A smart electric energy meter data acquisition method and system based on dual-mode communication

Abstract

The application relates to the technical field of smart grids, and discloses a smart electric energy meter data acquisition method and system based on dual-mode communication, which realizes differentiated acquisition frequency and time window configuration by constructing a parameter priority matrix through importance classification and time-sensitive analysis of multi-dimensional electric energy parameters such as voltage and current. The application also introduces an abnormality detection mechanism, dynamically adjusts the acquisition strategy when the parameters appear abnormal fluctuations, and improves the acquisition accuracy and frequency of key data. At the same time, the application optimizes bandwidth allocation and transmission resource scheduling in combination with the real-time state of the communication environment, and ensures the timely transmission of high-priority data. Through the adaptive data acquisition and transmission scheme, the application significantly improves the real-time performance and reliability of smart grid monitoring, and provides strong support for the safe and stable operation of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, and particularly relates to a smart electric energy meter data acquisition method and system based on dual-mode communication. BACKGROUND

[0002] In industrial data processing, as the core infrastructure of modern energy management system, the reliability and efficiency of the data acquisition system of the smart grid directly relate to the safe and stable operation of the power system. As the key sensing node at the end of the power grid, the smart electric energy meter undertakes the real-time monitoring and transmission tasks of massive power consumption data, and the advantages and disadvantages of its data acquisition capability will directly affect the intelligent level and operation efficiency of the entire power grid.

[0003] However, the current mainstream electric energy meter data acquisition scheme generally adopts a unified acquisition mode with a fixed frequency. When facing complex and changeable power consumption environments, the current mainstream electric energy meter data acquisition scheme will obviously lack adaptability. At the same time, this acquisition scheme often ignores the significant differences in importance and time sensitivity of different types of data, resulting in unreasonable allocation of system resources and inability to ensure the timeliness of key data while considering overall acquisition efficiency.

[0004] Therefore, the current mainstream smart grid acquisition scheme needs to solve the timeliness of key data acquisition and consider overall acquisition efficiency. SUMMARY

[0005] The present application provides a smart electric energy meter data acquisition method and system based on dual-mode communication to realize the timeliness of key data acquisition and consider overall acquisition efficiency.

[0006] In a first aspect, to solve the above technical problems, the present application provides a smart electric energy meter data acquisition method based on dual-mode communication, comprising:

[0007] Real-time acquisition of multi-dimensional electric energy parameters of the smart electric energy meter, marking each parameter with a label of high importance level or low-middle importance level, and configuring the time window of each parameter according to the label;

[0008] According to the time window, analyze the historical change trend of each parameter and obtain a time-sensitive score, construct a parameter priority matrix according to the label and the time-sensitive score, and determine a comprehensive priority score, and construct a distribution table of adaptive acquisition frequency according to the comprehensive priority score;

[0009] According to the distribution table, the change range of each parameter is obtained, and it is judged whether there is an abnormal fluctuation state, if yes, the corresponding abnormal characteristic information is obtained, and according to the abnormal characteristic information, the acquisition parameter configuration is obtained; the channel state information under the current dual-mode communication environment is obtained, and according to the channel state information, the communication channel configuration adaptive to the current network condition is obtained.

[0010] According to the acquisition parameter configuration and the communication channel configuration, the data transmission resource is distributed, and the final data acquisition and transmission execution scheme is determined.

[0011] Preferably, the multi-dimensional electric energy parameters of the smart electric energy meter are obtained in real time, and each parameter is marked with a high importance level or a low importance level label, including:

[0012] The multi-dimensional electric energy parameter data of the smart electric energy meter is obtained in real time.

[0013] If the multi-dimensional electric energy parameter data belongs to a preset core parameter, it is marked with a high importance level label, and if it belongs to a preset auxiliary parameter, it is marked with a low importance level label.

[0014] The multi-dimensional electric energy parameters include voltage, current, power, frequency and temperature.

[0015] Preferably, the time window of each parameter is configured according to the label, including:

[0016] If the label is the high importance level, a short time window is set, and if the label is the low importance level, a long time window is set.

[0017] Preferably, the historical change trend of each parameter is analyzed according to the time window, and a time-sensitive score is obtained, including:

[0018] The historical data of each parameter is obtained, the historical data is processed in segments, the change frequency of each parameter in each segment is calculated, and a preliminary change rate value set is obtained.

[0019] According to the preliminary change rate value set, the variance of the change rate value of each parameter is calculated, and the fluctuation characteristic data of each parameter is obtained.

[0020] If the fluctuation characteristic data is higher than a preset fluctuation threshold, the corresponding parameter is marked as a high time-sensitive category, and if it is lower than the preset fluctuation threshold, it is marked as a low time-sensitive category, and a classified parameter sensitivity label set is obtained.

[0021] The parameter sensitivity label set is scored and processed, and the time-sensitive score of each parameter is obtained.

[0022] Preferably, the constructing a parameter priority matrix according to the label and the time sensitivity score and determining a comprehensive priority score comprises:

[0023] constructing a parameter priority matrix according to the time sensitivity score;

[0024] if the label is the high importance level and the time sensitivity score is higher than a first threshold, a first weight is given, if the label is the low importance level or the time sensitivity score is lower than a second threshold, a second weight is given;

[0025] determining the comprehensive priority score according to the first weight, the second weight and the parameter priority matrix.

[0026] Preferably, the constructing an adaptive acquisition frequency distribution table according to the comprehensive priority score result comprises:

[0027] if the comprehensive priority score exceeds a third threshold, a high frequency acquisition mode is assigned, and an acquisition interval is set as a first interval; if the comprehensive priority score is lower than a fourth threshold, a low frequency acquisition mode is assigned, and an acquisition interval is set as a second interval;

[0028] constructing an adaptive acquisition frequency distribution table according to the high frequency acquisition mode and the low frequency acquisition mode.

[0029] Preferably, the acquiring a variation range of each parameter according to the distribution table and judging whether there is an abnormal fluctuation state, if yes, acquiring corresponding abnormal feature information comprises:

[0030] acquiring a current parameter value of the each parameter according to the distribution table, if a deviation of the current parameter value from a historical average value exceeds a dynamic threshold, acquiring abnormal feature information, the abnormal feature information including type, position and fluctuation degree information of a corresponding parameter.

[0031] Preferably, the acquiring channel state information under a current dual-mode communication environment, and obtaining a communication channel configuration adapted to a current network condition according to the channel state information comprises:

[0032] acquiring channel state information under a current dual-mode communication environment, the channel state information including signal strength, bandwidth occupancy and transmission delay;

[0033] judging an optimal communication mode by a channel quality evaluation function Q, and obtaining a communication channel configuration adapted to a current network condition;

[0034] wherein, the channel quality evaluation function is:

[0035]

[0036] Wherein Q is the channel quality score, S represents signal strength, S min is the weakest acceptable signal, S max is the best signal, B represents bandwidth utilization, D represents transmission delay, D max is the maximum tolerable delay, and a, b, and g are weight coefficients.

[0037] Preferably, the allocation of data transmission resources according to the acquisition parameter configuration and the communication channel configuration determines the final data acquisition and transmission execution scheme, which comprises:

[0038] If the parameter data volume of high importance level occupies bandwidth exceeding the bandwidth threshold of the total bandwidth, it is transmitted preferentially, and if the parameter transmission of low importance level causes congestion, the priority of transmission is temporarily reduced or the transmission is delayed, and the final data acquisition and transmission execution scheme is determined.

[0039] In a second aspect, the present application provides an intelligent electric energy meter data acquisition system based on dual-mode communication, which comprises:

[0040] A detection end is configured to acquire multi-dimensional electric energy parameters of an intelligent electric energy meter in real time, mark each parameter as a high importance level or a low importance level, and configure a time window for each parameter according to the label;

[0041] A configuration end is configured to analyze the historical change trend of each parameter according to the time window and obtain a time-sensitive score, construct a parameter priority matrix according to the label and the time-sensitive score, and determine a comprehensive priority score, construct an adaptive acquisition frequency allocation table according to the comprehensive priority score, acquire the change amplitude of each parameter according to the allocation table, and determine whether there is an abnormal fluctuation state, if so, acquire corresponding abnormal feature information, and acquire an acquisition parameter configuration according to the abnormal feature information; acquire channel state information under a current dual-mode communication environment, and obtain a communication channel configuration adapted to the current network condition according to the channel state information;

[0042] A processing end is configured to allocate data transmission resources according to the acquisition parameter configuration and the communication channel configuration, and determine the final data acquisition and transmission execution scheme.

[0043] Compared with the prior art, the present application has the following beneficial effects:

[0044] (1) The present application acquires multi-dimensional electric energy parameters of an intelligent electric energy meter in real time, marks each parameter as a high importance level or a low importance level, and configures a time window for each parameter according to the label, thereby improving data processing efficiency and safety, and ensuring the rapid response capability of the system to key parameters.

[0045] (2) The application analyzes the historical change trend of each parameter according to the time window and obtains a time-sensitive score, constructs a parameter priority matrix according to the label and the time-sensitive score and determines a comprehensive priority score, and constructs a distribution table of adaptive acquisition frequency according to the comprehensive priority score, so that the weight distribution is more scientific, the frequency distribution is more detailed, and the monitoring pertinence is improved.

[0046] (3) The application obtains the change amplitude of each parameter according to the distribution table and judges whether there is an abnormal fluctuation state, if yes, the corresponding abnormal characteristic information is obtained, and the acquisition parameter configuration is obtained according to the abnormal characteristic information; the channel state information in the current dual-mode communication environment is obtained, and the communication channel configuration adapted to the current network condition is obtained according to the channel state information, the differential analysis and recording mode provides a strong guarantee for the stable operation of the industrial power system, and the dynamic optimization mode can guarantee the efficient operation of the communication channel and provide stable support for industrial communication.

[0047] (4) The application allocates data transmission resources according to the acquisition parameter configuration and the communication channel configuration, determines the final data acquisition and transmission execution scheme, and can effectively support the continuous operation of the equipment monitoring system and ensure the efficient transmission of key data. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a kind of based on dual-mode communication intelligent electric energy meter data acquisition method flow chart provided by the embodiment of the application;

[0049] Figure 2 is a kind of based on dual-mode communication intelligent electric energy meter data acquisition system structure schematic diagram provided by the embodiment of the application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0051] Referring to Figure 1 , the first embodiment of the application provides a kind of based on dual-mode communication intelligent electric energy meter data acquisition method flow chart, including the following steps:

[0052] S1, real-time acquisition of multi-dimensional electric energy parameters of intelligent electric energy meter, each parameter is marked as high importance level or low importance level label, and the time window of each parameter is configured according to the label;

[0053] S2, according to the time window, analyze the historical change trend of each parameter and obtain the time-sensitive score, construct a parameter priority matrix according to the label and the time-sensitive score and determine a comprehensive priority score, and construct a distribution table of adaptive acquisition frequency according to the comprehensive priority score;

[0054] S3, according to the distribution table, obtain the change amplitude of each parameter and determine whether there is an abnormal fluctuation state, if yes, obtain the corresponding abnormal feature information, and obtain the acquisition parameter configuration according to the abnormal feature information, obtain the channel state information under the current dual-mode communication environment, and obtain the communication channel configuration adapted to the current network condition according to the channel state information;

[0055] S4, according to the acquisition parameter configuration and the communication channel configuration, allocate data transmission resources, and determine the final data acquisition and transmission execution scheme.

[0056] In step S1, multi-dimensional electric energy parameters of the smart electric energy meter are acquired in real time, each parameter is marked as a label of high importance level or low importance level, and the time window of each parameter is configured according to the label.

[0057] Preferably, the real-time acquisition of multi-dimensional electric energy parameters of the smart electric energy meter, each parameter is marked as a label of high importance level or low importance level, comprising:

[0058] Real-time acquisition of multi-dimensional electric energy parameter data of the smart electric energy meter;

[0059] If the multi-dimensional electric energy parameter data belongs to a preset core parameter, it is marked as a label of high importance level, and if it belongs to a preset auxiliary parameter, it is marked as a label of low importance level;

[0060] Among them, the multi-dimensional electric energy parameters include voltage, current, power, frequency and temperature.

[0061] For example, the electric energy parameter data monitored by the smart electric energy meter in real time includes voltage, current, power, frequency and temperature data. When collecting these data, it can be obtained from the electric energy meter through a pre-designed communication protocol such as Modbus.

[0062] For example, in a certain industrial scene, the electric energy meter collects data every second to obtain an initial data set of voltage 220V, current 10A, power 2.2kW, frequency 50Hz and temperature 40℃. The data acquisition interface classifies and arranges these data to form an initial electric energy parameter set, and the voltage and current are stored in the core parameter group, and the power, frequency and temperature are classified into the auxiliary parameter group. This classification facilitates subsequent processing and management, and improves data processing efficiency.

[0063] In one possible implementation, the data importance evaluation matrix is used for hierarchical processing. The matrix pre-set rules mark voltage and current as core parameters, belonging to the high importance level, because they directly affect power system stability; temperature data is marked as an auxiliary parameter, belonging to the low-medium importance level, because it is mainly used for equipment state monitoring.

[0064] For example, if the voltage exceeds ±10% of the rated value, it may trigger the system protection mechanism, while the temperature rise to 50°C only needs to pay attention to heat dissipation. After classification, a preliminary hierarchical data set is formed, and core parameters are processed first to ensure real-time response of the system.

[0065] Specifically, a label is added to each parameter. A high importance label such as "core_voltage" is attached to voltage data, and a low-medium importance label such as "auxiliary_temperature" is attached to temperature data.

[0066] For example, 220V data is labeled as "core_voltage_high", and 40°C data is labeled as "auxiliary_temperature_low-medium". After labeling, a final hierarchical electric energy data set is formed, which facilitates data retrieval and analysis and improves data traceability.

[0067] It can be understood that the above method has the benefits of improving data processing efficiency and security. Hierarchical processing and labeling enable core parameters to respond first, ensuring power system stability; encrypted storage and backup mechanisms enhance data security, facilitating fault troubleshooting and historical data analysis. These technical effects support the efficient operation of smart electric energy meters in industrial scenarios.

[0068] Preferably, the time window of each parameter is configured according to the label, comprising:

[0069] If the label is the high importance level, a short time window is set, and if the label is the low-medium importance level, a long time window is set.

[0070] In one possible implementation, electric energy parameter data with importance labels is obtained from the hierarchical electric energy data set, and label information is extracted.

[0071] For example, in a certain industrial scenario, the data set generated by the smart electric energy meter contains voltage 220V labeled as "core_voltage_high", current 10A labeled as "core_current_high", and temperature 40°C labeled as "auxiliary_temperature_low-medium". Based on the rule engine, the "core" or "auxiliary" field in the label is identified to extract the importance level. First, scan the data set to parse the "core_voltage_high" label and confirm that the voltage is a core parameter with high importance; similarly, parse "auxiliary_temperature_low-medium" to confirm that the temperature is an auxiliary parameter with low-medium importance. This parsing method ensures quick differentiation of parameter types for subsequent differentiated processing.

[0072] For example, when configuring the time window based on the importance level, core parameters such as voltage and current, which directly affect the stability of the power system, need to be configured with a short time window to capture rapid changes.

[0073] Specifically, the voltage and current are allocated a 1-second time window to ensure real-time monitoring of their fluctuations; while auxiliary parameters such as temperature change more slowly, a 10-second time window is configured to reduce system processing pressure. After the initial time window configuration scheme is generated, time-sensitive analysis is required.

[0074] For example, the frequency of voltage changes within a 1-second window, such as a 0.5V fluctuation per second, indicates that a short window is suitable for capturing its dynamics; temperature changes only 0.2℃ within 10 seconds, verifying the reasonableness of the long window. After optimization through analysis, the voltage window is adjusted to 0.8 seconds, and the temperature window remains at 10 seconds, forming the optimized configuration parameters.

[0075] In step S2, the historical change trend of each parameter is analyzed according to the time window, and a time-sensitive score is obtained, a parameter priority matrix is constructed according to the label and the time-sensitive score, and a comprehensive priority score is determined, and a distribution table of adaptive acquisition frequency is constructed according to the comprehensive priority score.

[0076] Preferably, the analysis of the historical change trend of each parameter according to the time window and the acquisition of the time-sensitive score comprise:

[0077] Obtain the historical data of each parameter, segment the historical data, calculate the change frequency of each parameter in each segment, and obtain a preliminary change rate value set;

[0078] Calculate the variance of the change rate value of each parameter according to the preliminary change rate value set, and obtain the fluctuation characteristic data of each parameter;

[0079] If the fluctuation characteristic data is higher than the preset fluctuation threshold, the corresponding parameter is marked as a high time-sensitive category, and if it is lower than the preset fluctuation threshold, it is marked as a low time-sensitive category, and a classified parameter sensitivity label set is obtained;

[0080] The parameter sensitivity label set is scored to obtain the time-sensitive score of each parameter.

[0081] In the field of smart grids, the time-sensitive score is an index used to quantify the time urgency and the severity of the consequences of delay of tasks, devices, or events in the power grid.

[0082] For example, in the processing of the set of preliminary change rate values, when calculating the variance of each parameter change rate value, attention can be paid to the fluctuation of the voltage in different time periods. Assuming that the change rate values of the voltage in 24 time periods are 0.5, 0.3, 0.7, etc., the mean is 0.5, and the dispersion degree is analyzed to obtain variance data. The variance reflects whether the voltage fluctuation is stable. If the variance is large, it means that the voltage fluctuation is severe and needs to be paid more attention to. This method directly reveals the fluctuation characteristics of the parameters, which is convenient for subsequent classification.

[0083] For example, when classifying the fluctuation characteristic data, if the preset fluctuation threshold is 0.4, the variance of the voltage is 0.6, which is higher than the threshold, and is marked as a high time-sensitive category; while the variance of the power factor is 0.1, which is lower than the threshold, and is marked as a low time-sensitive category. This classification method ensures that system resources are preferentially allocated to parameters with larger fluctuations. The parameter sensitivity label set after classification provides a basis for subsequent scoring, which helps to optimize the monitoring strategy.

[0084] For example, when generating a feature vector data set containing time-sensitive scores, the voltage of the high time-sensitive category can be assigned a score of 8, while the power factor of the low time-sensitive category is assigned a score of 3. Combining the score with the parameter characteristics forms a feature vector, such as the feature vector of the voltage containing the score 8 and the fluctuation frequency data, and the feature vector of the power factor containing the score 3 and the stability characteristic data. This scoring mechanism enables the system to reasonably allocate processing resources according to the importance of the parameters, improving the pertinence of monitoring.

[0085] It can be understood that the above classification and scoring mechanism ensures the rapid response capability of the system to key parameters through differential processing, while avoiding excessive attention to stable parameters. This way has important value in industrial power monitoring and can effectively improve the pertinence and resource utilization efficiency of data processing.

[0086] Preferably, the constructing a parameter priority matrix according to the label and the time-sensitive score and determining a comprehensive priority score comprises:

[0087] constructing a parameter priority matrix according to the time-sensitive score;

[0088] if the label is the high importance level and the time-sensitive score is higher than a first threshold, a first weight is assigned, and if the label is the low importance level or the time-sensitive score is lower than a second threshold, a second weight is assigned;

[0089] determining a comprehensive priority score according to the first weight, the second weight and the parameter priority matrix.

[0090] Assuming that in a power system of a factory, the monitored parameters include voltage, current, and power factor, among which the voltage is rated as high importance level, and the current and power factor are rated as medium-low level. The voltage is classified into the priority category, and the current and power factor are classified into the secondary category. This grouping helps the system to allocate limited monitoring resources to key parameters first, ensuring the stability of core equipment operation.

[0091] For example, when thresholding the time sensitivity score for the classified parameter grouping set, the upper limit of the score can be set to 7.0, and the lower limit to 3.0. Assuming that the time sensitivity score of the voltage is 8.5, which is higher than the upper limit, it is therefore marked as high sensitivity category; while the score of the power factor is 2.5, which is lower than the lower limit, it is therefore marked as low sensitivity category. By processing this process, parameters that need to be focused on can be quickly screened out, ensuring that the system remains highly vigilant to parameters with large fluctuations, while reducing unnecessary resource investment on stable parameters.

[0092] In the field of smart grid, constructing a parameter priority matrix based on time sensitivity score is a decision-making process that combines dynamic time constraints with multi-dimensional parameter evaluation. By embedding the time sensitivity score as the core parameter into the priority matrix and dynamically adjusting the weight, the following can be achieved: high emergency response (such as fault isolation), global optimization of resources (taking into account economy and safety), and adaptive decision-making (automatically adjusting strategies as the environment changes). This method is widely used in fault management, renewable energy scheduling, demand response, and other scenarios, significantly improving the resilience and efficiency of the power grid.

[0093] When generating the priority matrix and determining the weight assignment, the classified and labeled parameter dataset is integrated into a structured matrix. Assuming that the voltage is assigned a first weight, such as 0.6, due to its high sensitivity category and high importance level, while the power factor is assigned a second weight, such as 0.2, due to its low sensitivity category and medium-low importance level. This weight assignment intuitively reflects the degree of influence of parameters on system operation, helping to optimize monitoring strategies and improve resource utilization efficiency.

[0094] Generally, in the field of smart grid, priority matrix (Priority Matrix) and weight assignment (Weight Assignment) are key steps to determine the comprehensive priority score (Comprehensive Priority Score, CPS). After completing these two steps, the comprehensive priority score (CPS) can be calculated by linear weighting method (simple and efficient, most commonly used), TOPSIS (suitable for conflicting indicators), and fuzzy comprehensive evaluation (suitable for fuzzy language description).

[0095] At the same time, the construction of the priority matrix can also be optimized in combination with multi-dimensional data.

[0096] For example, the historical fluctuation frequency of the parameter, the probability of abnormal occurrence, and other information are included in the matrix construction process. If the voltage fluctuation frequency is high in the past 24 hours, the weight value will be further increased. This comprehensive consideration makes the weight allocation more scientific and provides a reliable basis for subsequent monitoring decisions.

[0097] Preferably, the construction of the adaptive acquisition frequency allocation table according to the comprehensive priority score result comprises:

[0098] If the comprehensive priority score exceeds the third threshold value, a high-frequency acquisition mode is allocated, and the acquisition interval is set to the first interval; if the comprehensive priority score is lower than the fourth threshold value, a low-frequency acquisition mode is allocated, and the acquisition interval is set to the second interval.

[0099] The adaptive acquisition frequency allocation table is constructed according to the high-frequency acquisition mode and the low-frequency acquisition mode.

[0100] For example, in the scenario of industrial power monitoring, when obtaining parameter classification data from the comprehensive priority score result, the score value can be analyzed first. Assuming that in the power system of a factory, the monitoring parameters include voltage, current, and power factor, and the preset score threshold upper limit is 8.0 and the lower limit is 4.0. The score value of the voltage is 8.5, which is higher than the upper limit, so it is classified into the high-frequency mode group; the current score is 3.5, which is lower than the lower limit, and is classified into the low-frequency mode group. This classification method helps to differentiate the importance of different parameters.

[0101] In one possible implementation, the voltage parameter in the high-frequency mode group is allocated to be acquired every 5 minutes, and the current parameter in the low-frequency mode group is set to be acquired every 30 minutes. In this way, the key parameters are paid more attention to, while the waste of resources on secondary parameters is avoided.

[0102] For example, when generating the adaptive acquisition frequency allocation table, the classification and frequency matching results are integrated into a structured table.

[0103] Specifically, the table records the mode group, score value, and corresponding acquisition interval of the voltage and current, and also records the mode switching of the parameter in different time periods, such as the voltage may maintain the high-frequency mode during the production peak period due to the continuous increase of the score. This recording method facilitates subsequent viewing and adjustment of the configuration scheme.

[0104] For example, for the acquisition and transmission of the complete configuration scheme, the acquisition interval information is sent to the acquisition module, and the consistency of the configuration is checked.

[0105] Specifically, if the factory's voltage score drops to 7.5 during the night low-load period, which is below the upper limit of the high-frequency threshold, the system will automatically switch it to the medium-frequency mode, with the collection interval adjusted to every 15 minutes. This dynamic adjustment method can optimize the collection strategy according to the actual operating environment and reduce unnecessary resource occupation.

[0106] For example, the implementation of differentiated collection configuration can also be optimized in combination with historical data throughout the entire process from parameter classification to frequency allocation.

[0107] In one possible implementation, the system analyzes the trend of voltage scores in the past week. If it finds that the score value is consistently above the upper limit, it will further shorten the collection interval to every 3 minutes to ensure timely response to potential abnormalities. This trend-based adjustment method enhances the flexibility and reliability of the system.

[0108] For example, for the specific implementation of threshold judgment, multiple thresholds can be set according to business needs. Assuming that in addition to the upper limit of 8.0 and the lower limit of 4.0, an intermediate threshold of 6.0 is also set to identify parameters in the medium-frequency mode group. For example, if the power factor score is 6.5, it can be classified as a medium-frequency mode, with a collection interval of every 20 minutes. This multi-level classification method makes the frequency allocation more detailed and improves the relevance of monitoring.

[0109] In step S3, the change amplitude of each parameter is obtained according to the allocation table, and it is judged whether there is an abnormal fluctuation state. If yes, the corresponding abnormal feature information is obtained, and the collection parameter configuration is obtained according to the abnormal feature information. Channel state information under the current dual-mode communication environment is obtained, and the communication channel configuration adapted to the current network condition is obtained according to the channel state information.

[0110] Preferably, the step of obtaining the change amplitude of each parameter according to the allocation table and judging whether there is an abnormal fluctuation state, if yes, obtaining the corresponding abnormal feature information, comprises:

[0111] According to the allocation table, the current parameter value of each parameter is obtained. If the deviation of the current parameter value from the historical mean value exceeds a dynamic threshold, abnormal feature information is obtained. The dynamic threshold is the sum of the historical mean value and 3 times the standard deviation. The abnormal feature information includes the type, position, and fluctuation degree information of the corresponding parameter.

[0112] For example, in the scenario of industrial power monitoring, when analyzing real-time monitoring data of power parameters, the acquisition and processing of value change information can be explored from multiple angles. For the topic of deviation calculation between value change and historical mean, assuming that in the power system of a factory, the monitoring parameters include voltage and current, and the historical mean is calculated from the data of the past month, the voltage mean is 220 volts, and the standard deviation is 5 volts. In real-time monitoring, if the voltage value is 230 volts at a certain time, the deviation is 10 volts, and the dynamic threshold range constructed in combination with the standard deviation is 215 volts to 225 volts. Obviously, 230 volts exceeds the upper limit of the threshold, so it is determined to be an abnormal fluctuation state. This method can quickly identify potential problems and ensure the timeliness of monitoring.

[0113] For example, in the process of obtaining specific information of abnormal parameters for preliminary abnormality judgment data, the type characteristics of abnormal parameters are extracted, such as voltage belonging to the core parameter category, and the location of abnormality occurrence is determined, such as abnormal voltage coming from the main distribution cabinet A area of the factory. Such detailed record data provides clear direction for subsequent analysis, which helps to quickly locate the problem source.

[0114] For example, when analyzing the fluctuation degree of abnormal parameters, it is found that the voltage fluctuates from 220 volts to 230 volts, with an amplitude of 4.5%. By associating this fluctuation degree with type and location information, a complete abnormal feature set is formed, such as "voltage-main distribution cabinet A area-fluctuation amplitude 4.5%". This association method facilitates comprehensive understanding of the overall picture of the abnormality, providing data support for subsequent processing.

[0115] For example, for the step of generating state change events based on abnormal feature sets, the above information is integrated into a structured event record, such as "voltage abnormal fluctuation event: main distribution cabinet A area, fluctuation amplitude 4.5%, time point 10:30 am". Such recording method is convenient for storage and query, and provides convenience for subsequent fault tracing and optimization decision-making.

[0116] Preferably, the channel state information in the current dual-mode communication environment is obtained, and the communication channel configuration adapted to the current network condition is obtained according to the channel state information, comprising:

[0117] Obtain the channel state information in the current dual-mode communication environment, and the channel state information includes signal strength, bandwidth occupancy and transmission delay;

[0118] Determine the optimal communication mode by a channel quality evaluation function to obtain a communication channel configuration adapted to the current network condition;

[0119] The channel quality evaluation function is:

[0120]

[0121] wherein Q is a channel quality score, S represents signal strength, S min is the weakest acceptable signal, S max is the best signal, B represents bandwidth utilization, D represents transmission delay, D max is the maximum tolerable delay, and a, b, g are weight coefficients.

[0122] For example, in a network monitoring scenario in a dual-mode communication environment, real-time monitoring of network conditions is performed to obtain key data. Signal strength, bandwidth occupancy, and transmission delay are core indicators that directly reflect the operating state of the communication channel. Suppose in an industrial communication network, it is found that the signal strength of a certain channel is low, only 70% of the normal value, the bandwidth occupancy rate is as high as 90%, and the transmission delay reaches 200 milliseconds. In this case, these data are classified and organized to form a first channel state record, providing a basis for subsequent evaluation.

[0123] For example, in processing the first channel state record, a preset weight coefficient is combined to score the channel quality. Signal strength may be given a higher weight because it directly affects communication stability; bandwidth occupancy and transmission delay are allocated different proportions of weight according to the specific scene. Suppose in an industrial device remote control scenario, the signal strength weight is higher, and if the scoring result shows that the channel quality is below the preset threshold, such as only 40 points out of a full score of 100 points, the system will determine that the current channel is not suitable for continued use and needs to be adjusted in mode.

[0124] For example, suppose the current mode is a low-bandwidth high-stability mode, but the network condition shows that the delay is too high, and it may be recommended to switch to a high-bandwidth low-delay mode to form a first communication mode scheme. This scheme will give priority to delay-sensitive industrial data transmission requirements to ensure smooth communication.

[0125] For example, in implementing the first communication mode scheme, dynamic optimization of channel configuration is performed. Signal strength and transmission delay are the key calibration objects, and if the signal strength is insufficient, the transmission power or antenna direction may be adjusted; if the delay is too high, the data packet transmission path may be optimized. In an industrial scenario, suppose the delay of a certain channel is reduced from 200 milliseconds to 50 milliseconds, and the system determines that it meets the current requirements to form an optimized communication channel configuration. This adjustment can significantly improve the real-time performance of data transmission.

[0126] For example, in a specific scenario, suppose a device monitoring system in a factory relies on dual-mode communication, and if the channel quality decreases, it may cause interruption of device state data transmission. Through the above-mentioned cooperative work, the system can timely switch to a more optimal mode and adjust the relevant parameters to ensure uninterrupted communication. This mechanism is crucial for the continuity of industrial production and can effectively avoid equipment downtime or data loss caused by communication problems.

[0127] For example, from another side, for signal strength calibration, if it is monitored that the signal in a certain area is consistently weak, it can be recommended to increase the relay device to enhance the coverage. In this way, not only the current problem can be solved, but also reference can be provided for similar scenarios in the future, reflecting the flexibility and foresight of system configuration. Overall, this dynamic optimization method can ensure the efficient operation of the communication channel and provide stable support for industrial communication.

[0128] In step S4, data transmission resources are allocated according to the acquisition parameter configuration and the communication channel configuration to determine the final data acquisition and transmission execution scheme.

[0129] Preferably, the allocation of data transmission resources according to the acquisition parameter configuration and the communication channel configuration to determine the final data acquisition and transmission execution scheme comprises:

[0130] If the bandwidth occupied by the parameter data of high importance level exceeds the bandwidth threshold of the total bandwidth, it is transmitted preferentially, and if the transmission of the parameter of low importance level causes congestion, the priority of transmission is temporarily reduced or the data is sent with delay to determine the final data acquisition and transmission execution scheme.

[0131] For example, in the bandwidth allocation scenario of an industrial communication network, real-time data acquisition is a core link. By periodically scanning the communication channel, the current traffic distribution is obtained, including the traffic of high importance parameters such as device control instructions and the traffic of low importance parameters such as log data. Assuming that in the device monitoring system of a factory, the traffic proportion of high importance parameters reaches 70%, and the traffic proportion of low importance parameters is 30%. If the preset threshold is that the traffic proportion of high importance parameters does not exceed 60%, it indicates that the high importance parameters are overloaded. This monitoring mechanism can timely find traffic anomalies and provide a basis for subsequent optimization. Real-time monitoring not only reflects the channel state, but also provides data support for dynamic adjustment.

[0132] In one possible implementation, the overloaded high importance parameters are optimized in path. For high importance parameters, an independent low-delay path is allocated to ensure fast transmission of instruction data.

[0133] For example, the system detects a surge in the traffic of device control instructions, which may be caused by a sudden task of the production line. These instruction data are allocated to a backup high-speed channel, and the transmission priority of low importance parameters such as log data is downgraded to a secondary channel. This adjustment can effectively ensure the real-time performance of critical data and avoid transmission interruption caused by bandwidth competition.

[0134] Specifically, the secondary channel plays a key role in the transmission of low importance parameters. If the secondary channel is congested, for example, the transmission rate of log data is reduced to 50% of the normal value, a delay sending mechanism is started to queue the data packets in batches.

[0135] For example, the system can be set to send only 100 low-importance data packets per second, and the remaining data packets wait in the queue for the next cycle to be sent. This mechanism can effectively alleviate channel pressure and ensure the stability of overall transmission. The implementation of delayed transmission can also avoid data loss due to congestion and improve the transmission efficiency of low-importance data.

[0136] For example, when scheduling the transmission tasks of high and low importance parameters, the channel resources and task requirements are considered comprehensively. Assuming that the control instructions of high importance parameters need to be transmitted within 10 milliseconds in a certain period, and the log data of low importance parameters allows a 50-millisecond delay. The control instructions are preferentially scheduled to the high-bandwidth channel, and the log data is allocated to the secondary channel in the idle period. The generated transmission execution scheme can ensure that critical tasks are completed first, while taking into account the transmission requirements of non-critical tasks. The advantage of such unified scheduling is to optimize resource utilization and improve the overall efficiency of the communication system.

[0137] In one possible implementation, predictive scheduling is also performed according to historical data.

[0138] For example, the system analyzes the traffic pattern of the past week and finds that the traffic of high importance parameters increases by 20% during the daily peak period. More bandwidth resources are reserved in advance for the peak period to ensure that critical data transmission is not affected. This forward-looking scheduling can significantly improve the adaptability of the system and ensure the continuous stability of communication.

[0139] It can be understood that the above-mentioned cooperation forms a complete bandwidth allocation optimization process. From traffic monitoring to priority adjustment, to congestion control and task scheduling, each link is closely connected to jointly ensure the efficient operation of the communication channel. This mechanism is particularly important in industrial scenarios and can effectively support the continuous operation of the device monitoring system to ensure efficient transmission of critical data.

[0140] In summary, the present application discloses an intelligent electric energy meter data acquisition method based on dual-mode communication. By performing importance classification and time-sensitive analysis on multi-dimensional electric energy parameters such as voltage and current, a parameter priority matrix is constructed to realize differentiated acquisition frequency and time window configuration. The present application also introduces an abnormality detection mechanism, which dynamically adjusts the acquisition strategy when the parameters exhibit abnormal fluctuations, thereby improving the acquisition accuracy and frequency of critical data. At the same time, the present application optimizes bandwidth allocation and transmission resource scheduling in combination with the real-time state of the communication environment, ensuring the timely transmission of high-priority data. Through this adaptive data acquisition and transmission scheme, the present application significantly improves the real-time performance and reliability of smart grid monitoring, providing strong support for the safe and stable operation of the power system.

[0141] Reference Figure 2The embodiment of the present application provides a kind of based on dual-mode communication's intelligent electric energy meter data acquisition system structure diagram, comprising:

[0142] Detection end, for real-time acquisition of the multidimensional electric energy parameter of intelligent electric energy meter, each parameter is marked as high importance level or low importance level label, according to the label configuration each parameter time window;

[0143] Configuration end, for according to the time window analysis each parameter historical change trend and obtains time-sensitive score, according to the label and the time-sensitive score constructs parameter priority matrix and determines comprehensive priority score, according to the comprehensive priority score result constructs adaptive acquisition frequency distribution table;According to the distribution table, the change amplitude of each parameter is obtained, and whether there is abnormal fluctuation state is judged, if yes, corresponding abnormal feature information is obtained, according to the abnormal feature information acquisition parameter configuration;Obtain the channel state information under current dual-mode communication environment, according to the channel state information obtains the communication channel configuration adapted to current network condition;

[0144] Processing end, for according to the acquisition parameter configuration and the communication channel configuration distribution data transmission resource, determines the final data acquisition and transmission execution scheme.

[0145] It should be noted that the embodiment of the present application provides a kind of based on dual-mode communication's intelligent electric energy meter data acquisition system for executing all process steps of the above-mentioned embodiment of a kind of based on dual-mode communication's intelligent electric energy meter data acquisition method, the working principle and beneficial effects of the two are one-to-one correspondence, thus no longer tedious.

[0146] The embodiment of the present application further provides a kind of terminal equipment. The terminal equipment includes: processor, memory and computer program stored in the memory and executable on the processor. The processor executes the computer program to realize the steps in each based on dual-mode communication's intelligent electric energy meter data acquisition method embodiment described above, for example Figure 1 The step S11 shown. Alternatively, the processor executes the computer program to realize the functions of each module / unit in each system embodiment described above.

[0147] Illustratively, the computer program can be divided into one or more modules / units, the one or more modules / units are stored in the memory, and are executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the terminal equipment.

[0148] The terminal device can be a desktop computer, a notebook computer, a palm computer, a smart tablet, and the like. The terminal device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the terminal device, and do not constitute a limitation on the terminal device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus, and the like.

[0149] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0150] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, and the like), and the like; and the data storage area can store data created according to use of the terminal device (such as audio data, a phone book, and the like), and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0151] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or system, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the computer readable medium can include or exclude contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0152] It should be noted that the above-described system embodiments are only illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiments provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0153] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A data acquisition method for smart energy meters based on dual-mode communication, characterized in that, include: The system acquires multi-dimensional energy parameters from smart meters in real time, labels each parameter as either high or medium-low importance, and configures the time window for each parameter based on the labels. Analyze the historical change trends of each parameter according to the time window and obtain the time sensitivity score. Construct a parameter priority matrix according to the label and the time sensitivity score and determine the comprehensive priority score. Construct an adaptive collection frequency allocation table according to the comprehensive priority score. The change range of each parameter is obtained according to the allocation table, and it is determined whether there is an abnormal fluctuation state. If so, the corresponding abnormal feature information is obtained, and the collection parameter configuration is obtained according to the abnormal feature information. The channel state information under the current dual-mode communication environment is obtained, and the communication channel configuration adapted to the current network conditions is obtained according to the channel state information. Based on the acquisition parameter configuration and the communication channel configuration, data transmission resources are allocated to determine the final data acquisition and transmission execution plan.

2. The smart energy meter data acquisition method based on dual-mode communication according to claim 1, characterized in that, The real-time acquisition of multi-dimensional energy parameters from smart meters, and the labeling of each parameter as either high or medium-low importance, includes: Real-time acquisition of multi-dimensional energy parameter data from smart meters; If the multi-dimensional electrical energy parameter data belongs to the preset core parameters, it is marked with a high importance level label; if it belongs to the preset auxiliary parameters, it is marked with a medium to low importance level label. The multi-dimensional electrical energy parameters include voltage, current, power, frequency, and temperature.

3. The smart energy meter data acquisition method based on dual-mode communication according to claim 1, characterized in that, The time window for configuring each parameter according to the label includes: If the label is of high importance, a short time window is set; if the label is of medium to low importance, a long time window is set.

4. The smart energy meter data acquisition method based on dual-mode communication according to claim 1, characterized in that, The step of analyzing the historical trends of each parameter based on the time window and obtaining a timeliness sensitivity score includes: Historical data of each parameter is obtained, the historical data is segmented, the frequency of change of each parameter in each segment is calculated, and a preliminary set of rate of change values ​​is obtained. The variance of the rate of change of each parameter is calculated based on the preliminary set of rate of change values ​​to obtain the fluctuation characteristic data of each parameter; If the fluctuation feature data is higher than the preset fluctuation threshold, the corresponding parameter is marked as a high time sensitivity category; if it is lower than the preset fluctuation threshold, it is marked as a low time sensitivity category, thus obtaining a set of parameter sensitivity labels after classification. The parameter sensitivity label set is scored to obtain the time sensitivity score of each parameter.

5. The smart energy meter data acquisition method based on dual-mode communication according to claim 1, characterized in that, The step of constructing a parameter priority matrix and determining a comprehensive priority score based on the label and the timeliness sensitivity score includes: Construct the parameter priority matrix based on the timeliness sensitivity score; If the label is of the high importance level and the timeliness sensitivity score is higher than the first threshold, a first weight is assigned; if the label is of the medium-low importance level or the timeliness sensitivity score is lower than the second threshold, a second weight is assigned. The comprehensive priority score is determined based on the first weight, the second weight, and the parameter priority matrix.

6. The smart energy meter data acquisition method based on dual-mode communication according to claim 1, characterized in that, The step of constructing an adaptive acquisition frequency allocation table based on the comprehensive priority scoring results includes: If the overall priority score exceeds the third threshold, a high-frequency acquisition mode is assigned, and the acquisition interval is set to the first interval; if the overall priority score is lower than the fourth threshold, a low-frequency acquisition mode is assigned, and the acquisition interval is set to the second interval; wherein, the first interval is smaller than the second interval. An adaptive acquisition frequency allocation table is constructed based on the high-frequency acquisition mode and the low-frequency acquisition mode.

7. The data acquisition method for smart energy meters based on dual-mode communication according to any one of claims 1-6, characterized in that, The step involves obtaining the variation range of each parameter according to the allocation table and determining whether there are abnormal fluctuations. If so, the corresponding abnormal feature information is obtained, including: The current parameter value of each parameter is obtained according to the allocation table. If the deviation between the current parameter value and the historical average exceeds the dynamic threshold, abnormal feature information is obtained. The abnormal feature information includes the type, position and fluctuation degree information of the corresponding parameter.

8. The data acquisition method for smart energy meters based on dual-mode communication according to any one of claims 1-6, characterized in that, The step of obtaining channel state information under the current dual-mode communication environment and obtaining a communication channel configuration adapted to the current network conditions based on the channel state information includes: Obtain channel state information under the current dual-mode communication environment, the channel state information including signal strength, bandwidth utilization, and transmission delay; The optimal communication mode is determined by the channel quality evaluation function, and a communication channel configuration that is suitable for the current network conditions is obtained. The channel quality evaluation function is as follows: in It is a comprehensive score of channel quality. Indicates signal strength. For the weakest acceptable signal, For the best signal, Indicates bandwidth utilization. Indicates transmission delay. For maximum tolerable delay, , , These are the weighting coefficients.

9. The data acquisition method for smart energy meters based on dual-mode communication according to any one of claims 1-6, characterized in that, The step of allocating data transmission resources based on the acquisition parameter configuration and the communication channel configuration, and determining the final data acquisition and transmission execution plan, includes: If the amount of data for parameters with high importance exceeds the bandwidth threshold of the total bandwidth, they will be transmitted first. If congestion occurs during the transmission of parameters with low importance, the transmission priority will be temporarily reduced or the transmission will be delayed. The final data collection and transmission execution plan will then be determined.

10. A smart energy meter data acquisition system based on dual-mode communication, characterized in that, include: The detection end is used to acquire multi-dimensional energy parameters of the smart energy meter in real time, and to label each parameter as either high importance or medium-low importance. The time window for each parameter is configured according to the label. The configuration end is used to analyze the historical change trends of each parameter according to the time window and obtain the timeliness sensitivity score; construct a parameter priority matrix and determine a comprehensive priority score based on the tag and the timeliness sensitivity score; construct an adaptive acquisition frequency allocation table based on the comprehensive priority score result; obtain the change amplitude of each parameter according to the allocation table and determine whether there is an abnormal fluctuation state; if so, obtain the corresponding abnormal feature information; obtain the acquisition parameter configuration based on the abnormal feature information; obtain the channel state information under the current dual-mode communication environment; and obtain the communication channel configuration adapted to the current network conditions based on the channel state information. The processing end is used to allocate data transmission resources according to the acquisition parameter configuration and the communication channel configuration, and to determine the final data acquisition and transmission execution plan.

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