Intelligent meter reading method and device, electronic equipment and storage medium

Abstract

The invention provides an intelligent meter reading method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent power grids, the method comprises the following steps: obtaining a group of communication index parameters of each node in a node set, the node set comprising a group of agent nodes and a group of station nodes in a target area; obtaining a communication quality score corresponding to each node according to the group of communication index parameters of each node; according to the communication quality scores corresponding to all the nodes, meter reading operation is executed on all the nodes in sequence from high to low, so that all the nodes collect electricity utilization data and send the electricity utilization data to a central node, and a target communication network is formed by one group of agent nodes, one group of station nodes and the central node. By implementing the technical scheme provided by the invention, the effect of improving the meter reading efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a smart meter reading method, device, electronic device and storage medium. Background Art

[0002] The popularity of smart meters and smart grids has further promoted the innovation of meter reading technology. Smart meters have the functions of automatically collecting, storing and transmitting data, and can monitor users' electricity consumption in real time. In the existing smart meter reading system, common practices include optimizing the networking path or improving the physical environment. Specifically, optimizing the networking path can reduce the distance and number of signal transmissions by changing the connection method between nodes; improving the physical environment involves measures such as shielding interference sources and adjusting the installation location of equipment; these methods have improved the performance of the meter reading system to a certain extent, but in the dynamically changing power carrier communication network, these static optimization methods are often difficult to cope with frequent interference and changes. Especially in a high-interference environment, the signal quality fluctuates greatly, resulting in low meter reading efficiency. Therefore, how to improve meter reading efficiency in a dynamically changing network environment has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides an intelligent meter reading method, device, electronic device and storage medium.

[0004] In a first aspect, the present application provides an intelligent meter reading method, comprising: obtaining a set of communication index parameters for each node in a node set, wherein the node set includes a set of proxy nodes and a set of station nodes in a target substation; obtaining a communication quality score corresponding to each node according to a set of communication index parameters of each node; performing meter reading operations on each node in order from high to low according to the communication quality scores corresponding to each node, so that each node collects electricity consumption data and sends it to a central node, wherein a set of proxy nodes, a set of station nodes and a central node constitute a target communication network, any station node is directly connected to the central node for communication or is connected to the central node through a proxy node, and any proxy node is directly connected to the central node for communication or is connected to the central node through other proxy nodes.

[0005] By adopting the above technical solution, the meter reading efficiency and reliability of the intelligent meter reading system can be effectively improved. First, by obtaining a set of communication index parameters of each node in the node set and calculating the communication quality score, the communication capability of each node can be accurately evaluated. Secondly, the meter reading operation is performed on the nodes in order from high to low according to the communication quality score, ensuring that nodes with higher communication quality are used for data collection and transmission first, thereby reducing data loss and retransmission caused by signal quality problems. This solution not only improves the anti-interference ability and stability of the meter reading system, but also maintains efficient meter reading performance for dynamically changing network environments.

[0006] Optionally, a communication quality score corresponding to each node is obtained based on a set of communication indicator parameters of each node, including: the current node is any node in the node set, and for the current node, a target communication quality score corresponding to the current node is obtained in the following manner: each communication indicator parameter in a set of communication indicator parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain the target communication quality score.

[0007] By adopting the above technical solution, the communication quality score of each node can be obtained by weighted summation based on a set of communication index parameters of each node. This method makes the communication quality score more scientific and reasonable, and can more accurately reflect the actual communication status of each node. In a dynamically changing communication network, by selecting nodes with higher communication quality for meter reading operations in this way, the meter reading efficiency and the success rate of data transmission can be effectively improved, the impact of signal interference can be reduced, and thus the performance of the entire meter reading system can be improved.

[0008] Optionally, each communication indicator parameter in a set of communication indicator parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain a target communication quality score, including: calculating the target communication quality score according to the following formula: target communication quality score = K1*SNR + K2*RSSI + K3*SUCC, K1+K2+K3=1, wherein a set of communication indicator parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, the preset weight coefficients include K1, K2 and K3, K1, K2 and K3 are allowed to be adjusted, SNR, RSSI and SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

[0009] By adopting the above technical solution, the performance of the intelligent meter reading system can be effectively improved. Specifically, a set of communication index parameters corresponding to the current node (signal-to-noise ratio parameter SNR, received signal strength indicator parameter RSSI and success rate parameter SUCC) are weighted and summed according to the preset weight coefficient to obtain the target communication quality score, thereby realizing an accurate evaluation of the communication quality of each node. This evaluation method can more comprehensively reflect the actual communication status of the node and avoid misjudgment caused by the influence of a single indicator. On this basis, the nodes are sorted according to the communication quality score and the meter reading operation is performed in sequence, ensuring that high-quality communication nodes participate in the meter reading process first, significantly improving the meter reading efficiency and the reliability of data transmission, and is particularly suitable for dynamically changing networks in high interference environments.

[0010] Optionally, each communication index parameter in a set of communication index parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain a target communication quality score, including: calculating the target communication quality score according to the following formula: target communication quality score = L*(K1*SNR + K2*RSSI + K3*SUCC), K1+K2+K3=1, wherein a set of communication index parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, and the preset weight coefficients include L, K1, K2 and K3, L, K1, K2 and K3 are allowed to be adjusted, L is used to represent the weight coefficient corresponding to the level of the current node in the target communication network, SNR, RSSI and SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

[0011] By adopting the above technical solution, by introducing the hierarchical weight coefficient L, not only the three key communication indicators of signal-to-noise ratio (SNR), received signal strength indication (RSSI) and success rate (SUCC) are considered, but also the hierarchy of the node in the target communication network. This method can evaluate the communication quality of each node more comprehensively and accurately; the weight coefficients L, K1, K2 and K3 can be adjusted so that the system can better adapt to different network environments and application scenarios; giving priority to nodes with high communication quality scores can reduce failed attempts due to poor signals and improve the overall meter reading efficiency; through more accurate communication quality evaluation, the meter reading order can be arranged more reasonably to ensure that the data of high-priority nodes are collected and transmitted first; comprehensive consideration of multiple communication indicators and the hierarchy of nodes can improve the reliability of data transmission, reduce data loss and errors, and ensure the accuracy of meter reading data.

[0012] Optionally, after performing meter reading operations on each node in sequence according to the communication quality scores corresponding to each node in descending order, the above method also includes: re-obtaining an updated set of communication indicator parameters for each node in the node set at preset time intervals; obtaining an updated communication quality score corresponding to each node according to the updated set of communication indicator parameters for each node; and performing meter reading operations on each node in sequence according to the updated communication quality scores in descending order.

[0013] By adopting the above technical solution, by re-obtaining a set of communication indicator parameters of the node at preset intervals and updating the communication quality score, it can be ensured that the meter reading operation is always based on the latest communication status; dynamically updating the communication quality score can timely discover and process nodes with reduced communication quality, reduce failed attempts and retransmissions due to poor signals, and improve the overall meter reading efficiency; regularly updating the communication quality score can timely discover potential problems in the network, such as signal interference, equipment failure, etc., and take corresponding measures to optimize them; by dynamically adjusting the meter reading order, the system can better adapt to changes in the network environment, ensuring that the meter reading task can be completed efficiently and reliably under any circumstances.

[0014] Optionally, obtaining a set of communication index parameters of each node in the node set includes: the central node obtaining a set of communication index parameters of each node in the node set according to a preset period.

[0015] By adopting the above technical solution, the central node can regularly obtain a set of communication index parameters of each node in the node set to ensure the timeliness and accuracy of the communication quality score. This helps to dynamically adjust the meter reading order, improve meter reading efficiency and reliability, especially in high interference environments, and can effectively cope with signal quality fluctuations, improving the stability and performance of the entire meter reading system.

[0016] Optionally, after performing meter reading operations on each node in order from high to low according to the communication quality scores corresponding to each node, the method further includes: receiving power consumption data sent by each node to obtain a power consumption data set; and uploading the power consumption data set to a remote server.

[0017] By adopting the above technical solution, the collected electricity consumption data is uploaded to the remote server, which enables centralized management and analysis of the data, facilitates subsequent data processing and anomaly detection, and further improves the intelligence level of the entire meter reading system.

[0018] Optionally, after receiving the electricity consumption data sent by each node respectively and obtaining the electricity consumption data set, the above method also includes: performing an abnormality analysis on the target electricity consumption data, wherein the target electricity consumption data is the electricity consumption data corresponding to any one of the nodes; when it is determined that there is an abnormality in the target electricity consumption data, triggering an alarm mechanism.

[0019] By adopting the above technical solution, it is possible to perform an abnormal analysis on the target electricity consumption data after receiving the electricity consumption data sent by each node, and promptly discover and handle abnormal situations in the electricity consumption data. When it is determined that the target electricity consumption data is abnormal, the alarm mechanism is triggered to ensure that the management personnel can take quick measures to avoid losses or safety hazards caused by data abnormalities.

[0020] Optionally, each node in the node set supports power line carrier communication HPLC and high-speed wireless communication HRF.

[0021] By adopting the above technical solutions and supporting power line carrier communication (HPLC) and high-speed wireless communication (HRF), nodes can switch between the two communication modes to ensure that the best communication path can be found under any circumstances. When one communication mode is interfered with or fails, it can quickly switch to another communication mode to reduce the risk of data transmission interruption and improve the overall reliability of the system. The support of multiple communication modes makes the system more robust and anti-interference, and can operate stably in complex and changeable network environments.

[0022] In the second aspect of the present application, an intelligent meter reading device is also provided, including: an acquisition module, used to acquire a set of communication index parameters of each node in a node set, wherein the node set includes a set of proxy nodes and a set of station nodes in a target substation; an acquisition module, used to obtain a communication quality score corresponding to each node according to a set of communication index parameters of each node; a meter reading module, used to perform meter reading operations on each node in sequence from high to low according to the communication quality scores corresponding to each node, so that each node collects electricity consumption data and sends it to a central node, wherein a group of proxy nodes, a group of station nodes and a central node constitute a target communication network, any station node is directly connected to the central node for communication or is connected to the central node through a proxy node, and any proxy node is directly connected to the central node for communication or is connected to the central node through other proxy nodes.

[0023] In a third aspect of the present application, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, any of the above method steps is implemented.

[0024] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the above method steps is performed.

[0025] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By accurately evaluating the communication capabilities of each node and giving priority to nodes with better communication quality, the meter reading efficiency and reliability of the intelligent meter reading system can be effectively improved; 2. Selecting nodes with higher communication quality to perform meter reading operations first can effectively improve meter reading efficiency and data transmission success rate, reduce the impact of signal interference, and thus improve the performance of the entire meter reading system; 3. By re-obtaining the communication index parameters of the node at preset intervals, the communication quality score can be dynamically updated, reducing failed attempts and retransmissions caused by poor signals, and improving the overall meter reading efficiency; 4. The support of multiple communication methods makes the system more robust and anti-interference, and can operate stably in complex and changing network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of an intelligent meter reading method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of a communication network provided in an embodiment of the present application; Figure 3 is a flow chart of another intelligent meter reading method provided in an embodiment of the present application; Figure 4 It is a structural block diagram of an intelligent meter reading device provided in an embodiment of the present application; Figure 5 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application.

[0027] Description of the reference numerals: 500 - electronic device; 501 - processor; 502 - communication bus; 503 - user interface; 504 - network interface; 505 - memory. DETAILED DESCRIPTION

[0028] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0030] In the description of the embodiments of the present application, the term "plurality" means two or more. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0031] The following is combined with Figure 1-Figure 5 The embodiments of the present application are described.

[0032] This application provides an intelligent meter reading method, referring to Figure 1 , Figure 1 This is a flow chart of an intelligent meter reading method provided by an embodiment of the present application, comprising the following steps: Step S101, obtaining a set of communication index parameters of each node in a node set, wherein the node set includes a set of proxy nodes and a set of station nodes in a target area; Step S102, obtaining a communication quality score corresponding to each node according to a set of communication indicator parameters of each node; Step S103, according to the communication quality score corresponding to each node, meter reading operation is performed on each node in order from high to low, so that each node collects power consumption data and sends it to the central node, wherein a group of proxy nodes, a group of station nodes and the central node constitute a target communication network, any station node is directly connected to the central node for communication or connected to the central node through a proxy node, and any proxy node is directly connected to the central node for communication or connected to the central node through other proxy nodes.

[0033] Through the above steps, the meter reading efficiency and reliability of the intelligent meter reading system can be effectively improved. First, by obtaining a set of communication index parameters of each node in the node set and calculating the communication quality score, the communication capability of each node can be accurately evaluated. Secondly, the meter reading operation is performed on the nodes in the order of communication quality scores from high to low, ensuring that nodes with higher communication quality are used for data collection and transmission first, thereby reducing data loss and retransmission caused by signal quality problems. In addition, the method can update the communication quality score in time when the network environment changes, ensuring that the meter reading operation is always in the optimal state. In general, this embodiment not only improves the anti-interference ability and stability of the meter reading system, but also maintains efficient meter reading performance in a dynamically changing network environment. The above-mentioned target area can refer to an electricity supply area or unit, for example, usually powered by a transformer and covering users within a certain range. In this area, electric meters are installed in the homes or power consumption places of each user to measure the user's electricity consumption. The central node is responsible for reading data from these electric meters and uploading the data to the superior power consumption information collection system for subsequent data processing and analysis.

[0034] The existing intelligent meter reading system mainly improves performance through static optimization means (such as optimizing the networking path and improving the physical environment). However, in the dynamically changing power carrier communication network, these static means are often difficult to cope with frequent interference and changes. This embodiment evaluates the communication quality of the nodes in real time and dynamically adjusts the meter reading order according to the quality score, thereby more effectively coping with the dynamically changing network environment. In a high-interference environment, the signal quality fluctuates greatly, resulting in low meter reading efficiency. The method of this embodiment reduces the meter reading failures and retries caused by poor signal quality by giving priority to nodes with better communication quality, thereby improving the meter reading efficiency. Giving priority to nodes with better communication quality reduces the risk of meter reading failures and improves the overall meter reading efficiency. This method can adapt well to the dynamically changing network environment and improve the stability and reliability of the intelligent meter reading system.

[0035] Figure 2 is a schematic diagram of a communication network provided in an embodiment of the present application, such as Figure 2 In this example, CCO is the central node, PCO1-PCO5 are proxy nodes, and STA1-STA7 are station nodes. Figure 2 Some nodes support dual-mode communication, that is, support HPLC and HRF communication, such as PCO1, PCO3 and STA2 nodes are dual-mode nodes, and some nodes support single communication mode, such as PCO2 only supports HPLC communication mode, PCO2 is HPLC node. It should be noted that Figure 2 This is just an example. In actual applications, all proxy nodes and station nodes can support dual communication mode. Figure 2All proxy nodes and station nodes represent electricity meters (or energy meters). Optionally, in practical applications, all electricity meters can become proxy nodes.

[0036] In an optional embodiment, a communication quality score corresponding to each node is obtained based on a set of communication indicator parameters of each node, including: the current node is any node in the node set, and for the current node, a target communication quality score corresponding to the current node is obtained in the following manner: each communication indicator parameter in a set of communication indicator parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain a target communication quality score.

[0037] In the above embodiment, the communication quality score of each node can be obtained by weighted summation based on the communication index parameters of each node, for example, comprehensively considering factors such as signal-to-noise ratio, received signal strength and success rate. This method makes the communication quality score more scientific and reasonable, and can more accurately reflect the actual communication status of each node. In a dynamically changing communication network, by selecting nodes with higher communication quality for meter reading operations in this way, the meter reading efficiency and the success rate of data transmission can be effectively improved, and the impact of signal interference can be reduced, thereby improving the performance of the entire meter reading system.

[0038] By weighted summation, the communication quality of each node can be evaluated more comprehensively and accurately, thereby ensuring the efficiency and reliability of meter reading operations; the weight coefficient can be adjusted according to actual needs, so that the system can better adapt to different network environments and application scenarios; giving priority to nodes with high communication quality scores can reduce failed attempts due to poor signals and improve the overall meter reading efficiency; through more accurate communication quality evaluation, unnecessary retransmissions and data loss can be reduced, energy can be saved, and the service life of the equipment can be extended. This embodiment improves the accuracy and rationality of the score calculation by introducing weight coefficients and weighted summation methods. Different network environments may have different effects on communication quality. For example, in a high interference environment, some communication indicators may be more important; by allowing different weight coefficients to be set, it can be more flexible to adapt to different network environments, thereby improving the adaptability and stability of the intelligent meter reading system, and helping to improve the overall efficiency of the intelligent meter reading system. By giving priority to nodes with better communication quality, the number of meter reading failures and retries caused by poor signal quality can be reduced, thereby speeding up the meter reading speed and improving the meter reading success rate.

[0039] In an optional embodiment, each communication indicator parameter in a set of communication indicator parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain a target communication quality score, including: calculating the target communication quality score according to the following formula: target communication quality score = K1*SNR + K2*RSSI + K3*SUCC, K1+K2+K3=1, wherein a set of communication indicator parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, the preset weight coefficients include K1, K2 and K3, K1, K2 and K3 are allowed to be adjusted, SNR, RSSI and SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

[0040] In the above embodiment, the performance of the intelligent meter reading system can be effectively improved. Specifically, a set of communication index parameters corresponding to the current node (signal-to-noise ratio parameter SNR, received signal strength indication parameter RSSI and success rate parameter SUCC) are weighted and summed according to the preset weight coefficient to obtain the target communication quality score, thereby achieving an accurate evaluation of the communication quality of each node. This evaluation method can more comprehensively reflect the actual communication status of the node and avoid misjudgment due to the influence of a single indicator. On this basis, the nodes are sorted according to the communication quality score and the meter reading operation is performed in sequence, ensuring that high-quality communication nodes participate in the meter reading process first, significantly improving the meter reading efficiency and the reliability of data transmission, and is particularly suitable for dynamically changing networks in high interference environments.

[0041] By comprehensively considering the signal-to-noise ratio (SNR), received signal strength indication (RSSI) and success rate (SUCC), and assigning different weights, the communication quality of each node can be evaluated more comprehensively and accurately; the weight coefficients K1, K2 and K3 can be adjusted according to actual needs, so that the system can better adapt to different network environments and application scenarios; giving priority to nodes with high communication quality scores can reduce failed attempts due to poor signals and improve the overall meter reading efficiency; comprehensively considering multiple communication indicators can improve the reliability of data transmission, reduce data loss and errors, and ensure the accuracy of meter reading data. Different network environments may have different effects on communication quality. For example, in some environments, signal strength may be more important; while in other environments, communication success rate may be more critical. By allowing the weight coefficient to be adjusted, it can be more flexible to adapt to different network environments, thereby improving the adaptability and stability of the intelligent meter reading system. In the prior art, the communication quality score may be based on a single indicator or a simple average value, which cannot fully reflect the comprehensive communication performance of the node. In a complex and changing network environment, a single indicator or a simple average value may not accurately evaluate the communication quality of the node, resulting in low meter reading efficiency and unstable data transmission.

[0042] In an optional embodiment, each communication indicator parameter in a set of communication indicator parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain a target communication quality score, including: calculating the target communication quality score according to the following formula: target communication quality score = L*(K1*SNR + K2*RSSI + K3*SUCC), K1+K2+K3=1, wherein a set of communication indicator parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, and the preset weight coefficients include L, K1, K2 and K3, L, K1, K2 and K3 are allowed to be adjusted, L is used to represent the weight coefficient corresponding to the level of the current node in the target communication network, SNR, RSSI and SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

[0043] In the above embodiment, by introducing the hierarchical weight coefficient L, not only the three key communication indicators of signal-to-noise ratio (SNR), received signal strength indication (RSSI) and success rate (SUCC) are considered, but also the hierarchy of the node in the target communication network is considered. This method can evaluate the communication quality of each node more comprehensively and accurately; the weight coefficients L, K1, K2 and K3 can be adjusted so that the system can better adapt to different network environments and application scenarios; giving priority to nodes with high communication quality scores can reduce failed attempts due to poor signals and improve the overall meter reading efficiency; through more accurate communication quality evaluation, the meter reading order can be arranged more reasonably to ensure that the data of high-priority nodes are collected and transmitted first; comprehensive consideration of multiple communication indicators and the hierarchy of nodes can improve the reliability of data transmission, reduce data loss and errors, and ensure the accuracy of meter reading data.

[0044] By introducing a new weight coefficient L, it is allowed to consider the hierarchical position of the current node in the communication network when calculating the target communication quality score. This helps to more accurately evaluate the communication quality of nodes at different levels, and to optimize and adjust accordingly for the characteristics of different levels. For example, for nodes with smaller levels, a higher L value can be assigned to them, and the larger the weight coefficient value, the better the communication quality. For example, the level weight coefficient corresponding to the first level is 0.9 (or other), and the level weight coefficient corresponding to the second level is 0.8 (or other). By comprehensively considering the level influence and the flexibility of the weight coefficient, a more accurate and comprehensive communication quality score can be provided. This helps the smart meter reading system or other communication networks to more accurately identify and optimize communication bottlenecks and improve overall communication efficiency and reliability. This embodiment can comprehensively evaluate the communication quality according to the level of the current node in the network and its communication channel characteristic parameters (signal-to-noise ratio, received signal strength indication and success rate), thereby more accurately reflecting the actual communication capability of the node. In addition, by adjusting the weight coefficients K1, K2, K3 and L, it can be flexibly configured according to the actual application scenario to improve the adaptability and robustness of the system.

[0045] As an optional implementation, the target communication quality score of the current node may also be calculated according to the following formula: , r+s+q=1, and , where E represents the target communication quality score of the current node, and a set of communication indicator parameters includes the signal-to-noise ratio parameter R ( l ), received signal strength indicator parameter S ( l ) and success rate parameters Q ( l ), R ( l ) indicates the current node is located on the communication link l The signal-to-noise ratio parameter of the level node, S ( l ) indicates the current node is located on the communication link l The received signal strength indicator parameter of the hierarchical node, Q ( l ) indicates the current node is located on the communication link l The success rate parameters of the level nodes, K ( l ) indicates the l The weight coefficient corresponding to the level, n represents the level number of the current node, and the preset weight coefficients include K ( l ), r , s and q , K ( l), r , s and q All are allowed to be adjusted.

[0046] Combine the following Figure 2 To illustrate with examples, Figure 2 For example, the PCO5 node in the communication network is located at the second level, corresponding to the above n=2. When calculating the communication quality score of the PCO5 node, it is necessary to consider the communication quality score of its previous node (such as PCO2), that is, the communication quality score of the PCO5 node E= K ( 1 )[ r*R ( 1 )+ s*S ( 1 )+ q*Q ( 1 )]+ K ( 2 )[ r*R ( 2 )+ s*S ( 2 )+ q*Q ( 2 )] / 2, where R (1) S (1) and Q (1) respectively represent the signal-to-noise ratio parameter, received signal strength indicator parameter and success rate parameter of the first-level node (such as PCO2 node) on the communication link where PCO5 node is located, R (2) S (2) and Q (2) respectively represent the signal-to-noise ratio parameter, received signal strength indicator parameter and success rate parameter of the PCO5 node itself. The above signal-to-noise ratio parameter, received signal strength indicator parameter and success rate parameter all represent the communication parameters between a node and its parent node.

[0047] In a multi-level communication network, nodes at different levels may have different effects on communication quality. However, existing evaluation methods often ignore such hierarchical differences. The preset weight coefficients (including K(l), r, s and q) can be adjusted so that the evaluation method can adapt to changes in network status. When the network topology, communication environment or user needs change, the evaluation method can be dynamically adjusted by adjusting the corresponding weight coefficients, reducing the complexity and cost of the operation. By introducing the number of levels n and the weight coefficient K(l) corresponding to the level, the evaluation method can fully consider the impact of nodes at different levels on communication quality, so that the evaluation results can accurately reflect the actual contribution of nodes at each level, thereby improving the reliability and accuracy of the evaluation results.

[0048] Optionally, meter reading operations are performed on each node in order from high to low according to the communication quality scores corresponding to each node, including: sorting the communication quality scores corresponding to each node from high to low to obtain a sorting result; and performing meter reading operations on each node in order according to the sorting result.

[0049] In the above embodiment, the meter reading operation can be performed on each node in order from high to low according to the communication quality score of each node, ensuring that the nodes with higher communication quality are selected for meter reading first, thereby improving the success rate and reliability of meter reading. At the same time, this method can timely adjust the meter reading order in a dynamically changing network environment, effectively deal with the situation of large fluctuations in signal quality, and improve the efficiency and stability of the entire meter reading system.

[0050] In the existing intelligent meter reading system, meter reading operations are often performed in a fixed order or in a random manner without fully considering the communication quality of the nodes. In a high-interference environment, the signal quality fluctuates greatly. A fixed or random meter reading order may result in multiple retransmissions and data loss, reducing the meter reading efficiency and success rate, which may result in spending too much time on nodes with poor communication quality, thereby reducing the overall meter reading efficiency; performing meter reading operations on nodes with poor communication quality may result in an increase in the communication failure rate, thereby requiring more retries and additional resource investment. This will not only reduce the meter reading efficiency, but also increase the maintenance cost of the system. This embodiment can significantly reduce the time spent on nodes with poor communication quality by sorting the nodes according to the communication quality score and giving priority to performing meter reading operations on nodes with good communication quality. Prioritizing high-scoring nodes can ensure that the data of these nodes is collected and transmitted as soon as possible, reduce the system waiting time, and improve the overall response speed. By dynamically adjusting the meter reading order, this helps to improve the overall meter reading efficiency and enable the system to complete more meter reading tasks in a shorter time.

[0051] In an optional embodiment, after performing meter reading operations on each node in sequence from high to low according to the communication quality scores corresponding to each node, the above method also includes: re-acquiring an updated set of communication index parameters for each node in the node set at preset time intervals; obtaining an updated communication quality score corresponding to each node according to the updated set of communication index parameters for each node; and performing meter reading operations on each node in sequence from high to low according to the updated communication quality scores.

[0052] In the above embodiment, by re-acquiring the communication indicator parameters of the node and updating the communication quality score at preset intervals, it can be ensured that the meter reading operation is always based on the latest communication status; dynamically updating the communication quality score can timely discover and process nodes with reduced communication quality, reduce failed attempts and retransmissions due to poor signals, and improve the overall meter reading efficiency; regularly updating the communication quality score can timely discover potential problems in the network, such as signal interference, equipment failure, etc., and take corresponding measures to optimize them; by dynamically adjusting the meter reading order, the system can better adapt to changes in the network environment, ensuring that the meter reading task can be completed efficiently and reliably under any circumstances.

[0053] This embodiment reacquires the communication index parameters of the node at preset intervals and updates the communication quality score based on these parameters, so that the system can dynamically adapt to changes in the communication environment. This helps to ensure that the meter reading operation is always based on the latest communication quality information, thereby improving the efficiency and accuracy of the operation; by dynamically updating the communication quality score, the system can reallocate resources based on the latest score results; by dynamically updating the communication quality score and adjusting the meter reading strategy, it helps to enhance the reliability and stability of the system, and also helps to reduce the risk of meter reading failure, and improve the overall performance of the system and user satisfaction. The meter reading method in the prior art lacks the ability to dynamically adjust the meter reading strategy, so that the system cannot respond in a timely manner when facing changes in communication quality.

[0054] In an optional embodiment, obtaining a set of communication index parameters of each node in the node set includes: the central node obtaining a set of communication index parameters of each node in the node set according to a preset period.

[0055] In the above embodiment, the central node can regularly obtain a set of communication index parameters of each node in the node set to ensure the timeliness and accuracy of the communication quality score. This helps to dynamically adjust the meter reading order, improve the efficiency and reliability of meter reading, especially in a high interference environment, and can effectively cope with signal quality fluctuations, thereby improving the stability and performance of the entire meter reading system.

[0056] In this embodiment, the central node obtains a set of communication index parameters of each node in the node set according to a preset period, which can ensure the timeliness and consistency of data. Regular data acquisition can timely reflect the latest communication status of the node and ensure the accuracy of communication quality evaluation; periodic acquisition of communication index parameters can timely discover potential problems in the network, such as signal interference, equipment failure, etc., and take corresponding measures to optimize. Timely acquisition of communication index parameters helps the system to more accurately evaluate the communication quality of each node, thereby optimizing the order and strategy of meter reading operations. This can not only improve the efficiency of meter reading operations, but also reduce the number of retries and resource waste caused by communication failures.

[0057] In an optional embodiment, after performing meter reading operations on each node in order from high to low according to the communication quality scores corresponding to each node, the above method also includes: receiving power consumption data sent by each node to obtain a power consumption data set; and uploading the power consumption data set to a remote server.

[0058] In the above embodiment, firstly, by acquiring a set of communication index parameters of each node in the node set and calculating the communication quality score of each node based on these parameters, the orderliness and efficiency of the meter reading operation are ensured. Secondly, the meter reading operation is performed on each node in turn from high to low according to the communication quality score, which not only improves the success rate of meter reading, but also reduces the data loss caused by poor signal quality. Finally, by centrally uploading the collected power consumption data to the remote server, the centralized management and analysis of the data is realized, which is convenient for subsequent data processing and anomaly detection, and further improves the intelligence level of the entire meter reading system. After the meter reading operation is completed, the power consumption data is immediately uploaded to the remote server to ensure the real-time data transmission. This helps the energy management system to obtain the latest power consumption data in a timely manner, so as to make more accurate energy scheduling and decision-making.

[0059] In an optional embodiment, after receiving the electricity consumption data sent by each node respectively and obtaining the electricity consumption data set, the above method also includes: performing an abnormality analysis on the target electricity consumption data, wherein the target electricity consumption data is the electricity consumption data corresponding to any one of the nodes; when it is determined that the target electricity consumption data has an abnormality, triggering an alarm mechanism.

[0060] In the above embodiment, after receiving the electricity consumption data sent by each node, it is possible to perform an abnormal analysis on the target electricity consumption data, promptly discover and handle abnormal situations in the electricity consumption data, and when it is determined that the target electricity consumption data is abnormal, trigger an alarm mechanism to ensure that management personnel can take quick measures to avoid losses or safety hazards caused by data anomalies.

[0061] This embodiment can detect abnormal situations in the data in real time by performing abnormal analysis after receiving the power consumption data. Real-time abnormal detection can promptly discover and handle data abnormalities to ensure the normal operation of the system and the user's service experience. When data abnormalities are detected, the triggering alarm mechanism can immediately notify relevant personnel to handle them, reducing the delay in fault response. Rapid response and processing of abnormal data can improve the overall reliability of the system and reduce system failures and user complaints caused by data abnormalities. Real-time abnormal detection and alarm mechanisms can reduce the workload of operation and maintenance personnel and improve operation and maintenance efficiency. By introducing real-time abnormal analysis and automatic alarm mechanisms, the real-time, accuracy and reliability of the power data management and monitoring system are significantly improved, providing a strong guarantee for the stable operation and energy efficiency management of the power system.

[0062] In an optional embodiment, each node in the node set supports power line carrier communication HPLC and high-speed wireless communication HRF.

[0063] In the above embodiment, by supporting power line carrier communication (HPLC) and high-speed wireless communication (HRF), the node can switch between the two communication modes to ensure that the best communication path can be found under any circumstances. When one communication mode is interfered with or fails, it can be quickly switched to another communication mode to reduce the risk of data transmission interruption and improve the overall reliability of the system. The support of multiple communication modes makes the system more robust and anti-interference, and can operate stably in a complex and changeable network environment.

[0064] Supporting both HPLC and HRF communication modes at the same time allows nodes to flexibly select the best communication path according to environmental conditions and communication requirements. This helps to improve the reliability and stability of communication and reduce communication interruptions caused by failures in a single communication mode; nodes that support multiple communication modes are easier to integrate and interoperate with other systems and networks. This helps to build more complex and large-scale communication networks and improve the overall performance and scalability of the system; by introducing nodes that support both HPLC and HRF communication modes, the reliability, flexibility, transmission rate and bandwidth of the power communication network, as well as network scalability and compatibility are significantly improved. For example, in a high-interference environment, HPLC with strong anti-interference ability can be selected for communication, while in a low-interference environment, HRF with fast transmission speed can be selected for communication. This dual-mode communication method not only improves the reliability of communication, but also enhances the flexibility and adaptability of the system, thereby effectively improving the overall performance of the meter reading system.

[0065] It should be noted that the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application will be described in detail below in conjunction with specific embodiments.

[0066] The embodiment of the present application provides an intelligent meter reading method and system based on network signal quality, which statistically analyzes and sorts the signal quality in the network, including SNR (signal-to-noise ratio) and RSSI (received signal strength indication), and selects the current optimal node for meter reading, thereby improving meter reading efficiency and accuracy. The embodiment of the present application is described in detail below.

[0067] 1. Node signal quality parameter collection and calculation In the HPLC network, all nodes collect and analyze the communication quality of surrounding nodes and upper and lower level routers in real time, including SNR and RSSI, communication success rate, etc. The quality parameters are counted through the communication of the link layer, including beacon frames and proxy frames sent by CCO devices. Each node performs weighted statistics on the communication signals and success rates of itself and its parent node.

[0068] 2.CCO collects signal quality During network maintenance, CCO collects signal quality parameters from all nodes in the network and stores and processes the collected data.

[0069] 3. Statistical Analysis CCO performs statistical analysis on the collected signal quality parameters and calculates the average, standard deviation, median and other statistics of the SNR and RSSI of each node to understand the signal quality distribution of the entire network.

[0070] Since the network is a multi-level network, each node can also serve as a relay node, and the signal quality is weightedly calculated according to the hierarchical relationship of the node in the network.

[0071] The network structure is as follows Figure 2 As shown, the weighting factor K of the number of levels and the weighting factor of SNR s , weighting factor of RSSI r , success rate SUCC weighted coefficient q , the signal quality evaluation of the node is , where r+s+q=1, ,The smaller the node number is in level, the larger the weighted coefficient value is, the better the communication quality is,and the signal quality evaluation corresponds to the aforementioned communication quality score.

[0072] In the above formula, R ( l ) indicates the node on the communication link where a node (such as the current node mentioned above) is located. l The signal-to-noise ratio parameter of the level node, S ( l ) indicates the node is located on the communication link l The received signal strength indicator parameter of the hierarchical node, Q ( l ) indicates the node is located on the communication link l The success rate parameters of the level nodes, K ( l ) indicates the l The weight coefficient corresponding to the level, n represents the level number of the current node, K ( l ), r , s andq All are allowed to be adjusted.

[0073] 4. Sorting According to the results of statistical analysis, a comprehensive evaluation index is established to combine parameters such as SNR and RSSI according to different weights to rank the nodes. The weight setting can be adjusted according to actual needs. For example, in an environment with large signal interference, the weight of SNR can be appropriately increased.

[0074] For example, the single-node signal quality assessment = 0.5 × RSSI + 0.3 × SNR + 0.2 × SUCC. All nodes in the network are sorted according to this indicator to determine the node with the best signal quality.

[0075] It should be noted that when calculating the comprehensive evaluation index of a single node (corresponding to the aforementioned communication quality score), you can choose not to consider the weighting coefficient of the number of levels, that is, only weighted sum of the communication index parameters (such as SNR, RSSI and SUCC) can be performed, or the weighting coefficient of the number of levels can be comprehensively considered.

[0076] 5. Optimal Node Selection According to the sorting results, the node with the best current signal quality is selected as the meter reading node. The meter reading node is responsible for collecting the data of the meter connected to it and sending the data to the concentrator.

[0077] For example, if the comprehensive evaluation index of node A is the highest, node A is preferentially selected as the meter reading node.

[0078] 6. Dynamic Adjustment As time goes by and the network environment changes, the signal quality may change. Therefore, the system needs to regularly re-analyze the signal quality statistics, sort and select the best node to ensure that the current best node is always selected for meter reading.

[0079] The signal quality in the network is comprehensively evaluated and adjusted every hour or half an hour to adapt to dynamic changes in the network.

[0080] 7. Flowchart Figure 3 : is a flow chart of another intelligent meter reading method provided in an embodiment of the present application, including: S301, system networking; for example, HPLC+HRF dual-mode networking is adopted, and CCO serves as a central node (or coordinator) to maintain all nodes; S302, CCO collects node signal quality; CCO regularly collects signal quality parameters of each node (corresponding to the aforementioned set of communication indicator parameters) through the link layer, including SNR, RSSI, SUCC, etc.; S303, node quality assessment: CCO calculates all collected node information and assesses signal quality; S304, sorting the nodes; CCO sorts all the nodes according to signal quality; S305, selecting the best node for meter reading; the CCO performs meter reading according to the ranking result of the nodes.

[0081] CCO collects node signal quality periodically during idle time periods.

[0082] The embodiment of the present application provides a method for dynamically adjusting the meter reading strategy. The method does not read the meter according to the meter reading order in the related art, the order of network access or the order of storage of meter files, but sorts the nodes according to their signal quality, and gives priority to nodes with good communication quality for meter reading, which can improve the success rate and efficiency of meter reading.

[0083] Compared with the related art, the embodiments of the present application have at least the following technical effects: 1) Improve meter reading efficiency. By selecting the node with the best signal quality for meter reading, signal interference and data transmission errors can be reduced, and meter reading speed and efficiency can be improved; 2) Improve data accuracy. Nodes with good signal quality can collect meter data more accurately, reduce data errors, and improve data accuracy; 3) Dynamically adapt to network changes. The system can regularly perform statistical analysis on signal quality and select the optimal node, dynamically adapt to changes in the network environment, and ensure the stability and reliability of meter reading.

[0084] The present application also provides an intelligent meter reading device, such as Figure 4 As shown, Figure 4 : is a structural block diagram of an intelligent meter reading device provided in an embodiment of the present application, the device comprising: The acquisition module 401 is used to acquire a set of communication index parameters of each node in the node set, wherein the node set includes a set of proxy nodes and a set of station nodes in the target area; An acquisition module 402 is used to obtain a communication quality score corresponding to each node according to a set of communication indicator parameters of each node; The meter reading module 403 is used to perform meter reading operations on each node in order from high to low according to the communication quality score corresponding to each node, so that each node collects power consumption data and sends it to the central node, wherein a group of proxy nodes, a group of station nodes and the central node constitute a target communication network, any station node is directly connected to the central node or is connected to the central node through a proxy node, and any proxy node is directly connected to the central node or is connected to the central node through other proxy nodes.

[0085] Other device embodiments correspond to the aforementioned method embodiments, and other technical features can be found in the previous embodiments, which will not be repeated here.

[0086] The present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed, any one of the method steps described above is executed.

[0087] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0088] The present application also discloses an electronic device. Figure 5 As shown, Figure 5 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0089] The communication bus 502 is used to realize the connection and communication between these components.

[0090] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0091] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0092] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts of the entire electronic device (such as a server), and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and can be implemented separately through a chip.

[0093] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Refer to Figure 5 , the memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an intelligent meter reading method.

[0094] exist Figure 5In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application program of an intelligent meter reading method stored in the memory 505. When executed by one or more processors 501, the electronic device 500 executes one or more methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0100] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0101] This application is intended to cover any modifications, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field not recorded in the present disclosure.

Claims

1. An intelligent meter reading method, characterized in that: include: Obtaining a set of communication index parameters of each node in the node set, wherein the node set includes a set of proxy nodes and a set of station nodes in the target area; Obtaining a communication quality score corresponding to each node according to the set of communication indicator parameters of each node; According to the communication quality scores corresponding to the nodes, meter reading operations are performed on the nodes in order from high to low, so that the nodes collect electricity consumption data and send them to the central node respectively, wherein the group of proxy nodes, the group of station nodes and the central node constitute a target communication network, any station node is directly connected to the central node for communication or is connected to the central node through a proxy node, and any proxy node is directly connected to the central node for communication or is connected to the central node through other proxy nodes.

2. The method according to claim 1, characterized in that Obtaining communication quality scores corresponding to each node according to the set of communication indicator parameters of each node, including: The current node is any node in the node set. For the current node, a target communication quality score corresponding to the current node is obtained in the following manner: Each communication index parameter in the group of communication index parameters corresponding to the current node is weighted and summed according to a preset weight coefficient to obtain the target communication quality score.

3. The method according to claim 2, characterized in that The target communication quality score is obtained by weighting and summing each communication indicator parameter in the set of communication indicator parameters corresponding to the current node according to a preset weight coefficient, including: The target communication quality score is calculated according to the following formula: The target communication quality score = K1*SNR + K2*RSSI + K3*SUCC, K1+K2+K3=1, Among them, the set of communication indicator parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, the preset weight coefficients include K1, K2 and K3, K1, K2 and K3 are allowed to be adjusted, and the SNR, the RSSI and the SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

4. The method according to claim 2, characterized in that: The target communication quality score is obtained by weighting and summing each communication indicator parameter in the set of communication indicator parameters corresponding to the current node according to a preset weight coefficient, including: The target communication quality score is calculated according to the following formula: The target communication quality score = L*(K1*SNR + K2*RSSI + K3*SUCC), K1+K2+K3=1, Among them, the set of communication indicator parameters includes a signal-to-noise ratio parameter SNR, a received signal strength indication parameter RSSI and a success rate parameter SUCC, the preset weight coefficients include L, K1, K2 and K3, L, K1, K2 and K3 are allowed to be adjusted, L is used to represent the weight coefficient corresponding to the level of the current node in the target communication network, the SNR, the RSSI and the SUCC all represent the communication channel characteristic parameters between the current node and the parent node of the current node.

5. The method according to claim 1, characterized in that After performing meter reading operations on each node in order from high to low according to the communication quality scores corresponding to each node, the method further includes: Re-acquiring an updated set of communication indicator parameters of each node in the node set at a preset time interval; Obtaining updated communication quality scores corresponding to each node according to the updated set of communication indicator parameters of each node; The meter reading operation is performed on each of the nodes in order from high to low according to the updated communication quality scores.

6. The method according to claim 1, characterized in that Get a set of communication indicator parameters for each node in the node set, including: The central node obtains the set of communication indicator parameters of each node in the node set according to a preset period.

7. The method according to claim 1, characterized in that Each node in the node set supports power line carrier communication HPLC and high-speed wireless communication HRF.

8. An intelligent meter reading device, characterized in that: include: An acquisition module, used to acquire a set of communication index parameters of each node in a node set, wherein the node set includes a set of proxy nodes and a set of station nodes in a target area; An acquisition module, configured to obtain a communication quality score corresponding to each node according to the set of communication indicator parameters of each node; The meter reading module is used to perform meter reading operations on each node in order from high to low according to the communication quality scores corresponding to each node, so that each node collects power consumption data and sends it to the central node, wherein the group of proxy nodes, the group of station nodes and the central node constitute a target communication network, any station node is directly connected to the central node in communication or connected to the central node through a proxy node, and any proxy node is directly connected to the central node in communication or connected to the central node through other proxy nodes.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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