Dual-mode communication-based electric energy meter data acquisition method, device, equipment and medium

Abstract

The present application relates to the technical field of electric power, and especially relates to an electric energy meter data acquisition method and device based on dual-mode communication, equipment and medium, the method comprising: inputting predicted communication state information of each communication mode at the current moment into a mode selection model to obtain initial selection probability of each communication mode; determining target communication state information dimensions from all preset communication state information dimensions according to historical communication state information of any communication mode at all historical moments; optimizing the initial selection probability according to all target communication state information dimensions of each communication mode to obtain target selection probability of each communication mode, and selecting an optimal communication mode for a central coordinator (CCO) to acquire electric energy meter data. Through the present application, data transmission delay can be reduced, the overall response speed of the system can be improved, the communication distance can be prolonged, and the anti-interference capability can be greatly enhanced.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to a method, apparatus, equipment, and medium for acquiring electricity meter data based on dual-mode communication. Background Technology

[0002] In modern power systems, low-voltage centralized meter reading systems are one of the key technologies for realizing automated collection and management of electricity consumption information. The concentrator, as the core equipment of the system, is responsible for connecting the master station and the electricity meters, collecting, storing, and forwarding the electricity meter data, which is then uniformly calculated and processed by the master station.

[0003] However, with the increasing intelligence of electricity information collection terminals and the large-scale integration of new equipment such as distributed energy and charging piles, existing low-voltage centralized meter reading systems face numerous challenges. The amount and types of data that the concentrator needs to collect are constantly increasing, leading to increased data transmission latency and impacting the overall system response speed. Simultaneously, traditional low-voltage centralized meter reading systems suffer from short communication distances and poor anti-interference capabilities. These issues limit the system's coverage and data transmission reliability, making it difficult to meet the needs of complex electromagnetic environments or long-distance transmission. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, device, and medium for data acquisition from electricity meters based on dual-mode communication, which can effectively reduce data transmission latency, significantly improve the overall response speed of the system, extend the communication distance, and greatly enhance anti-interference capabilities. This not only helps to expand the coverage of the system but also significantly improves the reliability of data transmission, thereby better meeting the needs of complex electromagnetic environments or long-distance transmission.

[0005] In a first aspect, embodiments of this application provide a method for acquiring electricity meter data based on dual-mode communication, the method comprising:

[0006] The historical communication status information of each communication mode in the central coordinator (CCO) at each continuous historical moment is obtained. The historical communication status information is obtained by the concentrator when it uses the CCO to transmit electricity meter data or communication status monitoring data through each communication mode during the continuous communication with the electricity meter device.

[0007] For each communication mode, based on the historical communication status information of the communication mode at all historical moments, predict the predicted communication status information of the communication mode at the current moment.

[0008] Input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode;

[0009] Based on the historical communication status information of the communication mode at all historical moments, at least one target communication status information dimension is determined from all preset communication status information dimensions; the target communication status information dimension refers to the communication status information dimension that causes a reduction in the communication quality of the communication mode.

[0010] The initial selection probability of each communication mode is optimized based on the dimensions of all target communication status information corresponding to each communication mode, so as to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect electricity meter data.

[0011] In one possible implementation, predicting the predicted communication state information of the communication mode at the current moment based on the historical communication state information of the communication mode at all historical moments includes:

[0012] For each historical moment, based on the historical communication status information of the communication mode at that historical moment, a historical communication status identifier of the communication mode at that historical moment is obtained; the historical communication status identifier is used to characterize the communication quality of the communication mode.

[0013] Based on the historical communication status identifiers of the communication mode at each historical moment, predict the predicted communication status identifier of the communication mode at the current moment.

[0014] Based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments, the predicted communication status information of the communication mode at the current moment is determined.

[0015] In one possible implementation, determining the predicted communication status information of the communication mode at the current moment based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments further includes:

[0016] By fusing the historical communication status information corresponding to the predicted communication status identifier, the first predicted communication status information of the communication mode at the current moment is obtained.

[0017] Input all historical communication status information at all historical moments into the communication status information prediction model to obtain the second predicted communication status information of the communication mode at the current moment;

[0018] The first predicted communication state information and the second predicted communication state information are fused to obtain the final predicted communication state information of the communication mode at the current moment.

[0019] In one possible implementation, determining at least one target communication state information dimension from all preset communication state information dimensions based on the historical communication state information of the communication mode at all historical moments includes:

[0020] For each historical moment, based on the historical communication status information at that historical moment and the preset feature value range corresponding to each preset communication status information dimension, an initial evaluation value corresponding to each preset communication status information dimension at that historical moment is determined; the initial evaluation value is used to measure the degree to which each preset communication status information dimension causes a reduction in the communication quality of the communication mode.

[0021] For each preset communication status information dimension, the initial evaluation values ​​corresponding to the preset communication status information dimension at each historical moment are fused to obtain the target evaluation value corresponding to the preset communication status information dimension.

[0022] The target communication status information dimension is determined based on the target evaluation value corresponding to all preset communication status information dimensions.

[0023] In one possible implementation, determining the initial evaluation value of the preset communication status information dimension at the historical moment based on the historical communication status information at that historical moment and a preset feature value range corresponding to any preset communication status information dimension includes:

[0024] Based on the feature values ​​corresponding to the preset communication status information dimension in the historical communication status information, and the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension, calculate the original evaluation value of the preset communication status information dimension at the historical moment.

[0025] If the preset communication status information dimension is positively correlated with communication quality, then the reciprocal of the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical moment.

[0026] If the preset communication status information dimension is negatively correlated with communication quality, then the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical moment.

[0027] In one possible implementation, the step of optimizing the initial selection probability corresponding to each communication mode based on the dimension of all target communication state information corresponding to each communication mode to obtain the target selection probability corresponding to each communication mode includes:

[0028] For each communication mode, the preset influence weights corresponding to all target communication state information dimensions corresponding to the communication mode are integrated to obtain the initial optimization weights corresponding to the communication mode.

[0029] The initial optimization weights corresponding to each communication mode are normalized to obtain the target optimization weights corresponding to each communication mode.

[0030] For each communication mode, the initial selection probability corresponding to the communication mode is weighted according to the target optimization weight corresponding to the communication mode to obtain the target selection probability corresponding to the communication mode.

[0031] In one possible implementation, the pattern selection model is trained according to the following steps:

[0032] Obtain communication status information samples for each communication mode at each sample time and the selection probability label corresponding to each communication status information;

[0033] The mode selection model is trained based on the communication status information samples and the corresponding selection probability labels, so as to predict the selection probability of each communication mode based on the trained mode selection model.

[0034] Secondly, embodiments of this application also provide a data acquisition device for an energy meter based on dual-mode communication, the device comprising:

[0035] The acquisition module is used to acquire historical communication status information of each communication mode in the central coordinator (CCO) at each continuous historical moment. The historical communication status information is obtained by the concentrator when it uses the CCO to transmit electricity meter data or communication status monitoring data through each communication mode during the continuous communication with the electricity meter device.

[0036] The prediction module is used to predict the predicted communication status information of each communication mode at the current moment based on the historical communication status information of the communication mode at all historical moments.

[0037] The input module is used to input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode.

[0038] The determining module is used to determine at least one target communication status information dimension from all preset communication status information dimensions based on the historical communication status information of the communication mode at all historical moments; the target communication status information dimension refers to the communication status information dimension that causes the communication quality of the communication mode to decrease.

[0039] The optimization module is used to optimize the initial selection probability of each communication mode based on the dimension of all target communication status information corresponding to each communication mode, so as to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect electricity meter data.

[0040] In one possible implementation, the prediction module is specifically configured to, for each historical moment, obtain a historical communication status identifier of the communication mode at that historical moment based on the historical communication status information of the communication mode at that historical moment; the historical communication status identifier is used to characterize the communication quality of the communication mode; predict the predicted communication status identifier of the communication mode at the current moment based on the historical communication status identifiers of the communication mode at each historical moment; and determine the predicted communication status information of the communication mode at the current moment based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments.

[0041] In one possible implementation, the prediction module is specifically used to fuse historical communication status information corresponding to the predicted communication status identifier to obtain first predicted communication status information of the communication mode at the current moment; input historical communication status information of all historical moments into the communication status information prediction model to obtain second predicted communication status information of the communication mode at the current moment; and fuse the first predicted communication status information and the second predicted communication status information to obtain final predicted communication status information of the communication mode at the current moment.

[0042] In one possible implementation, the determining module is specifically configured to, for each historical moment, determine an initial evaluation value corresponding to each preset communication status information dimension at that historical moment, based on the historical communication status information at that historical moment and the preset feature value range corresponding to each preset communication status information dimension; the initial evaluation value is used to measure the degree to which each preset communication status information dimension causes a reduction in the communication quality of the communication mode; for each preset communication status information dimension, the initial evaluation values ​​corresponding to the preset communication status information dimension at each historical moment are fused to obtain a target evaluation value corresponding to the preset communication status information dimension; and the target communication status information dimension is determined based on the target evaluation values ​​corresponding to all preset communication status information dimensions.

[0043] In one possible implementation, the determining module is specifically configured to calculate the original evaluation value of the preset communication status information dimension at the historical time based on the feature values ​​corresponding to the preset communication status information dimension in the historical communication status information, and the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension; if the preset communication status information dimension is positively correlated with communication quality, then the reciprocal of the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical time; if the preset communication status information dimension is negatively correlated with communication quality, then the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical time.

[0044] In one possible implementation, the optimization module is specifically used to, for each communication mode, fuse the preset influence weights corresponding to all target communication state information dimensions of the communication mode to obtain the initial optimization weights corresponding to the communication mode; normalize the initial optimization weights corresponding to each communication mode to obtain the target optimization weights corresponding to each communication mode; and for each communication mode, weight the initial selection probability corresponding to the communication mode according to the target optimization weights corresponding to the communication mode to obtain the target selection probability corresponding to the communication mode.

[0045] In one possible implementation, the device further includes: a training module; the training module is configured to train the pattern selection model according to the following steps:

[0046] Obtain communication status information samples for each communication mode at each sample time and the selection probability label corresponding to each communication status information;

[0047] The mode selection model is trained based on the communication status information samples and the corresponding selection probability labels, so as to predict the selection probability of each communication mode based on the trained mode selection model.

[0048] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the energy meter data acquisition method based on dual-mode communication as described in any of the first aspects.

[0049] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the energy meter data acquisition method based on dual-mode communication as described in any of the first aspects.

[0050] This application provides a method, apparatus, device, and medium for acquiring electricity meter data based on dual-mode communication. The method includes: predicting the predicted communication state information of each communication mode at the current moment based on historical communication state information of each communication mode at each historical time; inputting the predicted communication state information corresponding to all communication modes into a mode selection model to obtain the initial selection probability corresponding to each communication mode; determining at least one target communication state information dimension from all preset communication state information dimensions based on the historical communication state information of any communication mode at all historical times; and optimizing the initial selection probability based on all target communication state information dimensions corresponding to each communication mode to obtain the target selection probability corresponding to each communication mode, so as to select the communication mode with the highest target selection probability for the Central Coordinator (CCO) to acquire electricity meter data. This application can effectively reduce data transmission latency, significantly improve the overall response speed of the system, extend the communication distance, and greatly enhance anti-interference capabilities. This not only helps to expand the system's coverage but also significantly improves the reliability of data transmission, thereby better meeting the needs of complex electromagnetic environments or long-distance transmission. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a method for acquiring electricity meter data based on dual-mode communication, provided in an embodiment of this application, is shown.

[0053] Figure 2 This application provides a flowchart illustrating a prediction process for predicting communication status information.

[0054] Figure 3 This invention provides another flowchart for predicting communication status information according to an embodiment of the present application.

[0055] Figure 4 A flowchart illustrating the determination of the weights corresponding to the first and second predicted communication state information provided in an embodiment of this application is shown.

[0056] Figure 5 A flowchart illustrating the determination of the target communication status information dimension provided in an embodiment of this application is shown;

[0057] Figure 6This document illustrates a flowchart showing the determination of the initial evaluation value of the preset communication status information dimension at a historical moment, as provided in an embodiment of this application.

[0058] Figure 7 This illustration shows a schematic diagram of a data acquisition device for an energy meter based on dual-mode communication, provided in an embodiment of this application.

[0059] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0061] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0062] To enable those skilled in the art to utilize the content of this application, and in conjunction with the specific application scenario of "electric power technology," the following implementation methods are provided. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application is primarily described within the "electric power technology field," it should be understood that this is merely an exemplary embodiment.

[0063] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0064] The following is a detailed description of a method for acquiring electricity meter data based on dual-mode communication, provided by an embodiment of this application.

[0065] Reference Figure 1 The diagram shown is a flowchart illustrating a method for acquiring electricity meter data based on dual-mode communication, as provided in an embodiment of this application. The exemplary steps of this embodiment are described below:

[0066] S101. Obtain the historical communication status information of each communication mode in the Central Coordinator (CCO) at each consecutive historical moment.

[0067] In this embodiment, the concentrator establishes a connection signal with the Central Coordinator (CCO) and issues a minute-level high-frequency data acquisition task to the CCO. Then, the CCO initiates minute-level high-frequency data acquisition and acquires electricity meter data from the electricity meter device through a specific communication mode. Additionally, this embodiment continuously transmits communication status monitoring data with the electricity meter device through communication modes not used for acquiring electricity meter data; communication status monitoring data refers to data used to monitor the communication status information of the communication mode. Therefore, historical communication status information is obtained by the concentrator using the CCO during continuous communication with the electricity meter device, transmitting electricity meter data or communication status monitoring data through various communication modes.

[0068] Here, "historical moments" refers to all moments within a preset time period (such as one hour, one day, two days, etc.) closest to the current moment. The historical communication status information for each historical moment includes feature values ​​corresponding to various preset communication status information dimensions. These preset communication status information dimensions refer to preset data dimensions related to communication quality, such as communication signal strength, bit error rate, communication transmission delay, packet loss rate, bandwidth utilization, and communication network load.

[0069] Here, traditional data acquisition schemes communicate with the energy meter device using only one communication mode at a time. However, traditional methods can only monitor the communication status of one communication mode and determine whether to switch modes, but cannot select the optimal communication mode for data acquisition based on the communication status of all modes.

[0070] This application embodiment communicates with the energy meter device simultaneously through all communication modes, thereby monitoring the communication status of all communication modes. The optimal communication mode can be selected for data collection, improving the accuracy of subsequent communication status prediction, thus increasing the data acquisition speed, reducing data transmission latency, and improving the overall response speed of the low-voltage centralized meter reading system. Furthermore, to avoid excessive noise communication data due to the use of all communication modes, the amount of communication status monitoring data should be less than a preset amount (meaning the amount of data that does not affect the acquisition speed of the concentrator), thus avoiding a reduction in the concentrator's acquisition speed and consequently affecting the overall response speed of the low-voltage centralized meter reading system. Further, to avoid excessive storage space on the concentrator due to monitoring the communication status of all communication modes by transmitting communication status monitoring data, this application embodiment, when monitoring the communication status of any communication mode using transmitted communication status monitoring data at any given time, after calculating the communication status information of that communication mode at that time, deletes all data other than the communication status information of that communication mode at that time from all related data used to calculate the communication status information of that communication mode at that time.

[0071] Furthermore, traditional data acquisition methods may result in situations where no data is collected during practical applications, leading to the inability to acquire corresponding communication status information and thus affecting subsequent communication status predictions. In contrast, the embodiment of this application continuously communicates with the electricity meter, enabling the acquisition of continuous communication status information and improving the accuracy of communication status prediction.

[0072] Furthermore, the communication modes in this application embodiment include HPLC communication mode and HRF communication mode. HRF communication mode: HRF (High-Rate Fieldbus) is a high-speed fieldbus communication protocol commonly used in industrial automation. HRF has advantages such as high communication rate, low bit error rate, scalability, long-distance transmission, high anti-interference capability, and high reliability, and therefore has been applied in many automation systems. The implementation of the HRF communication protocol includes multiple layers such as the physical layer, data link layer, and application layer. The physical layer is mainly responsible for the transmission of communication signals, the data link layer is responsible for the transmission control of data frames, and the application layer is responsible for the transmission of actual data.

[0073] Here, this application embodiment employs an HPLC+HRF dual-mode communication method for data acquisition. In practical applications, different communication protocols have different advantages and applicable scopes, thus often requiring the simultaneous use of multiple communication protocols to meet diverse needs. The HPLC+HRF dual-mode communication method is one such method that uses two communication protocols simultaneously to achieve data transmission. In this method, HPLC and HRF can be used for different data transmission tasks to improve data transmission efficiency and reliability. For example, HRF can be used for high-speed transmission of real-time data, while HPLC can be used for transmitting lower-speed but high-resolution data. In summary, the research content of the high-frequency data acquisition algorithm research project based on the HPLC+HRF dual-mode communication method is to research and implement a high-speed, long-distance transmission, and highly anti-interference high-frequency data acquisition algorithm.

[0074] S102. For each communication mode, based on the historical communication status information of that communication mode at all historical moments, predict the predicted communication status information of that communication mode at the current moment.

[0075] In this embodiment, the predicted communication status information at the current moment includes feature values ​​corresponding to each preset communication status information dimension. This embodiment can input historical communication status information from all historical moments into the communication status information prediction model to obtain the predicted communication status information of the communication mode at the current moment. Alternatively, the predicted communication status information of the communication mode at the current moment can be predicted using the following method:

[0076] Reference Figure 2 The diagram shown is a flowchart illustrating a prediction process for communication status information provided in an embodiment of this application.

[0077] S201. For each historical moment, based on the historical communication status information of the communication mode at that historical moment, obtain the historical communication status identifier of the communication mode at that historical moment; the historical communication status identifier is used to characterize the communication quality of the communication mode.

[0078] In this embodiment, the historical communication status identifier for each historical moment can be excellent, good, passable, or poor, used to characterize the communication quality of the communication mode at each historical moment. For example, the historical communication status identifier for the communication mode at the first historical moment is good. The historical communication status identifier for the communication mode at the second historical moment is excellent.

[0079] Here, the embodiments of this application may use various methods such as machine learning (e.g., decision tree, random forest, support vector machine, etc.) and deep learning (e.g., gated recurrent unit) to determine the historical communication status identifier of the communication mode at each historical moment.

[0080] For example, a communication status identifier determination model can be constructed using random forests to obtain a large number of communication status information samples and corresponding communication status identifier labels (which can be manually labeled). Then, the communication status identifier determination model can be trained using the communication status information samples and corresponding communication status identifier labels. Then, the historical communication status information of the communication mode at that historical moment can be input into the trained communication status identifier determination model to obtain the historical communication status identifier of the communication mode at that historical moment.

[0081] S202. Based on the historical communication status identifiers of the communication mode at each historical moment, predict the predicted communication status identifier of the communication mode at the current moment.

[0082] In this embodiment, the predicted communication status identifier of the communication mode at the current moment can be excellent, good, passable, or poor, used to characterize the communication quality of the communication mode at each historical moment. For example, the predicted communication status identifier of the communication mode at the current moment is excellent.

[0083] Here, the embodiments of this application can use various methods such as machine learning (such as decision tree regression, random forest regression and support vector regression, etc.) and time series analysis (such as autoregressive moving average model (ARMA) or autoregressive integral moving average model, etc.) to learn the changing trend of communication quality from historical communication status identifiers, so as to predict the current communication status identifier.

[0084] S203. Based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments, determine the predicted communication status information of the communication mode at the current moment.

[0085] Here, compared to directly predicting the current communication status information based on historical communication status information, this application adopts a two-step prediction strategy. First, it predicts the current communication status identifier based on historical communication status information. Then, it determines the current communication status information for that communication mode based on the current communication status identifier. This strategy has the following advantages: On the one hand, predicting the communication status identifier can better capture the global trend and pattern of the communication status. By predicting the communication status identifier first, the overall quality of the current communication status can be judged more accurately, and then specific information can be further refined based on the identifier, thereby improving the overall prediction accuracy. On the other hand, directly predicting the current communication status information may be significantly affected by noise and outliers in the historical communication status information. Predicting the communication status identifier first can minimize the impact of data noise and outliers, improving prediction accuracy.

[0086] Reference Figure 3 The diagram shown is another flowchart for predicting communication status information provided in an embodiment of this application.

[0087] S301. The historical communication status information corresponding to the predicted communication status identifier is fused to obtain the first predicted communication status information of the communication mode at the current moment.

[0088] In this embodiment, the historical communication status information corresponding to the predicted communication status identifier refers to historical communication status information where the historical communication status identifier is the same as the predicted communication status identifier. The first predicted communication status information includes feature values ​​corresponding to each preset communication status information dimension.

[0089] For example, the predicted communication status is "Good," and the historical communication status information includes A, B, and C. Specifically, historical communication status information A has a historical communication status of "Good," historical communication status information B has a historical communication status of "Excellent," and historical communication status information C has a historical communication status of "Good." Therefore, the historical communication status information corresponding to the predicted communication status is A and B. Thus, based on historical communication status information A and B, the first predicted communication status information for this communication mode at the current moment is determined.

[0090] Specifically, the historical communication status information corresponding to the predicted communication status identifiers is weighted and summed to obtain the first predicted communication status information of the communication mode at the current time; wherein, the closer the historical time corresponding to the historical communication status information is to the current time, the greater the weight of the historical communication status information. Alternatively, the historical communication status information corresponding to all predicted communication status identifiers is averaged to obtain the first predicted communication status information of the communication mode at the current time.

[0091] S302. Input the historical communication status information of all historical moments into the communication status information prediction model to obtain the second predicted communication status information of the communication mode at the current moment.

[0092] S303. The first predicted communication state information and the second predicted communication state information are fused to obtain the final predicted communication state information of the communication mode at the current moment.

[0093] In this embodiment, the first and second predicted communication status information are weighted and summed according to their respective weights to obtain the final predicted communication status information of the communication mode at the current time. Alternatively, the first and second predicted communication status information are averaged to obtain the first predicted communication status information of the communication mode at the current time.

[0094] In addition, refer to Figure 4The diagram shown is a flowchart for determining the weights corresponding to the first and second predicted communication status information provided in an embodiment of this application.

[0095] S401. Group each historical communication status information according to a preset number to obtain multiple sets of historical communication status information.

[0096] S402. Merge all historical communication status information in the target historical communication status information set to obtain the first merged communication status information.

[0097] In this application embodiment, the target historical communication status information set is the historical communication status information set with the highest accuracy of the communication status information prediction model under each historical communication status information set.

[0098] S403. Merge the historical communication status information of all historical moments to obtain the second merged communication status information.

[0099] In this embodiment of the application, the second fused communication status information is used to characterize the features of historical communication status information at all historical moments, that is, the data features used to predict the communication status information in this application.

[0100] Among them, the historical communication status information of all historical moments can be fused by weighted summation or average fusion to obtain the second fused communication status information.

[0101] S404. The similarity between the first fused communication status information and the second fused communication status information is determined as the weight corresponding to the second predicted communication status information.

[0102] In this embodiment, the higher the similarity between the first fused communication status information and the second fused communication status information, the more similar the data characteristics of the historical communication status information at all historical moments are to the data characteristics of the target historical communication status information set. Therefore, the accuracy of predicting the current communication status information using the communication status information prediction model is higher. Thus, the similarity between the first fused communication status information and the second fused communication status information is determined as the weight corresponding to the second predicted communication status information.

[0103] S405. Subtract the weight corresponding to the second predicted communication status information from the value 1 to obtain the weight corresponding to the first predicted communication status information.

[0104] S103. Input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode.

[0105] In this embodiment, the mode selection model is pre-trained based on communication status information samples and corresponding selection probability labels. The initial selection probability refers to the probability of selecting each communication mode to collect electricity meter data.

[0106] Here, the mode selection model is trained according to the following steps: obtaining communication state information samples of each communication mode at each sample time and selection probability labels corresponding to each communication state information; training the mode selection model based on the communication state information samples and corresponding selection probability labels, so as to predict the selection probability corresponding to each communication mode based on the trained mode selection model.

[0107] S104. Based on the historical communication status information of this communication mode at all historical moments, determine at least one target communication status information dimension from all preset communication status information dimensions.

[0108] In the embodiments of this application, the target communication status information dimension refers to the communication status information dimension that causes a reduction in the communication quality of the communication mode.

[0109] Reference Figure 5 The diagram shown is a flowchart for determining the dimension of target communication status information provided in an embodiment of this application.

[0110] S501. For each historical moment, based on the historical communication status information at that historical moment and the preset feature value range corresponding to each preset communication status information dimension, determine the initial evaluation value corresponding to each preset communication status information dimension at that historical moment.

[0111] In the embodiments of this application, the initial evaluation value is used to measure the degree to which each preset communication status information dimension causes a reduction in the communication quality of the communication mode. Therefore, the larger the initial evaluation value, the lower the communication quality.

[0112] Reference Figure 6 The diagram shows a flowchart for determining the initial evaluation value of a preset communication status information dimension at a historical moment, as provided in this application embodiment. Specifically, based on the historical communication status information at the historical moment and the preset feature value range corresponding to any preset communication status information dimension, the initial evaluation value of the preset communication status information dimension at the historical moment is determined, including:

[0113] S601. Based on the feature values ​​in the historical communication status information that correspond to the preset communication status information dimension, and the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension, calculate the original evaluation value of the preset communication status information dimension at that historical moment.

[0114] In this embodiment, the absolute value of the difference between the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension is calculated; the ratio between the feature value corresponding to the preset communication status information dimension in the historical communication status information and the absolute value of the difference is calculated to obtain the original evaluation value corresponding to the preset communication status information dimension.

[0115] Taking bit error rate as a preset communication status information dimension as an example, the preset feature value range corresponding to bit error rate is: The feature value corresponding to the bit error rate in this historical communication status information is 0.5. First, the absolute value of the difference between the maximum and minimum values ​​within the preset feature value range corresponding to the bit error rate is calculated. Then, calculate the ratio between the characteristic value of 0.5 corresponding to the bit error rate in the historical communication status information and the absolute value of the difference, 0.78. The original evaluation value corresponding to this bit error rate is 0.641.

[0116] S602. If the preset communication status information dimension is positively correlated with communication quality, then the reciprocal of the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at that historical moment.

[0117] In this embodiment, if the preset communication status information dimension is positively correlated with communication quality, it means that the larger the feature value corresponding to the preset communication status information dimension, the higher the communication quality. As can be seen from the calculation method of the original evaluation value, the larger the feature value corresponding to the preset communication status information dimension, the larger the original evaluation value, and therefore the higher the communication quality. However, the larger the initial evaluation value, the lower the communication quality. Therefore, there is a negative correlation between the original evaluation value and the initial evaluation value. Thus, the reciprocal of the original evaluation value is determined as the initial evaluation value.

[0118] S603. If the preset communication status information dimension is negatively correlated with communication quality, then the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at that historical moment.

[0119] In this embodiment, if the preset communication status information dimension is negatively correlated with communication quality, it means that the larger the feature value corresponding to the preset communication status information dimension, the lower the communication quality. However, as shown by the calculation method of the original evaluation value in this application, the larger the feature value corresponding to the preset communication status information dimension, the larger the original evaluation value, and the lower the communication quality. But, the larger the initial evaluation value, the lower the communication quality. Therefore, the original evaluation value and the initial evaluation value are positively correlated, so the original evaluation value can be determined as the initial evaluation value.

[0120] Furthermore, since the initial evaluation value calculated by the aforementioned method may be too large, the initial evaluation value corresponding to the preset communication status information dimension at each historical moment is normalized to obtain the final initial evaluation value corresponding to the preset communication status information dimension at each historical moment.

[0121] S502. For each preset communication status information dimension, the initial evaluation values ​​corresponding to the preset communication status information dimension at each historical moment are fused to obtain the target evaluation value corresponding to the preset communication status information dimension.

[0122] In this embodiment of the application, the initial evaluation values ​​of the preset communication status information dimension at each historical moment are fused by weighted summation fusion or average fusion to obtain the target evaluation value corresponding to the preset communication status information dimension.

[0123] S503. Determine the target communication status information dimension based on the target evaluation value corresponding to all preset communication status information dimensions.

[0124] In this embodiment, a preset communication status information dimension whose target evaluation value is greater than a preset evaluation value is determined as the target communication status information dimension. Alternatively, a preset number of preset communication status information dimensions with the largest target evaluation value are determined as the target communication status information dimensions.

[0125] S105. Optimize the initial selection probability of each communication mode based on the dimensions of all target communication status information corresponding to each communication mode, so as to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect the electricity meter data.

[0126] In this application embodiment, the degree of influence of the same preset communication status information dimension on the communication quality of different communication modes may vary, and the degree of influence of different preset communication status information dimensions on the communication quality of the same communication mode may also vary. Therefore, the target communication status information dimension corresponding to a communication mode will affect the selection probability of the corresponding communication mode. Therefore, experts in the art pre-set the influence weight of each preset communication status information dimension on the communication quality of each communication mode; this preset influence weight is used to measure the degree of influence of each preset communication status information dimension on the communication quality of each communication mode.

[0127] Specifically, the initial selection probability for each communication mode is optimized based on the dimension of all target communication state information corresponding to each communication mode, resulting in the target selection probability, including:

[0128] Step 1: For each communication mode, integrate the preset influence weights corresponding to all target communication status information dimensions of that communication mode to obtain the initial optimization weights for that communication mode.

[0129] In this embodiment of the application, the preset influence weights corresponding to all target communication status information dimensions corresponding to the communication mode are added together to obtain the initial optimization weights corresponding to the communication mode.

[0130] Step 2: Normalize the initial optimization weights corresponding to each communication mode to obtain the target optimization weights corresponding to each communication mode.

[0131] Step 3: For each communication mode, the initial selection probability corresponding to the communication mode is weighted according to the target optimization weight corresponding to the communication mode to obtain the target selection probability corresponding to the communication mode.

[0132] In this embodiment of the application, the product of the target optimization weight corresponding to the communication mode and the initial selection probability is determined as the target selection probability corresponding to the communication mode.

[0133] This application provides a method, apparatus, device, and medium for acquiring electricity meter data based on dual-mode communication. The method includes: predicting the predicted communication state information of each communication mode at the current moment based on historical communication state information of each communication mode at each historical time; inputting the predicted communication state information corresponding to all communication modes into a mode selection model to obtain the initial selection probability corresponding to each communication mode; determining at least one target communication state information dimension from all preset communication state information dimensions based on the historical communication state information of any communication mode at all historical times; and optimizing the initial selection probability based on all target communication state information dimensions corresponding to each communication mode to obtain the target selection probability corresponding to each communication mode, so as to select the communication mode with the highest target selection probability for the Central Coordinator (CCO) to acquire electricity meter data. This application can effectively reduce data transmission latency, significantly improve the overall response speed of the system, extend the communication distance, and greatly enhance anti-interference capabilities. This not only helps to expand the system's coverage but also significantly improves the reliability of data transmission, thereby better meeting the needs of complex electromagnetic environments or long-distance transmission.

[0134] Based on the same inventive concept, this application also provides a dual-mode communication-based energy meter data acquisition device corresponding to the dual-mode communication-based energy meter data acquisition method. Since the principle of the device in this application is similar to the dual-mode communication-based energy meter data acquisition method described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0135] Reference Figure 7The diagram shown is a schematic of a data acquisition device for an energy meter based on dual-mode communication, according to an embodiment of this application. The device includes:

[0136] The acquisition module 701 is used to acquire historical communication status information of each communication mode in the central coordinator (CCO) at each continuous historical moment; the historical communication status information is obtained by the concentrator when it uses the CCO to continuously communicate with the electricity meter device and transmits electricity meter data or communication status monitoring data through each communication mode.

[0137] The prediction module 702 is used to predict the predicted communication status information of each communication mode at the current moment based on the historical communication status information of the communication mode at all historical moments.

[0138] The input module 703 is used to input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode.

[0139] The determining module 704 is used to determine at least one target communication status information dimension from all preset communication status information dimensions based on the historical communication status information of the communication mode at all historical moments; the target communication status information dimension refers to the communication status information dimension that causes the communication quality of the communication mode to decrease.

[0140] The optimization module 705 is used to optimize the initial selection probability of each communication mode according to the dimension of all target communication status information corresponding to each communication mode, so as to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect the electricity meter data.

[0141] This application provides a data acquisition device for electricity meters based on dual-mode communication. This device effectively reduces data transmission latency, significantly improves the overall system response speed, extends communication distance, and greatly enhances anti-interference capabilities. This not only helps expand the system's coverage area but also significantly improves the reliability of data transmission, thereby better meeting the needs of complex electromagnetic environments or long-distance transmission.

[0142] like Figure 8 As shown in the embodiment of this application, an electronic device 800 includes a processor 801, a memory 802, and a bus. The memory 802 stores machine-readable instructions that can be executed by the processor 801. When the electronic device is running, the processor 801 communicates with the memory 802 via the bus. The processor 801 executes the machine-readable instructions to perform the steps of the above-described method for acquiring electricity meter data based on dual-mode communication.

[0143] Specifically, the memory 802 and processor 801 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 801 runs the computer program stored in the memory 802, it can execute the above-mentioned energy meter data acquisition method based on dual-mode communication.

[0144] Corresponding to the above-described method for acquiring electricity meter data based on dual-mode communication, this application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the steps of the above-described method for acquiring electricity meter data based on dual-mode communication.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0146] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0148] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the dual-mode communication-based electricity meter data acquisition method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0149] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for acquiring electricity meter data based on dual-mode communication, characterized in that, The method includes: The historical communication status information of each communication mode in the central coordinator (CCO) at each continuous historical moment is obtained. The historical communication status information is obtained by the concentrator when it uses the CCO to transmit electricity meter data or communication status monitoring data through each communication mode during the continuous communication with the electricity meter device. For each communication mode, based on the historical communication status information of the communication mode at all historical moments, predict the predicted communication status information of the communication mode at the current moment. Input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode; Based on the historical communication status information of the communication mode at all historical moments, at least one target communication status information dimension is determined from all preset communication status information dimensions; the target communication status information dimension refers to the communication status information dimension that causes a reduction in the communication quality of the communication mode. The initial selection probability of each communication mode is optimized based on the dimensions of all target communication status information corresponding to each communication mode to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect electricity meter data. The step of determining at least one target communication status information dimension from all preset communication status information dimensions based on the historical communication status information of the communication mode at all historical moments includes: for each historical moment, determining an initial evaluation value corresponding to each preset communication status information dimension at that historical moment based on the historical communication status information at that historical moment and the preset feature value range corresponding to each preset communication status information dimension; the initial evaluation value is used to measure the degree to which each preset communication status information dimension causes a reduction in the communication quality of the communication mode; for each preset communication status information dimension, fusing the initial evaluation values ​​corresponding to the preset communication status information dimension at each historical moment to obtain a target evaluation value corresponding to the preset communication status information dimension; and determining the target communication status information dimension based on the target evaluation values ​​corresponding to all preset communication status information dimensions. Based on the historical communication status information at the historical moment and the preset feature value range corresponding to any preset communication status information dimension, the initial evaluation value corresponding to the preset communication status information dimension at the historical moment is determined, including: calculating the original evaluation value corresponding to the preset communication status information dimension at the historical moment based on the feature value corresponding to the preset communication status information dimension in the historical communication status information, and the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension; if the preset communication status information dimension is positively correlated with communication quality, then the reciprocal of the original evaluation value is determined as the initial evaluation value corresponding to the preset communication status information dimension at the historical moment; if the preset communication status information dimension is negatively correlated with communication quality, then the original evaluation value is determined as the initial evaluation value corresponding to the preset communication status information dimension at the historical moment.

2. The method for acquiring electricity meter data based on dual-mode communication according to claim 1, characterized in that, The step of predicting the predicted communication state information of the communication mode at the current moment based on the historical communication state information of the communication mode at all historical moments includes: For each historical moment, based on the historical communication status information of the communication mode at that historical moment, a historical communication status identifier of the communication mode at that historical moment is obtained; the historical communication status identifier is used to characterize the communication quality of the communication mode. Based on the historical communication status identifiers of the communication mode at each historical moment, predict the predicted communication status identifier of the communication mode at the current moment. Based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments, the predicted communication status information of the communication mode at the current moment is determined.

3. The method for acquiring electricity meter data based on dual-mode communication according to claim 2, characterized in that, The step of determining the predicted communication status information of the communication mode at the current moment based on the predicted communication status identifier and the historical communication status information of the communication mode at all historical moments further includes: By fusing the historical communication status information corresponding to the predicted communication status identifier, the first predicted communication status information of the communication mode at the current moment is obtained. Input all historical communication status information at all historical moments into the communication status information prediction model to obtain the second predicted communication status information of the communication mode at the current moment; The first predicted communication state information and the second predicted communication state information are fused to obtain the final predicted communication state information of the communication mode at the current moment.

4. The method for acquiring electricity meter data based on dual-mode communication according to claim 1, characterized in that, The optimization of the initial selection probability for each communication mode based on the dimension of all target communication state information corresponding to each communication mode, to obtain the target selection probability for each communication mode, includes: For each communication mode, the preset influence weights corresponding to all target communication state information dimensions corresponding to the communication mode are integrated to obtain the initial optimization weights corresponding to the communication mode. The initial optimization weights corresponding to each communication mode are normalized to obtain the target optimization weights corresponding to each communication mode. For each communication mode, the initial selection probability corresponding to the communication mode is weighted according to the target optimization weight corresponding to the communication mode to obtain the target selection probability corresponding to the communication mode.

5. The method for acquiring electricity meter data based on dual-mode communication according to claim 1, characterized in that, The pattern selection model is trained according to the following steps: Obtain communication status information samples for each communication mode at each sample time and the selection probability label corresponding to each communication status information; The mode selection model is trained based on the communication status information samples and the corresponding selection probability labels, so as to predict the selection probability of each communication mode based on the trained mode selection model.

6. A data acquisition device for an electricity meter based on dual-mode communication, characterized in that, The device includes: The acquisition module is used to acquire historical communication status information of each communication mode in the central coordinator (CCO) at each continuous historical moment. The historical communication status information is obtained by the concentrator when it uses the CCO to transmit electricity meter data or communication status monitoring data through each communication mode during the continuous communication with the electricity meter device. The prediction module is used to predict the predicted communication status information of each communication mode at the current moment based on the historical communication status information of the communication mode at all historical moments. The input module is used to input the predicted communication state information corresponding to all communication modes into the mode selection model to obtain the initial selection probability corresponding to each communication mode. The determining module is used to determine at least one target communication status information dimension from all preset communication status information dimensions based on the historical communication status information of the communication mode at all historical moments; the target communication status information dimension refers to the communication status information dimension that causes the communication quality of the communication mode to decrease. The optimization module is used to optimize the initial selection probability of each communication mode based on the dimension of all target communication status information corresponding to each communication mode, so as to obtain the target selection probability of each communication mode, so as to select the communication mode with the highest target selection probability for the central coordinator (CCO) to collect electricity meter data. The determining module is specifically used to, for each historical moment, determine an initial evaluation value corresponding to each preset communication status information dimension at that historical moment based on the historical communication status information at that historical moment and the preset feature value range corresponding to each preset communication status information dimension; the initial evaluation value is used to measure the degree to which each preset communication status information dimension causes a reduction in the communication quality of the communication mode; for each preset communication status information dimension, the initial evaluation values ​​corresponding to the preset communication status information dimension at each historical moment are fused to obtain a target evaluation value corresponding to the preset communication status information dimension; and the target communication status information dimension is determined based on the target evaluation values ​​corresponding to all preset communication status information dimensions. The determining module is specifically used to calculate the original evaluation value of the preset communication status information dimension at the historical time based on the feature values ​​corresponding to the preset communication status information dimension in the historical communication status information, and the maximum and minimum values ​​in the preset feature value range corresponding to the preset communication status information dimension; if the preset communication status information dimension is positively correlated with communication quality, then the reciprocal of the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical time; if the preset communication status information dimension is negatively correlated with communication quality, then the original evaluation value is determined as the initial evaluation value of the preset communication status information dimension at the historical time.

7. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the energy meter data acquisition method based on dual-mode communication as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the electricity meter data acquisition method based on dual-mode communication as described in any one of claims 1 to 5.

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