Method and system for cross-regional sharing of power grid supervision and manufacture data based on cloud platform

Abstract

The invention discloses a cross-regional sharing method and system for power grid supervision and manufacture data based on a cloud platform, and relates to the technical field of cross-regional sharing of data, and the method comprises the following steps: obtaining power grid equipment nodes of a to-be-detected region, labeling the power grid equipment nodes, setting priority levels, obtaining data transmission influence data of power grid equipment, the data transmission influence data comprises a channel influence coefficient, a communication equipment performance coefficient and a data compression precision distortion coefficient; constructing a data transmission influence model based on a machine learning algorithm according to the data transmission influence data to obtain a data transmission influence coefficient; performing threshold division on the data transmission influence coefficient according to the priority level of the power grid equipment node; according to the method, the transmission influence coefficient model is constructed, and the data transmission influence coefficient is subjected to threshold division according to the priority of the power grid equipment nodes, so that the problem of insufficient processing of the relationship between the equipment priority and the data transmission influence is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-regional data sharing, and more specifically, to a method and system for cross-regional sharing of power grid monitoring data based on a cloud platform. Background Art

[0002] Grid supervision is one of the important tasks in the power industry, involving real-time monitoring, recording and analysis of various data during the grid construction process. With the continuous development and modernization of the power system, the scale and complexity of grid construction have gradually increased, and the management and sharing of grid supervision data have become particularly important. Traditional grid supervision data management methods usually rely on a local single information system, resulting in the inability to efficiently share data between different regions, and serious information island problems, which brings many inconveniences to grid construction and operation. In order to improve the efficiency of grid construction and monitoring, ensure the real-time, accuracy and cross-regional accessibility of data, cloud-based cross-regional sharing technology of grid supervision data has emerged.

[0003] The cloud platform provides a unified storage and management architecture for power grid monitoring data. Through efficient data integration and standardized interfaces, it enables data connection and interoperability between different regions and systems. The cloud platform also has powerful data analysis and processing capabilities, monitors project progress in real time, and promptly identifies potential problems and issues early warnings. In addition, the cloud platform's high availability and data security mechanisms effectively prevent data loss and leakage, ensuring the integrity and security of power grid monitoring data.

[0004] The above disclosed technical solutions have at least the following technical problems: In the traditional cross-regional sharing method of power grid monitoring data, the priorities of power grid equipment are different, and the roles and importance of device nodes in the entire power grid vary. Therefore, when transmitting data between different devices, it is often necessary to determine the processing order and quality assurance of data transmission based on the priority of the device. The traditional method does not divide the threshold interval of data transmission quality based on the priority of the device node, resulting in the data transmission of some key equipment being affected due to resource limitations or network problems, and unable to receive timely attention.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for cross-regional sharing of power grid monitoring data based on a cloud platform, which constructs a transmission influence coefficient model and divides the data transmission influence coefficient into thresholds according to the priority level of power grid equipment nodes to solve the problem of insufficient processing of the relationship between equipment priority and data transmission impact.

[0007] To achieve the above object, the present invention provides the following technical solutions: A cross-regional sharing method for power grid monitoring data based on a cloud platform comprises obtaining power grid equipment nodes in a to-be-tested area, labeling the power grid equipment nodes and setting priority levels, obtaining data transmission impact data of the power grid equipment, wherein the data transmission impact data comprises a channel impact coefficient, a communication equipment performance coefficient and a data compression precision distortion coefficient; constructing a data transmission impact model based on a machine learning algorithm according to the data transmission impact data to obtain a data transmission impact coefficient; and performing threshold division on the data transmission impact coefficient according to the priority levels of the power grid equipment nodes.

[0008] In a preferred embodiment, the specific steps for obtaining the channel influence coefficient are as follows: obtaining signal power and noise power data of the bandwidth between the power grid node to be tested and the server, and setting a fluctuation interval threshold for the signal power and noise power data, and removing data that is not within the fluctuation interval threshold range; obtaining the distribution of noise power through software analysis, comparing the noise power in different frequency ranges, and identifying the noise power of the main noise source; plotting the signal power data into a line graph to obtain multiple line segments, obtaining the absolute value of the slope of each line segment through machine learning and comparing them, and selecting a relatively stable signal power data segment according to the absolute value of the slope; adding and averaging the relatively stable signal power data segments to obtain the signal power, and using it as the benchmark data; obtaining the power grid node to be tested; The distance between the power grid device and the server is determined by sending multiple signals to the server through the power grid device node, and the signal strength of the power grid device signal when it is transmitted and the signal strength after the server receives it are recorded. According to the multiple groups of data of the signal strength when the power grid device signal is transmitted and the signal strength after the server receives it, the average value of the signal strength when the signal is transmitted and the signal strength after the server receives it is calculated; according to the signal strength when the signal is transmitted and the signal strength after the server receives it and the distance between the power grid device and the server, the attenuation coefficient is calculated based on the preset signal attenuation formula; the bandwidth between the power grid device to be tested and the server is obtained, and the channel influence coefficient is calculated based on the bandwidth, attenuation coefficient, signal power and noise power based on the preset channel influence coefficient formula.

[0009] In a preferred embodiment, the specific steps for obtaining the performance coefficient of the communication equipment are as follows: a piece of data is sent to the server side through the power grid equipment to be tested, a noise source is started during the data sending process, the output power of the noise source is set, and multiple experiments are performed to obtain multiple groups of bit error rates under normal conditions and bit error rates under the influence of the noise source; the bit error rates under normal conditions and the bit error rates under the influence of the noise source are plotted into a curve graph, and the influencing factor between the bit error rate and the noise power is obtained through linear regression analysis; the distance between the power grid equipment to be tested and the server side is set, the power grid equipment to be tested sends multiple groups of signals, the server side receives the signals, the magnetic field transmitting device is turned on between the power grid equipment and the server side, the magnetic field frequency is adjusted, and the strength of the power grid equipment signal when it is transmitted and the strength of the server-side signal when it is received under different magnetic field frequency states are recorded; according to The signal attenuation under different magnetic field frequency states is obtained from the strength of the power grid equipment signal when it is transmitted and the strength of the server-side signal when it is received, and the signal attenuation constant is obtained through linear regression analysis; the magnetic permeability, electrical conductivity and dielectric constant of the signal transmission medium are obtained, and the penetration ability of the power grid equipment to be tested in the magnetic field is obtained based on the preset magnetic field penetration formula according to the attenuation constant, magnetic field frequency, magnetic permeability, electrical conductivity and dielectric constant; the actual data transmission rate and bandwidth between the power grid equipment to be tested and the server are obtained, and the network load capacity is obtained based on the actual data transmission rate and bandwidth. The communication equipment performance coefficient is calculated based on the preset communication equipment performance coefficient formula according to the influencing factors between the network load, magnetic field penetration and bit error rate, and bit error rate and noise power.

[0010] In a preferred embodiment, the specific steps for obtaining the data compression precision distortion coefficient are as follows: select a group of image data to be tested, compress the image data through the power grid equipment to be tested and send it to the server for multiple times, and collect the total number of pixels of the original data and the compressed data to construct a data set; construct a data set based on the total number of pixels of the collected original data and compressed data, clean the data, remove abnormal values ​​in the data, and obtain the mean of the original data and the mean of the compressed data based on the mean calculation formula for the processed data; construct a data set based on the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, and calculate the variance of the original data and the variance of the compressed data based on the variance formula; construct a data set based on the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, and calculate the covariance between the original data and the compressed data based on the covariance formula; calculate the data compression precision distortion coefficient based on the preset data compression precision distortion coefficient formula based on the mean of the original data, the mean of the compressed data, the variance of the original data and the variance of the compressed data, and the covariance between the original data and the compressed data.

[0011] In a preferred embodiment, the threshold division of the data transmission impact coefficient according to the priority level of the power grid equipment node is specifically as follows: in the process of entering the power grid equipment node, each power grid equipment node is manually evaluated for priority, and sorted in descending order according to the priority level of the power grid equipment to obtain a descending sorting form; all priority levels are divided into N areas according to the descending sorting form, and a threshold interval is set for the data transmission impact coefficient of each area; if it is lower than the preset threshold interval, an early warning will be issued to prompt the staff to inspect the power grid equipment and update the equipment technology. During the maintenance of the power grid equipment, a time period is set for regular data updates; if it is higher than the preset threshold interval and higher than the threshold interval of the upper-level area, the management personnel will be prompted to replace the two power grid devices.

[0012] A system for a cross-regional sharing method of power grid monitoring data based on a cloud platform is characterized in that it includes a data acquisition module, a data transmission impact model construction module and a data classification module, and there is a connection between the modules; the data acquisition module is used to obtain power grid equipment nodes in the area to be tested, label the power grid equipment nodes and set priority levels, and obtain data transmission impact data of the power grid equipment, wherein the data transmission impact data includes a channel impact coefficient, a communication equipment performance coefficient and a data compression accuracy distortion coefficient; the data transmission impact model construction module is used to construct a data transmission impact model based on a machine learning algorithm according to the data transmission impact data to obtain a data transmission impact coefficient; the data classification module is used to perform threshold division on the data transmission impact coefficient according to the priority level of the power grid equipment node.

[0013] Technical effects and advantages of a cross-regional sharing method of power grid monitoring data based on a cloud platform of the present invention: 1. The present invention divides the data transmission impact coefficient into threshold values ​​according to the priority levels of power grid equipment, issues timely warnings when data transmission performance deteriorates, and prompts staff to perform maintenance and equipment technology updates, thereby ensuring the stable operation of power grid equipment. The present invention divides the data transmission impact coefficient into threshold values ​​according to the priority levels of power grid equipment, issues timely warnings when data transmission performance deteriorates, and prompts staff to perform maintenance and equipment technology updates, thereby ensuring the stable operation of power grid equipment.

[0014] 2. The present invention introduces machine learning algorithms to model factors affecting data transmission, which can more accurately evaluate the specific impact of various factors on power grid data transmission. By analyzing different data transmission influencing coefficients, the system can provide a scientific decision-making basis to help power companies and operation and maintenance teams formulate reasonable data transmission plans.

[0015] 3. The present invention accurately analyzes and calculates the compression accuracy distortion coefficient of power grid data. The system can optimize the data compression strategy under the premise of ensuring data transmission accuracy, thereby improving data transmission rate and storage efficiency. This not only reduces the consumption of bandwidth resources, but also reduces the demand for storage space, providing technical support for the processing and cross-regional sharing of large-scale power grid data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a schematic structural diagram of a cross-regional sharing method of power grid monitoring data based on a cloud platform.

[0017] Figure 2 The present invention is a schematic diagram of the structure of a cross-regional sharing system for power grid monitoring data based on a cloud platform. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, Figure 1 The present invention provides a cross-regional sharing method of power grid monitoring data based on a cloud platform, comprising the following steps: S1, obtaining the power grid equipment nodes in the area to be tested, labeling the power grid equipment nodes and setting priority levels, and obtaining data transmission impact data of the power grid equipment, wherein the data transmission impact data includes a channel impact coefficient, a communication equipment performance coefficient, and a data compression accuracy distortion coefficient.

[0020] In this embodiment, the power grid device nodes in the area to be tested are obtained, the power grid device nodes are numbered and priority levels are set, and data transmission impact data of the power grid devices are obtained. The data transmission impact data includes a channel impact coefficient, a communication device performance coefficient, and a data compression accuracy distortion coefficient, which are specifically as follows: Channel influence coefficient. The channel contains multiple characteristics such as bandwidth, noise, attenuation, delay and interference. Each characteristic has different effects, including bandwidth determines the amount of data that the channel can transmit; noise and interference reduce signal quality, resulting in increased error rate, which in turn affects the transmission rate; attenuation will cause the signal strength to weaken, shortening the effective distance of data transmission and requiring a stronger signal to maintain the transmission rate; delay affects the response speed of data transmission, especially in real-time communication.

[0021] The specific steps for obtaining the channel influence coefficient are as follows: Acquire the signal power and noise power data of the bandwidth between the power grid node to be tested and the server, set a fluctuation interval threshold for the signal power and noise power data, and remove the data that is not within the fluctuation interval threshold range; Obtain the distribution of noise power through software analysis, compare the noise power in different frequency ranges, and identify the noise power of the main noise source; The signal power data is plotted into a line graph to obtain a plurality of line segments, the absolute value of the slope of each line segment is obtained through machine learning and compared, and a relatively stable signal power data segment is selected according to the absolute value of the slope; Adding and averaging the relatively stable signal power data segments to obtain signal power, and using it as reference data; Obtain the distance between the power grid device to be tested and the server, send multiple signals to the server through the power grid device node, and record the signal strength of the power grid device signal when it is transmitted and the signal strength after the server receives it, and calculate the average value of the signal strength when the signal is transmitted and the signal strength after the server receives it according to multiple sets of data of the signal strength when the power grid device signal is transmitted and the signal strength after the server receives it; The attenuation coefficient is calculated based on the signal strength when the signal is transmitted, the signal strength after the server receives the signal, and the distance between the power grid equipment and the server based on the preset signal attenuation formula; The bandwidth between the power grid device to be tested and the server is obtained, and the channel influence coefficient is calculated based on a preset channel influence coefficient formula according to the bandwidth, attenuation coefficient, signal power and noise power.

[0022] The calculation formula of the channel influence coefficient is as follows:

[0023] In the formula, is the channel influence coefficient, is the bandwidth between the power grid equipment and the server, is the signal power, is the signal attenuation coefficient, is the noise power.

[0024] Communication equipment performance coefficient, the performance of communication equipment plays a vital role in data transmission rate. Data transmission rate refers to the amount of data that can be transmitted per unit time, measured in bits per second. The performance of communication equipment will affect the upper limit, stability and efficiency of the rate.

[0025] The specific steps for obtaining the performance coefficient of the communication device are as follows: A piece of data is sent from the power grid device to the server. During the data transmission process, the noise source is started, the output power of the noise source is set, and multiple experiments are performed to obtain multiple groups of bit error rates under normal conditions and bit error rates under the influence of the noise source. The bit error rate data in the normal state and the bit error rate data under the influence of the noise source are plotted into a curve graph, and the influence factor between the bit error rate and the noise power is obtained through linear regression analysis; Set the distance between the power grid device to be tested and the server, make the power grid device to be tested send multiple groups of signals, and the server receives the signals. Turn on the magnetic field transmitting device between the power grid device and the server, adjust the magnetic field frequency, and record the signal intensity of the power grid device when transmitting and the signal intensity of the server when receiving under different magnetic field frequency states; The attenuation of the signal under different magnetic field frequency states is obtained according to the intensity of the signal transmitted by the power grid equipment under different magnetic field frequency states and the intensity of the signal received by the server end, and the attenuation constant of the signal is obtained through linear regression analysis; Obtaining the magnetic permeability, electrical conductivity and dielectric constant of the signal transmission medium, and calculating based on a preset magnetic field penetration formula according to the attenuation constant, magnetic field frequency, magnetic permeability, electrical conductivity and dielectric constant to obtain the penetration ability of the power grid equipment to be tested in the magnetic field; Acquire the actual data transmission rate and bandwidth between the power grid device to be tested and the server, and calculate the network load capacity based on the actual data transmission rate and bandwidth based on a preset network load formula; The communication equipment performance coefficient is obtained by calculating based on a preset communication equipment performance coefficient formula according to the network load, magnetic field penetration and bit error rate, and the influencing factors between the bit error rate and noise power.

[0026] The calculation formula of the communication equipment performance coefficient is as follows:

[0027] The calculation formula of the magnetic field penetration is as follows:

[0028] The calculation formula of the network load is as follows:

[0029] In the formula, is the communication equipment performance coefficient, is a modulation constant, is a natural logarithm, is the signal power, is the noise power, is the network load, It is the magnetic field penetration, is the angular frequency of the wave, is the magnetic permeability of the medium, is the dielectric constant of the medium, is the conductivity of the medium, is the magnetic field frequency, is the decay constant, is the actual data transfer rate, It is the bandwidth between the power grid equipment and the server.

[0030] Data compression accuracy distortion coefficient, the data compression accuracy distortion coefficient mainly evaluates the image quality by comparing the brightness, contrast and structural information of two images. The data compression accuracy distortion coefficient can not only be used to evaluate the image compression quality, but also can be used in image processing, denoising and image enhancement.

[0031] The specific steps for obtaining the data compression precision distortion coefficient are as follows: A set of image data to be tested is selected, the image data is compressed by the power grid device to be tested and sent to the server for multiple times, and the total number of pixels of the original data and the compressed data is collected to construct a data set; Constructing a data set according to the total number of pixels of the collected original data and compressed data, cleaning the data, removing outliers in the data, and obtaining the mean of the original data and the mean of the compressed data based on the mean calculation formula of the processed data; According to the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, a data set is constructed, and the variance of the original data and the variance of the compressed data are calculated based on the variance formula; Constructing a data set according to the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, and calculating the covariance between the original data and the compressed data based on the covariance formula; The data compression precision distortion coefficient is calculated based on a preset data compression precision distortion coefficient formula according to the original data mean, the compressed data mean, the original data variance, the compressed data variance, and the covariance between the original data and the compressed data.

[0032] The calculation formula of the data compression precision distortion coefficient is as follows:

[0033] The calculation formulas for the mean of the original data and the mean of the compressed data are as follows:

[0034] The calculation formulas for the variance of the original data and the variance of the compressed data are as follows:

[0035] The calculation formula of the covariance between the original data and the compressed data is as follows:

[0036] In the formula, is the data compression precision distortion coefficient, is the original data mean, is the mean of the compressed data, is the covariance between the original and compressed data, is the variance of the original data, is the variance of the compressed data, and is a constant that prevents the denominator from being zero.

[0037] It should be noted that the value range of the data compression precision distortion coefficient is between [0, 1]. The closer the data compression precision distortion coefficient is to 1, the lower the data compression precision distortion rate is, indicating that the data quality is closer to the original data.

[0038] S2, constructing a data transmission impact model based on the data transmission impact data and a machine learning algorithm to obtain a data transmission impact coefficient.

[0039] In this embodiment, the data transmission impact model is constructed based on the machine learning algorithm according to the data transmission impact data to obtain the data transmission impact coefficient, which is specifically: According to the channel influence coefficient, the communication equipment performance coefficient and the data compression precision distortion coefficient, a data transmission influence model is constructed based on a machine learning algorithm to obtain a data transmission influence coefficient, which is specifically as follows: The calculation formula of the data transmission impact model is as follows:

[0040] In the formula, is the data transmission impact coefficient, is the channel influence coefficient, is the communication equipment performance coefficient, is the data compression precision distortion coefficient.

[0041] It should be noted that when the data transmission impact coefficient is high, it means that the negative impact of factors such as channels, equipment and compression is small, and the data transmission quality and efficiency are good; when the data transmission impact coefficient is low, it means that the channel quality is poor, the equipment performance is insufficient, and the compression distortion is large, resulting in poor data transmission efficiency and quality. In this case, network transmission will have problems including but not limited to delays, high error rates and low transmission bandwidth.

[0042] Further, the advantages of obtaining the data transmission influence coefficient through the channel influence coefficient, the communication equipment performance coefficient and the data compression accuracy distortion coefficient are analyzed: Through comprehensive evaluation of communication equipment performance, channel interference, noise source impact and other factors, the system can better adapt to complex power grid environments and external interference. Especially in areas with strong electromagnetic interference, the anti-interference ability of power grid equipment is enhanced through magnetic field frequency adjustment and attenuation coefficient calculation, which improves the stability and reliability of data transmission and reduces data loss and transmission errors caused by external factors. By accurately analyzing and calculating the compression accuracy distortion coefficient of power grid data, the system optimizes the data compression strategy while ensuring data transmission accuracy, thereby improving data transmission rate and storage efficiency. This not only reduces the consumption of bandwidth resources, but also reduces the demand for storage space, providing technical support for the processing and cross-regional sharing of large-scale power grid data. By evaluating the priority of power grid equipment nodes and accurately calculating their related data transmission impact coefficients, the data transmission strategy is dynamically adjusted according to the actual operation of the power grid equipment. Based on the impact analysis of channel influence, equipment performance and multi-dimensional data of data compression distortion, the system effectively predicts and optimizes data transmission links to ensure the efficiency and accuracy of data sharing between power grid areas. By dividing the thresholds of device node priority levels, the data transmission stability of key nodes is prioritized to avoid cross-regional data transmission failures caused by equipment failures or signal interference.

[0043] S3, dividing the data transmission impact coefficient into thresholds according to the priority levels of the power grid equipment nodes.

[0044] In this embodiment, the threshold division of the data transmission influence coefficient according to the priority level of the power grid device node is specifically as follows: In the process of entering the power grid equipment nodes, each power grid equipment node is manually evaluated for priority, and the nodes are sorted in descending order according to the priority of the power grid equipment to obtain a descending sorting form; Dividing all priority levels into N areas according to the descending sorting table, and setting a threshold interval for the data transmission impact coefficient of each area; If it is lower than the preset threshold range, an early warning will be issued to remind the staff to carry out maintenance and equipment technology update of the power grid equipment. During the maintenance of the power grid equipment, a time period will be set to regularly update the data; If it is higher than the preset threshold range and higher than the threshold range of the upper level area, the management personnel will be prompted to replace the two power grid devices to improve the utilization rate of the equipment.

[0045] Embodiment 2, Figure 2The present invention provides a system for a cross-regional sharing method of power grid monitoring data based on a cloud platform, including a data acquisition module, a data transmission impact model construction module and a data classification module, and there are connections between the modules; A data acquisition module is used to obtain the power grid equipment nodes in the area to be tested, label the power grid equipment nodes and set the priority level, and obtain the data transmission impact data of the power grid equipment, wherein the data transmission impact data includes the channel impact coefficient, the communication equipment performance coefficient and the data compression accuracy distortion coefficient; A data transmission impact model building module is used to build a data transmission impact model based on the data transmission impact data and a machine learning algorithm to obtain a data transmission impact coefficient; The data classification module is used to divide the data transmission impact coefficient into thresholds according to the priority level of the power grid equipment nodes.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0048] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0049] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0050] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0051] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cross-regional sharing method of power grid monitoring data based on a cloud platform, characterized in that: The steps include: Obtaining power grid equipment nodes in the area to be tested, labeling the power grid equipment nodes and setting priority levels, and obtaining data transmission impact data of the power grid equipment, wherein the data transmission impact data includes a channel impact coefficient, a communication equipment performance coefficient, and a data compression accuracy distortion coefficient; Constructing a data transmission impact model based on the data transmission impact data and a machine learning algorithm to obtain a data transmission impact coefficient; The data transmission impact coefficient is divided into thresholds according to the priority level of the power grid equipment nodes.

2. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 1 is characterized in that: The specific steps for obtaining the channel influence coefficient are as follows: Acquire the signal power and noise power data of the bandwidth between the power grid node to be tested and the server, set a fluctuation interval threshold for the signal power and noise power data, and remove the data that is not within the fluctuation interval threshold range; Obtain the distribution of noise power through software analysis, compare the noise power in different frequency ranges, and identify the noise power of the main noise source; The signal power data is plotted into a line graph to obtain a plurality of line segments, the absolute value of the slope of each line segment is obtained through machine learning and compared, and a relatively stable signal power data segment is selected according to the absolute value of the slope; Adding and averaging the relatively stable signal power data segments to obtain signal power, and using it as reference data; Obtain the distance between the power grid device to be tested and the server, send multiple signals to the server through the power grid device node, and record the signal strength of the power grid device signal when it is transmitted and the signal strength after the server receives it, and calculate the average value of the signal strength when the signal is transmitted and the signal strength after the server receives it according to multiple sets of data of the signal strength when the power grid device signal is transmitted and the signal strength after the server receives it; The attenuation coefficient is calculated based on the signal strength when the signal is transmitted, the signal strength after the server receives the signal, and the distance between the power grid equipment and the server based on the preset signal attenuation formula; The bandwidth between the power grid device to be tested and the server is obtained, and the channel influence coefficient is calculated based on a preset channel influence coefficient formula according to the bandwidth, attenuation coefficient, signal power and noise power.

3. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 2 is characterized in that: The specific steps for obtaining the performance coefficient of the communication device are as follows: A piece of data is sent from the power grid device to the server. During the data transmission process, the noise source is started, the output power of the noise source is set, and multiple experiments are performed to obtain multiple groups of bit error rates under normal conditions and bit error rates under the influence of the noise source. The bit error rate in the normal state and the bit error rate under the influence of the noise source are plotted into a curve graph, and the influence factor between the bit error rate and the noise power is obtained through linear regression analysis; Set the distance between the power grid device to be tested and the server, make the power grid device to be tested send multiple groups of signals, and the server receives the signals. Start the magnetic field transmitting device between the power grid device and the server, adjust the magnetic field frequency, and record the signal intensity of the power grid device when transmitting and the signal intensity of the server when receiving under different magnetic field frequency states; The attenuation of the signal under different magnetic field frequency states is obtained according to the intensity of the signal transmitted by the power grid equipment under different magnetic field frequency states and the intensity of the signal received by the server end, and the attenuation constant of the signal is obtained through linear regression analysis; Obtaining the magnetic permeability, electrical conductivity and dielectric constant of the signal transmission medium, and calculating based on a preset magnetic field penetration formula according to the attenuation constant, magnetic field frequency, magnetic permeability, electrical conductivity and dielectric constant to obtain the penetration ability of the power grid equipment to be tested in the magnetic field; Acquire the actual data transmission rate and bandwidth between the power grid device to be tested and the server, and calculate the network load capacity based on the actual data transmission rate and bandwidth based on a preset network load formula; The communication equipment performance coefficient is obtained by calculating based on a preset communication equipment performance coefficient formula according to the network load, magnetic field penetration and bit error rate, and the influencing factors between the bit error rate and noise power.

4. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 3 is characterized in that: The specific steps for obtaining the data compression precision distortion coefficient are as follows: A set of image data to be tested is selected, the image data is compressed by the power grid device to be tested and sent to the server for multiple times, and the total number of pixels of the original data and the compressed data is collected to construct a data set; Constructing a data set according to the total number of pixels of the collected original data and compressed data, cleaning the data, removing outliers in the data, and obtaining the mean of the original data and the mean of the compressed data based on the mean calculation formula of the processed data; According to the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, a data set is constructed, and the variance of the original data and the variance of the compressed data are calculated based on the variance formula; Constructing a data set according to the mean of the original data, the mean of the compressed data and the total number of pixels of the original data and the compressed data, and calculating the covariance between the original data and the compressed data based on the covariance formula; The data compression precision distortion coefficient is calculated based on a preset data compression precision distortion coefficient formula according to the original data mean, the compressed data mean, the original data variance, the compressed data variance, and the covariance between the original data and the compressed data.

5. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 4 is characterized in that: The threshold division of the data transmission influence coefficient according to the priority level of the power grid equipment node is specifically as follows: In the process of entering the power grid equipment nodes, each power grid equipment node is manually evaluated for priority, and the nodes are sorted in descending order according to the priority of the power grid equipment to obtain a descending sorting form; Dividing all priority levels into N areas according to the descending sorting table, and setting a threshold interval for the data transmission impact coefficient of each area; If it is lower than the preset threshold range, an early warning will be issued to remind the staff to carry out maintenance and equipment technology update of the power grid equipment. During the maintenance of the power grid equipment, a time period will be set to regularly update the data; If it is higher than the preset threshold range and higher than the threshold range of the upper level area, the management personnel will be prompted to replace the two power grid devices.

6. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 5 is characterized in that: The calculation formula of the channel influence coefficient is as follows: In the formula, is the channel influence coefficient, is the bandwidth between the power grid equipment and the server, is the signal power, is the signal attenuation coefficient, is the noise power.

7. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 6 is characterized in that: The calculation formula of the communication equipment performance coefficient is as follows: The calculation formula of the magnetic field penetration is as follows: The calculation formula of the network load is as follows: In the formula, is the communication equipment performance coefficient, is a modulation constant, is a natural logarithm, is the signal power, is the noise power, is the network load, It is the magnetic field penetration, is the angular frequency of the wave, is the magnetic permeability of the medium, is the dielectric constant of the medium, is the conductivity of the medium, is the magnetic field frequency, is the decay constant, is the actual data transfer rate, It is the bandwidth between the power grid equipment and the server.

8. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 7 is characterized in that: The calculation formula of the data compression precision distortion coefficient is as follows: The calculation formulas for the mean of the original data and the mean of the compressed data are as follows: The calculation formulas for the variance of the original data and the variance of the compressed data are as follows: The calculation formula of the covariance between the original data and the compressed data is as follows: In the formula, is the data compression precision distortion coefficient, is the original data mean, is the mean of the compressed data, is the covariance between the original and compressed data, is the variance of the original data, is the variance of the compressed data, and is a constant that prevents the denominator from being zero.

9. The cross-regional sharing method of power grid monitoring data based on cloud platform according to claim 8 is characterized in that: The data transmission impact model is constructed based on the machine learning algorithm according to the data transmission impact data to obtain the data transmission impact coefficient, specifically: According to the channel influence coefficient, the communication equipment performance coefficient and the data compression precision distortion coefficient, a data transmission influence model is constructed based on a machine learning algorithm to obtain a data transmission influence coefficient, which is specifically as follows: The calculation formula of the data transmission impact model is as follows: In the formula, is the data transmission impact coefficient, is the channel influence coefficient, is the communication equipment performance coefficient, is the data compression precision distortion coefficient.

10. A system using the cloud platform-based cross-regional sharing method for power grid monitoring data as claimed in any one of claims 1 to 9, characterized in that: It includes a data collection module, a data transmission impact model building module and a data classification module, and there are connections between the modules; A data acquisition module is used to obtain the power grid equipment nodes in the area to be tested, label the power grid equipment nodes and set the priority level, and obtain the data transmission impact data of the power grid equipment, wherein the data transmission impact data includes the channel impact coefficient, the communication equipment performance coefficient and the data compression accuracy distortion coefficient; A data transmission impact model building module is used to build a data transmission impact model based on the data transmission impact data and a machine learning algorithm to obtain a data transmission impact coefficient; The data classification module is used to divide the data transmission influence coefficient into thresholds according to the priority level of the power grid equipment node.

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