A micro-power wireless communication-based collector optimal point arrangement method and system

Abstract

The application discloses a kind of optimal point distribution method and system of collector based on micro-power wireless communication, belong to wireless communication collector point distribution technical field, its method includes the following steps: S1, obtains basic parameter;S2, generates initial population;S3, evaluates communication performance;S4, filters high fitness solution;S5, iteration updates output optimum;It aims at overcoming the deficiencies of prior art, solve the current collector point distribution relies on artificial experience, extensive layout leads to the problem of insufficient electric energy meter acquisition coverage, poor wireless communication reliability, wiring cost exceeds standard;Solve the problem that existing optimization algorithm is not synchronized into cable length hard constraint, cannot meet construction specification;While solving the problem that conventional evolutionary algorithm random disturbance update leads to slow convergence speed, easy to fall into local optimum, cannot realize the coordinated optimization of coverage rate and connectivity rate, realize the multi-objective global optimum of collector point distribution under complex building scene.

Description

Technical Field

[0001] This invention relates to the field of wireless communication data acquisition device deployment technology, and more specifically, to an optimal data acquisition device deployment method and system based on low-power wireless communication. Background Technology

[0002] With the deepening of the construction of smart grids and ubiquitous power Internet of Things in my country, low-voltage electricity consumption information collection systems have achieved nationwide coverage and become the core foundation for the digital and lean management of power grid marketing. Low-power wireless communication technology, with its advantages of flexible deployment, low transformation cost, and strong network scalability, has become one of the mainstream communication methods for low-voltage electricity consumption information collection systems. Its typical system architecture consists of a concentrator, a collector, and a smart energy meter. The collector connects to the energy meter through a cable to complete the collection of electricity consumption data and communicates with the concentrator through a low-power wireless self-organizing network to realize the remote centralized transmission of electricity consumption data.

[0003] Currently, the on-site deployment of data collectors largely relies on the engineering experience of construction personnel, and generally adopts a rough approach of "uniform deployment and subsequent additions." This approach is prone to problems such as excessive wireless signal attenuation and communication link interruption due to obstacles such as building walls, pipeline shafts, and equipment rooms. Alternatively, excessive spacing between the data collector and the electricity meter can lead to excessive wiring costs and high wiring losses. Ultimately, this results in problems such as insufficient electricity meter coverage, poor network connectivity reliability, and high maintenance and rectification costs, making it difficult to meet the core management requirements of the electricity information collection system: "full collection, full coverage, and full cost control."

[0004] To address the aforementioned issues, the industry has gradually introduced intelligent optimization algorithms such as genetic algorithms and particle swarm optimization to optimize data collector deployment. However, existing solutions still have significant technical shortcomings: most algorithms focus only on the single-objective optimization of wireless communication coverage performance, without simultaneously incorporating the hard constraint of cable length between the data collector and the electricity meter, resulting in optimized deployment schemes failing to meet on-site construction specifications; some algorithms do not fully integrate the 3D building model to accurately model the signal attenuation of obstacles, leading to significant discrepancies between simulation optimization results and actual on-site deployment effects; conventional evolutionary algorithms often use random perturbation to complete population updates, resulting in slow convergence speeds and a tendency to get trapped in local optima, failing to achieve coordinated optimization of electricity meter coverage and network connectivity, and making it difficult to adapt to the refined deployment requirements of data collectors in complex buildings and dense residential areas; furthermore, with the continuous advancement of urban and rural power grid transformation, complex building scenarios such as high-rise residential buildings and commercial complexes are increasing, electricity load density is constantly rising, and the number of electricity meters deployed is increasing significantly, placing higher demands on the rationality, economy, and reliability of data collector deployment, and existing deployment methods are no longer suitable for the electricity information collection needs under the new circumstances. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention aims to provide an optimal data collector deployment method and system based on low-power wireless communication. This method aims to overcome the shortcomings of existing technologies and solve the problems of insufficient electricity meter coverage, poor wireless communication reliability, and excessive wiring costs caused by the reliance on manual experience and extensive deployment of current data collectors. It also addresses the issues of existing optimization algorithms failing to incorporate hard constraints on cable length and failing to accurately model the attenuation of wireless links by building obstacles, resulting in large deviations between the deployment plan and the actual site conditions, and failure to meet construction specifications. Furthermore, it solves the problems of slow convergence speed and susceptibility to local optima caused by random perturbation updates in conventional evolutionary algorithms, which prevent the coordinated optimization of coverage and connectivity. The goal is to achieve multi-objective global optimization of data collector deployment in complex building scenarios.

[0006] To solve the above problems, the present invention adopts the following technical solution.

[0007] An optimal deployment method for data collectors based on low-power wireless communication, wherein the data collectors are connected to the energy meters via cables and networked with the concentrators via wireless communication, includes the following steps:

[0008] S1. Obtain basic parameters: Obtain the building model, deployment area, coordinates of electricity meter and concentrator, as well as transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight.

[0009] S2. Generate the initial population: Randomly generate multiple coordinate sets containing the number of collectors within the deployment area; Based on the building model, assign the electricity meter to the nearest collector and calculate the wiring distance; Filter all coordinate sets whose wiring distances meet the upper limit of cable length until the number of retained sets reaches the population size, thus forming the initial population.

[0010] S3. Evaluate communication performance: For each scheme of the population, calculate the link attenuation and received signal strength between each communication node based on the building model, and remove links that are below the sensitivity threshold; with the concentrator as the root node, construct a wireless communication tree based on the received signal strength, and count the coverage rate and connectivity of the electricity meters accessing the communication tree.

[0011] S4. Selecting high-fitness schemes: Based on the evaluation weights, the coverage and connectivity of each scheme are weighted and summed to calculate the fitness value and sort them in descending order; according to the retention ratio, high-fitness schemes are directly put into the new generation population, and the schemes that are not retained are recorded as remaining schemes waiting for position updates;

[0012] S5, Iterative Update Output Optimal: For the remaining schemes, move the collectors not connected to the communication tree to the nearest connected node according to the perturbation step size; if all collectors are connected, randomly offset the collector with the weakest signal; if the movement exceeds the limit or the wiring exceeds the limit, re-initialize the coordinates randomly; merge the updated remaining schemes into the new generation population, repeat S3 to S5 for the number of iterations and output the optimal scheme, simultaneously taking into account the hard constraint of cable length and wireless communication performance, and achieve global optimal deployment through directional iterative optimization, effectively improving the coverage rate of electricity meters and network connectivity, and reducing construction and maintenance costs.

[0013] As a further technical solution of the present invention, S1 obtaining basic parameters specifically includes:

[0014] S11. Deployment Scene Digital Modeling and Coordinate System Calibration: Define the three-dimensional coordinate boundary of the deployment area, extract obstacle information based on the building BIM model and construct an obstacle attenuation mapping table, and calibrate the three-dimensional fixed coordinates of the concentrator and all energy meters.

[0015] S12. Calibration of basic parameters of core equipment and communication links: Calibrate the global unified parameters of carrier frequency, transmit power, transceiver antenna gain, receive sensitivity threshold, upper limit of cable length between collector and energy meter, and total number of collectors to be deployed for low-power wireless communication.

[0016] S13. Configuration of Hyperparameters and Evaluation System for Layout Optimization Algorithm: Configure the hyperparameters of the algorithm, such as population size, elite retention ratio, coordinate perturbation step size, and maximum number of iterations, as well as the evaluation weights corresponding to electricity meter coverage and network connectivity. Standardize the digital modeling of the scene, core communication parameters, and algorithm hyperparameters to ensure the modeling accuracy of layout optimization and provide unified and accurate basic data support for subsequent optimization calculations.

[0017] As a further technical solution of the present invention, the S2 step of generating the initial population specifically includes:

[0018] S21. Definition of coding rules for collection point layout: Each collection point layout scheme is treated as a single individual in the population, and real number coding is adopted, with the three-dimensional coordinate matrix of the collection point as the coding format of the individual.

[0019] S22. Random generation of candidate deployment schemes: Within the deployment area, a uniformly distributed random number generation algorithm is used to generate collector coordinates that conform to the coordinate boundary constraints, forming a single set of candidate deployment schemes.

[0020] S23. Nearest neighbor matching and wiring distance calculation between electricity meter and data collector: The nearest neighbor allocation algorithm is used to complete the one-to-one matching between the electricity meter and the data collector, and the wiring distance after matching is calculated.

[0021] S24. Cable length constraint verification and effective scheme selection: Perform full hard constraint verification on candidate layout schemes, discard invalid schemes with wiring distances exceeding the upper limit of cable length, and retain valid schemes where all wiring distances meet the requirements.

[0022] S25. Initial Population Construction: Repeat the candidate solution generation, matching, and verification process until the number of valid solutions reaches the preset population size, forming the initial population and initializing the number of iterations. Real number encoding and constraint pre-screening ensure that all solutions in the initial population meet the hard constraint of cable length, avoid invalid solution iterations, and effectively improve the algorithm convergence efficiency and the feasibility of solution construction.

[0023] As a further technical solution of the present invention, the S23 energy meter-collector nearest neighbor matching and wiring distance calculation specifically involves: for the current candidate deployment scheme, using a three-dimensional Manhattan distance algorithm combined with the building BIM model, calculating the actual wiring distance between the energy meter and each collector based on the building topology path; when there are impenetrable obstacles between the energy meter and the collector, extracting the passable path information in the BIM model to correct the wiring distance by detour; assigning each energy meter to the collector with the closest wiring distance, completing a one-to-one matching and recording the corresponding wiring distance; simultaneously calculating the free space propagation attenuation, obstacle penetration attenuation, and received signal strength between communication nodes, and combining the BIM model to correct the wiring distance and link attenuation, accurately matching the actual wiring and communication environment on the building site, and significantly reducing the deviation between the simulation optimization results and the on-site deployment effect.

[0024] As a further technical solution of the present invention, the S3 evaluation of communication performance specifically includes:

[0025] S31. Calculation of communication node link parameters: For each deployment scheme in the current iteration, define a set of communication nodes including the concentrator and all collectors, and calculate the total link attenuation and received signal strength of all communication node pairs.

[0026] S32. Sensitivity threshold comparison and weak link elimination: Compare the received signal strength of all node pairs with the received sensitivity threshold, eliminate invalid links with received signal strength below the threshold, and retain valid communication links.

[0027] S33. Construct a wireless communication tree with the concentrator as the root node: Based on the filtered effective communication links, the improved Prim maximum spanning tree algorithm is used to construct a multi-hop wireless communication tree with the concentrator as the root node, and divide the set of connected collectors into sets of unconnected collectors.

[0028] S34. Statistical calculation of electricity meter coverage and network connectivity: Statistically calculate the electricity meter coverage and network connectivity of the current scheme, accurately calculate link parameters and construct the maximum spanning tree, accurately quantify the communication performance of the scheme, realize accurate evaluation of coverage and connectivity, and provide a reliable quantitative basis for scheme selection.

[0029] As a further technical solution of the present invention, the statistical calculation of the S34 electricity meter coverage rate and network connectivity rate is specifically as follows: the total number of electricity meters matched by the connected collectors is counted, and the ratio of the number of connected collectors to the total number of electricity meters is calculated to obtain the electricity meter coverage rate; the total number of connected collectors accessing the communication tree is counted, and the ratio of the number of connected collectors to the total number of collectors to be deployed is calculated to obtain the network connectivity rate. This clarifies the standardized and quantitative calculation method of coverage rate and connectivity rate, accurately characterizes the core performance of the deployment scheme, and ensures the accuracy of fitness calculation and the objectivity and fairness of scheme evaluation.

[0030] As a further technical solution of the present invention, the S4 screening of high-fitness schemes specifically includes:

[0031] S41. Calculation of individual fitness value: Based on the preset evaluation weights, the electricity meter coverage rate and network connectivity rate of each scheme are weighted and summed to calculate the fitness value of each scheme.

[0032] S42. Population Fitness Descending Order Sort: Sort all individuals in the current population in descending order of fitness value from highest to lowest.

[0033] S43. Elite Retention and Updating Scheme Division: Based on the preset elite retention ratio, calculate and directly retain the top-ranked high-fitness elite individuals to the new generation population, and mark the remaining individuals as the updating scheme set. Through the elite retention strategy, the high-fitness high-quality schemes are directly inherited, avoiding the loss of high-quality genes, effectively accelerating the algorithm's convergence speed, and significantly improving the algorithm's global optimization ability.

[0034] As a further technical solution of the present invention, the individual fitness value calculation in S41 is specifically as follows: based on the electricity meter coverage rate and network connectivity rate calculated in S34, combined with the electricity meter coverage rate evaluation weight and network connectivity rate evaluation weight configured in S13, the two indicators are weighted and summed to obtain the fitness value of the corresponding deployment scheme. The fitness value is quantified by weighted summation, which can flexibly adapt to the differentiated requirements of coverage rate and connectivity rate in different scenarios, and greatly improve the scenario adaptability and engineering practicality of the method.

[0035] As a further technical solution of the present invention, the S5 iterative update output optimization specifically includes:

[0036] S51. Connectivity status determination of the scheme to be updated: For each scheme in the set of schemes to be updated, based on the wireless communication tree results constructed in S33, divide the set of connected collectors into the set of disconnected collectors.

[0037] S52, Conditional Branch Update of Collector Coordinates: Based on the state of the set of unconnected collectors, execute mutually exclusive coordinate update logic. If the set of unconnected collectors is not empty, move the collectors not connected to the communication tree to the nearest connected node according to the perturbation step size. If the set of unconnected collectors is empty, traverse the communication tree to find the link with the weakest signal, and apply a random offset to the leaf collector at the end of the link.

[0038] S53. Constraint Condition Verification and Violation Coordinate Reset: For the data acquisition unit that has completed coordinate update, perform deployment area boundary verification and cable length constraint verification, and randomly re-initialize the violation coordinates;

[0039] S54. Complete construction of the new generation population: The updated and verified schemes to be updated are incorporated into the new generation population, so that the size of the new generation population reaches the preset value.

[0040] S55. Iteration Termination Judgment and Optimal Solution Output: Update the iteration count. If the iteration count has not reached the maximum iteration count, return to S3 to execute a new round of iterations. If the iteration count has reached the maximum iteration count, terminate the loop and output the optimal placement scheme with the highest fitness value in the final population. The branch update logic with directional perturbation is adopted to optimize disconnected nodes and weak links in a targeted manner, avoid the blindness of random perturbation, effectively speed up the convergence of the algorithm, and avoid getting trapped in local optima.

[0041] An optimal deployment system for data collectors based on low-power wireless communication, wherein the data collectors are connected to the energy meter via cables and networked with the concentrator via low-power wireless communication, includes a basic parameter acquisition module, an initial population generation module, a communication performance evaluation module, a high fitness scheme screening module, and an iterative update and optimal scheme output module.

[0042] The basic parameter acquisition module is used to acquire the building model, deployment area, coordinates of the electricity meter and concentrator, as well as the transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight.

[0043] The initial population generation module is used to randomly generate multiple coordinate sets containing a preset number of collectors within the deployment area; to allocate the electricity meter to the nearest collector based on the building model and calculate the wiring distance; to filter all coordinate sets whose wiring distances meet the upper limit of cable length until the number of retained sets reaches the population size, thus forming the initial population.

[0044] The communication performance evaluation module is used to calculate the link attenuation and received signal strength between each communication node based on the building model for each deployment scheme within the population, and to remove links that are below the sensitivity threshold; with the concentrator as the root node, a wireless communication tree is constructed based on the received signal strength, and the coverage rate of electricity meters and the network connectivity rate of the access communication tree are statistically analyzed.

[0045] The high fitness scheme screening module is used to perform weighted summation of the coverage and connectivity of each layout scheme based on preset evaluation weights, calculate the fitness value of each scheme and sort them in descending order; according to the preset retention ratio, the high fitness schemes are directly put into the new generation population, and the schemes that are not retained are recorded as remaining schemes waiting for position updates.

[0046] The iterative update and optimal solution output module is used to move the collectors that are not connected to the communication tree to the nearest connected node according to the perturbation step size for the remaining solutions; if all collectors in the solution are connected to the communication tree, a random offset is applied to the collector with the weakest signal; if the collector moves out of bounds or the wiring distance exceeds the limit, the coordinates of the collector are re-randomly initialized; the updated remaining solutions are incorporated into the new generation population, and the communication performance evaluation, high fitness solution screening, and coordinate iterative update process are repeated until the preset number of iterations is reached, and the optimal collector placement solution is output. This system can automatically complete the entire process of collector placement optimization calculation and output the optimal solution that takes into account both construction feasibility and communication reliability, which greatly reduces the workload and experience reliance of manual placement.

[0047] Compared with the prior art, the advantages of this invention are:

[0048] This invention addresses many shortcomings of existing low-power wireless data acquisition device deployment technologies by providing an optimal deployment method that balances construction feasibility and communication reliability. Compared to existing technologies, it possesses significant technical advantages and engineering application value. The core beneficial effects are as follows:

[0049] This invention places the hard constraint on the cable length between the data collector and the electricity meter at the initial population generation stage. By screening effective solutions through nearest neighbor matching and full constraint verification, it avoids invalid solutions that exceed the wiring distance limit from the source. It completely solves the pain points of traditional manual extensive deployment, excessive wiring costs of existing optimization algorithms, and the inability of the solution to be implemented in construction. It significantly reduces the cost of on-site wiring construction and subsequent rectification and maintenance investment, and ensures that the deployment solution fully complies with the on-site construction specifications.

[0050] This invention accurately calculates wireless communication link attenuation and received signal strength based on building models, and constructs a wireless communication tree with a concentrator as the root node. It achieves accurate quantitative evaluation of communication performance in complex building scenarios, effectively solving the problems of existing solutions not fully considering signal attenuation due to building obstacles and the large deviation between simulation optimization results and actual on-site deployment effects. It significantly improves the connectivity reliability of the data acquisition network and the data acquisition coverage of electricity meters, fully meeting the core management requirements of "full acquisition, full coverage, and full cost control" of the electricity information acquisition system.

[0051] This invention employs an iterative optimization strategy that combines elite retention with directional perturbation. It executes differentiated coordinate update logic for disconnected nodes and weak links, abandoning the blindness of random perturbation in conventional evolutionary algorithms, significantly accelerating the algorithm's convergence speed, effectively avoiding getting trapped in local optima, and achieving multi-objective collaborative global optimization of electricity meter coverage and network connectivity. It can perfectly adapt to the refined deployment needs of complex building scenarios such as high-rise residential buildings and commercial complexes, providing reliable technical support for the large-scale construction of low-voltage data acquisition systems for smart grids and ubiquitous power Internet of Things. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] This invention provides an embodiment 1

[0055] An optimal deployment method for data collectors based on low-power wireless communication, wherein the data collectors are connected to the energy meters via cables and networked with the concentrators via wireless communication, includes the following steps:

[0056] S1. Obtain basic parameters: Obtain the building model, deployment area, coordinates of electricity meter and concentrator, as well as transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight.

[0057] S2. Generate the initial population: Randomly generate multiple coordinate sets containing the number of collectors within the deployment area; Based on the building model, assign the electricity meter to the nearest collector and calculate the wiring distance; Filter all coordinate sets whose wiring distances meet the upper limit of cable length until the number of retained sets reaches the population size, thus forming the initial population.

[0058] S3. Evaluate communication performance: For each scheme of the population, calculate the link attenuation and received signal strength between each communication node based on the building model, and remove links that are below the sensitivity threshold; with the concentrator as the root node, construct a wireless communication tree based on the received signal strength, and count the coverage rate and connectivity of the electricity meters accessing the communication tree.

[0059] S4. Selecting high-fitness schemes: Based on the evaluation weights, the coverage and connectivity of each scheme are weighted and summed to calculate the fitness value and sort them in descending order; according to the retention ratio, high-fitness schemes are directly put into the new generation population, and the schemes that are not retained are recorded as remaining schemes waiting for position updates;

[0060] S5, Iterative Update Output Optimal: For the remaining schemes, move the collectors not connected to the communication tree to the nearest connected node according to the perturbation step size; if all collectors are connected, randomly offset the collector with the weakest signal; if the movement exceeds the limit or the wiring exceeds the limit, re-initialize the coordinates randomly; merge the updated remaining schemes into the new generation population, repeat S3 to S5 for the number of iterations and output the optimal scheme, simultaneously taking into account the hard constraint of cable length and wireless communication performance, and achieve global optimal deployment through directional iterative optimization, effectively improving the coverage rate of electricity meters and network connectivity, and reducing construction and maintenance costs;

[0061] The S1 step of obtaining basic parameters specifically includes:

[0062] S11. Deployment Scene Digital Modeling and Coordinate System Calibration: Define the three-dimensional coordinate boundary of the deployment area, extract obstacle information based on the building BIM model and construct an obstacle attenuation mapping table, and calibrate the three-dimensional fixed coordinates of the concentrator and all energy meters.

[0063] S12. Calibration of basic parameters of core equipment and communication links: Calibrate the global unified parameters of carrier frequency, transmit power, transceiver antenna gain, receive sensitivity threshold, upper limit of cable length between collector and energy meter, and total number of collectors to be deployed for low-power wireless communication.

[0064] S13. Configuration of hyperparameters and evaluation system for site optimization algorithm: Configure hyperparameters of the algorithm such as population size, elite retention ratio, coordinate perturbation step size, and maximum number of iterations, as well as evaluation weights corresponding to electricity meter coverage and network connectivity. Standardize and calibrate the scene digital modeling, core communication parameters and algorithm hyperparameters to ensure the modeling accuracy of site optimization and provide unified and accurate basic data support for subsequent optimization calculations.

[0065] The S2 step of generating the initial population specifically includes:

[0066] S21. Definition of coding rules for collection point layout: Each collection point layout scheme is treated as a single individual in the population, and real number coding is adopted, with the three-dimensional coordinate matrix of the collection point as the coding format of the individual.

[0067] S22. Random generation of candidate deployment schemes: Within the deployment area, a uniformly distributed random number generation algorithm is used to generate collector coordinates that conform to the coordinate boundary constraints, forming a single set of candidate deployment schemes.

[0068] S23. Nearest neighbor matching and wiring distance calculation between electricity meter and data collector: The nearest neighbor allocation algorithm is used to complete the one-to-one matching between the electricity meter and the data collector, and the wiring distance after matching is calculated.

[0069] S24. Cable length constraint verification and effective scheme selection: Perform full hard constraint verification on candidate layout schemes, discard invalid schemes with wiring distances exceeding the upper limit of cable length, and retain valid schemes where all wiring distances meet the requirements.

[0070] S25. Initial population construction: Repeat the candidate solution generation, matching and verification process until the number of effective solutions reaches the preset population size, form the initial population and initialize the number of iterations, use real number encoding and constraint pre-screening to ensure that the initial population solutions all meet the hard constraint of cable length, avoid invalid solution iteration, and effectively improve the algorithm convergence efficiency and solution construction feasibility.

[0071] The S23 energy meter-collector nearest neighbor matching and wiring distance calculation specifically involves: for the current candidate deployment scheme, using a three-dimensional Manhattan distance algorithm combined with the building BIM model, calculating the actual wiring distance between the energy meter and each collector based on the building topology path; when there are impenetrable obstacles between the energy meter and the collector, extracting the passable path information from the BIM model to correct the wiring distance by detour; assigning each energy meter to the collector with the closest wiring distance, completing a one-to-one match and recording the corresponding wiring distance; simultaneously calculating the free space propagation attenuation, obstacle penetration attenuation, and received signal strength between communication nodes, and combining the BIM model to correct the wiring distance and link attenuation, accurately matching the actual wiring and communication environment on the building site, and significantly reducing the deviation between the simulation optimization results and the on-site deployment effect;

[0072] The S3 evaluation of communication performance specifically includes:

[0073] S31. Calculation of communication node link parameters: For each deployment scheme in the current iteration, define a set of communication nodes including the concentrator and all collectors, and calculate the total link attenuation and received signal strength of all communication node pairs.

[0074] S32. Sensitivity threshold comparison and weak link elimination: Compare the received signal strength of all node pairs with the received sensitivity threshold, eliminate invalid links with received signal strength below the threshold, and retain valid communication links.

[0075] S33. Construct a wireless communication tree with the concentrator as the root node: Based on the filtered effective communication links, the improved Prim maximum spanning tree algorithm is used to construct a multi-hop wireless communication tree with the concentrator as the root node, and divide the set of connected collectors into sets of unconnected collectors.

[0076] S34. Statistical calculation of electricity meter coverage and network connectivity: Statistically calculate the electricity meter coverage and network connectivity of the current scheme, accurately calculate link parameters and construct the maximum spanning tree, accurately quantify the communication performance of the scheme, realize accurate evaluation of coverage and connectivity, and provide a reliable quantitative basis for scheme selection.

[0077] The S34 electricity meter coverage rate and network connectivity rate statistical calculation are as follows: The total number of electricity meters matched by the connected collectors is counted, and the ratio of this number to the total number of electricity meters is calculated to obtain the electricity meter coverage rate; the total number of connected collectors accessing the communication tree is counted, and the ratio of this number to the total number of collectors to be deployed is calculated to obtain the network connectivity rate. This clarifies the standardized and quantitative calculation method for coverage rate and connectivity rate, accurately characterizes the core performance of the deployment scheme, and ensures the accuracy of the fitness calculation and the objectivity and fairness of the scheme evaluation.

[0078] The S4 screening of high-fitness schemes specifically includes:

[0079] S41. Calculation of individual fitness value: Based on the preset evaluation weights, the electricity meter coverage rate and network connectivity rate of each scheme are weighted and summed to calculate the fitness value of each scheme.

[0080] S42. Population Fitness Descending Order Sort: Sort all individuals in the current population in descending order of fitness value from highest to lowest.

[0081] S43. Elite Retention and Scheme to be Updated: Based on the preset elite retention ratio, calculate and directly retain the high-fitness elite individuals with the highest ranking to the new generation population, and mark the remaining individuals as the scheme set to be updated. The elite retention strategy realizes the direct inheritance of high-fitness high-quality schemes, avoids the loss of high-quality genes, effectively accelerates the convergence speed of the algorithm, and significantly improves the global optimization ability of the algorithm.

[0082] The individual fitness value calculation in S41 is as follows: based on the electricity meter coverage rate and network connectivity rate calculated in S34, and combined with the electricity meter coverage rate evaluation weight and network connectivity rate evaluation weight configured in S13, the two indicators are weighted and summed to obtain the fitness value of the corresponding deployment scheme. The fitness value is quantified by weighted summation, which can flexibly adapt to the differentiated requirements of coverage rate and connectivity rate in different scenarios, and greatly improve the scenario adaptability and engineering practicality of the method.

[0083] The optimal output of the S5 iterative update specifically includes:

[0084] S51. Connectivity status determination of the scheme to be updated: For each scheme in the set of schemes to be updated, based on the wireless communication tree results constructed in S33, divide the set of connected collectors into the set of disconnected collectors.

[0085] S52, Conditional Branch Update of Collector Coordinates: Based on the state of the set of unconnected collectors, execute mutually exclusive coordinate update logic. If the set of unconnected collectors is not empty, move the collectors not connected to the communication tree to the nearest connected node according to the perturbation step size. If the set of unconnected collectors is empty, traverse the communication tree to find the link with the weakest signal, and apply a random offset to the leaf collector at the end of the link.

[0086] S53. Constraint Condition Verification and Violation Coordinate Reset: For the data acquisition unit that has completed coordinate update, perform deployment area boundary verification and cable length constraint verification, and randomly re-initialize the violation coordinates;

[0087] S54. Complete construction of the new generation population: The updated and verified schemes to be updated are incorporated into the new generation population, so that the size of the new generation population reaches the preset value.

[0088] S55. Iteration Termination Judgment and Optimal Solution Output: Update the iteration count. If the iteration count has not reached the maximum iteration count, return to S3 to execute a new round of iteration. If the iteration count has reached the maximum iteration count, terminate the loop and output the optimal placement scheme with the highest fitness value in the final population. The branch update logic with directional perturbation is used to optimize disconnected nodes and weak links in a targeted manner, avoid the blindness of random perturbation, effectively speed up the convergence of the algorithm, and avoid getting trapped in local optima.

[0089] This invention provides an embodiment 2

[0090] An optimal deployment system for data collectors based on low-power wireless communication, wherein the data collectors are connected to the energy meter via cables and networked with the concentrator via low-power wireless communication, includes a basic parameter acquisition module, an initial population generation module, a communication performance evaluation module, a high fitness scheme screening module, and an iterative update and optimal scheme output module.

[0091] The basic parameter acquisition module is used to acquire the building model, deployment area, coordinates of the electricity meter and concentrator, as well as the transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight.

[0092] The initial population generation module is used to randomly generate multiple coordinate sets containing a preset number of collectors within the deployment area; to allocate the electricity meter to the nearest collector based on the building model and calculate the wiring distance; to filter all coordinate sets whose wiring distances meet the upper limit of cable length until the number of retained sets reaches the population size, thus forming the initial population.

[0093] The communication performance evaluation module is used to calculate the link attenuation and received signal strength between each communication node based on the building model for each deployment scheme within the population, and to remove links that are below the sensitivity threshold; with the concentrator as the root node, a wireless communication tree is constructed based on the received signal strength, and the coverage rate of electricity meters and the network connectivity rate of the access communication tree are statistically analyzed.

[0094] The high fitness scheme screening module is used to perform weighted summation of the coverage and connectivity of each layout scheme based on preset evaluation weights, calculate the fitness value of each scheme and sort them in descending order; according to the preset retention ratio, the high fitness schemes are directly put into the new generation population, and the schemes that are not retained are recorded as remaining schemes waiting for position updates.

[0095] The iterative update and optimal solution output module is used to move the collectors that are not connected to the communication tree to the nearest connected node according to the perturbation step size for the remaining solutions; if all collectors in the solution are connected to the communication tree, a random offset is applied to the collector with the weakest signal; if the collector moves out of bounds or the wiring distance exceeds the limit, the coordinates of the collector are re-randomly initialized; the updated remaining solutions are incorporated into the new generation population, and the communication performance evaluation, high fitness solution screening, and coordinate iterative update process are repeated until the preset number of iterations is reached, and the optimal collector placement solution is output. This system can automatically complete the entire process of collector placement optimization calculation and output the optimal solution that takes into account both construction feasibility and communication reliability, which greatly reduces the workload and experience reliance of manual placement.

[0096] This invention provides an application example

[0097] The specific implementation steps of a method for optimal placement of data collectors based on low-power wireless communication are as follows:

[0098] S1 obtains basic parameters

[0099] S11 Deployment Scene Digital Modeling and Coordinate System Calibration

[0100] Define the three-dimensional coordinate boundary of the deployment area.

[0101]

[0102] in The three-dimensional coordinate boundary representing the deployment area, , These are the minimum and maximum coordinates along the X-axis of the deployment area, respectively. , These are the minimum and maximum coordinates along the Y-axis of the deployment area, respectively. , These are the minimum and maximum coordinates along the Z-axis of the deployment area, respectively, to define the legal spatial range for the data collector deployment;

[0103] Obstacle information is extracted from the building BIM model, an obstacle attenuation mapping table is constructed, and the single-penetration attenuation coefficient of different types of obstacles is determined. ,in Representing the The single-penetration attenuation coefficient of various types of obstacles, specifically for each type of obstacle. The value range is as follows: For concrete walls... The value range is 12~18, corresponding to lightweight partition walls. The value ranges from 3 to 6, corresponding to glass curtain walls. The value ranges from 2 to 4, corresponding to reinforced concrete floor slabs. The value range is 15~20;

[0104] Calibrate the fixed coordinates of the core equipment:

[0105] Concentrator 3D coordinates

[0106]

[0107] ,in The three-dimensional coordinates of the concentrator , , These are the coordinates of the concentrator in the X, Y, and Z axes, respectively.

[0108] Set of coordinates of all electricity meters

[0109]

[0110] ,in The set of coordinates representing all electricity meters. Representing the The three-dimensional coordinates of an electricity meter , , The first The coordinates of each electricity meter in the X, Y, and Z axes. This is the serial number of the electricity meter. This represents the total number of electricity meters.

[0111] S12 core equipment and communication link basic parameter calibration

[0112] Globally unified parameter symbols, definitions, and typical values ​​are used throughout the entire calculation process, as detailed below: This represents the carrier frequency of low-power wireless communication, with a value range of 470~510 (compliant with national metrological frequency band standards). This represents the transmit power of the collector / concentrator, with a value ranging from 10 to 17. This represents the transmit antenna gain, typically with a value of 2. This represents the receiver antenna gain, typically with a value of 2. This represents the receiver sensitivity threshold, with a value range of -110 to -120. This represents the upper limit of the cable length between the data collector and the energy meter, with a value ranging from 50 to 100. This represents the total number of data collectors to be deployed; the specific number is configured according to the needs of the scenario.

[0113] S13 Point Placement Optimization Algorithm Hyperparameters and Evaluation System Configuration

[0114] Configure the global hyperparameters and evaluation weights of the evolutionary optimization algorithm. The specific parameters are as follows: Represents the population size (the total number of placement schemes in a single iteration), with a value range of 50 to 200, and the constraint rule is a positive integer; This represents the elite retention rate (the percentage of high-fitness solutions directly retained in each round), with a value ranging from 0.1 to 0.3, and the constraint rule is as follows: ; This represents the coordinate perturbation step size, with a value range of 0.5 to 5. This represents the maximum number of iterations, with a value range of 100 to 500, and the constraint rule is a positive integer. This represents the weight for evaluating electricity meter coverage; it has no unit and ranges from 0.5 to 0.7. The constraint rules are as follows: And the value range is [0,1]; This represents the weight for evaluating network connectivity; it is unitless and ranges from 0.3 to 0.5. The constraint rules are the same as... Consistent.

[0115] S2 generates the initial population.

[0116] S21 Site Layout Scheme Coding Rules Definition

[0117] Each collector placement scheme is defined as one individual in the population, using real-number encoding. The encoding format for a single individual is as follows: The three-dimensional coordinate matrix of each data collector:

[0118]

[0119] in The first in the population The individual (the first) (Setting up the layout plan) For the first in this scheme The three-dimensional coordinates of each data collector The index of an individual in the population and , The serial number of the collector and , For population size, This represents the total number of data collectors to be deployed, and all coordinates must meet the following conditions. , This represents the three-dimensional coordinate boundary of the deployment area.

[0120] S22 candidate site layout schemes are generated randomly.

[0121] In the deployment area Internally, a uniformly distributed random number generation algorithm is used to generate... The coordinates of the data collectors that conform to the coordinate boundary constraints constitute a set of candidate point placement schemes, among which... This defines the three-dimensional coordinate boundary of the deployment area. This represents the total number of data collectors to be deployed.

[0122] S23 Energy Meter - Nearest Neighbor Matching and Wiring Distance Calculation for Data Collector

[0123] For the current candidate site selection scheme, the nearest neighbor allocation algorithm is used to complete the one-to-one matching of energy meters and data collectors. The specific calculation is as follows:

[0124] For the One electricity meter Calculate its relationship with all The wiring distance of each data collector is based on the building topology path, using a 3D Manhattan distance algorithm that combines the building BIM model (adapting to orthogonal wiring scenarios and avoiding impenetrable obstacles), as detailed below:

[0125]

[0126] in For the first The electricity meter and the first In the first scheme The actual wiring distance of each data collector For the first The three-dimensional coordinates of an electricity meter For the first In the first scheme The coordinates of each data collector , , These are the orthogonal cabling distances in the X and Y axes and the vertical direction (floor) in the plane, respectively. The actual cabling distance is obtained by summing these three distances.

[0127] When there are impenetrable obstacles such as load-bearing walls and shear walls between the electricity meter and the data collector, the algorithm extracts accessible paths (doorways, passageways, cable trays) from the building BIM model and corrects the Manhattan distance of the foundation. The correction formula is displayed separately and centered, as follows:

[0128]

[0129] In the formula The additional path length for cables to bypass impenetrable obstacles is calculated using the passable path data in the BIM model. It is the difference between the shortest orthogonal path and the directly orthogonal path along the passable area (passageway, doorway, cable tray) from the location of the electricity meter to the location of the data collector. This ensures that the wiring distance conforms to the actual engineering scenario and does not penetrate load-bearing walls or other obstacles.

[0130] Electricity meter Assign it to the nearest data collector. The specific data collector numbers are as follows:

[0131]

[0132] in In order to be with the first The data collector number matched with each electricity meter. The serial number of the collector and , This represents the total number of data collectors to be deployed. For the first The electricity meter and the first The actual wiring distance of each data collector.

[0133] Record the matching wiring distance of the electricity meter as follows: ,in For the first The first electricity meter and the matching first The wiring distance between each data collector;

[0134] For all communication nodes in the current candidate deployment scheme, calculate the free space propagation attenuation as follows:

[0135]

[0136] in For any two communication nodes and Free space propagation attenuation between them For low-power wireless communication carrier frequency, For nodes and The straight-line distance between them is distinct from the wiring distance and is used only for calculating wireless communication link attenuation.

[0137] The obstacle penetration attenuation between any two communication nodes is calculated as follows:

[0138]

[0139] in For nodes and Attenuation of penetration through obstacles between them The total number of obstacle types. For nodes and Between The number of obstacles penetrated is based on the building BIM model. It is obtained by establishing three-dimensional spatial straight line segments between communication nodes and using a three-dimensional ray-casting algorithm to count the number of various obstacles penetrated by the straight line segments. For the first Single-penetration attenuation coefficient of obstacle-like objects.

[0140] Based on free-space propagation attenuation and obstacle penetration attenuation, the Received Signal Strength Intensity (RSSI) between any two communication nodes is calculated as follows:

[0141]

[0142] in For nodes and The received signal strength between The transmit power of the collector / concentrator. For the transmit antenna gain, For receiving antenna gain, For free-space propagation attenuation, Attenuation for obstacle penetration.

[0143] S24 Cable Length Constraint Verification and Effective Solution Selection

[0144] For candidate deployment schemes that have completed electricity meter matching and communication parameter calculations, perform full hard constraint verification: traverse all Matching wiring distance for each electricity meter Determine if there exists any electricity meter whose wiring distance satisfies If such a candidate solution exists, it is deemed invalid, discarded, and the process returns to step S22 to randomly generate a new candidate placement solution; if the wiring distances of all energy meters meet the requirements... If the answer is yes, then the solution is considered valid and retained. This represents the total number of electricity meters. For the first The wiring distance between the electricity meter and the matching data collector This is the upper limit for the length of the cable between the data collector and the electricity meter.

[0145] S25 Initial Population Construction

[0146] Repeat steps S22 to S24, including candidate solution generation, matching, parameter calculation, and constraint verification, until the number of valid solutions retained reaches the preset population size. All valid solutions constitute the initial population. and initialize the number of iterations. .in For population size, For the initial population, This represents the number of iterations.

[0147] S3 evaluates communication performance

[0148] S31 communication node link parameter calculation

[0149] For each placement scheme in the current iteration of the population, define a set of communication nodes. (Including the concentrator and all collectors in this scheme), based on the free space propagation attenuation and obstacle penetration attenuation calculated in S23, determine the communication node pairs in each scheme. ( Total link attenuation The received signal strength of each node pair is obtained using the RSSI calculation formula. .

[0150] S32 Sensitivity Threshold Comparison and Weak Link Elimination

[0151] Perform sensitivity threshold verification on the links of all communication node pairs, and calculate the inter-node sensitivity thresholds. With receiver sensitivity threshold Compare: If If the link is weak and cannot achieve effective communication, it will be removed; if If the condition is met, the link is considered a valid communication link and will be retained. The received signal strength between nodes This is the receiving sensitivity threshold.

[0152] S33 constructs a wireless communication tree with the concentrator as the root node.

[0153] Based on the effective communication links filtered by S32, an improved Prim maximum spanning tree algorithm is used to construct a multi-hop wireless communication tree with the concentrator (node ​​0) as the root node. The specific steps are as follows:

[0154] Initialize the set of connected nodes Set of unconnected nodes , communication tree edge set ,in The set of connected nodes Represents the concentrator node. For a set of unconnected nodes, This represents the total number of data collectors to be deployed. For the set of edges of the communication tree;

[0155] Traverse all , Find satisfaction The node pair corresponding to the maximum RSSI value ,in The set of connected nodes Any node in, Set of unconnected nodes Any node in, These are the elements of the corresponding node pair in the adjacency matrix (i.e., the RSSI values ​​of the effective links). , These are the connected nodes and disconnected nodes that meet the conditions, respectively.

[0156] If a valid pair of nodes exists, Add to collection ,from Remove from , will the side Add to the communication tree edge set ,in For nodes to be added to the connected set, The set of connected nodes For a set of unconnected nodes, For the set of edges of the communication tree;

[0157] Repeat steps 2-3 until... If there are no valid links available for joining, then... It is a set of unconnected nodes;

[0158] Final set The collectors within are connected collectors, and the collection... The collectors inside are not connected collectors, among which The set of connected nodes This is the set of unconnected nodes.

[0159] S34 Electricity Meter Coverage and Network Connectivity Statistics Calculation

[0160] Electricity meter coverage Calculation: Count the total number of electricity meters matched with the connected data collector. The details are as follows:

[0161]

[0162] in This represents the electricity meter coverage rate, with a value ranging from [0, 100%]. This represents the total number of electricity meters matched with the connected data collector. The total number of electricity meters is counted, but only those electricity meters connected to the data collector are included in the effective coverage.

[0163] Network connectivity Calculate: Count the total number of connected collectors in the access communication tree. The details are as follows:

[0164]

[0165] in This represents network connectivity, with a value ranging from [0, 100%]. This represents the total number of connected collectors that have been added to the communication tree. This represents the total number of data collectors to be deployed.

[0166] S4 screening for high-fitness programs

[0167] S41 Individual Fitness Value Calculation

[0168] Electricity meter coverage calculated based on S34 and network connectivity Based on the evaluation weights configured in S13, the fitness value of the current solution is calculated. The calculation formula is:

[0169]

[0170] In the formula, For the first The fitness value of each scheme, As a weight for evaluating electricity meter coverage, Weights are used to evaluate network connectivity.

[0171] Based on the preset elite retention ratio The number of elite individuals that need to be directly retained in this round is calculated as follows:

[0172]

[0173] in This refers to the number of elite individuals that need to be directly retained in this round. To reserve a proportion for the elite For population size, This is a floor function that ensures the number of elite individuals is a positive integer.

[0174] S42 population fitness sorted in descending order

[0175] All of the current population Individuals, according to fitness value Sort the data in descending order from highest to lowest to obtain the sorted sequence of individuals. ,satisfy .in For population size, This represents the fitness value of the corresponding sorted individual.

[0176] S43 Elite Retention and Updating Plans

[0177] Based on the preset elite retention ratio The formula for calculating the number of elite individuals that need to be directly retained in this round is: in This refers to the number of elite individuals that need to be directly retained in this round. To reserve a proportion for the elite For population size, This is the floor function;

[0178] Take the sorted front A select few individuals were directly introduced into the new generation of the population. ,in For the number of elite individuals, For a new generation of population;

[0179] The remaining items after sorting Each individual is marked as a remaining set of solutions to be updated, awaiting coordinate perturbation updates. For population size, The number of elite individuals.

[0180] S5 iterative update outputs optimal

[0181] S51 Connectivity State Determination of the Solution to be Updated

[0182] For each scheme in the remaining set of schemes to be updated, based on the communication tree results of S33, the collector set is divided into: the set of connected collectors. (Collectors that connect to the communication tree and can communicate with the concentrator) and sets of unconnected collectors. (Collectors that are not connected to the communication tree and cannot complete data communication with the concentrator). Among them... This represents the set of connected collectors. This represents a set of unconnected collectors.

[0183] S52 collector coordinate conditional branch update (strictly follows if-else logic)

[0184] Based on the set of disconnected collectors In the given state, execute the mutually exclusive coordinate update logic, as follows:

[0185] like (Not empty set of unconnected collectors): For Each collector in Move directionally to the nearest connected node according to the perturbation step size.

[0186] Step 1: In the set of connected nodes (Including the concentrator), find the one that matches The nearest connected node by straight-line distance ,in Represents the concentrator node. For unconnected collectors, To and The nearest connected node;

[0187] Step 2: Calculate the pointer The unit direction vector is as follows:

[0188] in For pointing The unit direction vector, The nearest connected node For the first The coordinates before the unconnected collector was updated. for and The Euclidean distance between them (used only for direction vector calculation, not for wiring distance).

[0189] Step 3: Update the data collector coordinates based on the unit direction vector and the perturbation step size, as follows:

[0190] In the formula For the first The updated coordinates of the unconnected collectors The coordinates of the collector before the update. Let the coordinate perturbation step size be , It is a unit direction vector pointing to the nearest connected node.

[0191] like (The set of unconnected collectors is empty, and all collectors are connected to the communication tree): Traverse all edges of the communication tree, find the link with the weakest signal, and apply a random offset to the leaf collector at the end of the link.

[0192] Step 1: Traverse all edges of the communication tree to find the received signal strength. The smallest link, whose corresponding end leaf collector is denoted as ,in To receive signal strength, This is the leaf collector at the end of the weakest link;

[0193] Step 2: [The sentence is incomplete and requires more context.] The coordinates are subjected to random offset perturbation, and the update formula is centered separately, as follows:

[0194] In the formula for Updated coordinates for Coordinates before update Let the coordinate perturbation step size be , To generate three random numbers that follow a uniform distribution between 0 and 1, the final offset range is: This enables small-range random adjustments to the collector coordinates, optimizing communication quality on weak links.

[0195] S53 Constraint Verification and Violation Coordinate Reset

[0196] For the data collector that has completed coordinate updates, perform double hard constraint verification:

[0197] Deployment area boundary verification: Check the updated collector coordinates ( or Does it meet the requirements? or If the deployment exceeds the designated area, it will be considered a violation of the boundary. This defines the three-dimensional coordinate boundary of the deployment area.

[0198] Cable length constraint verification: Re-execute the nearest neighbor matching of the energy meter-collector in S23, using the same "actual wiring distance based on the building model" calculation method as in S23, and check the wiring distance of all energy meters connected to the collector. Do they all meet the requirements? If any limits are exceeded, it will be considered a wiring violation; it should be clarified here that the verification is performed using... This is based on the actual cabling distance according to the building topology path, rather than the straight-line distance in space, to ensure consistency with the distance calculation logic of S23 and guarantee a closed-loop data flow. For the first The actual wiring distance between the electricity meter and the matching data collector. This is the upper limit for the length of the cable between the data collector and the electricity meter.

[0199] If the data collector's coordinates exceed the specified limits or the wiring is incorrect, then the coordinates of that data collector within the deployment area will be affected. Within the boundary constraints, a uniform random initialization is re-executed to generate new coordinates that conform to the boundary constraints. This represents the three-dimensional coordinate boundary of the deployment area.

[0200] Complete S54 New Generation Population Construction

[0201] All remaining updates that have been completed and verified will be merged into the new generation population. This allows the total number of individuals in the new generation to reach the preset population size. ,in For a new generation of population, Population size.

[0202] S55 Iteration Termination Judgment and Optimal Solution Output

[0203] Update the iteration count to Execute termination judgment, where For the number of iterations:

[0204] like Then return to step S3 to process the new generation of the population. Perform communication performance evaluation, fitness calculation, and a new round of iterative updates, among which... The maximum number of iterations, The population in the current iteration;

[0205] like If the iteration loop terminates, the individual with the highest fitness value in the final population is output as the optimal placement scheme for the collectors. This represents the maximum number of iterations.

[0206] In summary, this invention addresses many shortcomings of existing low-power wireless data acquisition device deployment technologies by providing an optimal deployment method that balances construction feasibility and communication reliability. Compared to existing technologies, it possesses significant technical advantages and engineering application value. Its core beneficial effects are as follows:

[0207] This invention places the hard constraint on the cable length between the data collector and the electricity meter at the initial population generation stage. By screening effective solutions through nearest neighbor matching and full constraint verification, it avoids invalid solutions that exceed the wiring distance limit from the source. It completely solves the pain points of traditional manual extensive deployment, excessive wiring costs of existing optimization algorithms, and the inability of the solution to be implemented in construction. It significantly reduces the cost of on-site wiring construction and subsequent rectification and maintenance investment, and ensures that the deployment solution fully complies with the on-site construction specifications.

[0208] This invention accurately calculates wireless communication link attenuation and received signal strength based on building models, and constructs a wireless communication tree with a concentrator as the root node. It achieves accurate quantitative evaluation of communication performance in complex building scenarios, effectively solving the problems of existing solutions not fully considering signal attenuation due to building obstacles and the large deviation between simulation optimization results and actual on-site deployment effects. It significantly improves the connectivity reliability of the data acquisition network and the data acquisition coverage of electricity meters, fully meeting the core management requirements of "full acquisition, full coverage, and full cost control" of the electricity information acquisition system.

[0209] This invention employs an iterative optimization strategy that combines elite retention with directional perturbation. It executes differentiated coordinate update logic for disconnected nodes and weak links, abandoning the blindness of random perturbation in conventional evolutionary algorithms, significantly accelerating the algorithm's convergence speed, effectively avoiding getting trapped in local optima, and achieving multi-objective collaborative global optimization of electricity meter coverage and network connectivity. It can perfectly adapt to the refined deployment needs of complex building scenarios such as high-rise residential buildings and commercial complexes, providing reliable technical support for the large-scale construction of low-voltage data acquisition systems for smart grids and ubiquitous power Internet of Things.

[0210] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimal placement of data acquisition points based on low-power wireless communication, characterized in that, The data collector connects to the electricity meter via cable and networks with the concentrator via wireless communication, including the following steps: S1. Obtain basic parameters: Obtain the building model, deployment area, coordinates of electricity meter and concentrator, as well as transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight. S2. Generate the initial population: Randomly generate multiple coordinate sets containing the number of collectors within the deployment area; Based on the building model, assign the electricity meter to the nearest collector and calculate the wiring distance; Filter all coordinate sets whose wiring distances meet the upper limit of cable length until the number of retained sets reaches the population size, thus forming the initial population. S3. Evaluate communication performance: For each scheme of the population, calculate the link attenuation and received signal strength between each communication node based on the building model, and remove links that are below the sensitivity threshold; with the concentrator as the root node, construct a wireless communication tree based on the received signal strength, and count the coverage rate and connectivity of the electricity meters accessing the communication tree. S4. Selecting high-fitness schemes: Based on the evaluation weights, the coverage and connectivity of each scheme are weighted and summed to calculate the fitness value and sort them in descending order; according to the retention ratio, high-fitness schemes are directly put into the new generation population, and the schemes that are not retained are recorded as remaining schemes waiting for position updates; S5. Iterative update to output the optimal solution: For the remaining solutions, move the collectors that are not connected to the communication tree to the nearest connected node according to the perturbation step size; if all collectors are connected, randomly shift the collector with the weakest signal; if the movement exceeds the limit or the wiring exceeds the limit, re-initialize the coordinates randomly; merge the updated remaining solutions into the new generation population, repeat S3 to S5 for the number of iterations, and output the optimal solution.

2. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 1, characterized in that: The S1 step of obtaining basic parameters specifically includes: S11. Deployment Scene Digital Modeling and Coordinate System Calibration: Define the three-dimensional coordinate boundary of the deployment area, extract obstacle information based on the building BIM model and construct an obstacle attenuation mapping table, and calibrate the three-dimensional fixed coordinates of the concentrator and all energy meters. S12. Calibration of basic parameters of core equipment and communication links: Calibrate the global unified parameters of carrier frequency, transmit power, transceiver antenna gain, receive sensitivity threshold, upper limit of cable length between collector and energy meter, and total number of collectors to be deployed for low-power wireless communication. S13. Configuration of Hyperparameters and Evaluation System for Optimization Algorithm: Configure the hyperparameters of the algorithm, such as population size, elite retention ratio, coordinate perturbation step size, and maximum number of iterations, as well as the evaluation weights corresponding to electricity meter coverage and network connectivity.

3. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 1, characterized in that: The S2 step of generating the initial population specifically includes: S21. Definition of coding rules for collection point layout: Each collection point layout scheme is treated as a single individual in the population, and real number coding is adopted, with the three-dimensional coordinate matrix of the collection point as the coding format of the individual. S22. Random generation of candidate deployment schemes: Within the deployment area, a uniformly distributed random number generation algorithm is used to generate collector coordinates that conform to the coordinate boundary constraints, forming a single set of candidate deployment schemes. S23. Nearest neighbor matching and wiring distance calculation between electricity meter and data collector: The nearest neighbor allocation algorithm is used to complete the one-to-one matching between the electricity meter and the data collector, and the wiring distance after matching is calculated. S24. Cable length constraint verification and effective scheme selection: Perform full hard constraint verification on candidate layout schemes, discard invalid schemes with wiring distances exceeding the upper limit of cable length, and retain valid schemes where all wiring distances meet the requirements. S25. Initial population construction: Repeat the candidate solution generation, matching, and verification process until the number of valid solutions reaches the preset population size, thus forming the initial population and initializing the number of iterations.

4. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 3, characterized in that: The S23 electricity meter-collector nearest neighbor matching and wiring distance calculation specifically involves: for the current candidate deployment scheme, using a three-dimensional Manhattan distance algorithm combined with the building BIM model, calculating the actual wiring distance between the electricity meter and each collector based on the building topology path; when there are impenetrable obstacles between the electricity meter and the collector, extracting the passable path information from the BIM model, and correcting the wiring distance by detour; assigning each electricity meter to the collector with the closest wiring distance, completing a one-to-one matching and recording the corresponding wiring distance; and simultaneously calculating the free space propagation attenuation, obstacle penetration attenuation, and received signal strength between communication nodes.

5. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 1, characterized in that: The S3 evaluation of communication performance specifically includes: S31. Calculation of communication node link parameters: For each deployment scheme in the current iteration, define a set of communication nodes including the concentrator and all collectors, and calculate the total link attenuation and received signal strength of all communication node pairs. S32. Sensitivity threshold comparison and weak link elimination: Compare the received signal strength of all node pairs with the received sensitivity threshold, eliminate invalid links with received signal strength below the threshold, and retain valid communication links. S33. Construct a wireless communication tree with the concentrator as the root node: Based on the filtered effective communication links, the improved Prim maximum spanning tree algorithm is used to construct a multi-hop wireless communication tree with the concentrator as the root node, and divide the set of connected collectors into sets of unconnected collectors. S34. Statistical calculation of electricity meter coverage and network connectivity: Statistically calculate the electricity meter coverage and network connectivity of the current scheme.

6. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 5, characterized in that: The S34 electricity meter coverage rate and network connectivity rate statistical calculation are as follows: count the total number of electricity meters matched by the connected collectors, calculate the ratio of the number of connected collectors to the total number of electricity meters, and obtain the electricity meter coverage rate; count the total number of connected collectors accessing the communication tree, calculate the ratio of the number of connected collectors to the total number of collectors to be deployed, and obtain the network connectivity rate.

7. The optimal placement method for data collectors based on low-power wireless communication according to claim 1, characterized in that: The S4 screening of high-fitness schemes specifically includes: S41. Calculation of individual fitness value: Based on the preset evaluation weights, the electricity meter coverage rate and network connectivity rate of each scheme are weighted and summed to calculate the fitness value of each scheme. S42. Population Fitness Descending Order Sort: Sort all individuals in the current population in descending order of fitness value from highest to lowest. S43. Elite Retention and Updating Scheme Division: Based on the preset elite retention ratio, calculate and directly retain the top-ranked high-fitness elite individuals to the new generation population, and mark the remaining individuals as the updating scheme set.

8. The optimal placement method for data collectors based on low-power wireless communication according to claim 7, characterized in that: The individual fitness value calculation in S41 is specifically as follows: based on the electricity meter coverage rate and network connectivity rate calculated in S34, and combined with the electricity meter coverage rate evaluation weight and network connectivity rate evaluation weight configured in S13, the two indicators are weighted and summed to obtain the fitness value of the corresponding deployment scheme.

9. The optimal placement method for data acquisition devices based on low-power wireless communication according to claim 1, characterized in that: The optimal output of the S5 iterative update specifically includes: S51. Connectivity status determination of the scheme to be updated: For each scheme in the set of schemes to be updated, based on the wireless communication tree results constructed in S33, divide the set of connected collectors into the set of disconnected collectors. S52, Conditional Branch Update of Collector Coordinates: Based on the state of the set of unconnected collectors, execute mutually exclusive coordinate update logic. If the set of unconnected collectors is not empty, move the collectors not connected to the communication tree to the nearest connected node according to the perturbation step size. If the set of unconnected collectors is empty, traverse the communication tree to find the link with the weakest signal, and apply a random offset to the leaf collector at the end of the link. S53. Constraint Condition Verification and Violation Coordinate Reset: For the data acquisition unit that has completed coordinate update, perform deployment area boundary verification and cable length constraint verification, and randomly re-initialize the violation coordinates; S54. Complete construction of the new generation population: The updated and verified schemes to be updated are incorporated into the new generation population, so that the size of the new generation population reaches the preset value. S55. Iteration Termination Judgment and Optimal Solution Output: Update the iteration count. If the iteration count has not reached the maximum iteration count, return to S3 to execute a new round of iteration. If the iteration count has reached the maximum iteration count, terminate the loop and output the optimal placement scheme with the highest fitness value in the final population.

10. An optimal deployment system for data collectors based on low-power wireless communication, wherein the data collectors are connected to energy meters via cables and networked with concentrators via low-power wireless communication, characterized in that: It includes a basic parameter acquisition module, an initial population generation module, a communication performance evaluation module, a high fitness scheme selection module, and an iterative update and optimal scheme output module; The basic parameter acquisition module is used to acquire the building model, deployment area, coordinates of the electricity meter and concentrator, as well as the transmission power, sensitivity threshold, upper limit of cable length, number of collectors, population size, retention ratio, perturbation step size, number of iterations and evaluation weight. The initial population generation module is used to randomly generate multiple coordinate sets containing a preset number of collectors within the deployment area; Based on the building model, the electricity meter is assigned to the nearest data collector and the wiring distance is calculated; all sets of coordinates whose wiring distances meet the upper limit of cable length are filtered until the number of retained ones reaches the population size, thus forming the initial population. The communication performance evaluation module is used to calculate the link attenuation and received signal strength between each communication node based on the building model for each deployment scheme in the population, and to remove links that are below the sensitivity threshold. Using the concentrator as the root node, a wireless communication tree is constructed based on the received signal strength, and the coverage rate of electricity meters and the network connectivity rate of the access communication tree are statistically analyzed. The high fitness scheme screening module is used to perform weighted summation of the coverage and connectivity of each layout scheme based on preset evaluation weights, calculate the fitness value of each scheme, and sort them in descending order. Based on the preset retention ratio, high-fitness schemes are directly put into the new generation population, and schemes that are not retained are recorded as remaining schemes waiting for position updates; The iterative update and optimal solution output module is used to move the collectors that are not connected to the communication tree to the nearest connected node according to the perturbation step size for the remaining solutions; if all collectors in the solution are connected to the communication tree, a random offset is applied to the collector with the weakest signal. If the collector moves beyond its boundaries or the wiring distance exceeds the limit, the collector's coordinates are randomly re-initialized. The updated remaining schemes are then incorporated into the new generation population, and the communication performance evaluation, high fitness scheme selection, and coordinate iterative update process are repeated until the preset number of iterations is reached, at which point the optimal collector placement scheme is output.

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