A smart meter acquisition terminal load optimization configuration method and system

Abstract

The application discloses a kind of intelligent electric meter acquisition terminal load optimization configuration method, comprising the following steps: S1, the basic data of target area intelligent electric meter acquisition system, operating data and the cost parameters of each type acquisition terminal are obtained;Based on the basic data and operating data, the relationship model of acquisition success rate and acquisition terminal load quantity is established;Based on the cost parameters, total cost model is established;S2, with the total cost of all acquisition terminals in the target area as objective function, with the load configuration scheme of each acquisition terminal as decision variable, with the overall average acquisition success rate of the target area not less than the preset target value as constraint condition, optimization problem is constructed;S3, the optimization problem is solved using mathematical optimization solver, and the optimal load configuration scheme of each acquisition terminal is generated.The present application realizes scientific balance and global optimal configuration between system construction operation cost and data acquisition reliability.

Description

Technical Field

[0001] This invention relates to the field of power system automation and data acquisition technology, and in particular to a method and system for optimizing the load configuration of a smart meter data acquisition terminal. Background Technology

[0002] Smart meters and their automatic meter reading systems are the foundation for smart grids to achieve user-side sensing, advanced metering, and line loss analysis. A typical residential electricity consumption information collection system adopts a hierarchical architecture of "smart meter - collector - concentrator - master station." The collector is responsible for aggregating data from multiple meters, while the concentrator manages multiple collectors or directly connected meters. The collectors and concentrators are collectively referred to as the data acquisition terminal, which is the hub of the entire communication network.

[0003] Currently, in the construction and upgrading of data acquisition systems, the planning of the number of data collectors and concentrators, as well as their load (i.e., the number of connected meters or downstream devices), largely relies on the experience-based recommendations of equipment manufacturers, the on-site experience of technicians, or a simple principle of average allocation. This extensive configuration approach has significant drawbacks: 1. Resource waste and high costs: Conservatively configuring too many terminals to ensure reliability leads to high costs for equipment procurement, installation, and long-term operation and maintenance, such as... Figure 1 As shown on the right.

[0004] 2. Performance Bottlenecks and Data Acquisition Failures: Excessive load configuration, exceeding the processing capacity and communication bandwidth of the acquisition terminal, can easily lead to problems such as data packet conflicts, response timeouts, and processor overload. This directly manifests as a decrease in the daily success rate of frozen data acquisition. Figure 1 As shown on the left. This problem is particularly prominent in densely populated user areas.

[0005] 3. Lack of scientific basis and global optimization: It is impossible to make a scientific and quantitative trade-off between the two key objectives of "total system cost" and "overall data acquisition success rate". The decision lacks data support and it is difficult to maximize the return on investment.

[0006] Existing technological improvements largely focus on optimizing single-device communication protocols or enhancing hardware performance, such as improving HPLC (High-Speed ​​Power Line Carrier) communication chips. However, these improvements cannot fundamentally solve the resource misallocation problem caused by improper planning at the system level. Although some studies mention load balancing, they are usually limited to simple threshold alarms or dynamic adjustments based on real-time traffic, lacking methods for forward-looking, statically optimal configuration based on historical data and cost models during the planning phase.

[0007] Therefore, there is an urgent need for a method and tool that can provide the optimal configuration scheme for the load of the data acquisition terminal based on objective data and through modeling and optimization algorithms during the system planning, design, or modification and optimization stage.

[0008] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art. Summary of the Invention

[0009] The purpose of this invention is to provide a method for optimizing the load configuration of smart meter data acquisition terminals, in order to solve the technical problem that existing smart meter data acquisition systems rely on experience and lack quantitative basis in the load planning of data acquisition terminals (collectors / concentrators), and are unable to achieve a scientific balance and globally optimal configuration between system construction and maintenance costs and data acquisition reliability.

[0010] To achieve the above objectives, the present invention provides a method for optimizing the load configuration of a smart meter data acquisition terminal, comprising the following steps: S1. Obtain the basic data, operational data, and cost parameters of various types of acquisition terminals of the smart meter acquisition system in the target area; based on the basic data and operational data, establish a relationship model between the acquisition success rate and the number of acquisition terminals; based on the cost parameters, establish a total cost model. S2. An optimization problem is constructed with the objective function of minimizing the total cost of all acquisition terminals in the target area, the decision variable being the load configuration scheme of each acquisition terminal, and the constraint that the overall average acquisition success rate of the target area is not lower than a preset target value. S3. Use a mathematical optimization solver to solve the above optimization problem and generate the optimal load configuration scheme for each acquisition terminal.

[0011] Optionally, the data acquisition terminal includes a data collector or a concentrator.

[0012] Optionally, the basic data includes: a list of users in the target area, and the topological connection relationship between all smart meters and data acquisition terminals; the user list is used to determine the total number of users; the topological connection relationship is used to determine the total number of data acquisition terminals and the load of each data acquisition terminal in its current configuration state; The operational data includes historical operational data or field test data, which is used to determine the acquisition success rate under different load ranges. The cost parameters include: the unit price of various data acquisition terminals, standard installation and commissioning costs, and operation and maintenance costs under different load rates.

[0013] Optionally, the relationship model between the acquisition success rate and the number of acquisition terminals is a piecewise discrete function, which divides the number of terminals into multiple intervals and assigns a acquisition success rate value based on statistics or experiments to each interval.

[0014] Optionally, the total cost is the sum of equipment procurement cost, installation cost, and operation and maintenance cost, wherein the operation and maintenance cost of a single data acquisition terminal is related to its load quantity; the load configuration scheme includes at least the total number of data acquisition terminals and the load quantity of each data acquisition terminal; The total cost model is expressed by the following formula: In the formula, For total cost, This is the sum of the unit price of the data acquisition terminal and the standard installation and commissioning cost. This represents the total number of data collection terminals. For the first j The number of data acquisition terminals under load. For the first j The unit operation and maintenance cost of the data acquisition terminal throughout its entire life cycle.

[0015] Optionally, the overall average acquisition success rate constraint is expressed by the following formula: in, M This represents the total number of data collection terminals. U j For the first j The number of users covered by the data collection terminal L j For the first j The number of data acquisition terminals under load; P ( L j ) is the first j The data acquisition terminal is carrying L j The acquisition success rate corresponding to each load is determined by the relationship model between the acquisition success rate and the number of acquisition terminals under load. η This is the preset target value for the success rate of data collection.

[0016] Optionally, in step S3, the solution result is a set of optimal load configuration schemes. The optimal load configuration schemes include the total number of data acquisition terminals, the optimal load quantity configuration for each data acquisition terminal, and the type of data acquisition terminal to be deployed at each terminal location, as well as the smart meter identifier to be connected to each data acquisition terminal.

[0017] Optionally, the method for optimizing the load configuration of the smart meter data acquisition terminal further includes the following steps: S4. Based on the optimal load configuration scheme, deploy, install, debug, configure files and set communication parameters of the acquisition terminal on site, and collect subsequent running data to verify the effect and for model feedback optimization.

[0018] To achieve the above objectives, the present invention also provides a smart meter data acquisition terminal load optimization configuration system for implementing the above-described smart meter data acquisition terminal load optimization configuration method. The system includes: Data management module: used to perform the data acquisition part in step S1, access and store the basic data, historical operation data and cost parameter database of the smart meter data acquisition system in the target area, and clean and preprocess the data; Model building module: Connected to the data management module, used to execute the modeling part in step S1, and build the relationship model between the acquisition success rate and the number of acquisition terminals and the total cost model based on the input data; Optimization Solving Module: Connected to the model building module, used to execute steps S2 and S3, automatically construct the optimization problem, call the built-in or external optimization solver for calculation, and output the optimal load configuration scheme; Solution Deployment and Verification Module: Connects the optimization solution module and the production management system, and is used to execute step S4, convert the optimal load configuration scheme into equipment installation work orders and file configuration instructions, and receive feedback data for effect verification and model iteration.

[0019] Optionally, the system also includes a visualization module to display cost comparisons before and after optimization, data collection success rate predictions, and load distribution in chart form.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects: 1. High professionalism and accuracy: It transforms configuration work that relies on personal experience into a scientific decision-making process based on data and models, significantly improving the professionalism and accuracy of planning work by shifting from experience to science.

[0021] 2. Achieve global optimization: By optimizing the entire regional system, the overall performance or economic losses caused by local optimization are avoided. It can find the configuration scheme with the highest data acquisition success rate under strict cost budget, or find the lowest cost scheme while ensuring data acquisition success rate.

[0022] 3. Significant economic benefits: Through refined configuration, unnecessary investment in terminal equipment can be effectively reduced, long-term operation and maintenance pressure can be reduced, and capital expenditures and operating costs can be directly saved.

[0023] 4. Improve operational metrics: The planning process ensures the communication reliability of the data acquisition system, laying a solid foundation for improving key operational metrics such as the daily freeze data acquisition success rate and reducing line loss statistical errors.

[0024] 5. High flexibility and scalability: The cost parameters, performance curves and constraints in the model can be flexibly adjusted according to the actual conditions in different regions. The method can be applied to various electricity information collection systems of different scales, equipment types and communication standards. Attached Figure Description

[0025] Figure 1 This is a schematic diagram illustrating the principles of two scenarios in existing technologies: data acquisition failure due to excessive terminal load and high cost despite reasonable load. Figure 2 This is a schematic diagram of the piecewise discrete relationship model between the acquisition success rate and the number of terminal loads in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the overall process of the method of the present invention. Figure 4 This is a schematic diagram comparing the cost and success rate of two configuration schemes (uniform scheme and hybrid optimization scheme) generated by the optimization solution module in one embodiment of the present invention. Detailed Implementation

[0026] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the intelligent meter data acquisition terminal load optimization configuration method and system proposed in this invention. The advantages and features of this invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the embodiments of this invention. Please refer to the accompanying drawings to make the objectives, features, and advantages of this invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0027] This invention provides a method for optimizing the load configuration of a smart meter data acquisition terminal, such as... Figure 3 As shown, the method includes the following steps: S1. Data acquisition and modeling, specifically including the following steps: S1.1 Data Acquisition: Comprehensively acquire the basic data, operational data, and cost parameters of various types of acquisition terminals for the smart meter data acquisition system in the target area (such as a transformer substation or a power supply station). The acquisition terminals include data collectors or concentrators.

[0028] The basic data includes at least: a list of users in the target area and the topological connection relationship between all smart meters and data collection terminals; the user list is used to determine the total number of users; the topological connection relationship is used to determine the total number of data collection terminals and the load of each data collection terminal in its current configuration state; wherein, when the data collection terminal is a data collector, each load corresponds to one smart meter; when the data collection terminal is a concentrator, each load corresponds to one data collector or one smart meter. The basic data may also include the performance specifications of the acquisition terminals, which are used to determine the upper limit of the load capacity of each acquisition terminal.

[0029] The operational data includes historical operational data or field test data collected through a dedicated test, used to determine the acquisition success rate under different load ranges; wherein, the historical operational data includes historical daily frozen acquisition success rate logs.

[0030] The cost parameters include: the unit purchase price of various data acquisition terminals, standard installation and commissioning costs, and unit operation and maintenance costs under different load rates (such as the average annual operation and maintenance cost over the entire life cycle); wherein, the operation and maintenance costs include fault handling costs and inspection costs; The cost parameters may also include the concentrator's communication costs (such as 4G data charges).

[0031] S1.2 Performance Modeling: Based on the historical operating data or field test data, as well as the basic data, a key performance model is established—a model showing the relationship between the acquisition success rate and the number of acquisition terminals under load. Based on the aforementioned cost parameters, a total cost model is established that includes equipment procurement costs, installation costs, and operation and maintenance costs.

[0032] The relationship between the acquisition success rate and the number of acquisition terminals is typically represented as a piecewise discrete function. This is achieved by dividing the number of terminals into multiple intervals and assigning a constant acquisition success rate value based on statistics or experiments to each interval. Figure 2 As shown, as the load increases, the acquisition success rate starts to decline from close to 100%, and when the load exceeds a certain critical point, the acquisition success rate deteriorates sharply.

[0033] The total cost is the sum of equipment procurement cost, installation cost, and operation and maintenance cost. The operation and maintenance cost of a single data acquisition terminal is related to the range of its load capacity; different load capacity ranges correspond to different unit operation and maintenance cost values. The total cost model can be simplified as follows: In the formula, For total cost, This is the sum of the unit price of the data acquisition terminal and the standard installation and commissioning cost. This represents the total number of data collection terminals. For the first j The number of data acquisition terminals under load. For the first j The unit operation and maintenance cost of the data acquisition terminal throughout its entire life cycle.

[0034] When the data acquisition terminal is a concentrator, the total cost model can take into account communication costs in addition to equipment procurement costs, installation costs, and operation and maintenance costs.

[0035] Furthermore, the method of this invention is universally applicable, suitable for scenarios where only data acquisition devices are deployed (such as some older residential areas or specific substations), scenarios where only concentrators are deployed (such as novel HPLC direct connection solutions), and complex scenarios where both exist simultaneously. Regardless of the scenario, independent performance and cost models need to be established for each type of data acquisition terminal. That is, independent models relating the data acquisition success rate to the number of data acquisition terminals and the total cost model are constructed for both the data acquisition device and the concentrator.

[0036] S1.3 Differentiated Modeling of Collectors and Concentrators: It should be noted that the method described in this invention is applicable to load optimization configuration of various data acquisition terminals, but for different types of data acquisition terminals, differentiated modeling is required based on their technical characteristics and application scenarios. (1) Key points of data collector modeling a. Load is defined as the number of directly connected smart meters; b. The performance model is based on the load capacity of the RS-485 bus or the quality of power line carrier communication (i.e., the fundamental reason why the acquisition success rate decreases with the increase of the load is that the physical carrying capacity of the communication medium is limited), and it is usually expressed as a piecewise discrete function. c. The total cost model includes equipment procurement costs, installation costs, and operation and maintenance costs that are linearly related to the load quantity; d. In addition to the core constraint of acquisition success rate, other constraints are mainly physical connection reachability and bus load capacity limit.

[0037] (2) Key points of concentrator modeling a. Load is defined as the number of directly connected data collectors or smart meters (depending on the communication architecture); b. The performance model needs to consider multiple factors such as uplink communication bandwidth, CPU processing power, and memory capacity (that is, for the concentrator, when the number of loads increases, it will occupy more communication bandwidth, consume more CPU resources, and occupy more memory. When the above resources reach the bottleneck, it will lead to a decrease in the acquisition success rate). In addition to using piecewise discrete functions, continuous functions can also be used, and concurrent processing models can be introduced. c. In addition to equipment, installation, and maintenance costs, the total cost model can also consider communication costs (such as 4G data charges). d. In addition to the core constraint of acquisition success rate, other constraints mainly include uplink bandwidth constraint, concurrent processing capability constraint, and signal coverage constraint.

[0038] (3) Complex scene processing In complex scenarios where both collectors and concentrators exist, hierarchical optimization can be implemented: first, optimize the collector layer to obtain the optimal load configuration for each collector; then, use the collector optimization results as input to optimize the concentrator layer configuration. Alternatively, a joint optimization model can be constructed, incorporating the decision variables of both collectors and concentrators into a mixed-integer linear programming problem for solution.

[0039] S2. Optimize problem construction.

[0040] Construct a mixed-integer linear programming problem, the decision variable of which is the load configuration scheme of the data acquisition terminals. The load configuration scheme includes at least the total number of data acquisition terminals and the load of each data acquisition terminal (or a specific list of connected electricity meters). The objective function is to minimize the total lifecycle cost (T) of all acquisition terminals within the target area; the core constraint is that the overall average acquisition success rate of the optimized target area must reach or exceed the preset target value (e.g., 99.98%).

[0041] The overall average acquisition success rate constraint is expressed by the following formula: in, M This represents the total number of data collection terminals. U j For the first j The number of users covered by the data collection terminal L j For the first j The number of data acquisition terminals under load; P ( L j ) is the first j The data acquisition terminal is carrying L j The acquisition success rate corresponding to each load is determined by the relationship model between the acquisition success rate and the number of acquisition terminals under load. η This is the preset target value for the success rate of data collection.

[0042] Meanwhile, for the data collector, constraints can also be included, such as the upper limit of the load capacity of each terminal, physical connection reachability, and coverage of all users; for the concentrator, constraints can also be included, such as uplink bandwidth constraints, concurrent processing capacity constraints, and signal coverage constraints. Thus, a mixed-integer linear programming problem is constructed.

[0043] S3. Optimization and Solution Generation.

[0044] The above mixed-integer linear programming problem is solved using a mathematical optimization solver to generate the optimal load configuration scheme for each acquisition terminal.

[0045] The solution results in a set of optimal load configuration schemes, which include the total number of collectors or concentrators, the optimal load configuration for each collector or concentrator, and specify the type of data acquisition terminal to be deployed at each terminal point, as well as the specific electricity meters to be connected to each data acquisition terminal.

[0046] The mathematical optimization solver can employ optimization algorithms from the CPLEX, Gurobi, or SciPy libraries. Preferably, a linear programming or integer programming solver is used. When a continuous solution is obtained using linear programming, the continuous solution is rounded and its feasibility is adjusted to obtain an integer solution that conforms to engineering practice.

[0047] S4. Implementation and verification of the plan.

[0048] The optimal load configuration scheme is translated into work orders and configuration instructions to guide the deployment, installation, debugging, file configuration, and communication parameter settings of the data acquisition terminals on-site. After implementation, the data acquisition success rate is continuously monitored for one cycle and compared with the optimized prediction value to verify the effect and use it for model feedback optimization.

[0049] Furthermore, the present invention also provides a load optimization configuration system for a smart meter data acquisition terminal, used to implement the above method, the system comprising: Data management module: used to execute step S1.1, access and store basic data, historical operating data and cost parameter database of smart meter collection system in target area, and clean and preprocess the data; Model building module: connected to the data management module, used to execute step S1.2, and build the relationship model between the acquisition success rate and the number of acquisition terminals and the total cost model based on the input data; Optimization Solving Module: Connected to the model building module, it is used to execute steps S2 and S3, automatically construct the mixed integer linear programming problem, call the built-in or external optimization solver for calculation, and output the optimal load configuration scheme; Solution Deployment and Verification Module: Connects the optimization solution module and the production management system, and is used to execute step S4, convert the optimal load configuration scheme into specific equipment installation work orders and file configuration instructions, and receive feedback data for effect verification and model iteration.

[0050] The system also includes a visualization module, which displays cost comparisons before and after optimization, data collection success rate predictions, load distribution, etc., in chart form.

[0051] The system can be deployed in the cloud or on a local server to provide decision support tools for power grid planning and operation and maintenance personnel.

[0052] To further illustrate the specific implementation process and technical effects of the method described in this invention, the invention will be described in detail below with reference to specific embodiments. This embodiment takes the optimization of a low-voltage residential electricity consumption information collection system in a certain area of ​​Shanghai as an application scenario. This area has a large number of transformer substations using the collector mode, which face the problem of fluctuating collection success rates.

[0053] S1. Data Acquisition and Modeling.

[0054] S1.1 Data Acquisition.

[0055] A list of all users using the data collector mode within the target area, totaling 4374 households, was exported from the electricity information collection master station system. In this embodiment, each user corresponds to one smart meter. The historical daily frozen data collection success records for these users over the past three months were obtained. The unit price of the data collector (25 yuan / unit) and the standard installation cost (30 yuan / unit) were obtained from the materials department. Through research by the operations and maintenance department, the annual operation and maintenance cost of a single data collector under different load levels was estimated, as shown in Table 1.

[0056] Table 1 Relationship between data collector load range and operation and maintenance costs S1.2 Performance Modeling.

[0057] A special test was conducted using 10 data collectors of the same model, with different numbers of households (from 1 to 18) assigned to each. Each test was performed 100 times, and the success rate was calculated. The performance data is shown in Table 2.

[0058] Table 2. Data on data acquisition unit load and acquisition success rate The data in this table is abstracted into a piecewise discrete function P(L) as the performance model. The total cost model is simplified to: in T For total cost, N The total number of collectors. L k For the first k The number of data acquisition devices under load. b(L k ) For the first k The operation and maintenance cost of each data acquisition unit.

[0059] S2. Optimize problem construction.

[0060] Decision variable: Each data collector k Number of loads L k And the number of loads L k It is an integer variable; Objective function: Minimize total cost T ; Constraints: (1) Coverage for all users: .

[0061] (2) Load range per unit: .

[0062] (3) Overall success rate requirements: .

[0063] This problem is a typical mixed-integer linear programming problem.

[0064] S3. Optimization Solution and Solution Generation Using the Python programming language, the linear programming solver in the SciPy.optimize library was invoked, along with custom integer processing logic. The calculated optimal configuration scheme is not a traditional "one-size-fits-all" approach, but rather a differentiated mixed load scheme: some data collectors are configured to support 3 households per unit, while others are configured to support 4 households per unit. Specifically: 2190 households using the 3-household / unit scheme require 730 data collectors; 2184 households using the 4-household / unit scheme require 546 data collectors; a total of 1276 data collectors are needed.

[0065] To illustrate the optimization effect, it is compared with two traditional schemes: 1) Conservative uniform scheme: all units are configured at 3 households / unit, requiring 1458 units; 2) High-load uniform scheme: all units are configured at 10 households / unit, requiring 438 units. The comparison results are shown in Table 3 and... Figure 4 As shown.

[0066] Table 3 Comparison of different configuration schemes As can be seen from the table and figure, the present invention reduces the total cost by about 12.5% ​​compared with the conservative solution while ensuring a success rate of ≥99.99%, achieving the best balance between cost and reliability.

[0067] S4. Implementation and Verification of the Solution Following the aforementioned hybrid optimization scheme, representative transformer substations were selected for implementation. On-site staff added or adjusted data collectors at designated locations according to the scheme checklist, ensuring that the actual number of connected households for each collector matched the hybrid optimization scheme, and correctly configured the files in the main station system. After implementation, the daily frozen data collection success rate was monitored for two consecutive weeks, averaging over 99.99%, consistent with the optimization prediction, successfully increasing the original data collection success rate from approximately 99.97% to the target value.

[0068] It should be understood that the above embodiments are merely preferred embodiments of the present invention and not intended to limit it. Various modifications and substitutions can be made to the performance model establishment method (e.g., using a regression model), the refinement of cost items, and the selection of optimization algorithms without departing from the principles of the present invention. For example, for concentrator optimization, constraints such as uplink communication bandwidth and CPU processing power can be further considered to construct a more complex model. All such modifications and substitutions should be considered to fall within the protection scope of the present invention.

[0069] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0070] In the description of this invention, it should be understood that the terms "center," "height," "thickness," "upper," "lower," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0071] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0072] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0073] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A smart meter terminal load optimization configuration method, characterized in that, Includes the following steps: S1. Obtain the basic data, operational data, and cost parameters of various types of data acquisition terminals of the smart meter data acquisition system in the target area; Based on the aforementioned basic data and operational data, a model is established to show the relationship between the data acquisition success rate and the number of data acquisition terminals under load; based on the aforementioned cost parameters, a total cost model is established. S2. An optimization problem is constructed with the objective function of minimizing the total cost of all acquisition terminals in the target area, the decision variable being the load configuration scheme of each acquisition terminal, and the constraint that the overall average acquisition success rate of the target area is not lower than a preset target value. S3. Use a mathematical optimization solver to solve the above optimization problem and generate the optimal load configuration scheme for each acquisition terminal.

2. The smart meter terminal load optimization configuration method of claim 1, wherein, The data acquisition terminal includes a data collector or a data concentrator.

3. The smart meter terminal load optimization configuration method of claim 1, wherein, The basic data includes: a list of users in the target area, and the topological connection relationship between all smart meters and data acquisition terminals; the user list is used to determine the total number of users; the topological connection relationship is used to determine the total number of data acquisition terminals and the load of each data acquisition terminal in its current configuration state; The operational data includes historical operational data or field test data, which is used to determine the acquisition success rate under different load ranges. The cost parameters include: the unit price of various data acquisition terminals, standard installation and commissioning costs, and operation and maintenance costs under different load rates.

4. The smart meter terminal load optimization configuration method of claim 3, wherein, The relationship model between the acquisition success rate and the number of acquisition terminals is a piecewise discrete function. It divides the number of terminals into multiple intervals and assigns a acquisition success rate value based on statistics or experiments to each interval.

5. The smart meter terminal load optimization configuration method of claim 3, wherein, The total cost is the sum of equipment procurement cost, installation cost, and operation and maintenance cost, wherein the operation and maintenance cost of a single data acquisition terminal is related to its load quantity; the load configuration scheme includes at least the total number of data acquisition terminals and the load quantity of each data acquisition terminal. The total cost model is expressed by the following formula: In the formula, For total cost, This is the sum of the unit price of the data acquisition terminal and the standard installation and commissioning cost. This represents the total number of data collection terminals. For the first j The number of data acquisition terminals under load. For the first j The unit operation and maintenance cost of the data acquisition terminal throughout its entire life cycle.

6. The smart meter terminal load optimization configuration method of claim 5, wherein, The overall average acquisition success rate constraint is expressed by the following formula: in, M This represents the total number of data collection terminals. U j For the first j The number of users covered by the data collection terminal L j For the first j The number of data acquisition terminals under load; P ( L j ) is the first j The data acquisition terminal is carrying L j The acquisition success rate corresponding to each load is determined by the relationship model between the acquisition success rate and the number of acquisition terminals under load. η This is the preset target value for the success rate of data collection.

7. The smart meter terminal collection terminal load optimization configuration method of claim 1, wherein, In step S3, the solution result is a set of optimal load configuration schemes. The optimal load configuration schemes include the total number of data acquisition terminals, the optimal load quantity configuration for each data acquisition terminal, the type of data acquisition terminal to be deployed at each terminal point, and the smart meter identifier to be connected to each data acquisition terminal.

8. The smart meter terminal collection terminal load optimization configuration method of claim 1, wherein, It also includes the following steps: S4. Based on the optimal load configuration scheme, deploy, install, debug, configure files and set communication parameters of the acquisition terminal on site, and collect subsequent running data to verify the effect and for model feedback optimization.

9. A smart meter terminal load optimization configuration system, characterized in that, For implementing the method as described in any one of claims 1-8, the system comprises: Data management module: used to perform the data acquisition part in step S1, access and store the basic data, historical operation data and cost parameter database of the smart meter data acquisition system in the target area, and clean and preprocess the data; Model building module: Connected to the data management module, used to execute the modeling part in step S1, and build the relationship model between the acquisition success rate and the number of acquisition terminals and the total cost model based on the input data; Optimization Solving Module: Connected to the model building module, used to execute steps S2 and S3, automatically construct the optimization problem, call the built-in or external optimization solver for calculation, and output the optimal load configuration scheme; Solution Deployment and Verification Module: Connects the optimization solution module and the production management system, and is used to execute step S4, convert the optimal load configuration scheme into equipment installation work orders and file configuration instructions, and receive feedback data for effect verification and model iteration.

10. The smart metering terminal load optimization configuration system of claim 9, wherein, The system also includes a visualization module, which displays cost comparisons before and after optimization, data collection success rate predictions, and load distribution in chart form.

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