Distributed meteorological data processing method based on edge calculation

Abstract

The invention discloses a distributed meteorological data processing method based on edge computing, and relates to the technical field of data processing. The distributed meteorological data processing method based on edge calculation comprises the following steps: collecting data; evaluating data; and comprehensively evaluating. According to the invention, the edge device collects and preprocesses the meteorological data generated by the observation of the meteorological station to obtain the to-be-transmitted meteorological data, then collects the meteorological data information in the process of sending the meteorological data to the central node by the edge device, and carries out the data evaluation of the meteorological data transmission and synchronization process to obtain the data evaluation index. And performing comprehensive evaluation according to the data evaluation index to obtain a comprehensive meteorological data evaluation index. And when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data early warning threshold, an early warning mechanism is triggered, and the service personnel intervenes and maintains in time, so that the effect of improving the transmission and synchronization accuracy of the meteorological data is achieved, and the problem of low data accuracy in the meteorological data transmission and synchronization process in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a distributed meteorological data processing method based on edge computing. Background Art

[0002] As attention to climate change increases, meteorological data has become an important foundation and key resource in many fields such as weather forecasting, climate research, agricultural production, urban planning, traffic management, environmental protection, and scientific research, which require a lot of computing resources and data processing capabilities. Traditionally, these tasks are usually performed on centralized supercomputers. However, with the development of edge computing technology, these tasks can be performed on edge devices, thereby improving computing efficiency and reducing costs. Meteorological data is data on various meteorological elements collected by various meteorological observation stations, satellites, radars and other equipment, including temperature, humidity, air pressure, wind speed, precipitation, etc. Meteorological data not only provides convenience and security for our lives and work, but also provides important support and driving force for scientific research and technological innovation.

[0003] The existing meteorological data processing system integrates advanced observation equipment, high-speed data transmission, efficient database management, complex data processing algorithms and real-time visualization technology to achieve the automated and efficient collection, storage, processing, analysis and display of massive meteorological data, providing timely and accurate meteorological information services for multiple fields.

[0004] For example, the patent application with publication number: CN113032212A discloses a method, system, computer equipment and storage medium for monitoring meteorological data in the entire network, including: establishing a corresponding relationship between observation data and network links according to radar data identifiers and radar link identifiers, and associating observation data with key nodes in the transmission process to establish a transmission model; obtaining data information of observation data through information processing and warehousing models, and obtaining monitoring information of observation data by statistically analyzing the data information; obtaining status information of all key host nodes of observation data in the transmission process in real time through a host and file status information collection model; obtaining operating status information of network equipment in real time through a network device status information collection model; obtaining network connectivity information in real time through a network connectivity detection model, and marking each network link in the transmission model.

[0005] For example, the meteorological data transformation method, transmission method, server and data transmission system disclosed in the invention patent with announcement number: CN112686001 B include: finding the maximum and minimum values ​​from the original meteorological data; selecting a closed interval; the length of the closed interval is less than the difference between the maximum and minimum values; calculating the scaling factor and offset according to the maximum, minimum, right endpoint value of the closed interval and left endpoint value of the closed interval; using the calculated scaling factor and offset to transform the original meteorological data to obtain transformed data.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, distributed meteorological data processing based on edge computing completes the flow of meteorological data from the edge computing layer to the cloud center layer through preliminary processing and compression of edge devices, combined with data transmission mechanism and real-time synchronization technology. However, there is a problem of low data accuracy during the transmission and synchronization of meteorological data. Summary of the invention

[0008] The embodiment of the present application solves the problem of low data accuracy in the transmission and synchronization of meteorological data in the prior art by providing a distributed meteorological data processing method based on edge computing, and realizes real-time monitoring of the transmission and synchronization of meteorological data.

[0009] The embodiment of the present application provides a distributed meteorological data processing method based on edge computing, comprising the following steps: S1, pre-processing the meteorological data to be evaluated by the edge device to obtain the meteorological data to be transmitted, and collecting in real time the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node, wherein the meteorological data information comprises a first meteorological data information and a second meteorological data information, wherein the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; S2, obtaining a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, wherein the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation results to reflect the performance of the meteorological data transmission and synchronization process; S3, performing a comprehensive evaluation on the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index, wherein when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed, and the comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is required.

[0010] Furthermore, the specific method of the preprocessing is as follows: initial first meteorological data is obtained by performing a first preprocessing on the meteorological data to be evaluated, and the first preprocessing represents removing abnormal data in the meteorological data to be evaluated; initial second meteorological data is obtained by performing a second preprocessing on the initial first meteorological data, and the second preprocessing represents formatting the initial first meteorological data; meteorological data to be transmitted is obtained by performing a third preprocessing on the initial second meteorological data, and the third preprocessing represents data compression processing on the initial second meteorological data.

[0011] Furthermore, the data evaluation includes data transmission evaluation and data synchronization evaluation; the data transmission evaluation means evaluating the data transmission process according to the first meteorological data information to obtain a data transmission index, and the data transmission index is used to evaluate the overall efficiency of the meteorological data information in the data transmission process; the data synchronization evaluation means evaluating the data synchronization process according to the second meteorological data information to obtain a data synchronization index, and the data synchronization index is used to evaluate the overall quality of the meteorological data information in the data synchronization process.

[0012] Furthermore, the method for obtaining the data transmission index is as follows: obtaining first meteorological data information of the meteorological data to be transmitted during the data transmission process, wherein the first meteorological data information includes data throughput, data packet loss rate, data delay time, and data update frequency coefficient, wherein the data update frequency coefficient represents the average value of the ratio of the update frequency of the data to be transmitted in the central node to the update frequency of the meteorological data to be evaluated in the edge device; obtaining a data transmission evaluation weight from a preset database, wherein the data transmission evaluation weight includes a first transmission weight, a second transmission weight, and a third transmission weight; and calculating the data transmission index of the meteorological data to be transmitted in combination with the first meteorological data information and the data transmission evaluation weight, wherein the data transmission index is calculated using the following formula:

[0013]

[0014] Where DTI is the data transmission index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first transmission weight of the ith edge device, e represents a natural constant, T i is the data throughput of the ith edge device within the preset time interval, c is the first correction factor, represents the second transmission weight of the i-th edge device, PL i is the data packet loss rate of the ith edge device within the preset time interval, is the third transmission weight of the ith edge device, D i represents the data delay time of the i-th edge device, D max,iis the reference maximum data delay time of the i-th edge device, and GX represents the data update frequency coefficient.

[0015] Furthermore, the method for obtaining the data synchronization index is as follows: obtaining the second meteorological data information of the meteorological data to be transmitted during the data synchronization process, the second meteorological data information including the data packet loss rate, the number of data retransmissions, the data error rate and the data acquisition frequency coefficient, the data acquisition frequency coefficient representing the average value of the ratio of the actual acquisition frequency of the meteorological data to be evaluated in the edge device to the preset acquisition frequency; obtaining the data synchronization evaluation weight from the preset database, the data synchronization evaluation weight including the first synchronization weight, the second synchronization weight and the third synchronization weight; combining the second meteorological data information and the data synchronization evaluation weight to calculate the data synchronization index of the meteorological data to be transmitted, the data synchronization index is calculated using the following formula:

[0016]

[0017] Where DSI is the data synchronization index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first synchronization weight of the ith edge device, e represents a natural constant, PL i is the data packet loss rate of the ith edge device within the preset time interval, represents the second synchronization weight of the ith edge device, M i represents the number of data retransmissions of the ith edge device within the preset time interval, N is the total number of data packets transmitted by the edge device within the preset time interval, is the third synchronization weight of the ith edge device, ER i It represents the data error rate of the ith edge device within a preset time interval, and CX represents the data collection frequency coefficient.

[0018] Furthermore, the specific steps of comprehensively evaluating the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index are as follows: obtaining a preset data evaluation index threshold and a preset comprehensive meteorological data warning threshold from a preset database; if both the data transmission index and the data synchronization index are greater than the preset data evaluation index threshold, continuously monitoring changes in the data transmission index and the data synchronization index; if at least one of the data transmission index and the data synchronization index is not greater than the preset data evaluation index threshold, obtaining the comprehensive meteorological data evaluation index of the meteorological data to be transmitted according to the data evaluation index, and judging whether to trigger the warning mechanism according to the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data warning threshold.

[0019] Furthermore, the method for obtaining the comprehensive meteorological data evaluation index is as follows: obtaining a weight evaluation factor from a preset database, wherein the weight evaluation factor includes a data transmission weight and a data synchronization weight; obtaining a comprehensive meteorological data evaluation index according to the weight evaluation factor, the data transmission index and the data synchronization index, wherein the comprehensive meteorological data evaluation index is calculated using the following formula:

[0020]

[0021] In the formula, I QX is the comprehensive meteorological data evaluation index, e represents a natural constant, ω T is the data transmission weight, DTI is the data transmission index, ω S is the data synchronization weight and DSI is the data synchronization index.

[0022] Furthermore, the specific steps of triggering the early warning mechanism are as follows: comparing the comprehensive meteorological data evaluation index with the preset comprehensive meteorological data early warning threshold; when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data early warning threshold, the early warning mechanism is triggered and the generated early warning information is sent to the preset maintenance personnel, and the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data early warning threshold is continuously monitored, and the early warning information includes the comprehensive meteorological data evaluation index, the data evaluation index and the corresponding meteorological data information; when the comprehensive meteorological data evaluation index is not lower than the preset comprehensive meteorological data early warning threshold, the early warning mechanism is lifted.

[0023] The embodiment of the present application provides a distributed meteorological data processing system based on edge computing, including: a data collection module, a data evaluation module and a comprehensive evaluation module; wherein the data collection module is used to pre-process the meteorological data to be evaluated through the edge device to obtain the meteorological data to be transmitted, and collect the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node in real time, the meteorological data information includes first meteorological data information and second meteorological data information, the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; the data evaluation module is used to obtain a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation result to reflect the performance of the meteorological data transmission and synchronization process; the comprehensive evaluation module is used to perform a comprehensive evaluation on the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed, and the comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is required.

[0024] An embodiment of the present application provides an electronic device, characterized in that the electronic device includes a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the distributed meteorological data method based on edge computing.

[0025] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0026] 1. The meteorological data to be evaluated is preprocessed by the edge device to obtain the meteorological data to be transmitted, and then the meteorological data information is collected in the process of being sent from the edge device to the central node, and then the data evaluation is performed on the meteorological data transmission and synchronization process to obtain the data evaluation index, and then the meteorological data to be transmitted is comprehensively evaluated according to the data evaluation index to obtain the comprehensive meteorological data evaluation index. Finally, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered and the generated warning information is sent to the preset maintenance personnel for processing, thereby improving the accuracy of the meteorological data, and then realizing real-time monitoring of the transmission and synchronization of meteorological data, which effectively solves the problem of low data accuracy in the process of meteorological data transmission and synchronization in the prior art.

[0027] 2. By obtaining the first meteorological data information of the meteorological data to be transmitted during the data transmission process, and then obtaining the data transmission evaluation weight from the preset database, and finally combining the first meteorological data information and the data transmission evaluation weight to calculate the data transmission index of the meteorological data to be transmitted, the transmission efficiency of the meteorological data sent from the edge device to the central node is evaluated, thereby realizing the data transmission efficiency of the digitized meteorological data.

[0028] 3. By obtaining the second meteorological data information of the meteorological data to be transmitted during the data synchronization process, and then obtaining the data synchronization evaluation weight from the preset database, and finally combining the second meteorological data information and the data synchronization evaluation weight to calculate the data synchronization index of the meteorological data to be transmitted, the synchronization quality of the meteorological data sent from the edge device to the central node is evaluated, thereby realizing the data synchronization quality of the digitized meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a distributed meteorological data processing method based on edge computing provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of changes in the comprehensive meteorological data evaluation index provided in an embodiment of the present application;

[0031] Figure 3A schematic diagram of the structure of a distributed meteorological data processing system based on edge computing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The embodiment of the present application solves the problem of low data accuracy in the process of meteorological data transmission and synchronization in the prior art by providing a distributed meteorological data processing method based on edge computing. The meteorological data to be evaluated is pre-processed by the edge device to obtain the meteorological data to be transmitted, and then the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node is collected in real time. Then, the data evaluation index is obtained by performing data evaluation on the meteorological data transmission and synchronization process. Then, the meteorological data to be transmitted is comprehensively evaluated according to the data evaluation index to obtain the comprehensive meteorological data evaluation index. Finally, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed, thereby realizing real-time monitoring of the transmission and synchronization of meteorological data.

[0033] The technical solution in the embodiment of the present application is to solve the problem of low data accuracy in the above-mentioned meteorological data transmission and synchronization process. The overall idea is as follows:

[0034] The meteorological data to be evaluated is preprocessed by edge devices to obtain the meteorological data to be transmitted, and then the meteorological data information is collected in the process of being sent from the edge devices to the central node. Then, a comprehensive evaluation is performed based on the data evaluation index obtained by the data evaluation to obtain the comprehensive meteorological data evaluation index. Finally, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, thereby achieving the effect of improving the transmission and synchronization accuracy of meteorological data.

[0035] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] like Figure 1As shown, it is a flow chart of a distributed meteorological data processing method based on edge computing provided in an embodiment of the present application, and the method includes the following steps: S1, pre-processing the meteorological data to be evaluated by the edge device to obtain the meteorological data to be transmitted, and collecting the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node in real time, the meteorological data information includes the first meteorological data information and the second meteorological data information, the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; S2, obtaining a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation results to reflect the performance of the meteorological data transmission and synchronization process; S3, comprehensively evaluating the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, triggering the warning mechanism, otherwise no processing is performed, and the comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is needed.

[0037] In this embodiment, edge computing is a computing mode that distributes computing resources and data processing functions on edge devices and nodes close to the data source. This computing mode can significantly reduce the delay of data transmission, improve the real-time and efficiency of data processing, and enhance the security and privacy protection of data. Edge devices generally include but are not limited to meteorological stations and sensors. The edge devices pre-process the collected meteorological data to be evaluated to obtain the meteorological data to be transmitted, wherein the purpose of pre-processing is to remove abnormal values ​​in the meteorological data to be evaluated and to format and compress them, so that the subsequent transmission of the meteorological data to be transmitted will be more efficient; edge devices are distributed in different geographical locations, and real-time collection of meteorological parameters such as temperature, humidity, wind speed, wind direction, and air pressure. These terminal devices have the ability to collect and pre-process data, and they are regarded as edge devices in edge computing; the transmission and synchronization accuracy of meteorological data is improved through the cooperation between multiple edge devices.

[0038] Furthermore, the specific method of preprocessing is as follows: initial first meteorological data is obtained by performing a first preprocessing on the meteorological data to be evaluated, and the first preprocessing represents removing abnormal data in the meteorological data to be evaluated; initial second meteorological data is obtained by performing a second preprocessing on the initial first meteorological data, and the second preprocessing represents formatting the initial first meteorological data; meteorological data to be transmitted is obtained by performing a third preprocessing on the initial second meteorological data, and the third preprocessing represents data compression processing on the initial second meteorological data.

[0039] In this embodiment, the first preprocessing is used to ensure the accuracy of the meteorological data to be evaluated. Abnormal data is generally meteorological data that exceeds the normal range. For example, the temperature data in each data packet in the meteorological data to be evaluated is checked. If the temperature value is -300°C or 1000°C, these temperature values ​​are obviously beyond the normal range, and they are marked as abnormal data and deleted; the second preprocessing is used to ensure the consistency of the initial first meteorological data. Formatting means converting the timestamp in the initial first meteorological data into the format of Coordinated Universal Time (UTC), for example, converting the local time 2023-03-01 15:06:00 is converted to UTC time 2023-03-01T15:06:00Z; the third preprocessing is used to reduce the burden of data transmission. Data compression means using a compression algorithm to reduce the size of a data packet. For example, the LZ4 compression algorithm is used to compress the data packet, compressing the original data packet size from 1000 bytes to 500 bytes. Meteorological observation stations that collect meteorological data are usually located in remote areas, and the network bandwidth and storage resources of edge devices are limited. By compressing meteorological data to reduce the amount of data and storage space, meteorological data can be transmitted from edge devices to central nodes faster, thereby improving data transmission efficiency and storage efficiency, and realizing effective preprocessing of meteorological data to be transmitted.

[0040] Furthermore, data evaluation includes data transmission evaluation and data synchronization evaluation; data transmission evaluation means evaluating the data transmission process according to the first meteorological data information to obtain a data transmission index, and the data transmission index is used to evaluate the overall efficiency of the meteorological data information in the data transmission process; data synchronization evaluation means evaluating the data synchronization process according to the second meteorological data information to obtain a data synchronization index, and the data synchronization index is used to evaluate the overall quality of the meteorological data information in the data synchronization process.

[0041] In this embodiment, the data evaluation index includes a data transmission index and a data synchronization index. In a scenario where multiple edge devices (such as a meteorological observation station) send meteorological data to a central node, data transmission evaluation is particularly important. Each edge device collects and sends a large amount of meteorological data to the central node in real time for aggregation and analysis. The data transmission index is obtained through data transmission evaluation, and the data transmission efficiency of each edge device to the central node can be monitored in real time, and problems arising in the data transmission process can be discovered and resolved in a timely manner. For example, when the data transmission index is found to be lower than 0.6 (the preset data evaluation index threshold range is 0.6 to 1), the network connection status of the device can be checked immediately to ensure the real-time and accuracy of the meteorological data; when multiple edge devices send meteorological data to the central node, the network connection status of the device can be checked immediately to ensure the real-time and accuracy of the meteorological data; when multiple edge devices send meteorological data to the central node, the network connection status of the device can be checked immediately to ensure the real-time and accuracy of the meteorological data. In the data scenario, data synchronization evaluation also plays an important role. When the edge device synchronizes the meteorological data to the central node, it is necessary to ensure that the meteorological data maintains a high degree of consistency and accuracy during the synchronization process. The data synchronization index obtained through data synchronization evaluation can monitor the data synchronization status between each edge device and the central node in real time, and promptly discover and solve problems that arise during the synchronization process. For example, when the data synchronization index is found to be lower than 0.6, you can immediately check whether there is a problem with the synchronization configuration or synchronization software of the device, and take measures to repair and optimize it. This ensures that the meteorological data collected by the central node is complete, accurate and reliable, providing strong support for subsequent data analysis and application, and realizing data transmission evaluation and data synchronization evaluation based on meteorological data.

[0042] Furthermore, a method for obtaining a data transmission index is as follows: obtaining first meteorological data information of the meteorological data to be transmitted during the data transmission process, the first meteorological data information including data throughput, data packet loss rate, data delay time and data update frequency coefficient, the data update frequency coefficient representing the average value of the ratio of the update frequency of the data to be transmitted in the central node to the update frequency of the meteorological data to be evaluated in the edge device; obtaining a data transmission evaluation weight from a preset database, the data transmission evaluation weight including a first transmission weight, a second transmission weight and a third transmission weight; combining the first meteorological data information and the data transmission evaluation weight to calculate the data transmission index of the meteorological data to be transmitted, the data transmission index being calculated using the following formula:

[0043]

[0044] Where DTI is the data transmission index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first transmission weight of the ith edge device, e represents a natural constant, T i is the data throughput of the ith edge device within the preset time interval, c is the first correction factor, represents the second transmission weight of the i-th edge device, PL i is the data packet loss rate of the ith edge device within the preset time interval, is the third transmission weight of the ith edge device, D i represents the data delay time of the i-th edge device, D max,i is the reference maximum data delay time of the i-th edge device, and GX represents the data update frequency coefficient.

[0045] In this embodiment, data throughput refers to the amount of data successfully transmitted per unit time. iperf3 is an open source tool for testing network bandwidth, which is used to evaluate the performance and throughput of the network. During the test, the iperf3 server and client will send and receive data to each other, thereby calculating the data throughput; the data packet loss rate refers to the ratio of the number of data packets lost during data transmission to the total number of data packets sent. Wi reshark is a network packet analysis software. Wi reshark can capture data packets on the network and display detailed information for each data packet. By analyzing the captured data packets, the number of data packets sent and received can be counted, thereby calculating the data packet loss rate; the data delay time refers to the time it takes for data to travel from the sender to the receiver, which is usually calculated by measuring the timestamps of data packet sending and receiving. SolarWinds NPM (Network Performance Monitor) is a powerful network management tool that can monitor the performance of network devices in real time. After installing and configuring SolarWinds NPM, create monitoring items to measure the round-trip time of data packets to obtain data delay time.

[0046] Specifically, the formula combines data throughput, data packet loss rate, data delay time, data update frequency coefficient and data transmission evaluation weight to obtain the data transmission index. In this formula, there is a positive correlation between data throughput and data transmission index. The higher the data throughput, the higher the data transmission index. There is a negative correlation between data packet loss rate and data delay time and data transmission index. The lower the data packet loss rate and the shorter the data delay time, the higher the data transmission index. The value range of the data transmission index is 0-1. The closer the data transmission index is to 1, the higher the overall efficiency of the meteorological data to be transmitted in the data transmission process. The closer the data transmission index is to 0, the lower the overall efficiency of the meteorological data to be transmitted in the data transmission process. The data update frequency coefficient is positively correlated with the data transmission index. Specifically, the larger the data update frequency coefficient, the faster the corresponding data is transmitted from the edge device to the central node. The faster the transmission, the faster the corresponding data update frequency, which means that the data transmission efficiency is high. Meteorological data is different from other data and needs to be updated in time. Among them, data throughput, data packet loss rate and data delay time will affect each other in the process of meteorological data transmission. For example: from multiple edge devices to The central node sends meteorological data. In a certain period of time, due to the limited network bandwidth and multiple devices transmitting a large amount of data at the same time, the data throughput increases sharply. At this time, the network is congested due to the increase in data throughput, and some data packets may be lost due to buffer overflow, and the data packet loss rate increases. At the same time, due to the increase in data throughput, the queuing time of data packets in the network increases, and the data delay time also increases accordingly. The data throughput represents the total amount of meteorological data to be transmitted from the edge device to the central node within a preset time interval. The data packet loss rate represents the proportion of data packets lost during the data transmission process of the meteorological data to be transmitted within a preset time interval. The data delay time represents the time required for the meteorological data to be transmitted from the edge device to the central node for reception. The first transmission weight is used to reflect the influence of data throughput in the data transmission evaluation, the second transmission weight is used to reflect the influence of data packet loss rate in the data transmission evaluation, and the third transmission weight is used to reflect the influence of data delay time in the data transmission evaluation. The sum of the first transmission weight, the second transmission weight and the third transmission weight is 1, which represents the different influences of different factors on the data transmission index. Adjusting the distribution of weights can reflect the different influences of different factors on the data transmission index.

[0047] The first transmission weight is obtained from a preset database. In a specific embodiment, a mapping set of data throughput and its corresponding weight factor is constructed according to the relationship between historical data throughput and data transmission index, and the real-time data throughput is input into the mapping set to obtain the corresponding first transmission weight.

[0048] The second transmission weight is obtained from a preset database. In a specific embodiment, a mapping set of data packet loss rate and its corresponding weight factor is constructed according to the relationship between the historical data packet loss rate and the data transmission index, and the real-time data packet loss rate is input into the mapping set to obtain the corresponding second transmission weight.

[0049] The third transmission weight is obtained from a preset database. In a specific embodiment, a mapping set of data delay time and its corresponding weight factor is constructed according to the relationship between historical data delay time and data transmission index, and the real-time data delay time is input into the mapping set to obtain the corresponding third transmission weight.

[0050] The first correction factor is obtained from a preset database. In a specific embodiment, a mapping set of data throughput and its corresponding correction factor is constructed according to the relationship between historical data throughput and data transmission index, and the real-time data throughput is input into the mapping set to obtain the corresponding first correction factor.

[0051] It is important to understand that T i is the data throughput of the ith edge device within the preset time interval, c is the first correction factor, which is used to control the nonlinear degree of the impact of data throughput on the data transmission index, and its value range is 10 -3 ~10 -1 ,use To reflect the contribution of data throughput to the data transmission index, as T i increase, The value of gradually decreases, so The value of PL gradually increases, which means that the higher the data throughput, the higher the data transmission index. The first correction factor controls the speed of this nonlinear growth; i is the data packet loss rate of the ith edge device within the preset time interval, 1-PL i It reflects the contribution of data packet loss rate to the data transmission index. The lower the data packet loss rate, the higher the data transmission index. i represents the data delay time of the i-th edge device, D max,i is the reference maximum data delay time of the i-th edge device, using To reflect the contribution of data delay time to the data transmission index, the lower the data delay time, the higher the data transmission index, realizing the numerical evaluation of the data transmission efficiency of meteorological data.

[0052] Furthermore, the method for obtaining the data synchronization index is as follows: obtaining the second meteorological data information of the meteorological data to be transmitted during the data synchronization process, the second meteorological data information includes the data packet loss rate, the number of data retransmissions, the data error rate and the data acquisition frequency coefficient, the data acquisition frequency coefficient represents the average value of the ratio of the actual acquisition frequency of the meteorological data to be evaluated in the edge device to the preset acquisition frequency; obtaining the data synchronization evaluation weight from the preset database, the data synchronization evaluation weight includes the first synchronization weight, the second synchronization weight and the third synchronization weight; combining the second meteorological data information and the data synchronization evaluation weight to calculate the data synchronization index of the meteorological data to be transmitted, the data synchronization index is calculated using the following formula:

[0053]

[0054] Where DSI is the data synchronization index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first synchronization weight of the ith edge device, e represents a natural constant, PL i is the data packet loss rate of the ith edge device within the preset time interval, represents the second synchronization weight of the ith edge device, M i represents the number of data retransmissions of the ith edge device within the preset time interval, N is the total number of data packets synchronized by the edge device within the preset time interval, is the third synchronization weight of the ith edge device, ER i It represents the data error rate of the ith edge device within a preset time interval, and CX represents the data collection frequency coefficient.

[0055] In this embodiment, the data error rate generally refers to the proportion of data packets that are erroneous or damaged during the transmission process to the total number of transmitted data packets; the number of data retransmissions refers to the number of times the original data is resent when the original data fails to successfully reach the destination due to various reasons (such as data packet loss, errors or transmission failure) during the data transmission process; the SolarWidth NPM software is installed and configured in the meteorological data transmission network; secondly, monitoring items are defined to focus on key performance indicators of data packet transmission, including errors and retransmissions; then, real-time monitoring is started and SolarWidth NPM is configured to capture and record relevant data; finally, the error rate and retransmission times are extracted and counted through the data analysis tool provided by SolarWidth NPM.

[0056] The formula combines the data packet loss rate, the number of data retransmissions, the data error rate, the data acquisition frequency coefficient and the data synchronization evaluation weight to obtain the data synchronization index. In this formula, the data packet loss rate, the number of data retransmissions and the data error rate are all negatively correlated with the data synchronization index. The lower the data packet loss rate, the fewer the data retransmissions and the lower the data error rate, the higher the data synchronization index. The value range of the data synchronization index is 0-1. The closer the data synchronization index is to 1, the higher the overall quality of the meteorological data to be transmitted during the data synchronization process. The closer the data synchronization index is to 0, the lower the overall quality of the meteorological data to be transmitted during the data synchronization process. The data acquisition frequency coefficient is positively correlated with the data synchronization index. The larger the data acquisition frequency coefficient, the larger the corresponding data acquisition frequency coefficient, and the higher the accuracy of data synchronization. In actual situations, due to failures or delays in edge devices, the actual acquisition frequency may be less than the preset acquisition frequency, which means that some changes in meteorological data cannot be fully collected. Meteorological data is different from other data. It is necessary to ensure timely data collection to capture the corresponding meteorological changes. Among them, the data packet loss rate, the number of data retransmissions and the number of The data error rate will affect each other during the synchronization process of meteorological data. For example, when the network conditions are unstable during the synchronization of meteorological data from the edge device to the central node, the increase in the data packet loss rate will trigger more data retransmission times to ensure data integrity. Although the data packet loss rate is not directly equivalent to the data error rate, network problems also increase the risk of data packets being damaged during the synchronization process, thereby increasing the data error rate. The number of data retransmissions indicates the number of data packets that need to be resent during the synchronization process of the meteorological data to be transmitted within a preset time interval due to data packet loss or damage. The data error rate indicates the proportion of data packets that have errors during the synchronization process of the meteorological data to be transmitted within a preset time interval. The first synchronization weight is used to reflect the degree of influence of the data packet loss rate in the data synchronization evaluation, the second synchronization weight is used to reflect the degree of influence of the number of data retransmissions in the data synchronization evaluation, and the third synchronization weight is used to reflect the degree of influence of the data error rate in the data synchronization evaluation. The sum of the first synchronization weight, the second synchronization weight and the third synchronization weight is 1, indicating the different influences of different factors on the data synchronization index. Adjusting the distribution of weights can reflect the different influences of different factors on the data synchronization index.

[0057] Specifically, the first synchronization weight is obtained from a preset database. In a specific embodiment, a mapping set of data packet loss rate and its corresponding weight factor is constructed according to the relationship between the historical data packet loss rate and the data synchronization index, and the real-time data packet loss rate is input into the mapping set to obtain the corresponding first synchronization weight.

[0058] The second synchronization weight is obtained from a preset database. In a specific embodiment, a mapping set of the number of data retransmissions and their corresponding weight factors is constructed based on the relationship between the number of historical data retransmissions and the data synchronization index, and the real-time data retransmission number is input into the mapping set to obtain the corresponding second synchronization weight.

[0059] The third synchronization weight is obtained from a preset database. In a specific embodiment, a mapping set of data error rate and its corresponding weight factor is constructed according to the relationship between the historical data error rate and the data synchronization index, and the real-time data error rate is input into the mapping set to obtain the corresponding third synchronization weight.

[0060] It is important to understand that PL i is the data packet loss rate of the i-th edge device, 1-PL i It reflects the contribution of data packet loss rate to data synchronization index, which means that the lower the data packet loss rate, the higher the data synchronization index; M i It represents the number of data retransmissions of the ith edge device within the preset time interval, N is the total number of data packets synchronized by the edge device, and is used Reflects the contribution of data retransmission times to the data synchronization index. The fewer the data retransmission times, the higher the data synchronization index. Used to standardize the number of data retransmissions to match the value range of other ratio indicators, ER i It represents the data error rate of the ith edge device within a preset time interval, using 1-ER i It reflects the contribution of the data error rate to the data synchronization index. The lower the data error rate, the higher the data synchronization index, which realizes the numerical evaluation of the data synchronization quality of meteorological data.

[0061] Furthermore, the specific steps of comprehensively evaluating the meteorological data to be transmitted according to the data evaluation index to obtain the comprehensive meteorological data evaluation index are as follows: obtaining the preset data evaluation index threshold and the preset comprehensive meteorological data warning threshold from the preset database; if the data transmission index and the data synchronization index are both greater than the preset data evaluation index threshold, continuously monitoring the changes in the data transmission index and the data synchronization index; if at least one of the data transmission index and the data synchronization index is not greater than the preset data evaluation index threshold, obtaining the comprehensive meteorological data evaluation index of the meteorological data to be transmitted according to the data evaluation index, and judging whether to trigger the warning mechanism according to the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data warning threshold.

[0062] In this embodiment, the preset data evaluation index threshold is a boundary value set based on historical data and obtained from a preset database. The value range of the preset data evaluation index threshold is generally 0.6 to 1. When the data evaluation index is greater than the preset data evaluation index threshold, it means that the data transmission and synchronization process is stable and reliable, and can meet the timeliness and accuracy requirements of meteorological data, and no maintenance personnel are required to process it. When the data evaluation index is not greater than the preset data evaluation index threshold, it means that the data transmission efficiency and synchronization status cannot meet the timeliness and accuracy requirements of meteorological data. It is necessary to obtain the comprehensive meteorological data evaluation index of the meteorological data to be transmitted according to the data evaluation index, and then compare the comprehensive meteorological data evaluation index with the preset comprehensive meteorological data warning threshold to determine whether to trigger the warning mechanism, thereby realizing a comprehensive evaluation of the overall performance of meteorological data sent from the edge device to the central node.

[0063] Furthermore, the method for obtaining the comprehensive meteorological data evaluation index is as follows: obtain a weight evaluation factor from a preset database, the weight evaluation factor including a data transmission weight and a data synchronization weight; obtain the comprehensive meteorological data evaluation index according to the weight evaluation factor, the data transmission index and the data synchronization index, and the comprehensive meteorological data evaluation index is calculated using the following formula:

[0064]

[0065] In the formula, I QX is the comprehensive meteorological data evaluation index, e represents a natural constant, ω T is the data transmission weight, DTI is the data transmission index, ω S is the data synchronization weight and DSI is the data synchronization index.

[0066] In this embodiment, the formula combines the data transmission index, the data synchronization index, the data transmission weight and the data synchronization weight to obtain the comprehensive meteorological data evaluation index. In this formula, the data transmission index and the data synchronization index are positively correlated with the comprehensive meteorological data evaluation index. The value range of the comprehensive meteorological data evaluation index is 0 to e-1 (e is a natural constant, and its value is approximately 2.718281828459045). The closer the comprehensive meteorological data evaluation index is to e-1, the better the data transmission efficiency and data synchronization quality are, that is, the higher the comprehensive meteorological data evaluation index is, the better the overall performance of data transmission and data synchronization is. The closer the comprehensive meteorological data evaluation index is to 0, the worse the data transmission efficiency and data synchronization quality are, that is, the lower the comprehensive meteorological data evaluation index is, the worse the overall performance of data transmission and data synchronization is. Among them, there is a certain correlation between the data transmission index and the data synchronization index, for example: from multiple Meteorological stations collect real-time meteorological data and send these data from edge devices to central nodes for further processing and analysis. In this process, when network conditions are good, data transmission protocols are optimized, and system resources are sufficient, the data transmission index will remain at a high level, which means that meteorological data can be quickly and accurately transmitted from edge devices to central nodes. In this case, due to frequent and timely data updates, the data synchronization process will be smoother, reducing the risk of data delays and inconsistencies, thereby improving the data synchronization index. The data transmission weight is used to reflect the importance of data transmission evaluation in the comprehensive evaluation, and the data synchronization weight is used to reflect the importance of data synchronization evaluation in the comprehensive evaluation. The sum of the data transmission weight and the data synchronization weight is 1, indicating the different effects of different factors on the comprehensive meteorological data evaluation index. Adjusting the distribution of weights can reflect the different effects of different factors on the comprehensive meteorological data evaluation index.

[0067] Specifically, the data transmission weight is obtained from a preset database. In a specific embodiment, a mapping set of the data transmission index and its corresponding weight factor is constructed according to the relationship between the historical data transmission index and the comprehensive meteorological data evaluation index, and the real-time data transmission index is input into the mapping set to obtain the corresponding data transmission weight.

[0068] The data synchronization weight is obtained from a preset database. In a specific embodiment, a mapping set of the data synchronization index and its corresponding weight factor is constructed according to the relationship between the historical data synchronization index and the comprehensive meteorological data evaluation index, and the real-time data synchronization index is input into the mapping set to obtain the corresponding data synchronization weight.

[0069] It should be understood that, taking the data transmission weight as 0.5 and the data synchronization weight as 0.5 as an example, the statistical table of changes in the comprehensive meteorological data evaluation index is shown in Table 1:

[0070] Table 1 Statistics of changes in comprehensive meteorological data evaluation index

[0071]

[0072]

[0073] As can be seen from Table 1, these three sets of data show the changes in the comprehensive meteorological data evaluation index under different data transmission indexes and data synchronization indexes. In the first set of data, the data transmission index and the data synchronization index are both lower than 0.6 (the value range of the preset data evaluation index threshold is 0.6 to 1), and the comprehensive meteorological data evaluation index is 0.57, indicating that the overall performance of data transmission and data synchronization is not very ideal; in the second set of data, although the data transmission index is higher than 0.6, the data synchronization index is lower than 0.6, and the comprehensive meteorological data evaluation index is 0.73. Due to the equal weights, but the data synchronization index is smaller than the data transmission index, the negative impact of the data synchronization index on the comprehensive meteorological data evaluation index is relatively large, indicating that although The data transmission performed well, but the insufficient data synchronization quality is still the bottleneck of the overall performance, and attention should be paid to the areas that need to be improved in the data synchronization process; in the third group of data, although the data synchronization index is close to 1, the insufficient data transmission efficiency will limit the improvement of the comprehensive meteorological data evaluation index. Due to the equal weights, but the data transmission index is smaller than the data synchronization index, the data transmission index has a relatively large negative impact on the comprehensive meteorological data evaluation index, indicating that the system needs to improve the data transmission efficiency to give full play to the advantages of high-quality data synchronization; in the fourth group of data, the data transmission index and the data evaluation index are both higher than 0.6, and the comprehensive meteorological data evaluation index is 1.01, indicating that the overall performance of data transmission and data synchronization is relatively good.

[0074] like Figure 2 As shown, it is a schematic diagram of the change of the comprehensive meteorological data evaluation index provided in an embodiment of the present application, which shows the relationship between the comprehensive meteorological data evaluation index and the data transmission index and the data synchronization index, where it is assumed that the data transmission weight is 0.6, the data synchronization weight is 0.4, the value range of the data synchronization index is 0 to 1, and the value range of the data transmission index is 0 to 1. It can be seen from the figure that the larger the data transmission index and the data synchronization index, the larger the comprehensive meteorological data evaluation index, indicating that the overall performance of data transmission and data synchronization is better, and the smaller the data transmission index and the data synchronization index, the smaller the comprehensive meteorological data evaluation index, indicating that the overall performance of data transmission and data synchronization is worse; a more accurate numericalization of the comprehensive meteorological data evaluation index is achieved.

[0075] Furthermore, the specific steps of triggering the early warning mechanism are as follows: comparing the comprehensive meteorological data evaluation index with the preset comprehensive meteorological data early warning threshold; when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data early warning threshold, the early warning mechanism is triggered and the generated early warning information is sent to the preset maintenance personnel, and the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data early warning threshold is continuously monitored, and the early warning information includes the comprehensive meteorological data evaluation index, the data evaluation index and the corresponding meteorological data information; when the comprehensive meteorological data evaluation index is not lower than the preset comprehensive meteorological data early warning threshold, the early warning mechanism is lifted.

[0076] In this embodiment, the value range of the preset comprehensive meteorological data warning threshold obtained from the preset database is 1 to e-1. The comprehensive meteorological data evaluation index of the meteorological data is monitored in real time and compared with the preset comprehensive meteorological data warning threshold. When the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, it indicates that there is a problem with the transmission efficiency or synchronization status of the meteorological data, resulting in a poor comprehensive evaluation result. The early warning mechanism needs to be triggered immediately, and the generated early warning information is sent to the preset maintenance personnel through a preset communication channel (such as e-mail, text message, system notification, etc.). The early warning information should include the comprehensive meteorological data evaluation index, the specific data evaluation index (data transmission index, data synchronization index) and and the corresponding meteorological data information so that maintenance personnel can quickly understand the problem. After the preset maintenance personnel take corresponding measures (for example, if the problem is caused by network delay or failure, it may be necessary to optimize the network configuration or repair the network equipment; if the problem is caused by data processing errors, it may be necessary to improve the data processing algorithm or increase the data verification steps.) Afterwards, when the comprehensive meteorological data evaluation index recovers to a level not lower than the preset comprehensive meteorological data warning threshold, it means that the problem has been solved. At this time, the warning mechanism can be lifted, but it is still necessary to continuously monitor the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data warning threshold, thereby realizing the formulation of a warning mechanism based on the comprehensive meteorological data evaluation index.

[0077] like Figure 3As shown, it is a structural schematic diagram of a distributed meteorological data processing system based on edge computing provided in an embodiment of the present application. The distributed meteorological data processing system based on edge computing provided in an embodiment of the present application includes: a data collection module, a data evaluation module and a comprehensive evaluation module; wherein the data collection module is used to pre-process the meteorological data to be evaluated through the edge device to obtain the meteorological data to be transmitted, and collect the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node in real time, the meteorological data information includes the first meteorological data information and the second meteorological data information, the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; the data evaluation module is used to obtain a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation results to reflect the performance of the meteorological data transmission and synchronization process; the comprehensive evaluation module is used to perform a comprehensive evaluation on the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed, and the comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is required.

[0078] In this embodiment, the data collection module passes the collected meteorological data information to the data evaluation module. The data evaluation module calculates the data transmission index and the data synchronization index according to the collected meteorological data information, and passes them to the comprehensive evaluation module. After the comprehensive evaluation module conducts a comprehensive evaluation on the received data transmission index and data synchronization index, it calculates the comprehensive meteorological data evaluation index based on the weight evaluation factor, and decides whether to trigger the early warning mechanism based on the comprehensive meteorological data evaluation index. During the whole process, the changes of the comprehensive meteorological data evaluation index are continuously monitored, thereby realizing real-time monitoring of the overall performance of meteorological data sent from the edge device to the central node.

[0079] An embodiment of the present application provides an electronic device, characterized in that the electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the distributed meteorological data method based on edge computing.

[0080] In this embodiment, computer program instructions for executing a distributed meteorological data method based on edge computing are stored in the memory. When the processor receives a signal to execute the instruction, it reads the corresponding program instructions from the memory and performs various operations according to the requirements of the instruction, such as data processing, logical judgment, etc.; combining the functions of the memory and the processor, when the processor executes the computer program instructions stored in the memory, the electronic device can execute a distributed meteorological data method based on edge computing, and the electronic device can process and analyze meteorological data at the edge of the network (i.e., where the data is generated or near it). In a distributed manner, multiple electronic devices can work together to jointly process and analyze large-scale meteorological data, thereby achieving efficient transmission and synchronization of meteorological data.

[0081] To summarize, the embodiment of the present application pre-processes the meteorological data to be evaluated by the edge device to obtain the meteorological data to be transmitted, and then collects the meteorological data information in the process of being sent from the edge device to the central node; then performs data evaluation on the meteorological data transmission and synchronization process to obtain a data evaluation index; then, based on the data evaluation index, a comprehensive evaluation is performed on the meteorological data to be transmitted to obtain a comprehensive meteorological data evaluation index; finally, when the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered and the generated warning information is sent to the preset maintenance personnel for processing, thereby improving the accuracy of the meteorological data, and then improving the transmission and synchronization accuracy of the meteorological data, effectively solving the problem of low data accuracy in the meteorological data transmission and synchronization process in the prior art.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0087] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Distributed meteorological data processing method based on edge computing, It is characterized in that The following steps are involved: S1, preprocessing the meteorological data to be evaluated by the edge device to obtain the meteorological data to be transmitted, and collecting in real time the meteorological data information of the meteorological data to be transmitted during the process of being transmitted by the edge device and synchronized to the central node, wherein the meteorological data information includes first meteorological data information and second meteorological data information, wherein the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; S2, obtaining a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, wherein the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation result to reflect the performance of the meteorological data transmission and synchronization process; S3, comprehensively evaluate the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index. When the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed. The comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is needed.

2. The distributed meteorological data processing method based on edge computing as claimed in claim 1, It is characterized in that The specific method of the pretreatment is as follows: Initial first meteorological data are obtained by performing a first preprocessing on the meteorological data to be evaluated, wherein the first preprocessing means removing abnormal data from the meteorological data to be evaluated; The initial second meteorological data is obtained by performing a second preprocessing on the initial first meteorological data, wherein the second preprocessing means formatting the initial first meteorological data; The meteorological data to be transmitted is obtained by performing a third preprocessing on the initial second meteorological data, wherein the third preprocessing means performing a data compression process on the initial second meteorological data.

3. The distributed meteorological data processing method based on edge computing as claimed in claim 1, Features: The data evaluation includes data transmission evaluation and data synchronization evaluation; The data transmission evaluation indicates that the data transmission process is evaluated according to the first meteorological data information to obtain a data transmission index, and the data transmission index is used to evaluate the overall efficiency of the meteorological data information in the data transmission process; The data synchronization evaluation indicates that the data synchronization process is evaluated according to the second meteorological data information to obtain a data synchronization index, and the data synchronization index is used to evaluate the overall quality of the meteorological data information during the data synchronization process.

4. The distributed meteorological data processing method based on edge computing as claimed in claim 3, It is characterized in that The method for obtaining the data transmission index is as follows: Acquire first meteorological data information of the meteorological data to be transmitted during the data transmission process, wherein the first meteorological data information includes data throughput, data packet loss rate, data delay time, and data update frequency coefficient, wherein the data update frequency coefficient represents an average value of a ratio of an update frequency of the data to be transmitted in the central node to an update frequency of the meteorological data to be evaluated in the edge device; Acquire a data transmission evaluation weight from a preset database, wherein the data transmission evaluation weight includes a first transmission weight, a second transmission weight, and a third transmission weight; The data transmission index of the meteorological data to be transmitted is calculated by combining the first meteorological data information and the data transmission evaluation weight. The data transmission index is calculated using the following formula: Where DTI is the data transmission index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first transmission weight of the ith edge device, e represents a natural constant, T i is the data throughput of the ith edge device within the preset time interval, c is the first correction factor, represents the second transmission weight of the i-th edge device, PL i is the data packet loss rate of the ith edge device within the preset time interval, is the third transmission weight of the ith edge device, D i represents the data delay time of the i-th edge device, D max,i is the reference maximum data delay time of the i-th edge device, and GX represents the data update frequency coefficient.

5. The distributed meteorological data processing method based on edge computing as claimed in claim 3, Features: The method for obtaining the data synchronization index is as follows: Acquire second meteorological data information of the meteorological data to be transmitted during the data synchronization process, wherein the second meteorological data information includes a data packet loss rate, a number of data retransmissions, a data error rate, and a data acquisition frequency coefficient, wherein the data acquisition frequency coefficient represents an average value of a ratio of an actual acquisition frequency of the meteorological data to be evaluated in the edge device to a preset acquisition frequency; Acquire data synchronization evaluation weights from a preset database, wherein the data synchronization evaluation weights include a first synchronization weight, a second synchronization weight, and a third synchronization weight; The data synchronization index of the meteorological data to be transmitted is calculated by combining the second meteorological data information and the data synchronization evaluation weight. The data synchronization index is calculated using the following formula: Where DSI is the data synchronization index, i represents the number of the edge device, i = 1, 2..., n, n is the total number of edge devices, represents the first synchronization weight of the ith edge device, e represents a natural constant, PL i is the data packet loss rate of the ith edge device within the preset time interval, represents the second synchronization weight of the ith edge device, M i represents the number of data retransmissions of the ith edge device within the preset time interval, N is the total number of data packets transmitted by the edge device within the preset time interval, is the third synchronization weight of the ith edge device, ER i It represents the data error rate of the ith edge device within a preset time interval, and CX represents the data collection frequency coefficient.

6. The distributed meteorological data processing method based on edge computing as claimed in claim 5, It is characterized in that The specific steps of comprehensively evaluating the meteorological data to be transmitted according to the data evaluation index to obtain the comprehensive meteorological data evaluation index are as follows: Obtaining a preset data evaluation index threshold and a preset comprehensive meteorological data warning threshold from a preset database; If the data transmission index and the data synchronization index are both greater than the preset data evaluation index threshold, the changes of the data transmission index and the data synchronization index are continuously monitored; If at least one of the data transmission index and the data synchronization index is not greater than the preset data evaluation index threshold, the comprehensive meteorological data evaluation index of the meteorological data to be transmitted is obtained according to the data evaluation index, and whether to trigger the warning mechanism is determined based on the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data warning threshold.

7. The distributed meteorological data processing method based on edge computing as claimed in claim 6, It is characterized in that The method for obtaining the comprehensive meteorological data evaluation index is as follows: Obtaining a weight evaluation factor from a preset database, wherein the weight evaluation factor includes a data transmission weight and a data synchronization weight; The comprehensive meteorological data evaluation index is obtained according to the weight evaluation factor, the data transmission index and the data synchronization index. The comprehensive meteorological data evaluation index is calculated using the following formula: In the formula, I QX is the comprehensive meteorological data evaluation index, e represents the natural constant, ω T is the data transmission weight, DTI is the data transmission index, ω S is the data synchronization weight and DSI is the data synchronization index.

8. The distributed meteorological data processing method based on edge computing as claimed in claim 7, It is characterized in that The specific steps of triggering the early warning mechanism are as follows: Compare the comprehensive meteorological data evaluation index with the preset comprehensive meteorological data warning threshold; When the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered and the generated warning information is sent to the preset maintenance personnel, and the relationship between the comprehensive meteorological data evaluation index and the preset comprehensive meteorological data warning threshold is continuously monitored, and the warning information includes the comprehensive meteorological data evaluation index, the data evaluation index and the corresponding meteorological data information; When the comprehensive meteorological data evaluation index is not lower than the preset comprehensive meteorological data warning threshold, the warning mechanism will be lifted.

9. Distributed meteorological data processing system based on edge computing, It is characterized in that include: Data collection module, data evaluation module and comprehensive evaluation module; The data collection module is used to pre-process the meteorological data to be evaluated through the edge device to obtain the meteorological data to be transmitted, and collect the meteorological data information of the meteorological data to be transmitted in the process of being transmitted by the edge device and synchronized to the central node in real time, and the meteorological data information includes the first meteorological data information and the second meteorological data information, the first meteorological data information is used to describe the data transmission process of the meteorological data to be transmitted, and the second meteorological data information is used to describe the data synchronization process of the meteorological data to be transmitted; The data evaluation module is used to obtain a data evaluation index by performing data evaluation on the meteorological data transmission and synchronization process, wherein the data evaluation is used to evaluate the overall quality and efficiency of the meteorological data transmission and synchronization process, and the data evaluation index is used to quantify the data evaluation result to reflect the performance of the meteorological data transmission and synchronization process; The comprehensive evaluation module is used to comprehensively evaluate the meteorological data to be transmitted according to the data evaluation index to obtain a comprehensive meteorological data evaluation index. When the comprehensive meteorological data evaluation index is lower than the preset comprehensive meteorological data warning threshold, the warning mechanism is triggered, otherwise no processing is performed. The comprehensive meteorological data evaluation index is used to measure the overall performance of the meteorological data to be transmitted and determine whether a trigger mechanism is needed.

10. An electronic device, It is characterized in that The electronic device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute any one of the methods described in claims 1 to 8.

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