Method and device for processing electrical micro-data of power internet of things terminal

Abstract

The application discloses a kind of processing method and device of electrical micro data of power internet of things terminal, belong to data processing field.Method includes: obtaining the electrical micro data detected;Based on the position of the same class sensor of power internet of things terminal generates topological graph, to determine the size of the correlation area of this class sensor;For each set unit of each class sensor electrical micro data, the standard deviation corresponding to the correlation area mapped by the set unit is calculated in the electrical micro data, to determine the quantization bit rate of the set unit based on standard deviation, preset quality index and pre-constructed lookup table, to compress the micro data of the set unit based on quantization bit rate and then transmit to micro data processing center;Based on the electrical micro data of each class sensor and historical electrical micro data, determine whether to upload to cloud platform.This scheme can avoid micro data processing center calculation delay lag, improve monitoring real-time performance, while guaranteeing the accuracy of monitoring.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for processing electrical microdata from a power Internet of Things (IoT) terminal. Background Technology

[0002] With the development of the ubiquitous power Internet of Things, the amount of sensor signal data from power equipment is constantly increasing, and the demand for computing power is getting higher and higher. Now, micro data processing centers are often set up near power equipment. Because they are closer to the power equipment, the latency is low, and the speed of monitoring and processing power equipment anomalies is faster.

[0003] However, the processing capacity of the micro data processing center is limited. When there is too much micro data, it will cause computational delays and lag, affecting the real-time performance of monitoring.

[0004] Therefore, there is an urgent need for a method and device for processing electrical microdata from power Internet of Things (IoT) terminals. Summary of the Invention

[0005] This invention provides a method and apparatus for processing electrical micro-data from power Internet of Things (IoT) terminals, which can solve the problem of computational delays and lags in existing micro-data processing centers, affecting the real-time performance of monitoring. The technical solution is as follows: On the one hand, a method for processing electrical micro-data from power Internet of Things (IoT) terminals is provided, applied to edge computing in a micro-data processing center. The method includes: Acquire electrical microdata obtained from the test; A topology map is generated based on the location of the same type of sensors in the power Internet of Things terminal to determine the size of the correlation region of that type of sensor; For each setting unit of electrical microdata of each type of sensor, the standard deviation corresponding to the correlation region mapped by the setting unit is calculated in the electrical microdata. The quantization bit rate of the setting unit is determined based on the standard deviation, the preset quality index and the pre-built lookup table. The microdata of the setting unit is compressed based on the quantization bit rate and then transmitted to the microdata processing center. Based on the electrical microdata and corresponding standard deviation data of each type of sensor, as well as the historical electrical microdata and corresponding historical standard deviation data of each type of sensor, it is determined whether to transmit the current electrical microdata of each type of sensor to the cloud platform.

[0006] On the other hand, a power Internet of Things (IoT) terminal electrical micro-data processing device is provided, disposed at the edge of the micro-data processing center, for implementing the method described in any embodiment of the specification, the device comprising: The acquisition unit is used to acquire the electrical microdata obtained from the detection. The determining unit is used to generate a topology map based on the location of the same type of sensors in the power Internet of Things terminal, so as to determine the size of the correlation region of that type of sensor; The compression unit is used to calculate the standard deviation of the correlation region mapped by the setting unit in each setting unit of electrical micro data for each type of sensor, and to determine the quantization bit rate of the setting unit based on the standard deviation, the preset quality index and the pre-built lookup table, and to compress the micro data of the setting unit based on the quantization bit rate before transmitting it to the micro data processing center. The upload unit is used to determine whether to transmit the current electrical microdata of each type of sensor to the cloud platform based on the electrical microdata, the corresponding standard deviation data, and the historical electrical microdata and the corresponding historical standard deviation data of each type of sensor.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the method described above.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the method described above.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0010] The technical solution provided by this invention can bring at least the following beneficial effects: By applying adaptive compression to electrical microdata at the edge of the microdata processing center, the data processing burden on the center is reduced while avoiding data distortion and inaccurate monitoring results caused by over-compression, achieving an adaptive balance between data quality and compression effectiveness. Since the pre-built lookup table is stored in advance, processing speed is improved. Based on the electrical microdata and historical electrical microdata, it is determined whether the original electrical microdata needs to be uploaded to the cloud platform, saving bandwidth and cloud storage costs. Furthermore, since this solution is applied to the edge of the microdata processing center, not the center itself, it does not consume the center's computing resources, does not affect the center's data processing speed, and saves bandwidth by eliminating the need for the center to upload raw data to the cloud platform. Therefore, this solution avoids computational delays and lags at the microdata processing center, improves monitoring real-time performance, and ensures monitoring accuracy. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for processing electrical micro data of a power Internet of Things terminal according to an embodiment of the present invention; Figure 2 This is a structural diagram of a power Internet of Things terminal electrical microdata processing device according to an embodiment of the present invention; Figure 3 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] The following describes the specific implementation of the above concept.

[0015] Please refer to Figure 1 This invention provides a method for processing electrical microdata from a power Internet of Things (IoT) terminal, applied to edge computing in a microdata processing center. The method includes: Step 100: Obtain the electrical microdata obtained from the detection; Step 102: Generate a topology map based on the location of the same type of sensors in the power Internet of Things terminal to determine the size of the correlation region of that type of sensor; Step 104: For each setting unit of electrical micro data of each type of sensor, calculate the standard deviation corresponding to the correlation region mapped by the setting unit in the electrical micro data, and determine the quantization bit rate of the setting unit based on the standard deviation, the preset quality index and the pre-built lookup table, and then compress the micro data of the setting unit based on the quantization bit rate and transmit it to the micro data processing center. Step 106: Based on the electrical microdata and corresponding standard deviation data of each type of sensor, as well as the historical electrical microdata and corresponding historical standard deviation data of each type of sensor, determine whether to transmit the current electrical microdata of each type of sensor to the cloud platform.

[0016] In this embodiment of the invention, adaptive compression of electrical microdata is applied to the edge measurement system of the microdata processing center. This reduces the data processing burden on the microdata processing center while avoiding data distortion and inaccurate monitoring results caused by over-compression, achieving an adaptive balance between data quality and compression effect. Since the pre-built lookup table is stored in advance, processing speed is improved. Based on the electrical microdata and historical electrical microdata, it is determined whether the original electrical microdata needs to be uploaded to the cloud platform to save bandwidth and cloud storage costs. Furthermore, since this solution is applied to the edge measurement system of the microdata processing center, not the center itself, it does not occupy the center's computing resources, does not affect the center's data processing speed, and saves bandwidth by eliminating the need for the center to upload the original data to the cloud platform. Therefore, this solution avoids computational delays and lag in the microdata processing center, improves monitoring real-time performance, and ensures monitoring accuracy.

[0017] The following description Figure 1 The execution method for each step is shown.

[0018] For step 100: In this embodiment, the power IoT terminal is equipped with various electrical sensors, including at least voltage sensors, current sensors, electric field sensors, humidity sensors, and temperature sensors. These sensors collect electrical microdata from the power equipment in real time. This solution receives the electrical microdata from each sensor in real time via wireless or wired means.

[0019] Regarding step 102: In some implementations, step 102 includes: For each type of sensor, the following is performed: Generate a topology map based on the location of this type of sensor; For each pair of sensors, calculate the correlation coefficient at different time lengths; At each time length, the standard deviation of the correlation coefficient of all sensor pairs is determined. When the standard deviation stabilizes at a set value as the time length increases, that time length is taken as the data length of the correlation region of that type of sensor. Each sensor is taken as the starting target, and the correlation coefficient between the starting target sensor and each other sensor is calculated. The correlation coefficients are sorted in descending order to plot the decay curve of the correlation coefficient with the sorting number. The number of numbers before the elbow of the decay curve is used as the candidate dimension. From the candidate dimensions corresponding to all sensors as starting targets, the smallest candidate dimension is selected as the dimension width of the correlation region of this type of sensor. Combined with the data length, the size of the correlation region of this type of sensor is obtained.

[0020] In this embodiment, since the data collected by similar sensors can influence each other, there is correlation within a correlation region. Based on the standard deviation of the electrical micro data within the correlation region, a lookup table is used to determine the quantization bit rate of the setting unit. This ensures that the quantization bit rates of the correlated setting units are the same or similar, and enables the micro data processing center to obtain consistent data compression, avoiding abnormal analysis or inaccurate monitoring results in the micro data processing center.

[0021] In this embodiment, the elbow of the decay curve is the location where the correlation coefficient decreases abruptly and gradually slows down. Selecting the smallest candidate dimension as the dimensional width of the correlation region for this type of sensor can avoid data distortion. The size of the correlation region includes the data length and dimensional width. Considering both the stability of temporal correlation and the locality of spatial correlation, an adaptive method for determining the two-dimensional correlation scale is constructed based on spatial decay and information redundancy.

[0022] Regarding step 104: In some implementations, the lookup table is constructed as follows: Obtain several electrical microdata samples, divide the electrical microdata samples into blocks, and calculate the standard deviation of the data in each block; The optimal quantization level threshold is determined based on the standard deviation. The quantization code value is obtained by comparing each sample in each block of data according to the threshold table, so as to obtain the quantized data samples of electrical micro data samples at different quantization bit rates. Based on electrical micro data samples and quantized data samples, calculate the quality index of each block of quantized data sample at each quantization bit rate. The standard deviation of each data block and the quality index of the corresponding block at each quantization bit rate are used as data points, and smooth spline interpolation is performed on the data points to construct a three-dimensional relationship model to characterize the relationship between the standard deviation, quality index and quantization bit rate. A lookup table is generated based on a three-dimensional relational model.

[0023] In this embodiment, a lookup table is constructed to map the standard deviation, quality index, and quantization bit rate of the segmented data. This directly correlates the standard deviation, which characterizes local correlation, with the quality index, which characterizes the expected data quality of the microdata processing center. This makes the determination of the quantization bit rate more aligned with practical application needs, aiming to predict the dynamic adaptive bit rate distribution directly from the electrical microdata characteristics. This scheme avoids bit rate redundancy in similar useless data, reduces the overall data volume while ensuring global data quality, and optimizes overall data compression efficiency while improving data quality in key areas, achieving an adaptive balance between data quality and compression effect.

[0024] In some implementations, the quality index is calculated using the following formula: The quality coefficient is calculated based on the expected data redundancy, the storage capacity of the micro data processing center, the transmission delay and packet loss rate from the edge to the micro data processing center; Based on the electrical microdata samples and quantization data samples corresponding to the block, the signal-to-noise ratio of the block is calculated; Based on the quality coefficient and signal-to-noise ratio, the quality index of the block is calculated respectively.

[0025] In this embodiment, a quality coefficient is determined based on the expected data redundancy, the storage capacity of the micro data processing center, the transmission latency and packet loss rate from the edge to the micro data processing center, and a quality index is generated by combining the signal-to-noise ratio (SNR). This approach comprehensively evaluates the resource conditions of the micro data processing center and the transmission conditions to the micro data processing center as a standard for assessing the expected data quality of the micro data processing center. It does not simply use the SNR as a quality index, but comprehensively considers the actual conditions, which can reduce the data processing burden of the micro data processing center while avoiding data distortion and inaccurate monitoring results caused by excessive compression.

[0026] Specifically, the quality indicators are calculated using the following formula: in, In the formula, For quality indicators, For quality coefficient, The signal-to-noise ratio of the current block. , , and For tuning constants, This is the storage capacity for the micro data processing center. To account for the expected data redundancy, The transmission delay from the edge to the micro data processing center, The packet loss rate from the edge to the micro data processing center. This is a coupled spatial information potential matrix used to reflect the expected data redundancy, the storage capacity of the micro data processing center, the transmission delay and packet loss rate from the edge to the micro data processing center. It is an energy calculation function. This is the electrical microdata sample corresponding to this block. It is the difference between the electrical microdata sample and the quantized data sample corresponding to this block. To obtain the model.

[0027] In this embodiment, a larger storage capacity in the micro-data processing center indicates lower compression loss and a higher expected quality index. Higher expected data redundancy also indicates less compression loss and a higher expected data quality index. A greater transmission delay from the edge to the micro-data processing center means the center receives the data later; to reduce this delay, the compression ratio can be increased, resulting in a lower quality index. A higher packet loss rate from the edge to the micro-data processing center indicates higher data distortion; to reduce distortion, the compression ratio can be reduced, lowering compression loss and resulting in a higher quality index. This solution comprehensively evaluates the resource conditions of the micro-data processing center and the transmission conditions to it as criteria for assessing the expected data quality. It doesn't solely rely on the signal-to-noise ratio as a quality index but comprehensively considers practical conditions. This approach reduces the data processing burden on the micro-data processing center while avoiding data distortion and inaccurate monitoring results caused by excessive compression.

[0028] It is understandable that in edge testing, the preset quality indicators are also set based on the above formula. For the preset quality indicators and the standard deviation corresponding to the correlation region of each setting unit, the minimum required quantization bit rate to meet the conditions is obtained by looking up the table, which is used for micro data compression.

[0029] Regarding step 106: In some implementations, step 106 includes: For the current electrical microdata of each type of sensor, perform the following: Determine if the current electrical microdata is from the first K groups of data; if so, directly upload the current electrical microdata for all types of sensors. If not, then determine whether the current electrical microdata is continuous with the previous data. If the data is not uploaded to the cloud platform, then the current electrical microdata of all types of sensors will be uploaded directly. If not, determine whether the previous N consecutive sets of electrical microdata have triggered uploads to the cloud platform; if so, directly upload the current electrical microdata of all sensor types. If not, determine whether the difference between the mean of each set unit of the current electrical microdata and the mean of each set unit of the historical electrical microdata exceeds the corresponding historical standard deviation data of the historical electrical microdata and / or determine whether the ratio of the standard deviation data of each set unit of the current electrical microdata to the standard deviation data of each set unit of the historical electrical microdata exceeds the set range. If the conditions are met, the current electrical microdata of all types of sensors will be uploaded directly. If it does not meet the requirements, it will not be uploaded.

[0030] In this embodiment, the first K sets of data are all uploaded to establish an initial baseline; each Data is uploaded at least once to avoid prolonged periods of inactivity. If uploading is triggered for N consecutive groups, subsequent uploads will be retained even if the upload criteria are not met, to record the evolution process. In other cases, data is only uploaded when the current data distribution differs significantly from historical normal patterns; otherwise, it is discarded or stored locally to save bandwidth and cloud storage costs.

[0031] Please refer to Figure 2 This invention provides a device for processing electrical micro-data from a power Internet of Things (IoT) terminal, located at the edge of a micro-data processing center, for implementing the method of any embodiment in the specification. The device includes: Acquisition unit 201 is used to acquire electrical microdata obtained from the detection; The determining unit 202 is used to generate a topology map based on the location of the same type of sensors in the power Internet of Things terminal, so as to determine the size of the correlation region of the same type of sensor; Compression unit 203 is used to calculate the standard deviation of the correlation region mapped by the setting unit in each setting unit of electrical micro data for each type of sensor, and to determine the quantization bit rate of the setting unit based on the standard deviation, preset quality index and pre-built lookup table, and to compress the micro data of the setting unit based on the quantization bit rate and then transmit it to the micro data processing center. The upload unit 204 is used to determine whether to transmit the current electrical micro data of each type of sensor to the cloud platform based on the electrical micro data, the corresponding standard deviation data, and the historical electrical micro data and the corresponding historical standard deviation data of each type of sensor.

[0032] It should be noted that the electrical microdata processing device for the power Internet of Things terminal provided in the above embodiments is only an example of the division of the above functional units. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. In addition, the above device embodiments and method embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0033] Embodiments of this application also provide a computer device, please refer to... Figure 3 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the power Internet of Things terminal electrical microdata processing method provided in the above method embodiments.

[0034] The embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the power Internet of Things terminal electrical microdata processing method provided in the above method embodiments.

[0035] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform the electrical microdata processing method for a power Internet of Things terminal as described in any of the above embodiments.

[0036] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0037] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0038] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0039] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for processing electrical microdata from a power Internet of Things (IoT) terminal, characterized in that, Edge measurement applied to micro data processing centers includes: Acquire electrical microdata obtained from the test; A topology map is generated based on the location of the same type of sensors in the power Internet of Things terminal to determine the size of the correlation region of that type of sensor; For each setting unit of electrical microdata of each type of sensor, the standard deviation corresponding to the correlation region mapped by the setting unit is calculated in the electrical microdata. The quantization bit rate of the setting unit is determined based on the standard deviation, the preset quality index and the pre-built lookup table. The microdata of the setting unit is compressed based on the quantization bit rate and then transmitted to the microdata processing center. Based on the electrical microdata and corresponding standard deviation data of each type of sensor, as well as the historical electrical microdata and corresponding historical standard deviation data of each type of sensor, it is determined whether to transmit the current electrical microdata of each type of sensor to the cloud platform.

2. The method as described in claim 1, characterized in that, The method of generating a topology map based on the location of the same type of sensors in the power Internet of Things (IoT) terminal to determine the size of the relevance region of that type of sensor includes: For each type of sensor, the following is performed: Generate a topology map based on the location of this type of sensor; For each pair of sensors, calculate the correlation coefficient at different time lengths; At each time length, the standard deviation of the correlation coefficient of all sensor pairs is determined. When the standard deviation stabilizes at a set value as the time length increases, that time length is taken as the data length of the correlation region of that type of sensor. Each sensor is taken as the starting target, and the correlation coefficient between the starting target sensor and each other sensor is calculated. The correlation coefficients are sorted in descending order to plot the decay curve of the correlation coefficient with the sorting number. The number of numbers before the elbow of the decay curve is used as the candidate dimension. From the candidate dimensions corresponding to all sensors as starting targets, the smallest candidate dimension is selected as the dimension width of the correlation region of this type of sensor. Combined with the data length, the size of the correlation region of this type of sensor is obtained.

3. The method as described in claim 1, characterized in that, The lookup table is constructed in the following manner: Acquire several electrical microdata samples, divide the electrical microdata samples into blocks, and calculate the standard deviation of the data in each block; The optimal quantization level threshold is determined based on the standard deviation. The quantization code value is obtained by comparing each sample in each block of data according to the threshold table, so as to obtain the quantized data samples of the electrical micro data sample under different quantization bit rates. Based on the electrical micro data sample and the quantized data sample, calculate the quality index of each block of the quantized data sample at each quantized bit rate. The standard deviation of each data block and the quality index of the corresponding block at each quantization bit rate are used as data points, and smooth spline interpolation is performed on the data points to construct a three-dimensional relationship model to characterize the relationship between the standard deviation, quality index and quantization bit rate. A lookup table is generated based on the aforementioned three-dimensional relationship model.

4. The method as described in claim 3, characterized in that, The quality indicators are calculated using the following formula: The quality coefficient is calculated based on the expected data redundancy, the storage capacity of the micro data processing center, the transmission delay and packet loss rate from the edge to the micro data processing center; Based on the electrical microdata sample and the quantization data sample corresponding to the block, the signal-to-noise ratio of the block is calculated; Based on the quality coefficient and the signal-to-noise ratio, the quality index of the block is calculated respectively.

5. The method as described in claim 4, characterized in that, The quality indicators are calculated using the following formula: in, In the formula, The quality indicator is as described above. For quality coefficient, The signal-to-noise ratio of the current block. , , and For tuning constants, This refers to the storage capacity of the micro data processing center. To account for the expected data redundancy, The transmission delay from the edge to the micro data processing center. The packet loss rate from the edge to the micro data processing center. This is a coupled spatial information potential matrix used to reflect the expected data redundancy, the storage capacity of the micro data processing center, the transmission delay and packet loss rate from the edge to the micro data processing center. It is an energy calculation function. This is the electrical microdata sample corresponding to this block. It is the difference between the electrical microdata sample and the quantized data sample corresponding to that block. To obtain the model.

6. The method as described in claim 1, characterized in that, The process of determining whether to transmit the current electrical microdata of each type of sensor to the cloud platform based on the electrical microdata, corresponding standard deviation data, and historical electrical microdata and corresponding historical standard deviation data of each type of sensor includes: For the current electrical microdata of each type of sensor, perform the following: Determine if the current electrical microdata is from the first K groups of data; if so, directly upload the current electrical microdata for all types of sensors. If not, then determine whether the current electrical microdata is continuous with the previous data. If the data is not uploaded to the cloud platform, then the current electrical microdata of all types of sensors will be uploaded directly. If not, determine whether the previous N consecutive sets of electrical microdata have triggered uploads to the cloud platform; if so, directly upload the current electrical microdata of all sensor types. If not, then determine whether the difference between the mean of each set unit of the current electrical micro data and the mean of each set unit of the historical electrical micro data exceeds the corresponding historical standard deviation data of the historical electrical micro data and / or determine whether the ratio of the standard deviation data of each set unit of the current electrical micro data to the standard deviation data of each set unit of the historical electrical micro data exceeds the set range. If the conditions are met, the current electrical microdata of all types of sensors will be uploaded directly. If it does not meet the requirements, it will not be uploaded.

7. A device for processing electrical microdata from a power Internet of Things (IoT) terminal, disposed at the edge of a microdata processing center, for implementing the method according to any one of claims 1-6, characterized in that, The device includes: The acquisition unit is used to acquire the electrical microdata obtained from the detection. The determining unit is used to generate a topology map based on the location of the same type of sensors in the power Internet of Things terminal, so as to determine the size of the correlation region of that type of sensor; The compression unit is used to calculate the standard deviation of the correlation region mapped by the setting unit in each setting unit of electrical micro data for each type of sensor, and to determine the quantization bit rate of the setting unit based on the standard deviation, the preset quality index and the pre-built lookup table, and to compress the micro data of the setting unit based on the quantization bit rate before transmitting it to the micro data processing center. The upload unit is used to determine whether to transmit the current electrical microdata of each type of sensor to the cloud platform based on the electrical microdata, the corresponding standard deviation data, and the historical electrical microdata and the corresponding historical standard deviation data of each type of sensor.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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