An intelligent control system for meteorological data compression and transmission

Abstract

The present invention relates to the technical field of data transmission, and specifically to an intelligent control system for meteorological data compression and transmission. The system includes a network status evaluation module, a data priority evaluation module, a data compression and encryption module, and a data transmission control module. In the present invention, by evaluating network volatility and data urgency, it can effectively predict network conditions and give priority to transmitting critical data, improving the reliability and efficiency of data transmission. Through targeted data compression and encryption processing, it reduces delays and packet losses during data transmission while ensuring the security of data during transmission. Especially in a network environment with limited bandwidth, it can adjust data transmission bandwidth allocation and select appropriate transmission protocols to ensure the fast and accurate transmission of critical meteorological information, optimize resource allocation, strengthen the system's adaptability to complex network conditions, and enable it to work efficiently even under heavy or unstable network loads.

Description

Technical Field

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

[0002] The technical field of data transmission involves electronics, computer science, and information technology, mainly focusing on the systems and technologies for transmitting information from one device or system to another. In modern communication, data transmission is one of the fundamental technologies, including wired and wireless transmission technologies. The key technologies in this field include coding, modulation, signal processing, and compression technologies, aiming 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 telecommunication networks but also is the core for achieving efficient network operations, remote communication, and various network services.

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

[0004] Traditional transmission systems still face challenges of network fluctuations and bandwidth limitations in actual operations. Especially when transmitting a large amount of or critical data, traditional systems lack the ability to predict network fluctuations and a mechanism for dynamically adjusting the transmission strategy, resulting in the inability to effectively adjust the transmission strategy to adapt to changes when the network state is poor or the data volume is large. This limits the efficiency and reliability of data transmission. For example, traditional systems cannot give priority to ensuring the transmission of key data when encountering network congestion, resulting in delays in the transmission of important information and affecting the decision-making and response speed. In addition, fixed data processing strategies also fail to fully consider the importance and urgency of data, causing improper resource allocation and affecting the overall transmission performance. Summary of the Invention

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

[0006] To achieve the above purpose, the present invention adopts the following technical solution: An intelligent control system for meteorological data compression and transmission, the system includes:

[0007] The network status evaluation module evaluates the volatility of the current network based on the current meteorological data transmission environment, according to the network latency and packet loss data over a period of time, and predicts the network volatility in the future period according to the deviation between the weather forecast information in the future period and the current weather state, so as to obtain the network status prediction result;

[0008] The data priority evaluation module evaluates the urgency of meteorological data based on the meteorological data information to be transmitted, according to the deviation between the meteorological data and the normal data range, and combines the meteorological data type and the real-time requirement of the data to evaluate the transmission priority of the data, so as to obtain the transmission priority evaluation result;

[0009] The data compression and encryption module selects a matching compression algorithm based on the transmission priority evaluation result, compresses the data according to the target compression rate and target compression ratio of the meteorological data, and encrypts the data according to the encryption requirement, so as to obtain the compressed and encrypted meteorological data;

[0010] The data transmission control module selects a matching transmission protocol based on the compressed and encrypted meteorological data, transmission priority evaluation result and network status prediction result, according to the transmission efficiency requirement, transmission stability requirement and transmission latency requirement, and adjusts the transmission bandwidth allocation of meteorological data with different priorities according to the network stability in the future period, and performs meteorological data transmission, so as to obtain the meteorological data transmission result.

[0011] The improvement of the present invention is that the method for evaluating the volatility of the current network is:

[0012] Based on the current meteorological data transmission environment, collect network data over a period of time, including network latency and packet loss rate;

[0013] Based on the network data over the period of time, through the formula:

[0014]

[0015] Calculate the current network volatility score , and obtain the evaluation result of the volatility of the current network, where represents the network latency weight coefficient, represents the packet loss rate weight coefficient, is the average network latency, is the average packet loss rate, is the current network volatility score.

[0016] The improvement of the present invention is that the steps for obtaining the network status prediction result are:

[0017] Based on the evaluation result of the volatility of the current network, collect the weather forecast information in the future period through weather forecasting, so as to obtain the network status correlation data;

[0018] Based on the network status correlation data, through the formula:

[0019]

[0020] Calculate the network volatility score for a future time period , and obtain the network status prediction result, where is the current network volatility score, is the wind speed deviation between the future and the current, is the rainfall deviation between the future and the current, is the snowfall deviation between the future and the current, , and are influence coefficients, is the network volatility score for a future time period.

[0021] The improvement of the present invention is that the method for evaluating the urgency of meteorological data is:

[0022] Based on the meteorological data information to be transmitted, extract the current real-time meteorological data, and extract the corresponding data type, and obtain the urgency correlation data at the historical average value of the current detection point;

[0023] Based on the urgency correlation data, through the formula:

[0024]

[0025] Calculate the urgency score of meteorological data , and obtain the evaluation result of the urgency of meteorological data, where is the real-time meteorological data, is the average value of historical meteorological data, is the standard deviation of historical meteorological data, is the adjustment parameter, is the urgency score, is the base of the natural logarithm.

[0026] The improvement of the present invention is that the steps for obtaining the transmission priority evaluation result are:

[0027] Based on the evaluation result of the urgency of meteorological data, extract the meteorological data type, and obtain the real-time requirement of the data, and obtain the priority correlation data;

[0028] Based on the priority correlation data, through the formula:

[0029]

[0030] Calculate the priority of data transmission , where is the urgency score, is the total urgency score of meteorological data, is the priority of data transmission, is the data type weight, is the real-time requirement index of data, is the adjustment coefficient;

[0031] Based on the priority of the data transmission , according to value size, prioritize the meteorological data transmission tasks to obtain the transmission priority evaluation result.

[0032] The improvement of the present invention is that the method for compressing the data is as follows:

[0033] Based on the transmission priority evaluation result, extract the target compression rate and target compression ratio of the meteorological data according to the meteorological data type to obtain data compression correlation information;

[0034] Based on the data compression correlation information, through the formula:

[0035]

[0036] Calculate the matching degree score of the data compression algorithm , where 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 are weight parameters, is the matching degree score of the data compression algorithm;

[0037] Based on the matching degree score of the data compression algorithm , by comparing the value sizes of different data compression algorithms, select the data compression algorithm with the largest value to compress the meteorological data.

[0038] The improvement of the present invention is that the method for selecting a matching transmission protocol is as follows:

[0039] Based on the meteorological data to be transmitted, extract the preset transmission efficiency requirement, transmission stability requirement and transmission delay requirement of the data according to the meteorological data type to obtain transmission protocol correlation data;

[0040] Based on the transmission protocol correlation data, through the formula:

[0041]

[0042] Calculate the matching degree score of the transmission protocol , where 、 and are weight parameters, is an adjustment coefficient, is the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, is the target transmission stability, is 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 the natural logarithm;

[0043] Based on the matching degree score of the transmission protocol , by comparing the magnitudes of the values, select the transmission protocol with the largest value to obtain the transmission protocol selection result.

[0044] The improvement of the present invention is that the obtaining step of the meteorological data transmission result is as follows:

[0045] Based on the compressed and encrypted meteorological data, the transmission priority evaluation result, the network status prediction result, and the transmission protocol selection result, extract the current network bandwidth, and obtain the total priority score of the parallel transmission data to obtain the data transmission association information;

[0046] Based on the data transmission association information, through the formula:

[0047]

[0048] 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 a redundancy coefficient, is the total current available network bandwidth, is the priority of data transmission, is the total priority score of the parallel transmission data, is an adjustment coefficient, is the network volatility score for the future time period;

[0049] Based on the bandwidth allocated to a single meteorological data transmission task , perform meteorological data transmission to obtain the meteorological data transmission result.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by evaluating network volatility and data urgency, the network conditions can be effectively predicted and critical data can be preferentially transmitted, improving the reliability and efficiency of data transmission. Through targeted data compression and encryption processing, the latency and packet loss during data transmission are reduced, while ensuring the security of data during transmission. Especially in a network environment with limited bandwidth, by adjusting the data transmission bandwidth allocation and selecting an appropriate transmission protocol, network fluctuations can be effectively addressed, thus ensuring the fast and accurate transmission of critical meteorological information, optimizing resource allocation, strengthening the system's adaptability to complex network conditions, and enabling efficient operation even under heavy or unstable network loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is the system flow chart of the present invention;

[0053] Figure 2 is the flow chart for evaluating the volatility of the current network in the present invention;

[0054] Figure 3 is the flow chart for obtaining the network state prediction result in the present invention;

[0055] Figure 4 is the flow chart for evaluating the urgency of meteorological data in the present invention;

[0056] Figure 5 is the flow chart for obtaining the transmission priority evaluation result in the present invention;

[0057] Figure 6 is the flow chart for compressing data in the present invention;

[0058] Figure 7 is the flow chart for selecting a matching transmission protocol in the present invention;

[0059] Figure 8 is the flow chart for obtaining the meteorological data transmission result in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0061] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 should not be construed as a limitation to the present invention. In addition, in the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0062] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent control system for meteorological data compression and transmission, the system includes:

[0063] The network status evaluation module evaluates the volatility of the current network based on the current meteorological data transmission environment, according to the network latency and packet loss data within a period of time, and predicts the network volatility in the future period according to the deviation between the weather prediction information in the future period and the current weather state, to obtain the network status prediction result;

[0064] The data priority evaluation module evaluates the urgency of the meteorological data based on the meteorological data information to be transmitted, according to the deviation of the meteorological data from the normal data range, and combines the meteorological data type and the real-time requirement of the data to evaluate the transmission priority of the data, to obtain the transmission priority evaluation result;

[0065] The data compression and encryption module selects a matching compression algorithm based on the transmission priority evaluation result, according to the target compression rate and target compression ratio of the meteorological data, performs compression processing on the data, and performs encryption processing on the data according to the encryption requirement, to obtain the compressed and encrypted meteorological data;

[0066] The data transmission control module selects a matching transmission protocol based on the compressed and encrypted meteorological data, the transmission priority evaluation result and the network status prediction result, according to the transmission efficiency requirement, transmission stability requirement, transmission latency requirement, and adjusts the transmission bandwidth allocation of meteorological data with different priorities according to the network stability in the future period, to perform meteorological data transmission, to obtain the meteorological data transmission result.

[0067] The network status prediction result includes a network latency index, a packet loss rate index and a network volatility score, the transmission priority evaluation result includes a data emergency level, a priority ranking and a real-time classification, the compressed and encrypted meteorological data includes a data compression rate, the size of the data after compression and an encryption security level, and the meteorological data transmission result includes a transmission completion time, a data integrity verification result, data availability information and transmission bandwidth allocation information.

[0068] Please refer to Figure 2, the method for evaluating the volatility of the current network is as follows:

[0069] Based on the current meteorological data transmission environment, collect network data over a period of time, including network latency and packet loss rate;

[0070] Based on the network data over a period of time, through the formula:

[0071]

[0072] Calculate the current network volatility score , and obtain the evaluation result of the current network volatility. Among them, represents the network latency weight coefficient, represents the packet loss rate weight coefficient, is the average network latency, is the average packet loss rate, is the current network volatility score.

[0073] Formula:

[0074]

[0075] Meanings and acquisition methods of parameters

[0076] 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 can effectively reflect the impact of latency and packet loss rate on network stability.

[0077] is the average value of network latency, which can be obtained by collecting network latency data within a certain time window and then calculating its average value. For example, if the network latency is measured once per minute, the average latency within an hour can be obtained by summing up the latency values of all minutes and then dividing by 60.

[0078] is the average value of network packet loss rate. Similarly, it can be obtained by collecting packet loss data within the same time window and then calculating its average value. If the packet loss situation is recorded once per minute, the average packet loss rate within an hour can be obtained by averaging the packet loss rates of all minutes.

[0079] Calculation example

[0080] Set the network latency values measured within an hour as follows: .

[0081] Calculate the average latency :

[0082]

[0083] Set the measured packet loss rate as .

[0084] Calculate the average packet loss rate :

[0085]

[0086] Set 2 and .

[0087] Calculate :

[0088]

[0089]

[0090]

[0091]

[0092] The level of the value reflects the volatility of the network. The current calculation result indicates that the network has a certain degree of volatility.

[0093] Please refer to Figure 3 , the steps to obtain the network status prediction result are as follows:

[0094] Based on the current network volatility assessment result, through weather forecasting, collect weather prediction information for future periods to obtain network status correlation data;

[0095] Based on the network status correlation data, through the formula:

[0096]

[0097] Calculate the network volatility score for future periods , to obtain the network status prediction result, where is the current network volatility score, is the wind speed deviation between the future and the current, is the rainfall deviation between the future and the current, is the snowfall deviation between the future and the current, , and are influence coefficients, is the network volatility score for future periods.

[0098] Formula:

[0099]

[0100] Detailed Explanation and Acquisition Method of Parameters:

[0101] : Current network volatility score, the parameter is obtained through the previous steps.

[0102] 、 and : Respectively wind speed, rainfall, and snowfall deviation. The deviation is obtained through simple arithmetic calculation. The calculation method is the future value minus the current value. For example, the wind speed deviation is , the rainfall deviation is , the snowfall deviation is , where and are the current and future wind speeds. The parameters are obtained from weather stations or weather forecast APIs and are usually expressed in meters per second (m / s). The data source provides immediate and future wind speed predictions to help network operators evaluate potential network interference. and are the current and future rainfall amounts, obtained from weather services and usually expressed in millimeters (mm). The data is based on observations from weather stations or satellite data analysis and can be used to evaluate the impact of rainfall on network equipment. and are the current and future snowfall amounts. The snowfall amount data is obtained from weather services and is measured in centimeters (cm) and is used to evaluate the potential impact of snow accumulation on the ground and wireless network facilities.

[0103] 、 and : Weather impact coefficient. The coefficient is determined based on historical data analysis and represents the influence weight of different weather factors on network volatility. The coefficient is determined through statistical methods such as regression analysis and reflects the degree of influence of specific weather factors such as wind, rain, and snow on network performance.

[0104] Calculation Example:

[0105] Set the current network volatility index , the current wind speed m / s, the predicted wind speed m / s, the wind speed impact coefficient , the current rainfall mm, the predicted rainfall mm, the rainfall impact coefficient , the current snowfall cm, the predicted snowfall cm, the snowfall impact coefficient .

[0106] Calculate the wind speed deviation m / s, rainfall deviation mm, snowfall deviation cm.

[0107] Calculate the future network volatility:

[0108]

[0109]

[0110]

[0111]

[0112] Calculation result , reflecting the increase in network volatility due to bad weather.

[0113] Please refer to Figure 4 , the method for evaluating the urgency of meteorological data is:

[0114] Based on the meteorological data information to be transmitted, extract the current real-time meteorological data, and extract the corresponding data type, the historical average value at the current detection point, to obtain the urgency-related data;

[0115] Based on the urgency-related data, through the formula:

[0116]

[0117] Calculate the urgency score of meteorological data , to obtain the evaluation result of the urgency of meteorological data, where, is the real-time meteorological data, is the average value of historical meteorological data, is the standard deviation of historical meteorological data, is the adjustment parameter, is the urgency score, is the base of the natural logarithm.

[0118] Formula:

[0119]

[0120] Parameter meaning and acquisition method:

[0121] : 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 for real-time analysis of environmental changes.

[0122] : The average of historical meteorological data, which is obtained by averaging the historical meteorological data of the current detection point during the same period. For example, if today is June 1st, then is the average temperature or the average value of other meteorological indicators on June 1st in the past five years.

[0123] : Meteorological data deviation, which is calculated by subtracting the real-time data from the historical average data, and is used to evaluate the deviation degree of the current meteorological conditions from the normal mode.

[0124] : The standard deviation of historical meteorological data, which is a statistic that describes the fluctuation magnitude of historical data and is often used in the calculation of standardized deviation to provide a scale for measuring volatility.

[0125] : Numerical adjustment parameter, which is adjusted according to specific application scenarios to control the sensitivity of the function, thereby affecting the sensitivity of the emergency level score.

[0126] Calculation example

[0127] Suppose the collected real-time meteorological data is 30°C, the historical average data during the same period is 25°C, and the standard deviation of historical data is 5°C. Calculate the deviation of meteorological data:

[0128]

[0129] Calculate the standardized deviation:

[0130]

[0131] Suppose the adjustment parameter is 1, and calculate the emergency level score according to the improved formula:

[0132]

[0133] Calculation result indicates that the current meteorological data has a high level of emergency, expressing that the current meteorological conditions deviate significantly from the historical average data and require an emergency response or priority handling.

[0134] Please refer to Figure 5 , and the steps to obtain the transmission priority evaluation result are as follows:

[0135] Based on the evaluation result of the meteorological data emergency level, extract the meteorological data type and obtain the real-time requirement of the data to get the priority-related data;

[0136] Based on the priority-related data, through the formula:

[0137]

[0138] Calculate the priority of data transmission , where is the urgency score, is the total sum of the urgency scores of meteorological data, is the priority of data transmission, is the data type weight, is the real-time requirement index of the data, is the adjustment coefficient;

[0139] Based on the priority of data transmission , according to the value size, rank the priority of meteorological data transmission tasks to obtain the transmission priority evaluation result.

[0140] Formula:

[0141]

[0142] Detailed Explanation of Parameters and Acquisition Methods

[0143] : The urgency score, obtained through previous steps of calculation.

[0144] : The data type weight, a preset weight value based on the influence degree of different meteorological data types on system decisions or operations. For example, for data types with greater influence, higher weights are assigned. These weights are usually determined by professional teams or historical data analysis and adjusted according to the importance and influence degree of data types.

[0145] : The real-time requirement index, the real-time requirement is defined according to the frequency of data update or the timeliness that must be processed. For real-time monitoring data (such as wind speed monitoring), its 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.

[0146] : The adjustment coefficient, a preset 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 requirements of the system and past experience.

[0147] Calculation Example:

[0148] Suppose we have the following parameter values: : The emergency level score reflecting an extreme rainfall event : The weight of rainfall data : The real-time requirement, as the data is real-time monitored rainfall : The adjustment coefficient : The total emergency level of all data in the entire system

[0149] Calculation process:

[0150]

[0151]

[0152]

[0153]

[0154] Calculation result , indicating that under the given parameter conditions, the transmission priority of this rainfall data is 0.36. This value can be used to compare with other priorities to evaluate the order of data transmission tasks.

[0155] Please refer to Figure 6 , the method for compressing data is as follows:

[0156] Based on the evaluation result of transmission priority, according to the meteorological data type, extract the target compression rate and target compression ratio of meteorological data to obtain data compression correlation information;

[0157] Based on the data compression correlation information, through the formula:

[0158]

[0159] Calculate the matching degree score of the data compression algorithm , where 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 are weight parameters, is the matching degree score of the data compression algorithm;

[0160] Based on the matching degree score of the data compression algorithm , by comparing the value sizes of different data compression algorithms, select the data compression algorithm with the largest value to compress the meteorological data.

[0161] Formula:

[0162]

[0163] Parameter meanings and acquisition methods:

[0164] Target compression ratio : It refers to the ratio of the expected size of the compressed data to the size of the original data. This value is usually preset according to the data type, system performance requirements, and storage limitations.

[0165] Target compression rate : It refers to the speed at which the expected compression process processes data, with the unit of megabytes per second (MB / s). The value is preset according to the data type, system real-time processing requirements, or time limitations.

[0166] Compression ratio of the algorithm : It refers to the ratio of the size of the compressed data to the size of the original data 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.

[0167] Compression rate of the algorithm : It refers to the processing speed when a specific compression algorithm processes a specific data set, with the unit of megabytes per second (MB / s). The value can be obtained through experimental measurement or by referring to the algorithm documentation.

[0168] Weight parameter and : Used to adjust the influence degree of the compression ratio and compression rate in the matching degree scoring. The weights are set according to the requirements of the application scenario to ensure that the scoring results meet the actual needs.

[0169] Calculation example:

[0170] Set the following parameter values:

[0171] Target compression ratio: (i.e., expecting to compress the data to 50% of the original size), target compression rate: MB / s, actual compression ratio of algorithm A: ,actual compression rate of algorithm A: MB / s, weight parameter: , (indicating that the compression ratio is more important than the compression rate).

[0172] According to the formula:

[0173]

[0174]

[0175]

[0176]

[0177] The matching degree 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 outstanding in terms of compression ratio. However, the compression rate is slightly lower than the target value. According to the specific application requirements, the most suitable compression algorithm can be selected.

[0178] Please refer to Figure 7 , the method for selecting a matching transmission protocol is as follows:

[0179] Based on the meteorological data to be transmitted, according to the meteorological data type, extract the preset transmission efficiency requirements, transmission stability requirements, and transmission delay requirements of the data to obtain transmission protocol-related data;

[0180] Based on the transmission protocol-related data, through the formula:

[0181]

[0182] Calculate the matching degree score of the transmission protocol , where , and are weight parameters, is the adjustment coefficient, is the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, is the target transmission stability, is 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 the natural logarithm;

[0183] Based on the matching degree score of the transmission protocol , by comparing the magnitudes of the values, select the transmission protocol with the largest value to obtain the transmission protocol selection result.

[0184] Formula:

[0185]

[0186] Meanings and acquisition methods of parameters:

[0187] : Transmission efficiency weight coefficient, reflecting the importance of transmission efficiency in the matching degree score. This coefficient can be determined by analyzing historical transmission data and evaluating the impact degree of transmission efficiency on the overall transmission quality.

[0188] : Transmission efficiency adjustment coefficient, which controls the impact amplitude of transmission efficiency differences on the matching degree score. By statistically analyzing the efficiency differences of different transmission protocols, this coefficient is determined to meet the actual application requirements.

[0189] : Target transmission efficiency, representing the transmission efficiency expected to be achieved in a specific meteorological data transmission task. This value can be preset according to the task requirements and the average value of historical data transmission efficiency.

[0190] : Actual transmission efficiency of the transmission protocol, referring to the transmission efficiency of the selected transmission protocol in actual applications. It can be obtained through experimental tests or by referring to the performance indicators in the protocol specifications.

[0191] : Transmission stability weight coefficient, reflecting the importance of transmission stability in the matching degree score. By analyzing indicators such as data packet loss rate and jitter during the transmission process, the impact of stability on transmission quality is evaluated, and this coefficient is determined.

[0192] : Target transmission stability, representing the expected transmission stability level in a specific meteorological data transmission task. It can be preset according to the requirements of the task for data integrity and continuity.

[0193] : Actual transmission stability of the transmission protocol, referring to the transmission stability of the selected transmission protocol in actual applications. It can be obtained through experimental tests or by referring to the performance indicators in the protocol specifications.

[0194] : Transmission delay weight coefficient, reflecting the importance of transmission delay in the matching degree score. By analyzing the impact of transmission delay on the requirements for data real-time, this coefficient is determined.

[0195] : Target transmission delay, representing the expected transmission delay in a specific meteorological data transmission task. It can be preset according to the requirements of the task for data real-time.

[0196] : Actual transmission delay of the transmission protocol, referring to the transmission delay of the selected transmission protocol in actual applications. It can be obtained through experimental tests or by referring to the performance indicators in the protocol specifications.

[0197] Calculation example:

[0198] In a meteorological data transmission task, the following parameters are set: Target transmission efficiency , Target transmission stability , Target transmission delay milliseconds, for a certain transmission protocol , whose actual performance indicators are: actual transmission efficiency , actual transmission stability , actual transmission delay milliseconds. Set the weight coefficient and adjustment coefficient as: , , , .

[0199] Calculate the matching degree score:

[0200]

[0201] Calculate each value:

[0202] Transmission efficiency matching item:

[0203]

[0204] Transmission stability matching item:

[0205]

[0206] Transmission delay matching item:

[0207]

[0208] Calculate the total matching degree score:

[0209]

[0210] The matching degree score of this transmission protocol is approximately , close to 1, indicating that this protocol is relatively well-matched with the target requirements in terms of transmission efficiency, stability, and delay, and is suitable for the current meteorological data transmission task.

[0211] Please refer to Figure 8 , and the steps to obtain the meteorological data transmission result are:

[0212] Based on the compressed and encrypted meteorological data, the transmission priority evaluation result, the network status prediction result, and the transmission protocol selection result, extract the current network bandwidth, and obtain the total priority score of the parallel transmission data to obtain the data transmission association information;

[0213] Based on the data transmission association information, through the formula:

[0214]

[0215] 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 available network bandwidth currently, is the priority of data transmission, is the total priority score for parallel data transmission, is the adjustment coefficient, is the network volatility score for the future time period;

[0216] Based on the bandwidth allocated to a single meteorological data transmission task , meteorological data is transmitted to obtain the meteorological data transmission result.

[0217] Formula:

[0218]

[0219] Parameter meaning and acquisition method:

[0220] : redundancy coefficient, used to adjust the flexibility of bandwidth allocation, control the redundant bandwidth allocation of data with different priorities during network fluctuations, and is preset according to requirements.

[0221] : the total available network bandwidth currently, indicating the maximum bandwidth value available for allocation in the current network. This value can be measured in real time through a network monitoring tool or obtained from the bandwidth information provided by the network service provider.

[0222] : priority score of data transmission, obtained through previous steps of calculation.

[0223] : total priority score for parallel data transmission, indicating the cumulative priority of all data in the current transmission task. Obtained by adding up the priority scores of all parallel data transmissions.

[0224] : adjustment coefficient, used to adjust the influence weight of the network volatility score to make the bandwidth allocation adapt to volatility changes. This coefficient can be determined through experiments or historical data analysis according to the impact degree of network volatility on transmission performance.

[0225] : network volatility score for the future time period, obtained through previous steps of calculation.

[0226] Calculation example:

[0227] Set the following parameter values: : redundancy coefficient, : total available network bandwidth currently, : priority score of single data, : total priority score for parallel data transmission, : Adjustment coefficient, : Network volatility score for future time periods.

[0228] Calculation process:

[0229] Calculate the priority weight:

[0230]

[0231] Calculate the redundant bandwidth allocation part:

[0232]

[0233] Calculate the network volatility adjustment part:

[0234]

[0235] Calculate the bandwidth allocation result:

[0236]

[0237] The calculated bandwidth allocation result is 20.5 Mbps, which is used to select an appropriate transmission protocol. This result balances the data priority requirements under the current network conditions and takes into account the impact of network volatility to ensure a relatively stable transmission performance.

[0238] The above is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent control system for meteorological data compression and transmission, characterized in that, The system includes: Based on the current meteorological data transmission environment, the network status evaluation module evaluates the volatility of the current network according to the network latency and packet loss data within a period of time, and predicts the network volatility in the future period according to the deviation between the weather forecast information in the future period and the current weather state, so as to obtain the network status prediction result; The steps for obtaining the network status prediction result are as follows: Based on the evaluation result of the volatility of the current network, collect the weather forecast information in the future period through weather forecasting to obtain the network status correlation data; Based on the network status correlation data, through the formula: Calculate the network volatility score for future time periods , and obtain the network state prediction result, where is the current network volatility score, is the wind speed deviation between the future and the current, is the rainfall deviation between the future and the current, is the snowfall deviation between the future and the current, , and are influence coefficients, is the network volatility score for future time periods; Based on the meteorological data information to be transmitted, the data priority evaluation module evaluates the urgency of the meteorological data according to the deviation between the meteorological data and the normal data range, and combines the meteorological data type and the real-time requirement of the data to evaluate the transmission priority of the data, so as to obtain the transmission priority evaluation result; Based on the transmission priority evaluation result, the data compression and encryption module selects a matching compression algorithm according to the target compression rate and target compression ratio of the meteorological data, performs compression processing on the data, and performs encryption processing on the data according to the encryption requirement to obtain the compressed and encrypted meteorological data; Based on the compressed and encrypted meteorological data, the transmission priority evaluation result and the network status prediction result, the data transmission control module selects a matching transmission protocol according to the transmission efficiency requirement, transmission stability requirement, and transmission delay requirement, and adjusts the transmission bandwidth allocation of the meteorological data with different priorities according to the network stability in the future period to perform meteorological data transmission, so as to obtain the meteorological data transmission result.

2. The intelligent control system for meteorological data compression and transmission according to claim 1, characterized in that, The method for evaluating the volatility of the current network is: based on the current meteorological data transmission environment, collect the network data within a period of time, including network latency and packet loss rate; Based on the network data within the period of time, through the formula: Calculate the current network volatility score , and obtain 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 is the current network volatility score 3. The intelligent control system for meteorological data compression and transmission according to claim 1, characterized in that The method for evaluating the urgency of the meteorological data is: based on the meteorological data information to be transmitted, extract the current real-time meteorological data, and extract the historical average value at the current detection point corresponding to the data type to obtain the urgency correlation data; Based on the urgency correlation data, through the formula: Calculate the emergency level score of meteorological data , and obtain the evaluation result of the emergency level of meteorological data, where is the real-time meteorological data, is the average value of historical meteorological data, is the standard deviation of historical meteorological data, is the adjustment parameter, is the emergency level score, is the base of the natural logarithm.

4. The intelligent control system for meteorological data compression and transmission according to claim 3, characterized in that, The steps for obtaining the transmission priority evaluation result are: based on the evaluation result of the meteorological data urgency, extract the meteorological data type, and obtain the real-time requirement of the data to obtain the priority correlation data; Based on the priority correlation data, through the formula: Calculate the priority of data transmission , where is the urgency score is the total urgency score of meteorological data is the priority of data transmission is the data type weight is the real-time requirement index of the data is the adjustment coefficient; based on the priority of the data transmission , according to value size, prioritize the meteorological data transmission tasks to obtain the transmission priority evaluation result 5. The intelligent control system for meteorological data compression and transmission according to claim 1, characterized in that, The method for performing compression processing on the data is: Based on the transmission priority evaluation result, extract the target compression rate and target compression ratio of the meteorological data according to the meteorological data type to obtain the data compression correlation information; Based on the data compression correlation information, through the formula: Calculate the matching degree score of the data compression algorithm , where 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 are weight parameters, is the matching degree score of the data compression algorithm; Match degree score based on the data compression algorithm , by comparing the values of different data compression algorithms, select the data compression algorithm with the largest value to compress the meteorological data.

6. The intelligent control system for meteorological data compression and transmission according to claim 1, characterized in that, The method for selecting a matching transmission protocol is: Based on the meteorological data to be transmitted, extract the preset transmission efficiency requirement, transmission stability requirement, and transmission delay requirement of the data according to the meteorological data type to obtain the transmission protocol correlation data; Based on the transmission protocol correlation data, through the formula: Calculate the matching degree score of the transmission protocol , where , and are weight parameters, is an adjustment coefficient, is the target transmission efficiency, is the actual transmission efficiency of the transmission protocol, is the target transmission stability, is 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 the natural logarithm; based on the matching degree score of the transmission protocol , by comparing the magnitudes of the values, select the transmission protocol with the largest value to obtain the transmission protocol selection result.

7. The intelligent control system for meteorological data compression and transmission according to claim 6, characterized in that, The steps for obtaining the meteorological data transmission result are: Based on the compressed and encrypted meteorological data, the transmission priority evaluation result, the network status prediction result, and the transmission protocol selection result, extract the current network bandwidth, and obtain the total priority score of the parallel transmission data to obtain data transmission correlation information; Based on the data transmission correlation 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 available network bandwidth currently is the priority of data transmission is the total priority score for parallel data transmission is the adjustment coefficient is the network volatility score for the future period Based on the bandwidth allocated to the single-item meteorological data transmission task , meteorological data is transmitted to obtain the meteorological data transmission result.

Images