Meteorological data compression and transmission intelligent control system

Abstract

The invention relates to the technical field of data transmission, in particular to a meteorological data compression and transmission intelligent control system which comprises a network state evaluation module, a data priority evaluation module, a data compression and encryption module and a data transmission control module. According to the invention, network conditions can be effectively predicted and key data can be transmitted preferentially by evaluating network volatility and data emergencies, the reliability and efficiency of data transmission are improved, delay and packet loss in the data transmission process are reduced through targeted data compression and encryption processing, and the data transmission efficiency is improved. Meanwhile, the safety of data in the transmission process is guaranteed, especially in a bandwidth-limited network environment, quick and accurate transmission of key meteorological information can be guaranteed by adjusting data transmission bandwidth allocation and selecting a proper transmission protocol, resource allocation is optimized, the adaptive capacity of the system to complex network conditions is enhanced, and the service life of the system is prolonged. Therefore, the system can work efficiently under the condition that the network load is large or unstable.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to an intelligent control system for meteorological data compression and transmission. Background Art

[0002] The field of data transmission technology involves electronics, computer science, and information technology, and focuses on systems and technologies for transmitting information from one device or system to another. In modern communications, data transmission is one of the basic technologies, including wired and wireless transmission technologies. Key technologies in this field include coding, modulation, signal processing, and compression technologies, which aim to improve transmission efficiency, reduce data loss, and ensure the security and integrity of data during transmission. Data transmission technology not only supports the infrastructure of the Internet and telecommunications networks, but is also the core for achieving efficient network operations, remote communications, and a variety of network services.

[0003] Among them, the meteorological data compression and transmission intelligent control system is a system used to optimize the efficiency of meteorological data transmission. Its core purpose is to achieve efficient management of data in each link of capture, compression, transmission and reception through algorithms. The system reduces the amount of data and reduces the network burden through compression technology. At the same time, it uses intelligent control technology to dynamically adjust the transmission strategy to adapt to different network conditions and data urgency. It aims to optimize the data compression ratio and transmission speed to ensure the rapid and accurate transmission of key meteorological information under limited network bandwidth.

[0004] Traditional transmission systems still face challenges of network fluctuations and bandwidth limitations in actual operations, especially when transmitting large amounts of or critical data. Traditional systems lack the ability to predict network fluctuations and the mechanism to dynamically adjust transmission strategies, resulting in the inability to effectively adjust transmission strategies to adapt to changes when the network status is poor or the amount of data is large, which limits the efficiency and reliability of data transmission. For example, when encountering network congestion, traditional systems cannot prioritize the transmission of critical data, resulting in delays in the transmission of important information, affecting decision-making and response speed. In addition, fixed data processing strategies also fail to fully consider the importance and urgency of data, resulting in improper resource allocation and affecting overall transmission performance. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent control system for meteorological data compression and transmission.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent control system for meteorological data compression and transmission, the system comprising: The network status assessment module is based on the current meteorological data transmission environment, network delay and packet loss data over a period of time, assesses the volatility of the current network, and predicts the network volatility in the future period based on the deviation between the weather forecast information in the future period and the current weather status, and obtains the network status prediction result; The data priority assessment module evaluates the urgency of the meteorological data based on the information of the meteorological data to be transmitted and the deviation between the meteorological data and the normal data range, and evaluates the priority of data transmission in combination with the type of meteorological data and the real-time requirements of the data to obtain the transmission priority assessment result; The data compression and encryption module selects a matching compression algorithm based on the transmission priority evaluation result and the target compression rate and target compression ratio of the meteorological data, compresses the data, and encrypts the data according to the encryption requirements to obtain compressed and encrypted meteorological data; The data transmission control module selects a matching transmission protocol based on the compressed and encrypted meteorological data, the transmission priority evaluation results and the network status prediction results, according to the transmission efficiency requirements, transmission stability requirements and transmission delay requirements, and adjusts the transmission bandwidth allocation of meteorological data with differentiated priorities according to the network stability in the future time period, performs meteorological data transmission, and obtains meteorological data transmission results.

[0007] The present invention is improved in that the method for evaluating the volatility of the current network is: Based on the current meteorological data transmission environment, collect network data over a period of time, including network delay and packet loss rate; Based on the network data during the period, the formula:

[0008] Calculate the current network volatility score , get the volatility evaluation result of the current network, where, represents the network delay weight coefficient, Represents the packet loss rate weight coefficient, is the average network delay, is the average packet loss rate, Score the current network volatility.

[0009] The present invention is improved in that the steps of obtaining the network status prediction result are: Based on the volatility assessment result of the current network, weather forecast information for future periods is collected through weather forecast to obtain network status related data; Based on the network status association data, by formula:

[0010] Calculate the network volatility score for future periods , and obtain the network status prediction result, where Score the current network volatility, is the deviation of future and current wind speed, is the deviation of future and current rainfall, For the deviation of future and current snowfall, , and is the influence coefficient, Score the network volatility for future periods.

[0011] The present invention is improved in that the method for evaluating the urgency of meteorological data is: Based on the meteorological data information to be transmitted, the current real-time meteorological data is extracted, and the corresponding data type is extracted, and the historical average value at the current detection point is obtained to obtain the urgency-related data; Based on the urgency correlation data, by the formula:

[0012] Calculating urgency scores for weather data , and obtain the emergency assessment results of meteorological data, among which, For real-time weather data, is the average value of historical meteorological data. is the standard deviation of historical meteorological data, To adjust the parameters, Rate the urgency, is the base of natural logarithms.

[0013] The present invention is improved in that the step of obtaining the transmission priority evaluation result is: Based on the meteorological data urgency assessment result, extract the meteorological data type, obtain the real-time requirements of the data, and obtain priority-related data; Based on the priority association data, by the formula:

[0014] Calculate the priority of data transmission ,in, Rate the urgency, is the sum of the urgency scores of the weather data. is the priority of data transmission, is the data type weight, is the real-time demand index of data, is the adjustment factor; Based on the priority of the data transmission ,according to The value is used to prioritize the meteorological data transmission tasks and obtain the transmission priority evaluation result.

[0015] The present invention is improved in that the method for compressing data is: Based on the transmission priority evaluation result, according to the type of meteorological data, a target compression rate and a target compression ratio of the meteorological data are extracted to obtain data compression related information; Based on the data compression association information, through the formula:

[0016] Calculate the matching score of the data compression algorithm ,in, represents the target compression ratio, is the target compression rate, represents the compression ratio of the algorithm, represents the compression rate of the algorithm, and is the weight parameter, Score the matching degree of the data compression algorithm; Matching score based on the data compression algorithm , by comparing the differential data compression algorithms Value size, select the maximum The data compression algorithm based on the value is used to compress the meteorological data.

[0017] The present invention is improved in that the method for selecting a matching transmission protocol is: Based on the meteorological data to be transmitted, according to the type of meteorological data, the transmission efficiency requirement, transmission stability requirement and transmission delay requirement of the data preset are extracted to obtain the transmission protocol associated data; Based on the transport protocol associated data, by formula:

[0018] Calculate the match score of the transport protocol ,in, , and is the weight parameter, is the adjustment coefficient, For the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, For target transmission stability, For the actual transmission stability of the transmission protocol, is the target transmission delay, is the actual transmission delay of the transmission protocol, is the base of natural logarithms; Matching score based on the transport protocol , by comparison Value size, select the maximum The transmission protocol of the value is selected to obtain the transmission protocol selection result.

[0019] The present invention is improved in that the step of obtaining the meteorological data transmission result is: Based on the compressed and encrypted meteorological data, the transmission priority evaluation result, the network status prediction result and the transmission protocol selection result, the current network bandwidth is extracted, and the total priority score of the parallel transmission data is obtained to obtain the data transmission association information; Based on the data transmission association information, through the formula:

[0020] Calculate the bandwidth allocated to a single meteorological data transmission task , where K is the bandwidth allocated to a single meteorological data transmission task, is the redundancy coefficient, is the total network bandwidth currently available, is the priority of data transmission, Score the overall priority of the parallel transmission data, is the adjustment coefficient, Score the network volatility for future periods; Based on the bandwidth allocated to a single meteorological data transmission task , carry out meteorological data transmission and obtain meteorological data transmission results.

[0021] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by evaluating network volatility and data urgency, it is possible to effectively predict network conditions and give priority to the transmission of key data, thereby improving the reliability and efficiency of data transmission. Through targeted data compression and encryption processing, delays and packet loss in the data transmission process are reduced, while ensuring the security of data in the transmission process. Especially in a network environment with limited bandwidth, it is possible to effectively respond to network fluctuations by adjusting the data transmission bandwidth allocation and selecting an appropriate transmission protocol, thereby ensuring the rapid and accurate transmission of key meteorological information, optimizing resource allocation, and enhancing the system's adaptability to complex network conditions, so that it can work efficiently even when the network load is large or unstable. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart for evaluating the volatility of the current network for the present invention; Figure 3A flowchart of obtaining a network status prediction result according to the present invention; Figure 4 A flow chart for evaluating the urgency of meteorological data for the present invention; Figure 5 A flow chart of obtaining a transmission priority evaluation result of the present invention; Figure 6 A flow chart of the present invention for compressing data; Figure 7 A flow chart for selecting a matching transmission protocol for the present invention; Figure 8 The present invention is a flow chart for obtaining meteorological data transmission results. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0025] See also Figure 1 The present invention provides a technical solution: an intelligent control system for meteorological data compression and transmission, the system comprising: The network status assessment module is based on the current meteorological data transmission environment, network delay and packet loss data over a period of time, assesses the volatility of the current network, and predicts the network volatility in the future period based on the deviation between the weather forecast information in the future period and the current weather status, and obtains the network status prediction result; The data priority assessment module evaluates the urgency of the meteorological data based on the information of the meteorological data to be transmitted and the deviation between the meteorological data and the normal data range, and evaluates the priority of data transmission in combination with the type of meteorological data and the real-time requirements of the data to obtain the transmission priority assessment result; The data compression and encryption module selects a matching compression algorithm based on the transmission priority evaluation result, the target compression rate and the target compression ratio of the meteorological data, compresses the data, and encrypts the data according to the encryption requirements to obtain compressed and encrypted meteorological data; The data transmission control module selects a matching transmission protocol based on the compressed and encrypted meteorological data, transmission priority evaluation results and network status prediction results, according to the transmission efficiency requirements, transmission stability requirements and transmission delay requirements, and adjusts the transmission bandwidth allocation of meteorological data with differentiated priorities according to the network stability in the future period, performs meteorological data transmission, and obtains meteorological data transmission results.

[0026] The network status prediction results include network delay index, packet loss rate index and network volatility score; the transmission priority assessment results include data urgency level, priority sorting and real-time classification; the compressed and encrypted meteorological data include data compression rate, compressed data size and encryption security level; the meteorological data transmission results include transmission completion time, data integrity verification results, data availability information and transmission bandwidth allocation information.

[0027] See also Figure 2 , the method to evaluate the volatility of the current network is: Based on the current meteorological data transmission environment, collect network data over a period of time, including network delay and packet loss rate; Based on network data over a period of time, the formula:

[0028] Calculate the current network volatility score , get the volatility evaluation result of the current network, where, represents the network delay weight coefficient, Represents the packet loss rate weight coefficient, is the average network delay, is the average packet loss rate, Score the current network volatility.

[0029] formula:

[0030] Parameter meaning and acquisition method and are weight coefficients, usually set by historical data analysis or expert experience. These weights can be determined through regression analysis to ensure that they effectively reflect the impact of latency and packet loss rate on network stability.

[0031] It is the average value of network latency, which can be obtained by collecting network latency data within a certain time window and calculating its average value. For example, if network latency is measured once a minute, the average latency in an hour can be obtained by adding up the latency values ​​of all minutes and then dividing by 60.

[0032] is the average value of the network packet loss rate. Similarly, it can be obtained by collecting packet loss data in the same time window and calculating its average value. If the packet loss is recorded once a minute, the average packet loss rate for one hour can be obtained by averaging the packet loss rates of all minutes.

[0033] Calculation Example Set the network delay value measured within one hour as follows: .

[0034] Calculating average latency :

[0035] Set the measured packet loss rate for .

[0036] Calculate the average packet loss rate :

[0037] set up 2 and .

[0038] calculate :

[0039]

[0040]

[0041]

[0042] The value reflects the volatility of the network. The current calculation result is This shows that the network has a certain volatility.

[0043] See also Figure 3 , the steps to obtain the network status prediction results are: Based on the volatility assessment results of the current network, collect weather forecast information for future periods through weather forecasts to obtain network status correlation data; Based on the network status association data, through the formula:

[0044] Calculate the network volatility score for future periods , and obtain the network status prediction result, where Score the current network volatility, is the deviation of future and current wind speed, is the deviation of future and current rainfall, For the deviation of future and current snowfall, , and is the influence coefficient, Score the network volatility for future periods.

[0045] formula:

[0046] Parameter details and how to obtain them: : Current network volatility score, the parameters are calculated by the previous step.

[0047] , and : They are wind speed, rainfall, and snowfall deviations, respectively. The deviations are obtained by simple arithmetic calculation, which is the future value minus the current value. For example, the wind speed deviation is , the rainfall deviation is , the snowfall deviation is ,in, and Current and future wind speeds are obtained from weather stations or weather forecast APIs, usually expressed in meters per second (m / s). Data sources provide immediate and future wind speed forecasts to help network operators assess potential network disruptions. and Current and future rainfall, obtained from meteorological services, usually expressed in millimeters (mm). The data is based on observations from weather stations or analysis of satellite data and can be used to assess the impact of rainfall on network equipment. and Snowfall data, obtained from meteorological services and measured in centimeters (cm), are used to assess the potential impact of snow accumulation on ground and wireless network facilities.

[0048] , and :Weather impact coefficient, which is determined based on historical data analysis and reflects the weight of the impact of different weather factors on network volatility. The coefficient is determined through statistical methods such as regression analysis, reflecting the degree of impact of specific weather factors such as wind, rain, and snow on network performance.

[0049] Calculation example: Set the current network volatility indicator , current wind speed m / s, predicted wind speed m / s, wind speed influence coefficient , current rainfall mm, predicted rainfall mm, rainfall influence coefficient , current snowfall cm, predicted snowfall cm, snowfall influence coefficient .

[0050] Calculate wind speed deviation m / s, rainfall deviation mm, snowfall deviation cm.

[0051] Calculate future network volatility:

[0052]

[0053]

[0054]

[0055] Calculation results , reflecting increased network volatility due to bad weather.

[0056] See also Figure 4 , the method to evaluate the urgency of meteorological data is: Based on the meteorological data information to be transmitted, the current real-time meteorological data is extracted, and the corresponding data type is extracted, and the historical average value at the current detection point is obtained to obtain the urgency-related data; Based on the urgency correlation data, the formula is:

[0057] Calculating urgency scores for weather data , and obtain the emergency assessment results of meteorological data, among which, For real-time weather data, is the average value of historical meteorological data. is the standard deviation of historical meteorological data, To adjust the parameters, Rate the urgency, is the base of natural logarithms.

[0058] formula:

[0059] Parameter meaning and acquisition method: :Real-time meteorological data, data collected through meteorological observation stations or weather forecast APIs, such as temperature, wind speed, rainfall, etc. The data is updated in real time and is used to analyze environmental changes in real time.

[0060] : The average value of historical meteorological data. The data is obtained by averaging the historical meteorological data of the current detection point during the same period. For example, if today is June 1, then It is the average temperature or other meteorological indicators on June 1st of the past five years.

[0061] :Weather data deviation, deviation is through real-time data Compared with historical average data The difference is calculated to assess the degree to which current meteorological conditions deviate from normal patterns.

[0062] : The standard deviation of historical meteorological data is a statistic that describes historical data The size of the fluctuation is often used in the calculation of standardized deviation to provide a measure of volatility.

[0063] : A numerical adjustment parameter, which is adjusted according to the specific application scenario to control the sensitivity of the function, thereby affecting the sensitivity of the urgency score.

[0064] Calculation Example Set the collected real-time weather data The average temperature during the same period is 30°C. The standard deviation of historical data is 25°C. is 5°C. Calculate the deviation of the meteorological data:

[0065] Calculate the standardized deviation:

[0066] Setting the tuning parameters The urgency score is calculated according to the improved formula:

[0067] Calculation results This indicates that the current meteorological data has a high degree of urgency, expressing that the current meteorological conditions deviate significantly from the historical average data and require emergency response or priority processing.

[0068] See also Figure 5 , the steps to obtain the transmission priority evaluation result are: Based on the results of the meteorological data urgency assessment, the meteorological data type is extracted, and the real-time requirements of the data are obtained to obtain priority-related data; Based on the priority associated data, through the formula:

[0069] Calculate the priority of data transmission ,in, Rate the urgency, is the sum of the urgency scores of the weather data. is the priority of data transmission, is the data type weight, is the real-time demand index of data, is the adjustment factor; Based on data transfer priority ,according to The value is used to prioritize the meteorological data transmission tasks and obtain the transmission priority evaluation result.

[0070] formula:

[0071] Parameter details and how to obtain them : Urgency score, calculated by the previous step.

[0072] :Data type weight, a preset weight value based on the impact of different meteorological data types on system decisions or operations. For example, higher weights are assigned to data types with greater impact. These weights are usually determined by professional teams or historical data analysis, and adjusted according to the importance and impact of the data type.

[0073] : Real-time demand index, real-time demand It is defined based on the frequency of data updates or the timeliness of data processing. For real-time monitoring data (such as wind speed monitoring), the real-time requirement will be set to a larger value, while non-real-time data (such as daily average temperature) will be set to a smaller value. The parameter reflects the urgency of data processing.

[0074] :The adjustment coefficient is a pre-set constant used to adjust the influence of each parameter in the priority calculation to ensure the balance and practicality of the formula output. The coefficient is usually set according to the specific needs of the system and past experience.

[0075] Calculation example: Assume we have the following parameter values: : reflects the urgency score of an extreme rainfall event, : The weight of rainfall data, :Real-time requirement, because the data is real-time monitoring of rainfall, : adjustment coefficient, : The sum of the urgency of all data in the entire system.

[0076] Calculation process:

[0077]

[0078]

[0079]

[0080] Calculation results , indicating that under the given parameter conditions, the transmission priority of the rainfall data is 0.36. This value can be used to compare with other priorities to evaluate the order of data transmission tasks.

[0081] See also Figure 6 , the method for compressing data is: Based on the transmission priority evaluation result and according to the meteorological data type, the target compression rate and target compression ratio of the meteorological data are extracted to obtain data compression related information; Based on data compression association information, through the formula:

[0082] Calculate the matching score of the data compression algorithm ,in, represents the target compression ratio, is the target compression rate, represents the compression ratio of the algorithm, represents the compression rate of the algorithm, and is the weight parameter, Score the matching degree of the data compression algorithm; Matching score based on data compression algorithm , by comparing the differential data compression algorithms Value size, select the maximum The data compression algorithm based on the value is used to compress the meteorological data.

[0083] formula:

[0084] Parameter meaning and acquisition method: Target compression ratio : Refers to the ratio of the expected compressed data size to the original data size. This value is usually pre-set based on the data type, system performance requirements, and storage limitations.

[0085] Target compression rate : Refers to the expected speed at which the compression process processes data, measured in megabytes per second (MB / s). The value is pre-set based on the type of data, system real-time processing requirements, or time constraints.

[0086] Compression ratio of the algorithm : Refers to the ratio of the compressed data size to the original data size when a specific compression algorithm processes a specific data set. The value can be obtained through experimental measurement or by referring to the algorithm documentation.

[0087] Compression rate of the algorithm : Refers to the processing speed of a specific compression algorithm when processing a specific data set, in megabytes per second (MB / s). The value can be obtained through experimental measurement or by referring to the algorithm documentation.

[0088] Weight Parameters and : Used to adjust the influence of compression ratio and compression rate in matching score. The weight is set according to the needs of the application scenario to ensure that the scoring results meet the actual needs.

[0089] Calculation example: Set the following parameter values: Target compression ratio: (i.e., it is expected to compress the data to 50% of its original size), target compression rate: MB / s, actual compression ratio of algorithm A: , the actual compression rate of algorithm A: MB / s, weight parameters: , (Indicating that compression ratio is more important than compression rate).

[0090] According to the formula:

[0091]

[0092]

[0093]

[0094] The matching score of algorithm A is 1.08. This score is greater than 1, indicating that the performance of algorithm A is better than the expected target, especially in terms of compression ratio. However, the compression rate is slightly lower than the target value. The most suitable compression algorithm can be selected according to the specific application requirements.

[0095] See also Figure 7 , the method to select the matching transport protocol is: Based on the meteorological data to be transmitted, according to the type of meteorological data, the transmission efficiency requirement, transmission stability requirement and transmission delay requirement of the data preset are extracted to obtain the transmission protocol associated data; Based on the transport protocol associated data, through the formula:

[0096] Calculate the match score of the transport protocol ,in, , and is the weight parameter, is the adjustment coefficient, For the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, For target transmission stability, For the actual transmission stability of the transmission protocol, is the target transmission delay, is the actual transmission delay of the transmission protocol, is the base of natural logarithms; Matching score based on transport protocol , by comparison Value size, select the maximum The transmission protocol of the value is selected to obtain the transmission protocol selection result.

[0097] formula:

[0098] Parameter meaning and acquisition method: : Transmission efficiency weight coefficient, which reflects the importance of transmission efficiency in the matching score. This coefficient can be determined by analyzing historical transmission data and evaluating the impact of transmission efficiency on the overall transmission quality.

[0099] : Transmission efficiency adjustment coefficient, which controls the impact of transmission efficiency differences on the matching score. This coefficient is determined by statistically analyzing the efficiency differences of different transmission protocols to meet actual application needs.

[0100] : Target transmission efficiency, which indicates the transmission efficiency expected to be achieved in a specific meteorological data transmission task. This value can be preset based on the task requirements and the average value of historical data transmission efficiency.

[0101] :The actual transmission efficiency of the transmission protocol refers to the transmission efficiency of the selected transmission protocol in actual applications. It can be obtained through experimental testing or by referring to the performance indicators in the protocol specification.

[0102] : Transmission stability weight coefficient, which reflects the importance of transmission stability in the matching score. This coefficient is determined by analyzing indicators such as data packet loss rate and jitter during transmission, evaluating the impact of stability on transmission quality, and determining the coefficient.

[0103] : Target transmission stability, which indicates the transmission stability level expected to be achieved in a specific meteorological data transmission mission. It can be preset according to the mission's requirements for data integrity and continuity.

[0104] :The actual transmission stability of the transmission protocol refers to the transmission stability of the selected transmission protocol in actual applications. It can be obtained through experimental testing or by referring to the performance indicators in the protocol specification.

[0105] : Transmission delay weight coefficient, which reflects the importance of transmission delay in matching score. This coefficient is determined by analyzing the impact of transmission delay on data real-time requirements.

[0106] : Target transmission delay, which indicates the transmission delay expected to be achieved in a specific meteorological data transmission task. It can be preset according to the task's requirements for data real-time performance.

[0107] :The actual transmission delay of the transmission protocol refers to the transmission delay of the selected transmission protocol in actual applications. It can be obtained through experimental testing or by referring to the performance indicators in the protocol specification.

[0108] Calculation example: In the meteorological data transmission task, the following parameters are set: target transmission efficiency , target transmission stability , target transmission delay milliseconds, for a certain transport protocol , its actual performance indicators are: actual transmission efficiency , actual transmission stability , the actual transmission delay Milliseconds. Set the weight coefficient and adjustment coefficient as: , , , .

[0109] Calculate the matching score:

[0110] Calculate the values: Transmission efficiency matching item:

[0111] Transmission stability matching items:

[0112] Transmission delay matching term:

[0113] Calculate the overall match score:

[0114] The matching score of this transport protocol is about , which is close to 1, indicating that the protocol is well matched with the target requirements in terms of transmission efficiency, stability and delay, and is suitable for current meteorological data transmission tasks.

[0115] See also Figure 8 ,The steps to obtain the meteorological data transmission results are: Based on the compressed and encrypted meteorological data, transmission priority evaluation results, network status prediction results and transmission protocol selection results, the current network bandwidth is extracted, and the total priority score of the parallel transmission data is obtained to obtain the data transmission association information; Based on the data transmission association information, the formula is:

[0116] Calculate the bandwidth allocated to a single meteorological data transmission task , where K is the bandwidth allocated to a single meteorological data transmission task, is the redundancy coefficient, is the total network bandwidth currently available, is the priority of data transmission, Score the overall priority of the parallel transmission data, is the adjustment coefficient, Score the network volatility for future periods; Based on the bandwidth allocated to a single meteorological data transmission task , carry out meteorological data transmission and obtain meteorological data transmission results.

[0117] formula:

[0118] Parameter meaning and acquisition method: : Redundancy coefficient, used to adjust the flexibility of bandwidth allocation, control the redundant bandwidth allocation of data of different priorities when the network fluctuates, and pre-set according to needs.

[0119] : The total available network bandwidth, which indicates the maximum bandwidth value that can be allocated on the current network. This value can be measured in real time through network monitoring tools, or obtained from bandwidth information provided by network service providers.

[0120] : The priority score of data transmission, calculated in the previous step.

[0121] : The total priority score of the parallel transmission data, which indicates the cumulative priority of all data in the current transmission task. It is obtained by adding up the priority scores of all parallel transmission data.

[0122] : Adjustment coefficient, used to adjust the impact weight of network volatility score, so that bandwidth allocation can adapt to volatility changes. This coefficient can be determined through experiments or historical data analysis based on the impact of network volatility on transmission performance.

[0123] : The network volatility score for the future period, calculated in the previous step.

[0124] Calculation example: Set the following parameter values: : Redundancy coefficient, : The total available network bandwidth, : Priority score of single data, : The total priority score of the parallel transmission data, : adjustment coefficient, : Network volatility score for future periods.

[0125] Calculation process: Calculate the priority weight:

[0126] Calculate the redundant bandwidth allocation part:

[0127] Calculate the network volatility adjustment part:

[0128] Calculate the bandwidth allocation result:

[0129] The calculated bandwidth allocation result is 20.5Mbps, which is used to select the appropriate transmission protocol. This result balances the data priority requirements under the current network conditions, while taking into account the impact of network volatility to ensure relatively stable transmission performance.

[0130] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A meteorological data compression and transmission intelligent control system, characterized in that: The system comprises: The network status assessment module is based on the current meteorological data transmission environment, network delay and packet loss data over a period of time, assesses the volatility of the current network, and predicts the network volatility in the future period based on the deviation between the weather forecast information in the future period and the current weather status, and obtains the network status prediction result; The data priority assessment module evaluates the urgency of the meteorological data based on the information of the meteorological data to be transmitted and the deviation between the meteorological data and the normal data range, and evaluates the priority of data transmission in combination with the type of meteorological data and the real-time requirements of the data to obtain the transmission priority assessment result; The data compression and encryption module selects a matching compression algorithm based on the transmission priority evaluation result and the target compression rate and target compression ratio of the meteorological data, compresses the data, and encrypts the data according to the encryption requirements to obtain compressed and encrypted meteorological data; The data transmission control module selects a matching transmission protocol based on the compressed and encrypted meteorological data, the transmission priority evaluation results and the network status prediction results, according to the transmission efficiency requirements, transmission stability requirements and transmission delay requirements, and adjusts the transmission bandwidth allocation of meteorological data with differentiated priorities according to the network stability in the future time period, performs meteorological data transmission, and obtains meteorological data transmission results.

2. The meteorological data compression and transmission intelligent control system according to claim 1 is characterized in that: The method for evaluating the volatility of the current network is: Based on the current meteorological data transmission environment, collect network data over a period of time, including network delay and packet loss rate; Based on the network data during the period, the formula: Calculate the current network volatility score , get the volatility evaluation result of the current network, where, represents the network delay weight coefficient, Represents the packet loss rate weight coefficient, is the average network delay, is the average packet loss rate, Score the current network volatility.

3. The meteorological data compression and transmission intelligent control system according to claim 2 is characterized in that: The steps for obtaining the network status prediction result are: Based on the volatility assessment result of the current network, weather forecast information for future periods is collected through weather forecast to obtain network status related data; Based on the network status association data, by formula: Calculate the network volatility score for future periods , and obtain the network status prediction result, where Score the current network volatility, is the deviation of future and current wind speed, is the deviation of future and current rainfall, For the deviation of future and current snowfall, , and is the influence coefficient, Score the network volatility for future periods.

4. The meteorological data compression and transmission intelligent control system according to claim 1 is characterized in that: The method for evaluating the urgency of meteorological data is: Based on the meteorological data information to be transmitted, the current real-time meteorological data is extracted, and the corresponding data type is extracted, and the historical average value at the current detection point is obtained to obtain the urgency-related data; Based on the urgency correlation data, by the formula: Calculating urgency scores for weather data , and obtain the emergency assessment results of meteorological data, among which, For real-time weather data, is the average value of historical meteorological data. is the standard deviation of historical meteorological data, To adjust the parameters, Rate the urgency, is the base of natural logarithms.

5. The meteorological data compression and transmission intelligent control system according to claim 4 is characterized in that: The steps for obtaining the transmission priority evaluation result are: Based on the meteorological data urgency assessment result, extract the meteorological data type, obtain the real-time requirements of the data, and obtain priority-related data; Based on the priority association data, by the formula: Calculate the priority of data transmission ,in, Rate the urgency, is the sum of the urgency scores of the weather data. is the priority of data transmission, is the data type weight, is the real-time demand index of data, is the adjustment factor; Based on the priority of the data transmission ,according to The value is used to prioritize the meteorological data transmission tasks and obtain the transmission priority evaluation result.

6. The meteorological data compression and transmission intelligent control system according to claim 1 is characterized in that: The method for compressing data is as follows: Based on the transmission priority evaluation result, according to the type of meteorological data, a target compression rate and a target compression ratio of the meteorological data are extracted to obtain data compression related information; Based on the data compression association information, through the formula: Calculate the matching score of the data compression algorithm ,in, represents the target compression ratio, is the target compression rate, represents the compression ratio of the algorithm, represents the compression rate of the algorithm, and is the weight parameter, Score the matching degree of the data compression algorithm; Matching score based on the data compression algorithm , by comparing the differential data compression algorithms Value size, select the maximum The data compression algorithm based on the value is used to compress the meteorological data.

7. The meteorological data compression and transmission intelligent control system according to claim 1 is characterized in that: The method for selecting a matching transmission protocol is: Based on the meteorological data to be transmitted, according to the type of meteorological data, the transmission efficiency requirement, transmission stability requirement and transmission delay requirement of the data preset are extracted to obtain the transmission protocol associated data; Based on the transport protocol associated data, by formula: Calculate the match score of the transport protocol ,in, , and is the weight parameter, is the adjustment coefficient, For the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, For target transmission stability, For the actual transmission stability of the transmission protocol, is the target transmission delay, is the actual transmission delay of the transmission protocol, is the base of natural logarithms; Matching score based on the transport protocol , by comparison Value size, select the maximum The transmission protocol of the value is selected to obtain the transmission protocol selection result.

8. The meteorological data compression and transmission intelligent control system according to claim 7 is characterized in that: The steps for obtaining the meteorological data transmission result are as follows: Based on the compressed and encrypted meteorological data, the transmission priority evaluation result, the network status prediction result and the transmission protocol selection result, the current network bandwidth is extracted, and the total priority score of the parallel transmission data is obtained to obtain the data transmission association information; Based on the data transmission association information, through the formula: Calculate the bandwidth allocated to a single meteorological data transmission task , where K is the bandwidth allocated to a single meteorological data transmission task, is the redundancy coefficient, is the total network bandwidth currently available, is the priority of data transmission, Score the overall priority of the parallel transmission data, is the adjustment coefficient, Score the network volatility for future periods; Based on the bandwidth allocated to a single meteorological data transmission task , carry out meteorological data transmission and obtain meteorological data transmission results.

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