A method and system for evaluating the installation location of electricity meter data collectors

Abstract

This invention discloses a method and system for evaluating the installation location of electricity meter collectors, relating to the field of power Internet of Things (IoT) data acquisition and management technology. The method includes collecting multi-source environmental data of the installation area, constructing a candidate installation location model to screen installation conditions; for each candidate installation location, using multi-factor inference rules to output a candidate location suitability score; and based on the candidate location suitability score, adjusting rules and indicator weights according to scenario weights to output a comprehensive evaluation score for the candidate location. This method, by collecting multi-source environmental data of the installation area, constructing a candidate installation location model, and dynamically optimizing it using multi-factor inference rules and scenario weights, scientifically evaluates the comprehensive suitability of candidate locations. This effectively improves the scientific rigor and precision of installation location planning, optimizes communication performance, reduces electromagnetic interference risks, enhances maintenance convenience and controllability, strengthens adaptability to complex environments, and improves the overall operational quality and reliability of the electricity meter acquisition system.

Description

Technical Field

[0001] This invention relates to the field of power Internet of Things (IoT) data acquisition and management technology, specifically to a method and system for evaluating the installation location of electricity meter collectors. Background Technology

[0002] Against the backdrop of the rapid development of smart grids and the Internet of Things (IoT) for electricity, electricity meter readers, as key information collection and transmission nodes, undertake the functions of efficient aggregation and remote transmission of electricity metering data. In recent years, with the widespread application of wireless communication technologies such as Low Power Wide Area Networks (LPWAN), NB-IoT, and LoRa in electricity meter reading systems, the deployment density of electricity meter readers has continued to increase, placing higher demands on the rationality of their installation locations. Scientifically planning the installation location of readers not only directly affects the quality of data communication but also relates to the stability of system operation and maintenance costs. Therefore, research on optimizing the installation location of electricity meter readers has gradually attracted industry attention, and some technical solutions have attempted to provide auxiliary installation guidance based on communication signal coverage maps and building structure diagrams.

[0003] Existing technologies often focus on simple references to static parameters, lacking a comprehensive evaluation method that is systematic, multi-dimensional, and dynamically optimized. They typically rely on manual experience combined with a single indicator (such as communication signal strength) for rough site selection, failing to fully consider the complex coupling relationships between multiple factors such as electromagnetic compatibility, structural stability, maintenance accessibility, and construction feasibility. This makes the actual installation effect susceptible to fluctuations in the site environment, and communication quality and subsequent maintenance reliability difficult to guarantee. Existing technologies generally fail to establish complete candidate location models, lacking dynamic screening and nonlinear reasoning optimization mechanisms. They cannot adaptively adjust indicator weights and target parameters for different installation scenarios (such as high-interference industrial areas and old building environments), resulting in insufficient intelligence and precision in installation decision-making. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing methods for evaluating the installation location of electricity meter collectors have single evaluation indicators, lack multi-dimensional comprehensive consideration, cannot achieve dynamic screening and optimization of candidate locations, lack adaptive optimization capabilities for complex scenarios, and how to achieve scientific planning and intelligent auxiliary decision-making for the installation location of electricity meter collectors.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for evaluating the installation location of an electricity meter collector, comprising collecting multi-source environmental data of the installation area and constructing a candidate installation location model to screen installation conditions; the multi-source environmental data of the installation area includes building structure spatial layout, electricity meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality, and electromagnetic interference source distribution; for candidate installation locations, multi-factor inference rules are invoked to output candidate location suitability scores; based on the candidate location suitability scores, scene weight adjustment rules and indicator weights are applied to output a comprehensive evaluation score for the candidate locations; constructing a candidate installation location model to screen installation conditions includes, based on the multi-source environmental data of the installation area, using preset spatial topology rules, automatically constructing a candidate installation location model, dynamically screening a set of location nodes that meet the installation conditions, and constructing a candidate installation location set; invoking multi-factor inference rules to output candidate location suitability scores includes, for the candidate location set, invoking a predefined multi-factor inference rule library, scoring each candidate location based on communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility indicators, and dynamically generating candidate location suitability scores.

[0007] As a preferred embodiment of the method for evaluating the installation location of the electricity meter collector described in this invention, the step of outputting a comprehensive evaluation score of the candidate location based on the scene weight adjustment rules and the indicator weights includes: dynamically adjusting the indicator weights according to the scene weight adjustment rules, outputting a comprehensive evaluation score of the candidate location, and generating a ranking optimization result.

[0008] As a preferred embodiment of the method for evaluating the installation location of the electricity meter collector described in this invention, the step of outputting a comprehensive evaluation score of the candidate location based on the scene weight adjustment rules and index weights includes outputting the installation location based on the candidate location adaptability score and generating an auxiliary decision-making report.

[0009] As a preferred embodiment of the method for evaluating the installation location of the electricity meter collector according to the present invention, the auxiliary decision report includes: reasons for recommendation, detailed location score, and precautions.

[0010] Another objective of this invention is to provide an electricity meter data collector installation location evaluation system that can output a comprehensive evaluation score for candidate locations based on candidate location adaptability scores, scene weight adjustment rules, and indicator weights. This solves the problems of current electricity meter data collector installation location evaluation methods, which cannot achieve dynamic screening and optimization of candidate locations and lack adaptive optimization capabilities for complex scenarios.

[0011] As a preferred embodiment of the meter data acquisition device installation location evaluation system of the present invention, it includes: a multi-source environmental data acquisition module for the installation area, a candidate installation location model construction module, a candidate location adaptability score output module, a comprehensive evaluation module, and an installation location recommendation module; the multi-source environmental data acquisition module for the installation area is used to collect multi-source environmental data of the installation area in real time, including building structure spatial layout, meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality, and electromagnetic interference source distribution; the candidate installation location model construction module is used to construct a candidate installation location model using preset spatial topology rules after the multi-source environmental data acquisition module for the installation area is completed, and to screen locations that meet the safety requirements. The system constructs a candidate installation location set by selecting a set of location nodes that meet the installation conditions. The candidate location suitability score output module, after the candidate installation location model construction module outputs the candidate installation location set, calls a predefined multi-factor inference rule base to infer and output a candidate location suitability score for each candidate location based on indicators such as communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility. The comprehensive evaluation module, after the candidate location suitability score output module outputs the candidate location suitability score, outputs a comprehensive evaluation score for the candidate locations based on the candidate location suitability score and scene weight adjustment rules, generating a ranking result. The installation location recommendation module outputs the installation location based on the ranking result from the comprehensive evaluation module.

[0012] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement a method for evaluating the installation location of an electricity meter collector.

[0013] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for evaluating the installation location of an electricity meter collector.

[0014] The beneficial effects of this invention are as follows: The method for evaluating the installation location of electricity meter collectors provided by this invention collects multi-source environmental data of the installation area and constructs a candidate installation location model, achieving a comprehensive perception and structured expression of the installation environment of the electricity meter collector, providing an accurate data foundation for subsequent evaluation. By using multi-dimensional indicators such as communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility, and invoking multi-factor inference rules to quantitatively score candidate locations, a non-linear evaluation of the comprehensive performance of each location is achieved, overcoming the limitations of traditional single-indicator, static evaluation methods. Furthermore, by dynamically adjusting the weights of each indicator according to scenario weight adjustment rules, the comprehensive evaluation score can be adapted to different application environments, achieving intelligent optimization tailored to local conditions and improving the adaptability of this method to complex and variable installation scenarios. Finally, by outputting the optimal installation location based on comprehensive ranking and generating an auxiliary decision-making report containing recommendation reasons, location score details, and precautions, the installation process becomes more standardized and scientific, reducing decision-making biases caused by differences in human experience. Overall, this invention effectively improves the scientific nature of the installation location planning of electricity meter collectors, the reliability of communication, and the convenience of subsequent maintenance, thereby enhancing the stability and controllability of system operation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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.

[0016] Figure 1 The above is an overall flowchart of a method for evaluating the installation location of an electricity meter data collector, provided in Embodiment 1 of the present invention. Detailed Implementation

[0017] 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.

[0018] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for evaluating the installation location of an electricity meter data collector is provided, comprising:

[0019] S1: Collect multi-source environmental data of the installation area and construct a candidate installation location model to screen installation conditions.

[0020] Furthermore, by using mobile terminals or portable data acquisition devices, on-site inspections can be conducted in the planned installation areas of the electricity meter collectors. Real-time collection of multi-source environmental data, including building structure and spatial layout, electricity meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality (such as NB-IoT / 4G / LoRa), and electromagnetic interference source distribution, is performed and standardized encoding is carried out to generate scene datasets.

[0021] It should be noted that the collected scene dataset In this context, the following subset features are defined: Spatial topology feature set (installation space dimensions, structural load-bearing capacity, obstacle avoidance, etc.) Power supply access feature set : Set of communication environment features Electromagnetic interference characteristic set Maintain the set of reachability features and define the candidate location space. Its mathematical expression is:

[0022] ,

[0023] in, This represents the set of all possible physical location points in the installation area. Indicates the candidate location point. Indicates position Evaluation functions for each feature dimension (with values ​​normalized to) ), This represents the filtering threshold parameter for the corresponding feature dimension.

[0024] S2: For candidate installation locations, call the multi-factor inference rules to output the candidate location suitability score.

[0025] Furthermore, in the selected candidate location set In the middle, for each candidate position By calling a predefined multi-factor inference rule base, a nonlinear multidimensional coupled scoring model is constructed for location. The adaptability is dynamically inferred and calculated to generate a preliminary position adaptability score. .

[0026] Define five core indicator systems, including communication signal quality. Electromagnetic compatibility Convenience of later maintenance Structural stability and construction feasibility For each indicator system, the data collection location is... The following engineering parameters can be quantified through normalization or mapping functions. Convert it into a rating input.

[0027] Candidate positions Calculate the location fit score , represented as:

[0028] ,

[0029] ,

[0030] in, Candidate positions Preliminary fit score, For the first Candidate positions, For the first The smoothing adjustment constant of each indicator. For the first The nonlinear stretching coefficient of each index, For the first Position under each indicator The normalized score, For position In the The original measured values ​​under each indicator For the first The minimum measured value of each indicator among all candidate positions. For the first The maximum measured value of each indicator across all candidate positions. For the first The power-law adjustment parameter for each indicator score. This is the error term suppression coefficient. For the first Position under each indicator The error term function, For the first Target reference values ​​for each indicator is the nonlinear weighting exponent of the error term.

[0031] The range of values ​​is usually ,like This indicates that the location is severely unsuitable for installation, with multiple non-compliance issues or excessive errors; if At the intermediate level (e.g., This indicates that the location has general adaptability and can be weighed according to project needs; if Larger (e.g.) This indicates that the location is highly optimal, has excellent overall performance, and is the preferred installation location.

[0032] S3: Based on the candidate position suitability score, and according to the scene weight adjustment rules and indicator weights, output the comprehensive evaluation score of the candidate position.

[0033] Furthermore, in the generated preliminary positional fit score matrix Based on this, for different installation scenarios (such as high-interference industrial areas, old building environments, complex building intranet environments, etc.), the scenario weight adjustment rule library is dynamically invoked to adjust the weights and target reference values ​​in the indicator system, driving the comprehensive evaluation optimization model to calculate the final comprehensive evaluation score for each candidate position. .

[0034] Define the scenario Corresponding characteristic factors Based on engineering experience bases or machine learning model training results, a scene-adaptive weight adjustment function is constructed. Target reference value adjustment function For each candidate position The following nonlinear composite optimization model is used to calculate its comprehensive evaluation score. :

[0035] ,

[0036] in, For the first The overall evaluation score of each candidate position. For candidate position index, For the total number of indicators, For the first The basic weight of each indicator For the first The scenario sensitivity coefficient of each indicator. For the scene , characteristic function For the first The candidate position is in the Preliminary scores under each indicator For the first Smoothing parameters for each indicator For the first The logarithmic stretching factor of each indicator For the first The power index of the indicators. This is the denominator error suppression coefficient. For the first The baseline target value for each indicator, For the first The scenario adjustment coefficient for each indicator. For the scene Influence factors For the first The power exponent of the error term of each indicator This represents the total suppression power of the overall error term.

[0037] It should be noted that, ,like This indicates a severe mismatch in candidate positions, resulting in overall low performance or multiple deviations from the threshold. If Median level (e.g.) (), indicating a relatively average location, which can be considered as an alternative. If This indicates excellent overall compatibility, and installation is highly recommended.

[0038] It should also be noted that the comprehensive evaluation score matrix based on the generated candidate positions... According to the set sorting optimization strategy (such as sorting in descending order by comprehensive evaluation score, or combining threshold to select priority intervals), a set of recommended installation locations is selected. Perform scene consistency verification on the sorting results, targeting specific scene labels. Dynamically adjust the number of recommended candidates The system includes a strategy for explaining the reasons for recommendations, ensuring that the output is adaptable to different scenarios and feasible for engineering applications. It generates a decision support report, which includes the following structured information: Recommendation reasons: based on various locations. The system automatically extracts the core recommendation criteria based on the contribution of key indicators and the adjustment process of scene weights, clearly explaining the main technical reasons for the preferred location (such as high communication signal strength, low electromagnetic interference, and good structural stability); Location scoring details: Displaying the scoring results and relative rankings of each recommended location across all indicators (communication signal quality, electromagnetic compatibility, ease of maintenance, structural stability, and construction feasibility) in tabular or visual chart form, facilitating intuitive understanding by engineers; Precautions and prompts: Automatically alerting potential risks or matters requiring attention based on on-site environmental data and scoring anomalies (e.g., if there are high-frequency interference sources nearby, it is recommended to strengthen shielding; or if the maintenance access at this location is limited, construction feasibility needs to be confirmed); Traceability information: Recording metadata such as scene weight parameters used in this round of evaluation, evaluation model version, and environmental data collection timestamps, facilitating subsequent review and optimization tracking.

[0039] Example 2, one embodiment of the present invention, provides an electricity meter data acquisition device installation location evaluation system, including an installation area multi-source environmental data acquisition module, a candidate installation location model construction module, a candidate location adaptability score output module, a comprehensive evaluation module, and an installation location recommendation module.

[0040] The multi-source environmental data acquisition module for the installation area is used to collect multi-source environmental data in the installation area in real time, including building structure spatial layout, electricity meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality, and electromagnetic interference source distribution, and generate a scene dataset after standardized coding processing.

[0041] It should also be noted that the multi-source environmental data acquisition module for the installation area completes the normalization encoding, timestamp annotation, and acquisition status verification of the above-mentioned multi-source environmental data, and uses the scene dataset as the input premise for the candidate installation location model construction module to start the candidate location modeling process.

[0042] The candidate installation location model building module is used to automatically build a candidate installation location model based on the scene dataset provided by the multi-source environmental data acquisition module of the installation area, and to filter the set of location nodes that meet the installation conditions to form a candidate installation location set.

[0043] It should also be noted that the candidate installation location model construction module completes spatial constraint verification, structural bearing capacity analysis, and obstacle avoidance strategy processing during the screening process. It also labels the candidate location set with spatial coordinate information and unique location identifiers, which serve as the input basis for the candidate location adaptability score output module and drive the subsequent reasoning and scoring process.

[0044] The candidate location suitability score output module is used to call a predefined multi-factor inference rule library after the candidate installation location model building module outputs the candidate installation location set. Based on indicators such as communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility, it performs inference calculations on each candidate location and outputs the candidate location suitability score.

[0045] It should also be noted that the candidate position suitability score output module completes the standardized mapping of scores for each indicator, dynamic loading of inference rules, and logging of inference calculations. It then directly transmits the output suitability score matrix to the comprehensive evaluation module and outputs the monitoring results of score anomalies for the comprehensive evaluation module to refer to when adjusting the weights.

[0046] The comprehensive evaluation module is used to dynamically adjust the weights of each indicator based on the adaptability score provided by the candidate position adaptability score output module and according to the scene weight adjustment rules, calculate the comprehensive evaluation score of the candidate position, and generate the ranking result.

[0047] It should also be noted that during the calculation process, the comprehensive evaluation module completes scene label recognition, weight adjustment parameter loading, target reference value dynamic correction, and generates a comprehensive score matrix and ranking optimization results for candidate positions, which serve as input to the installation location recommendation module and drive the final recommendation process.

[0048] The installation location recommendation module is used to optimize the target installation location based on the ranking results of the comprehensive evaluation module, and automatically generate an auxiliary decision-making report that includes the reasons for the recommendation, detailed location scores, and precautions.

[0049] It should also be noted that during the recommendation process, the installation location recommendation module completes the selection of preferred locations, generation of recommendation reasons, visualization and arrangement of scoring details, supplementation of risk warning items, and records the metadata of this round of recommendation results (such as evaluation rule version, timestamp, and scene tags) for system traceability and optimization.

Claims

1. A method for evaluating the installation location of an electricity meter data collector, characterized in that, include: Collect multi-source environmental data of the installation area and construct a candidate installation location model to screen installation conditions; Multi-source environmental data for the installation area includes: building structure and spatial layout, electricity meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality, and distribution of electromagnetic interference sources. For each candidate installation location, a multi-factor inference rule is invoked to output the candidate location suitability score. In the selected candidate location set In the middle, for each candidate position By calling a predefined multi-factor inference rule base, a nonlinear multidimensional coupled scoring model is constructed for location. The adaptability is dynamically inferred and calculated to generate a preliminary position adaptability score. ; Define five core indicator systems, including communication signal quality. Electromagnetic compatibility Convenience of later maintenance Structural stability and construction feasibility For each indicator system, the data collection location is... The following engineering parameters can be quantified through normalization or mapping functions. Convert into rating input; Candidate positions Calculate the location fit score , is represented as: ， ， in, Candidate positions Preliminary fit score, For the first Candidate positions, For the first The smoothing adjustment constant of each indicator, For the first The nonlinear stretching coefficient of each index, For the first Position under each indicator The normalized score, For position In the The original measured values ​​under each indicator For the first The minimum measured value of each indicator among all candidate positions. For the first The maximum measured value of each indicator across all candidate positions. For the first The power-law adjustment parameter for each indicator score. This is the error term suppression coefficient. For the first Position under each indicator The error term function, For the first Target reference values ​​for each indicator The nonlinear weighting exponent for the error term; The range of values ​​is ,like This indicates that the location is not suitable for installation; if In This indicates that the location is adaptable; This indicates the recommended installation location; Based on the candidate position suitability score, and according to the scene weight adjustment rules and indicator weights, the comprehensive evaluation score of the candidate position is output. The process of constructing a candidate installation location model and screening installation conditions includes automatically constructing a candidate installation location model based on multi-source environmental data of the installation area and using preset spatial topology rules, dynamically screening the set of location nodes that meet the installation conditions, and constructing a candidate installation location set. The process of calling multi-factor inference rules to output candidate location suitability scores includes calling a predefined multi-factor inference rule library for the candidate location set, scoring each candidate location based on indicators such as communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility, and dynamically generating candidate location suitability scores.

2. The method for evaluating the installation location of the electricity meter data collector as described in claim 1, characterized in that: The comprehensive evaluation score for the candidate positions, based on the scene weight adjustment rules and indicator weights, includes: Based on the scene weight adjustment rules, the indicator weights are dynamically adjusted, the comprehensive evaluation score of the candidate positions is output, and the ranking optimization results are generated.

3. The method for evaluating the installation location of the electricity meter data collector as described in claim 2, characterized in that: The comprehensive evaluation score for the candidate positions, based on the scene weight adjustment rules and indicator weights, includes: Based on the candidate location suitability score, the installation location is output, and an auxiliary decision-making report is generated.

4. The method for evaluating the installation location of the electricity meter data collector as described in claim 3, characterized in that: The decision support report includes, Reasons for recommendation, detailed location rating, and important notes / tips.

5. A system for evaluating the installation location of an electricity meter data collector, employing the method for evaluating the installation location of an electricity meter data collector as described in any one of claims 1 to 4, characterized in that: It includes a multi-source environmental data acquisition module for the installation area, a candidate installation location model building module, a candidate location adaptability score output module, a comprehensive evaluation module, and an installation location recommendation module; The multi-source environmental data acquisition module for the installation area is used to collect multi-source environmental data of the installation area in real time, including building structure spatial layout, electricity meter distribution density, obstruction information, wall material, power access conditions, wireless communication signal quality, and electromagnetic interference source distribution. The candidate installation location model construction module is used to construct a candidate installation location model and filter the set of location nodes that meet the installation conditions after the multi-source environmental data acquisition module in the installation area has completed the data acquisition. The candidate location suitability score output module is used to call a predefined multi-factor reasoning rule library after the candidate installation location model construction module outputs the candidate installation location set, and to reason and output the candidate location suitability score for each candidate location based on communication signal quality, electromagnetic compatibility, ease of later maintenance, structural stability, and construction feasibility indicators. The comprehensive evaluation module is used to output the comprehensive evaluation score of the candidate position based on the candidate position adaptability score and the scene weight adjustment rules after the candidate position adaptability score output module outputs the candidate position adaptability score, and generate the ranking result. The installation location recommendation module is used to output the installation location based on the ranking results of the comprehensive evaluation module.

6. 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 method for evaluating the installation location of the electricity meter collector as described in any one of claims 1 to 4.

7. 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 method for evaluating the installation location of the electricity meter collector as described in any one of claims 1 to 4.

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