Joint processing method of low power consumption control and data compression for multi-mode communication of smart meters

Abstract

The present invention relates to the technical field of electric meter data transmission, and in particular to a method for combining low-power control and data compression for multi-mode communication in smart electric meters. The method comprises the following steps: obtaining operating parameters; if the temperature of the electric meter is greater than a temperature threshold, calculating the heat dissipation coefficient of the electric meter based on the heat dissipation parameters; determining the maximum operating power of the electric meter based on the heat dissipation coefficient; determining whether the electric meter is in an abnormal state based on the operating parameters; if the electric meter is operating normally, selecting LPWAN technology for data transmission and selecting a data compression method based on the data change cycle; in response to abnormal operation of the electric meter, adjusting the transmission frequency based on the amount of key data accumulated; after the transmission frequency adjustment is completed, determining the data compression and communication mode based on the maximum operating power. By calculating the heat dissipation coefficient and determining the maximum operating power based on the heat dissipation coefficient, the present invention allows the electric meter to operate within a safe power range, thereby increasing the stability of the electric meter operation.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter data transmission, and in particular to a method for jointly processing low-power consumption control and data compression of multi-mode communication of an intelligent electric meter. Background Art

[0002] With the rapid development of network communication technology, traditional electricity meters have been generally upgraded to smart meters. As the core terminal equipment of the smart grid, the operational stability of electricity meters directly affects the reliability and energy efficiency management level of the power system.

[0003] Patent application number: CN201710230240.4 discloses a smart meter data compression method and system, wherein the method includes: each time the smart meter collects power load data, LZ encoding the power load data collected by the smart meter at that time; storing the LZ-encoded power load data in a temporary database through the smart grid communication channel; reading the power load data from the temporary database once every preset second time period, and the read power load data is the power load data stored in the temporary database within the second time period before the corresponding reading time; LZ decoding the read power load data, SAX compression of the LZ-decoded power load data, and storing the SAX-compressed power load data in a data center. It can be seen that the smart meter data compression method and system only focus on the data compression part during the data compression process. During the data compression process, the temperature of the meter may continue to rise due to the increase in the power required for data compression or other reasons, thereby causing the meter to operate in an unstable state. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for jointly processing low-power control and data compression of multi-mode communication of smart meters, so as to solve the problem that the existing technology only focuses on the data compression part. During the data compression process, the temperature of the meter may continue to rise due to the increase in the power required for data compression or other reasons, thereby causing the meter to have an unstable operating state.

[0005] The present invention provides a method for jointly processing low-power consumption control and data compression for multi-mode communication of a smart meter, comprising:

[0006] Periodically obtain the operating parameters and communication delay of the meter during operation, and select the communication method with the lowest communication delay as the wireless transmission path;

[0007] In response to the temperature of the electric meter being greater than a temperature threshold, calculating a comprehensive heat dissipation coefficient of the electric meter according to the heat dissipation parameter;

[0008] determining the maximum operating power of the electric meter according to the comprehensive heat dissipation coefficient;

[0009] determining whether the state of the electric meter is abnormal based on the operating parameters, and in response to the electric meter operating normally, selecting LPWAN technology for data transmission and selecting a data compression method based on a data change cycle;

[0010] In response to the abnormal operation of the electric meter, adjusting the transmission frequency according to the accumulation amount of key data, and after the transmission frequency adjustment is completed, determining the data compression and communication mode according to the maximum operating power;

[0011] The operating parameters include: current value, voltage value and temperature value during the operation of the meter.

[0012] As an optimal technical solution for the combined processing method of low power consumption control and data compression for multi-mode communication of smart meters, the current comprehensive heat dissipation coefficient of the meter is calculated based on the comprehensive heat dissipation parameters, wherein:

[0013] Obtain cooling parameters and operating parameters;

[0014] Inputting the operating parameters and the heat dissipation parameters into a heat dissipation model to obtain a comprehensive heat dissipation coefficient;

[0015] The heat dissipation model is built based on the relationship between the operating parameters, the heat dissipation parameters and the comprehensive heat dissipation coefficient. The operating parameters and the heat dissipation parameters are input, and the comprehensive heat dissipation coefficient is output.

[0016] The heat dissipation parameters include: effective heat dissipation area, external environment temperature, and ambient wind speed.

[0017] As an optimal technical solution for the combined processing method of low power consumption control and data compression for multi-mode communication of smart meters, the maximum operating power of the meter is determined according to the comprehensive heat dissipation coefficient, and the maximum operating power is set. , where: h is the comprehensive heat dissipation coefficient, A is the effective heat dissipation area, T surface is the surface temperature of the meter casing, T env is the external ambient temperature.

[0018] As a preferred technical solution for the method for jointly processing low-power consumption control and data compression for multi-mode communication of a smart meter, the determination of whether the meter state is abnormal based on the operating parameters is performed, and the meter state is determined to be abnormal if any of the following conditions is met:

[0019] The temperature is greater than the temperature threshold and the duration exceeds the preset duration;

[0020] The voltage or current exceeds the corresponding preset value;

[0021] The number of key data queues accumulated is greater than the preset number.

[0022] As a preferred technical solution for the combined processing method of low-power consumption control and data compression for multi-mode communication of smart meters, the method of selecting a data compression method according to the data change cycle, acquiring various types of data and analyzing the data change cycle, and selecting a corresponding data compression method according to the data change cycle includes:

[0023] In response to a data change period being greater than a period threshold, compressing the data using a lossless compression algorithm;

[0024] In response to the data variation period being less than or equal to the period threshold, a lossy compression algorithm is used to perform data compression.

[0025] As an optimal technical solution for the joint processing method of low-power control and data compression of multi-mode communication of smart meters, in response to abnormal operation of the meter, the accumulation of key data is obtained and the transmission frequency is increased according to the accumulation of key data. After the transmission power is increased, the current operating power is obtained, and the data compression method is selected according to the current operating power and the maximum operating power.

[0026] As an optimal technical solution for the joint processing method of low power consumption control and data compression of multi-mode communication of smart meters, the transmission frequency is increased according to the accumulation amount of key data, and the increase in the transmission frequency is positively correlated with the accumulation amount of key data.

[0027] As an optimal technical solution for the joint processing method of low-power control and data compression of multi-mode communication of smart meters, in response to the completion of data transmission and compression mode adjustment, the temperature is monitored in real time. In response to the continued rise in temperature, historical data and real-time monitoring data are obtained, and an adaptive correction algorithm is used to correct the comprehensive heat dissipation coefficient.

[0028] As an optimal technical solution for the joint processing method of low-power control and data compression for multi-mode communication of smart meters, the comprehensive heat dissipation coefficient of the meter is calculated based on the heat dissipation parameters. If data anomalies occur in the process of obtaining the heat dissipation parameters and operating parameters (such as data mutations caused by sensor failure), the data of the spare sensor is used for calculation, or historical data is used for interpolation or fitting to estimate the current heat dissipation parameters and operating parameters.

[0029] Compared with the prior art, the beneficial effects of the present invention are that, on the one hand, the present invention detects the temperature. When the temperature of the electric meter exceeds the threshold, it indicates that the electric meter may be in a state where the temperature is higher than the threshold due to heat accumulation or the power exceeds the standard, and the actual situation of the electric meter is judged according to the measured parameters to avoid tripping or damage of the electric meter. The present invention calculates the comprehensive heat dissipation coefficient and determines the maximum operating power according to the comprehensive heat dissipation coefficient, so that the electric meter can operate within a safe power range and avoid malfunctions caused by excessive power; on the other hand, the present invention determines the state of the electric meter by detecting the operating parameters and comparing them with the preset standards. Under normal circumstances, a low-power wide area network is used to transmit data and compress it on demand, which can reduce energy consumption and reduce interference; in abnormal circumstances, the transmission frequency is adjusted, and the data compression and communication mode are determined, which can quickly handle abnormalities and ensure timely and accurate transmission of data, thereby increasing the stability of the electric meter operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flowchart of the steps of the joint processing method of low power consumption control and data compression for multi-mode communication of a smart meter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0032] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0033] See also Figure 1As shown, it is a flowchart of the steps of the method for jointly processing low-power consumption control and data compression of multi-mode communication of a smart meter according to an embodiment of the present invention, including:

[0034] Step S1, periodically obtaining operating parameters and communication delays during the operation of the electric meter, and selecting the communication mode with the lowest communication delay as the wireless transmission path;

[0035] Step S2, in response to the temperature of the electric meter being greater than a temperature threshold, calculating a comprehensive heat dissipation coefficient of the electric meter according to the heat dissipation parameters;

[0036] Step S3, determining the maximum operating power of the electric meter according to the comprehensive heat dissipation coefficient;

[0037] Step S4: Determine whether the meter status is abnormal based on the operating parameters. If the meter is operating normally, select LPWAN technology for data transmission and select a data compression method based on the data change cycle. If the meter is operating abnormally, adjust the transmission frequency based on the amount of key data accumulated. After the transmission frequency is adjusted, determine the data compression and communication mode based on the maximum operating power.

[0038] The operating parameters include: the current value, voltage value and temperature value during the operation of the meter.

[0039] In detail, low-power wide area network (LPWAN) technologies include: LoRa and NB-IoT.

[0040] In implementation, the data change period is the time interval between two consecutive changes in the amount of data collected by the smart meter that are greater than or equal to the fluctuation threshold. The fluctuation threshold is the minimum peak value in the historical data. LPWAN technology is used for data transmission, and a data compression method is selected based on the data change period. The time required for each data compression method is determined, and the compression method with the highest priority and a compression period shorter than the data fluctuation period is prioritized. The data compression priority, from high to low, is as follows: lossless compression (such as Huffman coding or LZZW algorithm), lossy compression (such as scalar quantization or linear approximation), and difference compression.

[0041] Furthermore, key data is a set of abnormal data that needs to be processed first, including:

[0042] Safety alarm: overvoltage, undervoltage, overcurrent;

[0043] Equipment abnormalities: sudden temperature change, measurement error exceeding the limit;

[0044] Safety events: cover opening alarm, magnetic field interference alarm.

[0045] In practice, the critical data accumulation amount is: the critical data amount that has been in the queue for more than 5 minutes, or the critical data amount that exceeds 30% of the storage buffer (the buffer is configured to be 64KB by default).

[0046] Specifically, on the one hand, when the temperature of the electric meter exceeds the threshold, it indicates that the electric meter may be in a state where heat accumulation causes the temperature to be higher than the threshold or power exceeds the standard, causing the temperature to be higher than the threshold. In this state, if the data transmission method and compression method are not changed, tripping or damage will occur. The present invention calculates the comprehensive heat dissipation coefficient and determines the maximum operating power based on the comprehensive heat dissipation coefficient, so that the electric meter can operate within a safe power range and avoid malfunctions caused by excessive power; on the other hand, the state of the electric meter is judged according to the operating parameters. Under normal circumstances, low-power wide area network is used to transmit data and compressed on demand, which can reduce energy consumption and reduce interference; in abnormal circumstances, the transmission frequency is adjusted, and the data compression and communication mode are determined, which can quickly handle abnormalities and ensure timely and accurate transmission of data, thereby increasing the stability of the electric meter operation.

[0047] Furthermore, the comprehensive heat dissipation coefficient of the electric meter is calculated based on the heat dissipation parameters, where:

[0048] Obtain cooling parameters and operating parameters;

[0049] Input the operating parameters and heat dissipation parameters into the heat dissipation model to obtain the comprehensive heat dissipation coefficient;

[0050] The heat dissipation model is built based on the relationship between operating parameters, heat dissipation parameters, and comprehensive heat dissipation coefficients. The operating parameters and heat dissipation parameters are input, and the comprehensive heat dissipation coefficient is output.

[0051] Heat dissipation parameters include: effective heat dissipation area, external ambient temperature, and ambient wind speed.

[0052] In detail, heat dissipation is mainly carried out through three methods: convection, radiation, and conduction. Convective heat dissipation is related to factors such as the effective heat dissipation area, the external ambient temperature, and the ambient wind speed; radiative heat dissipation is related to the temperature of the meter surface; and conductive heat dissipation is related to factors such as the thermal conductivity of the internal material of the meter. It is understandable that the above parameters are all directly measurable during operation or the factory parameters are fixed. By inputting the measured values ​​of the parameters into the corresponding physical formulas, the heat dissipated by these three heat dissipation methods and the comprehensive heat dissipation coefficient can be calculated. The corresponding physical formulas are all existing technologies. Therefore, it is feasible to directly obtain the comprehensive heat dissipation coefficient by establishing a model, and the model can be directly implanted in the CPU at the initial stage of the meter operation or at the factory, without the need to collect actual data for construction later. The construction of the model is an existing technology and will not be described in detail here.

[0053] Specifically, by establishing a heat dissipation model, an accurate comprehensive heat dissipation coefficient can be obtained, which can more accurately determine the maximum operating power of the meter, making the control of the meter's operating power more reasonable, thereby further increasing the stability of the meter's operation.

[0054] Furthermore, the maximum operating power of the meter is determined based on the comprehensive heat dissipation coefficient, and the maximum operating power is set. , where: h is the comprehensive heat dissipation coefficient, unit: W / (m²·°C), A is the effective heat dissipation area, unit: m², T surface is the surface temperature of the meter casing, unit: °C, T env is the external environment temperature, unit: °C.

[0055] Specifically, the effective heat dissipation area A is determined by the design of the meter housing structure (such as the area of ​​the heat dissipation fins). It is understandable that the effective heat dissipation area can be directly measured and input into the model; it is the external ambient temperature T env Real-time acquisition is achieved through an integrated temperature sensor. The ambient wind speed v is estimated indirectly using a MEMS wind speed sensor or based on the fan speed. The comprehensive heat dissipation coefficient h can also be determined by direct detection or by integrating the thermal conductivity of the material (e.g., aluminum alloy housing k=205) and the missivity of the surface coating (e.g., heat dissipation coating ϵ=0.9).

[0056] It is understandable that a reasonable maximum operating power can prevent the meter from becoming unstable or damaged due to continued temperature increase, such as component overheating and damage, data transmission errors, etc., thereby further increasing the stability of the meter's operation.

[0057] Specifically, the meter status is determined to be abnormal based on the operating parameters. The meter status is determined to be abnormal if any of the following conditions is met:

[0058] The temperature is greater than the temperature threshold and the duration exceeds the preset duration;

[0059] The voltage is greater than the preset voltage or the current is greater than the preset current;

[0060] The number of key data queues accumulated is greater than the preset number.

[0061] Furthermore, the values ​​of the preset duration, preset voltage, preset current and preset number are determined based on actual conditions such as usage specifications and real-time requirements of data. Preferably, the preset time is 5 minutes, the preset voltage is 120% of the rated voltage allowed by the meter, the preset current is 120% of the rated current allowed by the meter, and the preset number is 10.

[0062] Specifically, data compression methods are selected based on the data change cycle. Various types of data are acquired and the data change cycle is analyzed. The corresponding data compression methods are selected based on the data change cycle, including:

[0063] In response to a data change period being greater than a period threshold, compressing the data using a lossless compression algorithm (such as Huffman coding or LZ77 algorithm);

[0064] In response to the data variation period being less than or equal to the period threshold, a lossy compression algorithm (such as discrete cosine transform (DCT) or wavelet transform) is used to perform data compression.

[0065] In detail, by selecting appropriate data compression methods for different types of data according to the data change cycle, lossless compression is used to ensure data accuracy when the data change cycle is large, and lossy compression is used to save resources when the data change cycle is small, so that the electricity meter can operate efficiently in a low-power state, reducing unstable factors caused by insufficient or wasted resources, thereby further increasing the stability of the electricity meter operation.

[0066] Furthermore, in response to an abnormal operation of the meter, the accumulated amount of key data is obtained and the transmission frequency is increased accordingly. After the transmission power is increased, the current operating power is obtained. A data compression method is selected based on the current operating power and the maximum operating power, so that after the data compression method is selected, the current operating power of the meter remains less than or equal to the maximum operating power. In practice, the operating power is the meter's own operating power, calculated using current and voltage values ​​obtained by built-in voltage and current detectors.

[0067] Specifically, when the electricity meter is working abnormally, increasing the transmission frequency according to the amount of accumulated key data can speed up data transmission and reduce data backlog, thereby ensuring the timely sending of key data. After the transmission power is increased, the data compression method is selected according to the current operating power and the maximum operating power. While ensuring data transmission, the electricity meter can operate within a reasonable power range, thereby avoiding system crashes caused by data backlogs or component damage caused by unreasonable power, thereby further increasing the stability of the electricity meter operation.

[0068] Specifically, the transmission frequency is increased according to the amount of key data accumulation. The increase in the transmission frequency is positively correlated with the amount of key data accumulation. An embodiment of the present invention provides a method for adjusting the transmission frequency. The difference between the amount of data accumulation and the preset amount is recorded as the standard deviation value. The corresponding transmission frequency adjustment coefficient is determined according to the standard deviation value, where:

[0069] If the standard deviation value is greater than the preset standard deviation value, the transmission frequency is adjusted to the corresponding value using the first adjustment coefficient;

[0070] If the standard deviation value is less than or equal to the preset standard deviation value, the transmission frequency is adjusted to a corresponding value using the second adjustment coefficient;

[0071] It can be understood that the values ​​of the first adjustment coefficient and the second adjustment coefficient are limited by the actual operation of the meter and related components such as data transmission. They will not be infinite, but are a finite range that can be obtained. The specific values ​​are determined according to the actual situation of the meter. Preferably, the first adjustment coefficient is 1.20, the second adjustment coefficient is 1.10, and the standard deviation value is 10.

[0072] Specifically, the increase in transmission frequency is positively correlated with the accumulation of critical data, allowing the transmission frequency to be dynamically adjusted based on the actual data backlog. This prevents a worsening data backlog due to a low transmission frequency, nor does it waste excessive energy due to a high transmission frequency. Proper transmission frequency adjustment ensures stable and efficient data transmission, preventing data transmission issues from impacting the meter's normal operation and further enhancing meter stability.

[0073] Specifically, in response to the completion of data transmission and compression mode adjustment, the temperature is monitored in real time. In response to the temperature continuing to rise, historical data and real-time monitoring data are obtained, and the comprehensive heat dissipation coefficient is corrected using an adaptive correction algorithm. Among them, the data correction process of the adaptive correction algorithm (such as the Kalman filter or the neural network algorithm) is all existing technology and will not be repeated here.

[0074] Specifically, during the actual use of the electricity meter, dust accumulation, paint oxidation, and other conditions will cause the comprehensive heat dissipation coefficient to change. At this time, the comprehensive heat dissipation coefficient obtained by inputting the operating parameters and heat dissipation parameters into the heat dissipation model is no longer accurate, resulting in the phenomenon that the temperature continues to rise after the adjustment is completed. At this time, the Kalman filter or neural network algorithm is used to update the comprehensive heat dissipation coefficient according to the changes in temperature and heat dissipation parameters, so that the comprehensive heat dissipation coefficient can be more in line with the actual heat dissipation conditions of the electricity meter, thereby avoiding power control errors caused by inaccurate comprehensive heat dissipation coefficient, thereby further increasing the stability of the electricity meter operation.

[0075] Furthermore, when calculating the comprehensive heat dissipation coefficient of the electricity meter based on the heat dissipation parameters, if data anomalies occur in the process of obtaining the heat dissipation parameters and operating parameters (such as data mutations caused by sensor failure), the data of the spare sensor is used for calculation, or historical data is used for interpolation or fitting to estimate the current heat dissipation parameters and operating parameters.

[0076] Furthermore, in the event of data anomalies, parameters can be estimated using alternate sensor data or by interpolating and fitting historical data. This allows for relatively accurate parameter acquisition even in the event of a sensor failure, allowing for the calculation of a reasonable comprehensive heat dissipation coefficient. This reasonable comprehensive heat dissipation coefficient ensures accurate determination of the subsequent maximum operating power, keeping the meter's operating power within a reasonable range and avoiding operational instability caused by inaccurate parameters, thereby further increasing the meter's operational stability.

[0077] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for combining low power consumption control and data compression for multi-mode communication of smart meters, characterized in that: include: Periodically obtain the operating parameters and communication delay of the meter during operation, and select the communication method with the lowest communication delay as the wireless transmission path; In response to the temperature of the electric meter being greater than a temperature threshold, calculating a comprehensive heat dissipation coefficient of the electric meter according to the heat dissipation parameter; determining the maximum operating power of the electric meter according to the comprehensive heat dissipation coefficient; determining whether the state of the electric meter is abnormal based on the operating parameters, and in response to the electric meter operating normally, selecting LPWAN technology for data transmission and selecting a data compression method based on a data change cycle; In response to the abnormal operation of the electric meter, adjusting the transmission frequency according to the accumulation amount of key data, and after the transmission frequency adjustment is completed, determining the data compression and communication mode according to the maximum operating power; The operating parameters include: current value, voltage value and temperature value during the operation of the meter; The comprehensive heat dissipation coefficient of the electric meter is calculated based on the heat dissipation parameters, wherein: Obtain cooling parameters and operating parameters; Inputting the operating parameters and the heat dissipation parameters into a heat dissipation model to obtain a comprehensive heat dissipation coefficient; The heat dissipation model is built based on the relationship between the operating parameters, the heat dissipation parameters and the comprehensive heat dissipation coefficient. The operating parameters and the heat dissipation parameters are input, and the comprehensive heat dissipation coefficient is output. The heat dissipation parameters include: effective heat dissipation area, external ambient temperature, and ambient wind speed; The maximum operating power of the electric meter is determined according to the comprehensive heat dissipation coefficient, and the maximum operating power is set. , where: h is the comprehensive heat dissipation coefficient, A is the effective heat dissipation area, T surface is the surface temperature of the meter casing, T env is the external ambient temperature; The data change period is the time interval between two consecutive changes in the amount of data collected by the smart meter that is greater than or equal to the fluctuation threshold. The fluctuation threshold is the minimum value of the peak value in the historical data. Key data is a collection of abnormal data that needs to be processed first; The critical data accumulation volume is: the critical data volume that has been in the queue for more than 5 minutes, or the critical data volume that exceeds 30% of the storage buffer. The default configuration of the buffer is 64KB.

2. The method for combined processing of low power consumption control and data compression for multi-mode communication of smart meters according to claim 1, characterized in that: The determination of whether the state of the electric meter is abnormal based on the operating parameters is performed, and the electric meter state is determined to be abnormal if any of the following conditions is met: The temperature is greater than the temperature threshold and lasts longer than the preset duration; The voltage or current exceeds the corresponding preset value; The number of key data queues accumulated is greater than the preset number.

3. The method for combined processing of low power consumption control and data compression for multi-mode communication of smart meters according to claim 2, characterized in that: The method of selecting a data compression method according to the data change cycle, obtaining various types of data and analyzing the data change cycle, and selecting a corresponding data compression method according to the data change cycle includes: In response to a data change period being greater than a period threshold, compressing the data using a lossless compression algorithm; In response to the data variation period being less than or equal to the period threshold, a lossy compression algorithm is used to perform data compression.

4. The method for combined processing of low power consumption control and data compression for multi-mode communication of smart meters according to claim 3, characterized in that: In response to the abnormal operation of the electric meter, the accumulation of key data is obtained and the transmission frequency is increased according to the accumulation of key data. After the transmission power is increased, the current operating power is obtained, and the data compression method is selected according to the current operating power and the maximum operating power.

5. The method for combined processing of low power consumption control and data compression for multi-mode communication of smart meters according to claim 4, characterized in that: The transmission frequency is increased according to the accumulation amount of key data, and the increase amount of the transmission frequency is positively correlated with the accumulation amount of key data.

6. The method for combined low power consumption control and data compression for multi-mode communication of smart meters according to claim 1, characterized in that: In response to the completion of data transmission and compression mode adjustment, the temperature is monitored in real time. In response to the temperature continuing to rise, historical data and real-time monitoring data are obtained, and the comprehensive heat dissipation coefficient is corrected using an adaptive correction algorithm.

7. The method for combined processing of low power consumption control and data compression for multi-mode communication of smart meters according to claim 1, characterized in that: The comprehensive heat dissipation coefficient of the electric meter is calculated based on the heat dissipation parameters. If data abnormalities occur during the process of obtaining the heat dissipation parameters and operating parameters, the data of the backup sensor is used for calculation, or historical data is used for interpolation or fitting to estimate the current heat dissipation parameters and operating parameters.

Images