perform bidirectional message exchange, accurately records the timestamp, and obtains the bidirectional ranging results. The distance matrix processing module collects all successful two-way ranging results, constructs a symmetric distance matrix, removes outliers, and averages the ranging results. The coordinate calculation module, based on the symmetric distance matrix and the known beacon node coordinates, calculates the coordinates of all unknown location energy meter modules through a two-stage hybrid algorithm and optimizes the Cartesian coordinate system; The coordinate projection module projects the optimized Cartesian coordinate system onto the latitude and longitude coordinates of the earth to obtain the final geographical coordinates of all electricity meters and their corresponding device identifiers, and packages and uploads them to the main station system for visualization on the electronic map. The positioning evaluation module periodically executes the automatic positioning process. It evaluates the overall accuracy by comparing the calculated coordinates of the beacon nodes with the actual coordinates. When the average error exceeds a set threshold, it automatically triggers repositioning and adjusts the algorithm parameters.

[0076] This embodiment also provides an electronic device suitable for a low-cost automatic location method for a large number of electricity meters in a distribution area, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a low-cost automatic location method for a large number of electricity meters in a distribution area as proposed in the above embodiment.

[0077] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a low-cost automatic location method for a large number of electricity meters in a distribution area, as proposed in the above embodiment.

[0078] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for automatically locating the geographical location of a large number of electricity meters in a distribution area proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0079] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-cost method for automatic location positioning of a large number of electricity meters in a distribution area, characterized in that: include, Select electricity meters with known geographical location information from the automated meter reading communication network of the low-voltage distribution area as beacon node coordinates; The communication network issues a positioning start command, coordinates the entire network in the station area to enter ranging mode, conducts two-way message exchange, records timestamps, and obtains two-way ranging results; Collect all successful two-way ranging results, construct a symmetric distance matrix, remove outliers, and measure the average value; Based on the symmetric distance matrix and the known beacon node coordinates, the coordinates of all unknown beacon node locations are obtained, and an optimized Cartesian coordinate system is derived. The optimized Cartesian coordinate system is projected onto the geodetic latitude and longitude coordinates to obtain the final geographical location coordinates and corresponding device identifiers of all electricity meters, and then uploaded to the main station system. Regularly perform automatic positioning to evaluate the overall accuracy of the positioning. If the error exceeds the set threshold, automatically trigger repositioning and adjust parameters.

2. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 1, characterized in that: The step of selecting electricity meters with known geographical location information as beacon node coordinates from the automated meter reading communication network of the low-voltage distribution area includes: The existing automated meter reading communication network of the low-voltage distribution area is utilized. The communication network adopts a mesh topology and includes a distribution area acquisition terminal and an electricity meter communication module. Electricity meters with known geographical location information are selected as beacon nodes.

3. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 2, characterized in that: The communication network issues a positioning start command, coordinates the entire network in the area to enter ranging mode, performs bidirectional message exchange, records timestamps, and obtains bidirectional ranging results, including... The data acquisition terminal in the distribution area issues a unified positioning start command to coordinate all networked energy meter modules in the distribution area to enter the ranging mode. Each energy meter module exchanges bidirectional messages with all neighboring modules within its communication range and accurately records the timestamp.

4. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 3, characterized in that: The process involves collecting all successful two-way ranging results, constructing a symmetric distance matrix, removing outliers, and measuring the average value, including... The data acquisition terminal in the distribution area serves as the data aggregation point, collecting all successful two-way ranging results from the entire network to construct an N×N symmetric distance matrix D, where N is the total number of electricity meters in the distribution area, and the matrix elements... This represents the measured distance between node i and node j; If the measured distance is greater than the physical dimensions of the transformer area, it is considered a measurement error and discarded. Bidirectional distance measurements are then performed between the same pair of modules, and the average value is taken as the final measurement. .

5. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 4, characterized in that: Based on the symmetric distance matrix and known beacon node coordinates, the coordinates of all unknown beacon node locations are obtained, resulting in an optimized Cartesian coordinate system, including... Based on the distance matrix D and the known coordinates of the beacon nodes, the two-dimensional geographic coordinates of all unknown location energy meter modules are solved. When the diameter of the transformer area is smaller than the set value, the latitude and longitude coordinates are projected into the local plane rectangular coordinate system, and a two-stage hybrid algorithm is used for calculation. Phase 1 involves generating a global relative map; Calculate the bicentric inner product matrix using the distance matrix. D Construct the squared distance matrix , element is The inner product matrix is ​​calculated through a double centering operation. B , in, , N This represents the total number of electricity meters in the distribution area. I It is an N×N identity matrix, and 1 is an N×1 column vector consisting entirely of 1s. It is an N×N matrix where each element is , J It is an N×N matrix, also known as a doubly centered matrix; inner product matrix B Perform eigenvalue decomposition. in, It is a diagonal matrix composed of eigenvalues. It is the corresponding eigenvector matrix; Take the two largest eigenvalues and and the corresponding feature vectors and The relative coordinate matrix of all nodes for, ; Phase two involves absolute coordinate transformation and precise optimization; Using the known true coordinates of the beacon nodes and the relative coordinates obtained in stage one Find an optimal similarity transformation, which is expressed as: in, s It is a scaling factor. R It is a rotation matrix. t It is a translation vector, which can be solved to obtain , This is the optimal solution for the scaling factor. The optimal solution for the rotation matrix. The optimal solution for the translation vector; The relative coordinate matrix of all nodes obtained in stage one When applied to similarity transformations, preliminary absolute coordinate estimates are obtained. , is represented as , Preliminary absolute coordinate estimation Using the initial values, we construct a weighted nonlinear least squares problem to optimize the coordinates and fit the actual distance measurement data. The objective function is: in, = ) are the coordinates to be optimized for node i. = ) are the coordinates to be optimized for node j. Coordinates to be optimized X Jacobian matrix, It is the set of neighbors that can successfully measure distances with node i. This represents the measured distance between node i and node j. It is a weighting factor, set according to the signal-to-noise ratio of the measured distance; measurements with a higher signal-to-noise ratio are assigned greater weight; G is the set of beacon nodes. It is a regularization parameter. The true coordinates of the energy meter k at the beacon node. The temporary coordinates for the beacon node currently being fine-tuned; The LM algorithm is used to solve the nonlinear least squares problem, and the iterative update formula is used to solve for the coordinate update in the current iteration step. Update the coordinate X until convergence, using the formula: in, H It is the objective function. W It is determined by the weighting factor The diagonal matrix formed r It is the residual vector. μ It is the damping factor. I It is an identity matrix.

6. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 5, characterized in that: The process involves projecting the optimized Cartesian coordinate system onto a geodetic latitude and longitude coordinate system to obtain the final geographical location coordinates and corresponding device identifiers of all electricity meters, and then uploading this data to the main station system. The optimized Cartesian coordinates are projected onto the geodetic latitude and longitude coordinates. The data acquisition terminal in the distribution area packages the final geographical coordinates of all electricity meters and their corresponding equipment identifiers, and uploads them to the main station system. The main station system updates this information to the power grid geographic information system and displays it on the electronic map.

7. The low-cost automatic location method for a large number of electricity meters in a distribution area as described in claim 6, characterized in that: The system periodically performs automatic positioning, evaluates the overall accuracy of the current positioning, and automatically triggers repositioning and parameter adjustment when the error exceeds a set threshold. The positioning process is executed periodically. The overall accuracy of the positioning is evaluated by comparing the error between the calculated coordinates of the beacon node and its actual coordinates. When the average error exceeds the set threshold, repositioning is automatically triggered and the algorithm parameters are adjusted.

8. A low-cost automatic location system for a large number of electricity meters in a distribution area, employing the low-cost automatic location system for a large number of electricity meters in a distribution area as described in any one of claims 1 to 7, characterized in that, include: The beacon node selection module selects energy meters with known geographical location information from the automated meter reading communication network of the low-voltage distribution area as beacon node coordinates, providing a reference benchmark for subsequent positioning. The ranging control module issues a positioning start command, coordinates the entire network to enter ranging mode, controls the energy meter module to perform bidirectional message exchange, accurately records the timestamp, and obtains the bidirectional ranging results. The distance matrix processing module collects all successful two-way ranging results, constructs a symmetric distance matrix, removes outliers, and averages the ranging results. The coordinate calculation module, based on the symmetric distance matrix and the known beacon node coordinates, calculates the coordinates of all unknown location energy meter modules through a two-stage hybrid algorithm and optimizes the Cartesian coordinate system; The coordinate projection module projects the optimized Cartesian coordinate system onto the latitude and longitude coordinates of the earth to obtain the final geographical coordinates of all electricity meters and their corresponding device identifiers, and packages and uploads them to the main station system for visualization on the electronic map. The positioning evaluation module periodically executes the automatic positioning process. It evaluates the overall accuracy by comparing the error between the calculated coordinates and the actual coordinates of the beacon nodes. When the average error exceeds a set threshold, it automatically triggers repositioning and adjusts the algorithm parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the low-cost automatic location method for a large number of electricity meters in a distribution area, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-cost automatic location method for a large number of electricity meters in a distribution area, as described in any one of claims 1 to 7.

Images