Power remote meter reading communication signal monitoring method and system

Abstract

The invention provides an electric power remote meter reading communication signal monitoring method and system. The method comprises the following steps: acquiring multi-dimensional electricity consumption basic information of an electricity consumption information acquisition terminal; terminal communication signal intensity data and environment parameters are obtained based on the multi-dimensional power utilization basic information, a signal quality evaluation matrix is constructed through a dynamic weight distribution algorithm, and the environment parameters comprise obstacle density; detecting the terminal communication signal strength data through the signal quality evaluation matrix to obtain a detection result; and performing dynamic matching according to the signal intensity grade and the time-sharing meter reading success rate in the corresponding time period, generating a signal optimization strategy, and executing the signal optimization strategy through the communication signal enhanced coverage device. Through multi-dimensional information integration, dynamic weight signal evaluation and signal optimization based on the meter reading success rate, the quality and stability of electric power remote meter reading communication signals can be accurately improved, and a powerful guarantee is provided for efficient operation of electric power remote meter reading services.

Description

Technical Field

[0001] The present invention relates to the field of power systems and communication technologies, and in particular to a method and system for monitoring power remote meter reading communication signals. Background Art

[0002] With the continuous advancement of power consumption information collection technology at power supply companies, higher requirements are being placed on the timeliness and integrity of data such as power consumption, voltage, and current transmitted via remote channels from terminals. The integrity of data collection directly impacts the accuracy and effectiveness of user load characteristics queries, daily power consumption inquiries, and potential equipment fault analysis. However, some power consumption information collection terminals are installed in weak or blind spots, such as basements, newly commissioned residential areas without operator signal coverage, and mountainous areas. This results in weak remote meter reading signals, severely hindering data transmission to the main power consumption information collection station. Traditionally, in areas with poor signal strength or blind spots, determining terminal device signal strength relies primarily on terminal disconnection and meter reading timeout reports displayed on the main station. This post-processing approach lacks proactive problem resolution, resulting in delayed problem detection and requiring significant on-site maintenance and maintenance efforts. Furthermore, wireless signal coverage for traditional power consumption information collection terminals is often achieved by installing an extension antenna on the terminal's antenna port. This not only requires frequent manual adjustments to the antenna position, but also offers limited improvement in remote communication signal strength in areas with poor native signal strength, making it difficult to meet the requirements for data transmission and meter reading efficiency. Summary of the Invention

[0003] In response to the defects and shortcomings of the existing technology, the present invention provides a method and system for monitoring communication signals for electric power remote meter reading, so as to further improve the signal coverage quality and meter reading efficiency, and realize accurate monitoring and intelligent optimization of electric power remote meter reading signals. The method includes obtaining multi-dimensional basic electricity consumption information of the electricity information collection terminal; based on the multi-dimensional basic electricity consumption information, obtaining terminal communication signal strength data and environmental parameters, and constructing a signal quality evaluation matrix through a dynamic weight allocation algorithm, the environmental parameters include obstacle density; through the signal quality evaluation matrix, the terminal communication signal strength data is detected to obtain the detection result; according to the signal strength level and the time-sharing meter reading success rate in the corresponding time period, dynamic matching is performed to generate a signal optimization strategy, and the signal optimization strategy is executed through a communication signal coverage enhancement device. Through multi-dimensional information integration, dynamic weight signal evaluation and signal optimization based on meter reading success rate, the quality and stability of the electric power remote meter reading communication signal can be accurately improved, providing a strong guarantee for the efficient operation of the electric power remote meter reading service.

[0004] The present invention specifically adopts the following technical means:

[0005] A method for monitoring communication signals of electric power remote meter reading, comprising:

[0006] Obtain multi-dimensional basic electricity consumption information of the electricity consumption information collection terminal, including terminal asset number, terminal installation location coordinates, communication card parameters and station area number;

[0007] Based on the multi-dimensional basic electricity usage information, terminal communication signal strength data and environmental parameters including obstacle density are obtained, and a signal quality evaluation matrix is constructed using a dynamic weight allocation algorithm based on the terminal communication signal strength data and the environmental parameters;

[0008] Using the signal quality evaluation matrix, the terminal communication signal strength data is detected to obtain a detection result including a signal strength level and a signal application scenario;

[0009] Dynamic matching is performed according to the signal strength level and the time-sharing meter reading success rate in the corresponding time period to generate a signal optimization strategy, and the signal optimization strategy is executed through the communication signal reinforcement coverage device.

[0010] Furthermore, the dynamic weight allocation algorithm includes:

[0011] performing noise suppression on the terminal communication signal strength data to obtain preprocessed signal data;

[0012] Performing environmental gradient calculation on the environmental parameters to obtain an environmental gradient vector, and determining the scene type of the terminal according to the terminal installation location coordinates to obtain a scene feature vector;

[0013] The signal-environment coupling matrix is constructed by the following formula using the preprocessed signal data, the environment gradient vector and the scene feature vector:

[0014]

[0015] Where Ψ(t) is the signal-environment coupling matrix, η is the preset coefficient used to adjust the weight of the dynamic sensitivity term, The preprocessed signal The derivative with respect to time t is used to characterize the dynamic rate of change of the signal, ω k (t) is the weight coefficient dynamically generated by the environment gradient vector and the scene feature vector through the LSTM network, SINR k is the kth signal to interference plus noise ratio, Γ and Δ are the preset threshold and preset interval respectively, which are used to adjust the input parameters of the sigmoid function, and λ is used to adjust the weight of the spatial gradient correction term. is the second-order spatial derivative of the signal-to-noise ratio;

[0016] The quantum annealing algorithm is used to solve the dynamic weight vector based on the signal-environment coupling matrix to obtain the optimized weight vector, and further construct the signal quality evaluation matrix

[0017] Furthermore, the calculation formula of the optimized weight vector is:

[0018]

[0019] Among them, W * is the optimized weight vector, γ is the sparsity control parameter, Ψ(t) is the signal-environment coupling matrix, Y pred is the meter reading success rate vector, is a constraint that ensures that the sum of the elements of the weight vector W is 1.

[0020] Furthermore, the generation signal optimization strategy includes:

[0021] Based on the coordinates of the low-level signal area and environmental parameters in the signal strength level, a cross-scenario interference source probability distribution model based on transfer learning is constructed using the following formula:

[0022]

[0023] Among them, α k is the weight coefficient of the kth component, generated by extracting the environmental gradient vector through ResNet-34, σ k It is a parameter that is dynamically adjusted according to the scene type and is used to control the width of the probability distribution. k ,y k ,z k )|| 2 For the space point (x,y,z) and (x k ,y k ,z k ), K is the total number of components involved in the summation, that is, the number of interference source distribution features;

[0024] According to the cross-scenario interference source probability distribution model, the quantum annealing algorithm is used to solve the maximum probability interference source coordinates, wherein the quantum annealing Hamiltonian in the quantum annealing algorithm is defined as:

[0025]

[0026] Where H is the quantum annealing Hamiltonian, J ij is the coupling coefficient between qubits i and j, and are the Pauli Z operators acting on quantum bits i and j respectively, Γ(t) is the parameter that decays with time, Γ0 is the initial value, γ is the decay rate, t is the time, is the Pauli X operator acting on quantum bit i;

[0027] The transmit power adjustment amount is calculated according to the signal quality evaluation matrix and the time-sharing meter reading success rate; and a signal optimization strategy is generated based on the maximum probability interference source coordinates and the transmit power adjustment amount.

[0028] Furthermore, the calculation formula of the transmit power adjustment amount is:

[0029]

[0030] Where ΔP tx (t) is the transmit power adjustment at time t, P base is the preset reference transmission power, N is the number of indicators involved in the calculation of the signal quality evaluation matrix Q, is the nth indicator in the signal quality evaluation matrix Q, and Ψ is the signal-environment coupling matrix.

[0031] Furthermore, the communication signal enhancement coverage device includes a wireless signal proximal device, a wireless signal remote device, a power supply unit and an optical fiber, wherein the wireless signal proximal device is used to receive terminal communication signal strength data, and the wireless signal remote device is used to dynamically adjust and output the transmission power according to the signal optimization strategy through multi-band adaptive modulation; the wireless signal remote device in the communication signal enhancement coverage device adjusts the transmission power and / or deployment position according to the signal optimization strategy.

[0032] Furthermore, before generating the signal optimization strategy, the method further includes obtaining the time-sharing meter reading success rate through the following steps:

[0033] Use RPA tools to obtain time-based meter reading data from electricity consumption information collection terminals. This data includes the terminal ID, meter reading timestamp, meter reading status, and power consumption value.

[0034] Align the meter reading timestamp with the time window of the terminal communication signal strength data to generate a time-aligned sequence;

[0035] Based on the time-aligned sequence, the initial meter reading success rate is calculated according to the preset time window, and the success rate fluctuation in the initial meter reading success rate is calculated by the average of the absolute differences of the meter reading success rates in adjacent time windows.

[0036] When the success rate fluctuation exceeds the preset threshold, the initial meter reading success rate is compensated based on the sliding window mean to obtain the time-sharing meter reading success rate.

[0037] And, a power remote meter reading communication signal monitoring system, comprising:

[0038] Electricity usage information collection terminal: used to obtain and store multi-dimensional electricity usage basic information and time-sharing meter reading data, including terminal asset number, terminal installation location coordinates, communication card parameters and station area number;

[0039] Signal strength monitoring module: used to perform the following operations:

[0040] Based on the multi-dimensional basic electricity usage information, obtaining terminal communication signal strength data and environmental parameters including obstacle density;

[0041] Based on the terminal communication signal strength data and environmental parameters, a signal quality assessment matrix is constructed through a dynamic weight allocation algorithm;

[0042] Detecting the terminal communication signal strength data using the signal quality assessment matrix to obtain a detection result including a signal strength level and a signal application scenario;

[0043] Dynamically matching the signal strength level and the time-sharing meter reading success rate within the corresponding time period to generate a signal optimization strategy;

[0044] Communication signal coverage enhancement device: used to adjust the transmission power and / or deployment position of the wireless signal remote device according to the signal optimization strategy to enhance the terminal communication signal strength.

[0045] And, a computer device includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0046] A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that the computer program implements the steps of the method described above when executed by a processor.

[0047] Compared with the existing technology, the present invention and its preferred solution comprehensively collect key terminal-related data by obtaining multi-dimensional basic electricity information such as the terminal asset number, terminal installation location coordinates, communication card parameters, and station area number of the electricity information collection terminal, providing a rich information basis for subsequent in-depth analysis of signal conditions. Secondly, based on the multi-dimensional basic electricity information, the terminal communication signal strength data and environmental parameters are obtained, and a signal quality evaluation matrix is constructed using a dynamic weight allocation algorithm. This fully considers the impact of different environmental factors on the signal, as well as the differences in the importance of each signal indicator in different scenarios, making the signal quality evaluation more accurate and in line with the actual situation, and providing a reliable basis for signal detection.

[0048] Furthermore, the signal quality assessment matrix is used to test terminal communication signal strength data, generating detection results that include signal strength levels and signal application scenarios. This clearly distinguishes different signal states and applicable scenarios, facilitating the development of targeted optimization strategies. Finally, a signal optimization strategy is generated by dynamically matching the signal strength level with the time-sharing meter reading success rate within the corresponding time period. This strategy is then executed by the communication signal enhancement coverage device. This dynamic optimization method, based on actual meter reading success rates, effectively addresses signal issues, significantly improving the pertinence and effectiveness of signal optimization and effectively meeting data transmission and meter reading requirements.

[0049] Compared with traditional power remote meter reading communication signal processing methods, the present invention significantly improves the accuracy, pertinence and real-time performance of power remote meter reading communication signal monitoring through multi-dimensional data collection, precise signal quality assessment, scenario-based signal detection and dynamic optimization based on meter reading success rate. It effectively solves the problems of insufficient signal coverage and lack of pertinence of optimization measures in traditional methods, and provides strong technical support for ensuring the efficiency and reliability of power consumption information collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0051] Figure 1 A flow chart of a method for monitoring communication signals for remote electric meter reading according to an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the communication signal coverage structure for remote power meter reading provided by an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of the power remote meter reading communication signal monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0055] In one embodiment, Figure 1 As shown, a method for monitoring communication signals for remote power meter reading is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] S101: Acquire multi-dimensional basic electricity usage information of an electricity usage information collection terminal, where the multi-dimensional basic electricity usage information includes the terminal asset number, terminal installation location coordinates, communication card parameters, and station area number.

[0057] Specifically, electricity usage information collection terminals are widely distributed across different regions. To fully understand the characteristics and environment of each terminal, multi-dimensional basic electricity usage information is required. The terminal asset number uniquely identifies each terminal within the power system, facilitating precise location and management of each terminal and tracking the entire equipment lifecycle, including installation, maintenance, and decommissioning. By obtaining the terminal's installation coordinates, it is possible to determine whether the terminal is located inside a distribution station, on an outdoor tower, or in a special area such as a basement. Different locations and environments have varying impacts on signal propagation. For example, metal equipment in distribution stations may shield signals, while basements are susceptible to signal attenuation due to structural factors. The communication card is a key component for data communication between the terminal and the master station, and its parameters directly impact communication quality. For example, communication card parameters include communication frequency band, transmit power, and communication protocol. Different communication frequency bands have different signal propagation characteristics in different environments, and transmit power determines the signal transmission distance and strength. The station number identifies the power supply area to which the terminal belongs. Different stations may have different power equipment layouts, user densities, and power consumption characteristics, which helps further analyze the indirect impact on the terminal's communication signal. For example, stations with higher power loads may experience more electromagnetic interference, thus affecting signal quality.

[0058] S102: Based on the multi-dimensional basic electricity consumption information, terminal communication signal strength data and environmental parameters are obtained, and based on the terminal communication signal strength data and environmental parameters, a signal quality evaluation matrix is constructed through a dynamic weight allocation algorithm, where the environmental parameters include obstacle density.

[0059] Specifically, based on multi-dimensional basic electricity usage information, the terminal's built-in signal monitoring module or related communication equipment can be used to obtain real-time terminal communication signal strength data such as interference plus noise ratio (INR), received signal strength indicator (RSSI), and bit error rate (BER). The INR reflects the extent to which the signal is affected by interference and noise during transmission; a higher ratio indicates better signal quality. The RSSI provides a visual representation of the received signal strength, and the BER measures the probability of errors during data transmission; a lower value indicates higher transmission accuracy. Furthermore, environmental factors such as temperature, humidity, and obstacle density have a significant impact on signal propagation. For example, within a distribution station, an installed camera can capture an image of the equipment layout. Image recognition algorithms can then be used to analyze the number and distribution of obstacles, such as metal equipment and walls, in the image to calculate the obstacle density. In outdoor scenarios, a geographic information system (GIS) combined with site survey data can be used to obtain information on the distribution of obstacles, such as buildings and trees, within a certain range around the terminal, thereby estimating obstacle density. A high obstacle density can easily lead to signal obstruction, reflection, and scattering, weakening signal strength and quality.

[0060] Furthermore, considering that different signal metrics and environmental parameters have varying degrees of impact on signal quality in different scenarios, a dynamic weighting algorithm can be used to construct a signal quality assessment matrix. For example, in environments with numerous obstacles, the received signal strength indicator may be more affected and its weighting should be increased; whereas in areas with numerous interference sources, the interference plus noise ratio (INR) is given greater weighting. By continuously adjusting the weightings, the assessment matrix can more accurately reflect actual signal quality.

[0061] S103: Detect terminal communication signal strength data using a signal quality assessment matrix to obtain a detection result, which includes a signal strength level and a signal application scenario.

[0062] Specifically, after constructing the signal quality assessment matrix, the matrix can be used to conduct in-depth analysis and detection of the terminal communication signal strength data, thereby obtaining comprehensive and accurate detection results. Among them, based on the calculation results of the signal quality assessment matrix, corresponding thresholds can be set to divide the signal strength levels. For example, the signal strength is divided into three levels: a signal strength of 1-2 bars is defined as a weak signal, which may frequently experience data transmission interruptions or errors; a signal strength of 3 bars is defined as a fair signal, with basically stable data transmission, but may fluctuate during certain periods or under specific circumstances; a signal strength of 4 bars is defined as a stable signal, which can ensure reliable data transmission. This level classification method helps to intuitively understand the strength of the signal.

[0063] In addition to signal strength levels, the signal's application scenario can also be determined. For example, machine learning algorithms, such as decision trees and neural networks, can be used to input relevant data from the signal quality assessment matrix, terminal installation location coordinates, environmental parameters, and other information. The trained model can then further identify signal application scenarios. Common scenarios include complex basement environments, base station signal transmission in remote areas, and electromagnetic interference within distribution substations. In complex basement environments, signals are susceptible to multiple reflections and absorption from building structures, resulting in severe signal attenuation. In remote basement signal transmission scenarios, signals gradually weaken during transmission due to the distance from the base station and are easily affected by topography. In electromagnetic interference scenarios within distribution substations, electrical equipment within the substation generates electromagnetic interference, impacting normal signal transmission. Accurately identifying signal application scenarios facilitates the development of targeted signal optimization strategies.

[0064] S104: Dynamically match the signal strength level and the time-sharing meter reading success rate in the corresponding time period to generate a signal optimization strategy, and execute the signal optimization strategy through the communication signal enhancement coverage device, wherein the wireless signal remote device in the communication signal enhancement coverage device adjusts the transmission power and / or deployment position according to the signal optimization strategy.

[0065] Specifically, to improve meter reading success rates and ensure efficient operation of the remote power meter reading system, a signal optimization strategy can be generated and implemented based on the correlation between signal strength levels and time-based meter reading success rates. For example, if the signal strength level is weak during a certain period and the meter reading success rate falls below a certain threshold, this indicates that the current signal conditions are severely impacting meter reading. By analyzing extensive historical data, patterns between different signal strength levels and meter reading success rates can be captured, determining the optimization measures required in different situations. Based on the dynamic matching results and the actual situation, a specific signal optimization strategy can be generated. For example, if the signal strength level is weak and the meter reading success rate is low, the signal optimization strategy might include increasing the transmit power of the wireless signal remote to enhance signal coverage and strength. Alternatively, the wireless signal remote could be positioned away from obstacles or closer to terminals with weaker signals to improve signal reception. For situations where the signal strength is average but the meter reading success rate fluctuates significantly, the communication protocol can be optimized or the signal transmission frequency adjusted to reduce interference and stabilize signal transmission. After receiving the signal optimization strategy, the communication signal enhancement coverage device's wireless signal remote units adjust their transmit power accordingly, precisely controlling the power increase or decrease to ensure signal requirements are met while avoiding excessive transmission that wastes energy and increases interference. When adjusting the deployment location, the wireless signal remote units are moved to the optimized position via remote control or on-site operation, and the results of the adjustments are monitored in real time. For example, after adjustments, terminal communication signal strength data and meter reading success rates are re-obtained. If the results do not meet expectations, the strategy is further adjusted, forming a closed-loop optimization process to continuously improve signal quality and meter reading success rates.

[0066] The aforementioned method for monitoring communication signals for remote power meter reading obtains multidimensional basic electricity usage information from the electricity information collection terminal, providing a comprehensive data foundation for subsequent in-depth understanding of terminal conditions and signal characteristics. Secondly, based on this multidimensional basic electricity usage information, terminal communication signal strength data and environmental parameters are obtained, and a signal quality assessment matrix is constructed using a dynamic weight allocation algorithm. This overcomes the shortcomings of traditional methods that rely solely on signal strength detection and provides a rich data foundation for subsequent signal optimization decisions. Furthermore, signal detection based on the signal quality assessment matrix not only accurately assesses signal strength levels but also clarifies signal application scenarios, enhancing adaptability to complex environments and avoiding signal assessment errors caused by environmental factors such as high obstacle density. Finally, by dynamically matching signal strength levels with time-of-use meter reading success rates, a signal optimization strategy is generated. This comprehensively considers the correlation between signal strength and actual meter reading results, enabling more precise adjustment of signal coverage strategies to avoid the impact of insufficient or excessive signal coverage on meter reading efficiency. In addition, this method implements signal optimization strategies through communication signal enhancement coverage devices, dynamically adjusts the transmission power and deployment location of wireless signal remote devices, and can optimize signal coverage range according to real-time signal requirements and environmental conditions, improve the scientificity and timeliness of signal coverage, and further enhance the efficiency and reliability of remote electricity meter reading.

[0067] In an exemplary embodiment, a signal quality assessment matrix is constructed based on terminal communication signal strength data and environmental parameters using a dynamic weight allocation algorithm, including:

[0068] According to the terminal communication signal strength data, an improved median filter algorithm is used to suppress noise to obtain preprocessed signal data;

[0069] Calculate the environmental gradient based on the environmental parameters to obtain the environmental gradient vector, and determine the scene type of the terminal based on the terminal installation location coordinates to obtain the scene feature vector;

[0070] By preprocessing the signal data, the environment gradient vector and the scene feature vector, the signal-environment coupling matrix is constructed using the following formula:

[0071]

[0072] Where Ψ(t) is the signal-environment coupling matrix, η is the preset coefficient used to adjust the weight of the dynamic sensitivity term, The preprocessed signal The derivative with respect to time t is used to characterize the dynamic rate of change of the signal, ω k (t) is the weight coefficient dynamically generated by the environment gradient vector and the scene feature vector through the LSTM (Long Short-Term Memory) network, SINR kis the kth signal to interference plus noise ratio, Γ and Δ are the preset threshold and preset interval respectively, which are used to adjust the input parameters of the sigmoid function, and λ is used to adjust the weight of the spatial gradient correction term. is the second-order spatial derivative of the signal-to-noise ratio;

[0073] The quantum annealing algorithm is used to solve the dynamic weight vector based on the signal-environment coupling matrix to obtain the optimized weight vector;

[0074] Based on the optimized weight vector, a signal quality evaluation matrix is constructed.

[0075] Specifically, terminal communication signals are highly susceptible to various noise interference during transmission, affecting the signal's authenticity and accuracy, and thus interfering with subsequent signal quality assessments. The improved median filter algorithm is a commonly used denoising method that can be optimized based on the traditional median filter algorithm. Traditional median filtering replaces the value of a particular point in the signal with the median of that point's neighborhood to eliminate noise. The improved median filter algorithm, however, optimizes the selection of neighborhoods and median calculation methods based on the characteristics of power remote meter reading communication signals. For example, when selecting neighborhoods, the time series characteristics of the signal can be considered, not just within the spatial neighborhood but also incorporating signal values within a certain time range. When calculating the median, different weights can be assigned based on the signal's importance or volatility. This improved median filter algorithm processes terminal communication signal strength data, removing noise, and produces more stable preprocessed signal data, laying the foundation for accurate subsequent analysis of signal characteristics.

[0076] Furthermore, environmental gradient calculation can reflect the spatial variation trends of environmental parameters, thereby demonstrating the degree of environmental impact on the signal. Environmental parameters can include data such as obstacle density, temperature, and humidity. For example, by monitoring and analyzing the terminal's surrounding environment and combining relevant algorithms, the rate of change of obstacle density in different spatial directions, such as horizontal and vertical variations, can be calculated. For parameters such as temperature and humidity, their spatial gradients can be calculated. By integrating the gradients of different environmental parameters, an environmental gradient vector can be obtained. This vector comprehensively reflects the spatial variation characteristics of the environment, providing a quantitative basis for subsequent analysis of the environmental impact on the signal. Based on the terminal's installation location coordinates, combined with geographic information system data and building structure information, it can be determined whether the terminal is located in different scenarios, such as a distribution station building, an outdoor tower, or a basement. Different scenarios have different signal propagation characteristics, and characteristic information can be extracted for each scenario, such as the equipment layout and metal object distribution within the distribution station building, the terrain and vegetation coverage surrounding the outdoor tower, and the building materials and number of floors in the basement. By quantifying and encoding this characteristic information, a scene feature vector can be formed to characterize the unique properties of different scenes and provide scene-related information for the subsequent construction of the signal-environment coupling matrix.

[0077] Specifically, based on the preprocessed signal data, the environmental gradient vector, and the scene feature vector, the above formula can be used to construct a signal-environment coupling matrix. This signal-environment coupling matrix comprehensively considers the dynamic changes of the signal itself, environmental factors, and the impact of scene features on the signal. Among them, the LSTM network is a special recursive neural network with the ability to handle long-term dependencies. It can dynamically generate the weight coefficient ω based on the input environmental gradient vector and scene feature vector combined with time series information. k (t). The weight coefficient can be adjusted with time and environmental changes to adapt to different signals and environmental conditions.

[0078] Specifically, the quantum annealing algorithm is an optimization algorithm that simulates the annealing process of a quantum system and is capable of finding the global optimal solution in a complex solution space. When constructing a signal quality assessment matrix, the signal quality can be accurately assessed by determining the weights of various signal characteristics and environmental factors. Based on this signal-environment coupling matrix, the quantum annealing algorithm can simulate the evolution of quantum bits in a quantum state and search for the optimal weight combination in the solution space. During the search process, the state of the quantum bit can change according to the laws of quantum mechanics, gradually tending towards the state with the lowest energy, corresponding to the optimal weight vector. Compared with traditional optimization algorithms, the quantum annealing algorithm has stronger global search capabilities, can avoid falling into local optimal solutions, and thus obtain a better weight vector, so that the constructed signal quality assessment matrix more accurately reflects the signal quality.

[0079] In an exemplary embodiment, the calculation formula of the optimized weight vector is:

[0080]

[0081] Among them, W * is the optimized weight vector, γ is the sparsity control parameter, Ψ(t) is the signal-environment coupling matrix, Y pred is the meter reading success rate vector, is a constraint that ensures that the sum of the elements of the weight vector W is 1.

[0082] After obtaining the optimized weight vector, the final signal quality assessment matrix can be constructed based on the pre-set evaluation model structure. This matrix integrates multiple aspects of information, such as preprocessed signal data, environmental gradient vectors, and scene feature vectors, and can be obtained through weighted calculation using the optimized weight vectors. For example, an element in the matrix can be obtained by multiplying a certain eigenvalue of the preprocessed signal by the corresponding weight, adding an element in the environmental gradient vector multiplied by its weight, and then multiplying the relevant element in the scene feature vector by the corresponding weight. This signal quality assessment matrix comprehensively considers the impact of various factors on signal quality, providing a powerful tool for subsequent accurate detection of signal strength levels and signal application scenarios.

[0083] In an exemplary embodiment, a signal optimization strategy is generated by dynamically matching the signal strength level and the time-sharing meter reading success rate in the corresponding time period, including:

[0084] Based on the coordinates of the low-level signal area and environmental parameters in the signal strength level, a cross-scenario interference source probability distribution model based on transfer learning is constructed using the following formula:

[0085]

[0086] Among them, α k is the weight coefficient of the kth component, generated by extracting the environmental gradient vector through ResNet-34, σ k It is a parameter that is dynamically adjusted according to the scene type and is used to control the width of the probability distribution. k ,y k ,z k )|| 2 For the space point (x,y,z) and (x k ,y k ,z k ), K is the total number of components involved in the summation, that is, the number of interference source distribution features;

[0087] According to the cross-scenario interference source probability distribution model, the quantum annealing algorithm is used to solve the coordinates of the interference source with the maximum probability. The quantum annealing Hamiltonian in the quantum annealing algorithm is defined as:

[0088]

[0089] Where H is the quantum annealing Hamiltonian, J ij is the coupling coefficient between qubits i and j, and are the Pauli Z operators acting on quantum bits i and j respectively, Γ(t) is the parameter that decays with time, Γ0 is the initial value, γ is the decay rate, t is the time, is the Pauli X operator acting on quantum bit i;

[0090] Calculate the transmission power adjustment amount based on the signal quality evaluation matrix and the time-sharing meter reading success rate;

[0091] Generate a signal optimization strategy based on the coordinates of the maximum probability interference source and the transmit power adjustment amount.

[0092] Specifically, in the signal strength level division, a low-level signal area means poor signal quality, and there may be interference sources that affect the success rate of meter reading. First, the coordinates of the low-level signal area can be accurately located through the spatial analysis of the terminal installation location coordinates and signal strength monitoring data. And obtain the environmental parameters of the area, such as obstacle density. Through the low-level signal area coordinates and environmental parameters, a cross-scenario interference source probability distribution model based on transfer learning can be constructed through the above formula. Among them, ResNet-34 is a deep learning model with excellent feature extraction capabilities. The calculated environmental gradient vector can be input into ResNet-34, which generates a weight coefficient α of the kth component by learning and analyzing various features in the environmental gradient vector. k The weight coefficient reflects the degree of influence of different environmental characteristics on the distribution of interference sources. Different scene types have different effects on signal propagation and interference source distribution. The parameter σ can be dynamically adjusted according to the scene type. k , used to control the width of the probability distribution. For example, in a distribution station scenario, the influence range of the interference source is relatively concentrated, σ k The value can be smaller; in the outdoor tower scene, σ kA larger value can be used to accommodate situations where interference sources may be widely distributed. A quantum annealing algorithm can then be used to find the optimal solution to the model by simulating the annealing process of a quantum system. This quantum annealing algorithm continuously adjusts the states of the qubits, causing the energy of the quantum system to approach its lowest state. The combination of qubit states corresponding to this lowest energy state represents the coordinates of the most probable interference source. During the solution process, the changes in qubit state are controlled by the quantum annealing Hamiltonian, defined by the formula above.

[0093] In an exemplary embodiment, the transmit power adjustment amount may be calculated according to the signal quality evaluation matrix and the time-sharing meter reading success rate using the following formula:

[0094]

[0095] Where ΔP tx (t) is the transmit power adjustment at time t, P base is the preset reference transmission power, N is the number of indicators involved in the calculation of the signal quality evaluation matrix Q, is the nth indicator in the signal quality evaluation matrix Q, and Ψ is the signal-environment coupling matrix. This represents the average rate of change of each indicator in the signal quality assessment matrix over time, reflecting the overall trend of signal quality over time. A large average indicates a significant change in signal quality, necessitating a significant adjustment in transmit power.

[0096] Based on the coordinates of the most probable interference source and the transmit power adjustment, a comprehensive signal optimization strategy can be generated. Specifically, once the coordinates of the most probable interference source are determined, targeted measures can be taken, such as adjusting the deployment location of the wireless signal remote unit to avoid the interference source or enhance signal coverage in the interference area. Combined with the transmit power adjustment, the transmit power of the wireless signal remote unit can be appropriately adjusted. If the transmit power adjustment is positive, the transmit power can be appropriately increased to improve signal strength; if it is negative, the transmit power can be reduced to avoid energy waste and unnecessary interference. By comprehensively considering the coordinates of the most probable interference source and the transmit power adjustment, a corresponding signal optimization strategy can be developed to improve the quality of the communication signal for remote power meter reading and increase the success rate of meter reading.

[0097] In an exemplary embodiment, the communication signal enhancement coverage device includes a wireless signal near-end machine, a wireless signal far-end machine, a power supply unit and an optical fiber, wherein the wireless signal near-end machine is used to receive terminal communication signal strength data, and the wireless signal far-end machine is used to dynamically adjust and output the transmission power according to the signal optimization strategy through multi-band adaptive modulation.

[0098] Specifically, the communication signal enhancement coverage device is used to provide a stable wireless communication signal with sufficient strength to ensure effective coverage of the area where the power consumption information collection terminal is installed. Figure 2 As shown in the figure, this embodiment provides a schematic diagram of the communication signal coverage structure for remote electricity meter reading. A high-power wireless signal near-end unit can be used as the front-end receiving device of the communication signal enhancement coverage device. Equipped with a highly sensitive receiving antenna and signal processing circuitry, it is installed in a location with relatively good signal reception conditions, such as near the signal source or in an open area, to ensure effective capture of the communication signals emitted by the electricity consumption information collection terminal. Furthermore, electricity consumption information collection terminals are widely distributed in various environments, such as basements and mountainous areas where signals are easily obstructed and attenuated. They continuously transmit information containing terminal communication signal strength data. The signal detection algorithm built into the wireless signal near-end unit can filter out weak signals emitted by the terminal from complex electromagnetic environments and perform preliminary amplification and filtering on the signal. During the amplification process, a low-noise amplifier is used to boost signal strength while minimizing the introduction of additional noise interference. Filtering, using devices such as bandpass filters, removes clutter and interference from the signal, retaining only useful communication signal strength data.

[0099] Furthermore, a high-power wireless signal remote can be used as a signal coverage device for the area where the electricity consumption information collection terminal is installed, transmitting signals to the area where the electricity consumption information collection terminal is installed, enabling synchronous signal reception by multiple terminal devices. The wireless signal remote can use multi-band adaptive modulation technology, allowing the transmission frequency to be flexibly adjusted according to the signal optimization strategy. For example, if the signal strength level is low, the meter reading success rate is unsatisfactory, and the signal optimization strategy requires an increase in transmission power, the wireless signal remote can increase the transmission power through a power amplifier to enhance the signal strength to a level sufficient to cover the weak signal area; conversely, if the signal strength is strong and stable, the transmission power can be reduced to save energy and reduce interference with other devices. This method of dynamically adjusting the transmission power not only meets the terminal's signal strength requirements, but also achieves efficient energy utilization and minimizes interference.

[0100] Specifically, the power supply unit ensures the normal operation of the communication signal coverage enhancement device, providing a stable power supply to both the near-end and remote wireless signal devices. This power supply unit can adopt various power supply methods to adapt to different installation environments. For example, in locations with mains power access, such as distribution stations, the power supply can be directly drawn from the distribution station's mains power supply, ensuring stable and reliable power supply and continuous operation of the device. In remote areas without mains power access, such as mountainous areas or newly commissioned residential communities without grid coverage, the power supply unit can be powered by a backup battery that can be charged by solar panels. During the day, the solar panels convert solar energy into electricity and store it in the backup battery. At night or when solar power is insufficient, the backup battery powers the device, ensuring normal operation in all environments. Furthermore, optical fiber, as a key medium for communication signal transmission, establishes a high-speed, stable communication link between the near-end and remote wireless signal devices. Optical fiber, with its thin diameter and light weight, is easier to lay and install than traditional coaxial cable. Its advantages are particularly evident in scenarios where space is limited or long-distance transmission is required. On the one hand, optical fiber offers long transmission distances and minimal signal loss, ensuring that terminal communication signal strength data and signal optimization strategy instructions received by the near-end wireless signal are accurately transmitted to the far-end wireless signal. Furthermore, optical fiber also exhibits excellent resistance to electromagnetic interference, effectively preventing the impact of external electromagnetic interference on signal transmission, ensuring stable and reliable communication.

[0101] In an exemplary embodiment, before dynamically matching the signal strength level and the time-sharing meter reading success rate in the corresponding time period and generating the signal optimization strategy, the method further includes obtaining the time-sharing meter reading success rate by the following steps:

[0102] Use RPA tools to obtain time-based meter reading data from electricity consumption information collection terminals. This data includes the terminal ID, meter reading timestamp, meter reading status, and power consumption value.

[0103] Align the meter reading timestamp with the time window of the terminal communication signal strength data to generate a time-aligned sequence;

[0104] Based on the time-aligned sequence, the initial meter reading success rate is calculated according to the preset time window, and the success rate fluctuation rate in the initial meter reading success rate is calculated using the following formula:

[0105] When the success rate fluctuation exceeds the preset threshold, the initial meter reading success rate is compensated based on the sliding window mean to obtain the time-sharing meter reading success rate.

[0106] Specifically, the RPA (Robotic Process Automation) tool can simulate manual operations and automatically obtain time-based meter reading data from electricity consumption information collection terminals. This time-based meter reading data contains important information such as the terminal ID, meter reading timestamp, meter reading status, and energy consumption value. The meter reading timestamp records the specific time of each meter reading operation with accuracy down to the second, providing a time reference for subsequent data time alignment and analysis. The meter reading status clearly indicates the successful completion of each meter reading task, such as "success," "failure," or "timeout," and serves as a direct indicator of the meter reading success rate. This RPA tool can quickly and accurately obtain time-based meter reading data from a large number of terminals, significantly reducing the workload of manual data acquisition and further improving the accuracy and timeliness of data acquisition. The collection times of terminal communication signal strength data and time-based meter reading data may differ. For example, terminal communication signal strength data is collected at regular intervals, such as every five minutes, while meter reading operations are performed at different times according to a fixed meter reading schedule. During the alignment process, the meter reading timestamps can be aligned with the collection time window of the terminal communication signal strength data. If a meter reading timestamp falls within a specific communication signal strength data collection time window, the corresponding meter reading data is associated with the communication signal strength data for that period. If the meter reading timestamp falls between two collection time windows, the corresponding communication signal strength data can be determined using algorithms such as time interpolation. This process generates a time-aligned sequence, allowing subsequent analysis of signal strength and meter reading success rate to be conducted on a unified time scale, avoiding analytical errors caused by time inconsistencies.

[0107] Based on this time-aligned sequence, the initial meter reading success rate can be calculated according to preset time windows. The preset time windows can be set based on actual needs and data analysis objectives, for example, 1 hour, 4 hours, or 1 day. Within each preset time window, the ratio of successful meter readings to the total number of meter readings is calculated to determine the initial meter reading success rate for that time window. To more comprehensively assess the stability of the meter reading success rate, the success rate fluctuation can be calculated, taking into account factors such as the differences in success rates within adjacent time windows and the number of time windows, for further analysis. This success rate fluctuation reflects the degree of variation in the meter reading success rate across different time windows and accurately measures fluctuations in the meter reading success rate.

[0108] When the success rate fluctuation exceeds a preset threshold, it indicates that the meter reading success rate fluctuates significantly, possibly due to abnormal factors affecting the meter reading process. Therefore, the initial meter reading success rate can be compensated using a sliding window average. Sliding window average is a data smoothing method that slides a fixed-size window across the time series data, calculates the average value of the data within the window, and uses this average value to replace the data at the center of the window. When compensating the initial meter reading success rate, the sliding window average calculation can eliminate fluctuations in the meter reading success rate caused by accidental factors, resulting in a more stable and reliable time-based meter reading success rate. For example, when calculating the time-based meter reading success rate for a terminal, it is found that its success rate fluctuation exceeds a preset threshold. Using the sliding window average method, the initial meter reading success rates within several adjacent time windows can be averaged and the resulting average value used as the compensated meter reading success rate for that time window, thereby obtaining the final time-based meter reading success rate.

[0109] Based on the same inventive concept, Figure 3 As shown, the embodiment of the present application also provides a power remote meter reading communication signal monitoring system 300. The implementation solution provided by the system is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more power remote meter reading communication signal monitoring system embodiments provided below can refer to the limitations of the power remote meter reading communication signal monitoring method above, and will not be repeated here. The system includes an electricity information collection terminal 301, a signal strength monitoring module 302 and a communication signal strengthening coverage device 303, wherein,

[0110] The signal strength monitoring module 302 is configured to:

[0111] Obtain multi-dimensional basic electricity consumption information of the electricity consumption information collection terminal, which includes the terminal asset number, terminal installation location coordinates, communication card parameters and station area number;

[0112] Based on multi-dimensional basic electricity consumption information, terminal communication signal strength data and environmental parameters are obtained. Based on the terminal communication signal strength data and environmental parameters, a signal quality evaluation matrix is constructed through a dynamic weight allocation algorithm. Environmental parameters include obstacle density.

[0113] The signal quality evaluation matrix is used to test the terminal communication signal strength data and obtain the test results, which include the signal strength level and signal application scenario;

[0114] Dynamic matching is performed based on the signal strength level and the time-sharing meter reading success rate within the corresponding time period to generate a signal optimization strategy, and the signal optimization strategy is executed by the communication signal enhancement coverage device 303, wherein the wireless signal remote device in the communication signal enhancement coverage device 303 adjusts the transmission power and / or deployment position according to the signal optimization strategy;

[0115] The electricity consumption information collection terminal 301 is used to obtain and store multi-dimensional electricity consumption basic information and time-sharing meter reading data;

[0116] The communication signal enhancement coverage device 303 is used to enhance the terminal communication signal strength data according to the signal optimization strategy.

[0117] In this system, comprehensive data collection by the electricity usage information collection terminal 301, precise evaluation and optimization decisions by the signal strength monitoring module 302, and effective signal enhancement by the communication signal enhancement device 303 significantly improve the monitoring effectiveness and data transmission quality of the remote power meter reading communication signal, providing strong support for the stable operation of the remote power meter reading service. Specifically, the electricity usage information collection terminal 301 is used to acquire and store multidimensional basic electricity usage information and time-of-day meter reading data, eliminating the one-sidedness of information acquisition and providing a comprehensive and critical data source for the entire monitoring system, thus providing a solid data foundation for subsequent signal analysis and optimization. The signal strength monitoring module 302 obtains multidimensional basic electricity usage information, further deriving terminal communication signal strength data and environmental parameters, and constructs a signal quality assessment matrix using a dynamic weight allocation algorithm. This avoids the limitations of a single data source in traditional signal monitoring systems, resulting in more accurate and comprehensive signal quality assessment. The signal strength monitoring module 302 also uses the signal quality assessment matrix to detect terminal communication signal strength data, clearly distinguishing between signal strength levels and signal application scenarios, eliminating ambiguity in signal status assessment and providing a clear direction for the subsequent development of targeted optimization strategies. Furthermore, the signal strength monitoring module 302 can dynamically match and generate a signal optimization strategy based on the signal strength level and the time-sharing meter reading success rate, so that the optimization strategy closely fits the actual meter reading situation, effectively improving the meter reading success rate and signal stability.

[0118] The communication signal enhancement coverage device 303 can enhance the terminal communication signal strength data according to the signal optimization strategy, directly process the weak links of the signal, avoid the obstruction of data transmission caused by weak signals, effectively solve the problem of unstable data transmission, ensure that the electricity consumption information collection terminal can stably obtain high-quality signals, and further ensure the reliability and efficiency of power remote meter reading communication.

[0119] In an exemplary embodiment, the present invention further provides a computer device comprising a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for monitoring communication signals for remote power meter reading according to the present application. A multi-core processor is preferred to improve the system's parallel processing capabilities. The memory provides sufficient temporary storage space to support program execution and data processing. The memory capacity should be large enough to accommodate large amounts of supply information and computing tasks.

[0120] In an exemplary embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for monitoring the communication signal of remote power meter reading of the present application. The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid-state drive (SSD), or an optical disk. The random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0121] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0122] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

[0123] The present invention is not limited to the above-mentioned best embodiment. Anyone can derive other forms of electric power remote meter reading communication signal monitoring methods and systems based on the inspiration of the present invention. All equivalent changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A method for monitoring electric power remote meter reading communication signals, characterized in that: include: Obtain multi-dimensional basic electricity consumption information of the electricity consumption information collection terminal, including terminal asset number, terminal installation location coordinates, communication card parameters and station area number; Based on the multi-dimensional basic electricity usage information, terminal communication signal strength data and environmental parameters including obstacle density are obtained, and a signal quality evaluation matrix is constructed using a dynamic weight allocation algorithm based on the terminal communication signal strength data and the environmental parameters; Using the signal quality evaluation matrix, the terminal communication signal strength data is detected to obtain a detection result including a signal strength level and a signal application scenario; Dynamic matching is performed according to the signal strength level and the time-sharing meter reading success rate in the corresponding time period to generate a signal optimization strategy, and the signal optimization strategy is executed through the communication signal reinforcement coverage device.

2. The method for monitoring electric power remote meter reading communication signals according to claim 1, characterized in that: The dynamic weight allocation algorithm includes: performing noise suppression on the terminal communication signal strength data to obtain preprocessed signal data; Performing environmental gradient calculation on the environmental parameters to obtain an environmental gradient vector, and determining the scene type of the terminal according to the terminal installation location coordinates to obtain a scene feature vector; The signal-environment coupling matrix is constructed by the following formula using the preprocessed signal data, the environment gradient vector and the scene feature vector: Where Ψ(t) is the signal-environment coupling matrix, η is the preset coefficient used to adjust the weight of the dynamic sensitivity term, The preprocessed signal The derivative with respect to time t is used to characterize the dynamic rate of change of the signal, ω k (t) is the weight coefficient dynamically generated by the environment gradient vector and the scene feature vector through the LSTM network, SINR k is the kth signal to interference plus noise ratio, Γ and Δ are the preset threshold and preset interval respectively, which are used to adjust the input parameters of the sigmoid function, and λ is used to adjust the weight of the spatial gradient correction term. is the second-order spatial derivative of the signal-to-noise ratio; A quantum annealing algorithm is used to solve the dynamic weight vector based on the signal-environment coupling matrix to obtain an optimized weight vector, and a signal quality evaluation matrix is further constructed.

3. The method for monitoring electric power remote meter reading communication signals according to claim 2, characterized in that: The calculation formula of the optimized weight vector is: Among them, W * is the optimized weight vector, γ is the sparsity control parameter, Ψ(t) is the signal-environment coupling matrix, Y pred is the meter reading success rate vector, is a constraint that ensures that the sum of the elements of the weight vector W is 1.

4. The method for monitoring electric power remote meter reading communication signals according to claim 3, characterized in that: The generated signal optimization strategy includes: Based on the coordinates of the low-level signal area and environmental parameters in the signal strength level, a cross-scenario interference source probability distribution model based on transfer learning is constructed using the following formula: Among them, α k is the weight coefficient of the kth component, generated by extracting the environmental gradient vector through ResNet-34, σ k It is a parameter that is dynamically adjusted according to the scene type and is used to control the width of the probability distribution. k ,y k ,z k )|| 2 For the space point (x,y,z) and (x k ,y k ,z k ), K is the total number of components involved in the summation, that is, the number of interference source distribution features; According to the cross-scenario interference source probability distribution model, the quantum annealing algorithm is used to solve the maximum probability interference source coordinates, wherein the quantum annealing Hamiltonian in the quantum annealing algorithm is defined as: Where H is the quantum annealing Hamiltonian, J ij is the coupling coefficient between qubits i and j, and are the Pauli Z operators acting on quantum bits i and j respectively, Γ(t) is the parameter that decays with time, Γ0 is the initial value, γ is the decay rate, t is the time, is the Pauli X operator acting on quantum bit i; The transmit power adjustment amount is calculated according to the signal quality evaluation matrix and the time-sharing meter reading success rate; and a signal optimization strategy is generated based on the maximum probability interference source coordinates and the transmit power adjustment amount.

5. The method for monitoring electric power remote meter reading communication signals according to claim 4, characterized in that: The calculation formula of the transmit power adjustment amount is: Where ΔP tx (t) is the transmit power adjustment at time t, P base is the preset reference transmission power, N is the number of indicators involved in the calculation of the signal quality evaluation matrix Q, is the nth indicator in the signal quality evaluation matrix Q, and Ψ is the signal-environment coupling matrix.

6. The method for monitoring electric power remote meter reading communication signals according to claim 1, characterized in that: The communication signal enhancement coverage device includes a wireless signal proximal device, a wireless signal remote device, a power supply unit and an optical fiber, wherein the wireless signal proximal device is used to receive terminal communication signal strength data, and the wireless signal remote device is used to dynamically adjust and output the transmission power according to the signal optimization strategy through multi-band adaptive modulation; the wireless signal remote device in the communication signal enhancement coverage device adjusts the transmission power and / or deployment position according to the signal optimization strategy.

7. The method for monitoring electric power remote meter reading communication signals according to claim 1, characterized in that: Before generating the signal optimization strategy, the following steps are also included to obtain the time-sharing meter reading success rate: Use RPA tools to obtain time-based meter reading data from electricity consumption information collection terminals. This data includes the terminal ID, meter reading timestamp, meter reading status, and power consumption value. Align the meter reading timestamp with the time window of the terminal communication signal strength data to generate a time-aligned sequence; Based on the time-aligned sequence, the initial meter reading success rate is calculated according to the preset time window, and the success rate fluctuation in the initial meter reading success rate is calculated by the average of the absolute differences of the meter reading success rates in adjacent time windows. When the success rate fluctuation exceeds the preset threshold, the initial meter reading success rate is compensated based on the sliding window mean to obtain the time-sharing meter reading success rate.

8. A power remote meter reading communication signal monitoring system, characterized in that: include: Electricity usage information collection terminal: used to obtain and store multi-dimensional electricity usage basic information and time-sharing meter reading data, including terminal asset number, terminal installation location coordinates, communication card parameters and station area number; Signal strength monitoring module: used to perform the following operations: Based on the multi-dimensional basic electricity usage information, obtaining terminal communication signal strength data and environmental parameters including obstacle density; Based on the terminal communication signal strength data and environmental parameters, a signal quality assessment matrix is constructed through a dynamic weight allocation algorithm; Detecting the terminal communication signal strength data using the signal quality assessment matrix to obtain a detection result including a signal strength level and a signal application scenario; Dynamically matching the signal strength level and the time-sharing meter reading success rate within the corresponding time period to generate a signal optimization strategy; Communication signal coverage enhancement device: used to adjust the transmission power and / or deployment position of the wireless signal remote device according to the signal optimization strategy to enhance the terminal communication signal strength.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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