Data security transmission system for vehicle-mounted meteorological monitoring equipment

Abstract

The present invention relates to the technical field of data transmission, and in particular to a data security transmission system for vehicle-mounted meteorological monitoring equipment. The system comprises a database, an information collection port, a data processing port and a data transmission port. The database is used to store historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information; the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the data processing port is used to pre-process the information collected by the information collection port, predict the packet loss rate during data transmission and judge the possibility of packet loss during data transmission; the data transmission port is used to calculate the importance of data that may be lost during data transmission, and give priority to transmitting data with high importance.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a data security transmission system for vehicle-mounted meteorological monitoring equipment. Background Art

[0002] Existing meteorological monitoring systems may encounter data packet loss during data transmission and storage, resulting in data integrity damage, which in turn affects the accuracy of meteorological forecasts; traditional network packet loss prediction methods usually rely on simple models or statistical analysis, which may not fully consider the impact of multiple variables, such as meteorological information, vehicle status, etc. on packet loss rate. Therefore, in complex environments such as vehicle-mounted meteorological monitoring equipment, the prediction accuracy may be low; in some traditional data transmission systems, the role of historical data may be ignored, resulting in the inability to use past transmission experience for intelligent adjustment. The lack of historical data will make it impossible for the system to make reasonable predictions and optimizations based on the packet loss during historical transmission; the data transmission schemes in the existing technology usually fail to intelligently adjust the priority of data transmission based on real-time conditions, and may not be able to effectively cope with the impact of factors such as network bandwidth fluctuations and vehicle status changes. Therefore, some important data may be ignored or delayed when the network is unstable, affecting the real-time and accuracy of meteorological monitoring results.

[0003] Therefore, it is necessary to propose a data security transmission system for vehicle-mounted meteorological monitoring equipment. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a data security transmission system for vehicle-mounted meteorological monitoring equipment, which combines data from multiple dimensions such as real-time meteorological monitoring information, network information, vehicle status and transmission information, and predicts packet loss rate through deep learning, which can more comprehensively capture the law of network packet loss, and can predict packet loss risk in real time, providing more accurate packet loss rate prediction results; by using historical transmission data and real-time monitoring data for comprehensive analysis, similarity calculation is used to evaluate the correlation between real-time data and historical packet loss data, so as to judge the importance of real-time data, and adjust the data transmission priority based on the predicted packet loss rate and importance calculation results. When the packet loss rate is high, important data will be transmitted first, so as to ensure that when the network condition is poor, key meteorological data can be transmitted first, which improves the reliability and real-time performance of the system, optimizes the efficiency of data transmission, and ensures the priority transmission of important information.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A data security transmission system for vehicle-mounted meteorological monitoring equipment, characterized in that the system includes a database, an information collection port, a data processing port and a data transmission port, the database is used to store historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information; the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the data processing port is used to pre-process the information collected by the information collection port, predict the packet loss rate during data transmission and determine the possibility of packet loss during data transmission; the data transmission port is used to calculate the importance of data that may be lost during data transmission, and give priority to transmitting data with high importance.

[0007] A further improvement of the present invention is that the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the real-time meteorological monitoring information includes rainfall intensity, snowfall, haze concentration, wind speed, temperature and humidity acquired in real time by the vehicle-mounted meteorological monitoring equipment; the real-time network information includes signal strength, network bandwidth, network delay and network switching frequency; the real-time vehicle status information includes vehicle speed, vehicle position and working status of the meteorological monitoring equipment; the real-time transmission information includes data packet size, data packet sending frequency and transmission load.

[0008] A further improvement of the present invention is that the data processing port includes a data preprocessing module, a packet loss rate prediction module and a judgment module; the data preprocessing module is used to extract the information collected by the information collection port and perform normalization and formatting processing in sequence; the packet loss rate prediction module is used to run the packet loss rate prediction model construction strategy and predict the packet loss rate during data transmission; the judgment module is used to run the judgment strategy, compare the predicted packet loss rate with the preset packet loss rate threshold, and judge the possibility of packet loss during data transmission.

[0009] A further improvement of the present invention is that the data transmission port includes an importance calculation module and a priority transmission module; the importance calculation module is used to run the data importance calculation strategy and arrange the data to be transmitted in descending order according to importance; the priority transmission module is used to extract the calculation result of the data importance and give priority to the transmission of data with high importance.

[0010] A further improvement of the present invention is that the packet loss rate prediction module runs a packet loss rate prediction model construction strategy and predicts the packet loss rate during data transmission; the packet loss rate prediction model construction strategy includes the following specific steps:

[0011] S11, extracting historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information from a database;

[0012] S12. Combine historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information into the form of feature vectors, take the set of all groups of feature vectors as the set of historical feature vectors, and construct a packet loss rate prediction model. The packet loss rate prediction model takes the set of all groups of feature vectors as input, takes the predicted packet loss rate of each group of feature vectors as output, takes the actual packet loss rate corresponding to each group of feature vectors as the prediction target, takes minimizing the mean square error between the predicted packet loss rate and the actual packet loss rate as the training target, stops training when the mean square error between the predicted packet loss rate and the actual packet loss rate converges, and outputs the packet loss rate prediction model.

[0013] A further improvement of the present invention is that the judgment module runs a judgment strategy, and the specific content of the judgment strategy is: combining the normalized and formatted real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information into a feature vector, taking the set of all feature vectors as a real-time feature vector set, inputting a single feature vector in the real-time feature vector set into a packet loss rate prediction model in the order of time nodes, outputting a predicted packet loss rate, wherein the packet loss rate value is between 0 and 1, comparing the predicted packet loss rate with a preset packet loss rate threshold, and when the predicted packet loss rate is greater than or equal to the packet loss rate threshold, determining that the data at the time node may be lost during transmission.

[0014] A further improvement of the present invention is that the packet loss rate prediction model is any one of a deep neural network model, a random forest model or a deep belief network model.

[0015] A further improvement of the present invention is that the importance calculation module is used to run a data importance calculation strategy, and the data importance calculation strategy includes the following specific steps:

[0016] S21. When the predicted packet loss rate is greater than or equal to the packet loss rate threshold, a real-time feature vector set is obtained, and a historical feature vector in which data packet loss occurs after a historical transmission process in the historical feature vector set is marked as a historical packet loss feature vector;

[0017] S22, importing the real-time feature vector and the historical packet loss feature vector into a cosine similarity calculation formula to perform similarity calculation, and outputting a similarity Xij, where Xij represents the similarity between the j-th real-time feature vector and the i-th historical packet loss feature vector in the real-time feature vector set;

[0018] S23, calculate the mean similarity between the jth real-time feature vector and all historical packet loss feature vectors ; Where n is the total number of historical packet loss feature vectors;

[0019] S24, preset a similarity threshold x, extract the number of times Qj when the similarity between the j-th real-time feature vector and the n historical packet loss feature vectors is greater than the similarity threshold, and calculate the importance of the j-th real-time feature vector. The calculation formula is: ;in, is the importance of the jth real-time feature vector;

[0020] S25. Arrange the real-time feature vectors in descending order according to their importance, and give priority to transmitting data with high importance.

[0021] The technical effects of the present invention are as follows:

[0022] The present invention combines data of multiple dimensions such as real-time meteorological monitoring information, network information, vehicle status and transmission information, and predicts packet loss rate through deep learning, which can not only capture the law of network packet loss more comprehensively, but also predict packet loss risk in real time, and provide more accurate packet loss rate prediction results; by using historical transmission data and real-time monitoring data for comprehensive analysis, similarity calculation is used to evaluate the correlation between real-time data and historical packet loss data, so as to judge the importance of real-time data; by calculating the importance of real-time data, the data transmission priority is adjusted based on the predicted packet loss rate. When the packet loss rate is high, important data will be transmitted first, so as to ensure that when the network condition is poor, key meteorological data can be transmitted first, improve the reliability and real-time performance of the system, optimize the efficiency of data transmission, and ensure the priority transmission of important information. While effectively utilizing historical data experience, the intelligent degree of data transmission is improved, so that the system can perform more accurate priority scheduling according to past packet loss patterns and the characteristics of real-time data; the stability and security of data transmission can be maintained under extreme network conditions, avoiding data loss or errors caused by packet loss, thereby improving the reliability of the vehicle-mounted meteorological monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0024] Figure 1 A schematic diagram of the structure of a data security transmission system for vehicle-mounted meteorological monitoring equipment according to an embodiment of the present invention;

[0025] Figure 2 Schematic diagram of a policy framework of a data security transmission system for vehicle-mounted meteorological monitoring equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] This embodiment proposes a data security transmission system for vehicle-mounted meteorological monitoring equipment, which combines data from multiple dimensions such as real-time meteorological monitoring information, network information, vehicle status, and transmission information, and predicts packet loss rate through deep learning. This method can not only capture the law of network packet loss more comprehensively, but also predict packet loss risks in real time and provide more accurate packet loss rate prediction results; by calculating the importance of real-time data, the data transmission priority is adjusted based on the predicted packet loss rate. When the packet loss rate is high, important data will be transmitted first, thereby ensuring that key meteorological data can be transmitted first when the network conditions are poor, improving the reliability and real-time performance of the system. This mechanism greatly optimizes the efficiency of data transmission and ensures the priority transmission of important information; by using historical transmission data and real-time monitoring data for comprehensive analysis, similarity calculation is used to evaluate the correlation between real-time data and historical packet loss data, thereby judging the importance of real-time data. This method can effectively utilize the experience of historical data to improve the intelligence of data transmission, allowing the system to perform more accurate priority scheduling based on past packet loss patterns and the characteristics of real-time data; by optimizing the prediction of packet loss during transmission and the priority transmission of important data, the system can ensure the stability and security of data transmission even under extreme network conditions, avoid data loss or errors caused by packet loss, and thus improve the reliability of on-board meteorological monitoring equipment.

[0027] like Figure 1-Figure 2 As shown, a data security transmission system for vehicle-mounted meteorological monitoring equipment is characterized in that the system includes a database, an information collection port, a data processing port and a data transmission port, the database is used to store historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information; the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the data processing port is used to pre-process the information collected by the information collection port, predict the packet loss rate during data transmission and determine the possibility of packet loss during data transmission; the data transmission port is used to calculate the importance of data that may be lost during data transmission, and give priority to transmitting data with high importance.

[0028] In this embodiment, the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the real-time meteorological monitoring information includes rainfall intensity, snowfall, haze concentration, wind speed, temperature and humidity obtained in real time by vehicle-mounted meteorological monitoring equipment; the real-time network information includes signal strength, network bandwidth, network delay and network switching frequency; the real-time vehicle status information includes vehicle speed, vehicle position and working status of meteorological monitoring equipment; the real-time transmission information includes data packet size, data packet sending frequency and transmission load.

[0029] In this embodiment, the data processing port includes a data preprocessing module, a packet loss rate prediction module and a judgment module; the data preprocessing module is used to extract the information collected by the information collection port and perform normalization and formatting processing in sequence; the packet loss rate prediction module is used to run the packet loss rate prediction model construction strategy and predict the packet loss rate during data transmission; the judgment module is used to run the judgment strategy, compare the predicted packet loss rate with the preset packet loss rate threshold, and judge the possibility of packet loss during data transmission.

[0030] In this embodiment, the data transmission port includes an importance calculation module and a priority transmission module; the importance calculation module is used to run the data importance calculation strategy and arrange the data to be transmitted in descending order according to importance; the priority transmission module is used to extract the calculation result of the data importance and give priority to the transmission of data with high importance.

[0031] In this embodiment, the packet loss rate prediction module runs a packet loss rate prediction model construction strategy and predicts the packet loss rate during data transmission; the packet loss rate prediction model construction strategy includes the following specific steps:

[0032] S11, extracting historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information from a database;

[0033] S12. Combine historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information into the form of feature vectors, take the set of all groups of feature vectors as the set of historical feature vectors, and construct a packet loss rate prediction model. The packet loss rate prediction model takes the set of all groups of feature vectors as input, takes the predicted packet loss rate of each group of feature vectors as output, takes the actual packet loss rate corresponding to each group of feature vectors as the prediction target, takes minimizing the mean square error between the predicted packet loss rate and the actual packet loss rate as the training target, stops training when the mean square error between the predicted packet loss rate and the actual packet loss rate converges, and outputs the packet loss rate prediction model.

[0034] In this embodiment, the judgment module runs a judgment strategy, and the specific content of the judgment strategy is: combining the normalized and formatted real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information into a feature vector, taking the set of all feature vectors as a real-time feature vector set, and inputting a single feature vector in the real-time feature vector set into a packet loss rate prediction model in the order of time nodes, and outputting a predicted packet loss rate, wherein the packet loss rate value is between 0 and 1, and comparing the predicted packet loss rate with a preset packet loss rate threshold. When the predicted packet loss rate is greater than or equal to the packet loss rate threshold, it is determined that the data at the time node may be lost during the transmission process.

[0035] In this embodiment, the packet loss rate prediction model is any one of a deep neural network model, a random forest model or a deep belief network model.

[0036] In this embodiment, the importance calculation module is used to execute a data importance calculation strategy, and the data importance calculation strategy includes the following specific steps:

[0037] S21. When the predicted packet loss rate is greater than or equal to the packet loss rate threshold, a real-time feature vector set is obtained, and a historical feature vector in which data packet loss occurs after a historical transmission process in the historical feature vector set is marked as a historical packet loss feature vector;

[0038] S22, importing the real-time feature vector and the historical packet loss feature vector into a cosine similarity calculation formula to perform similarity calculation, and outputting a similarity Xij, where Xij represents the similarity between the j-th real-time feature vector and the i-th historical packet loss feature vector in the real-time feature vector set;

[0039] S23, calculate the mean similarity between the jth real-time feature vector and all historical packet loss feature vectors ; Where n is the total number of historical packet loss feature vectors;

[0040] S24, preset a similarity threshold x, extract the number of times Qj when the similarity between the j-th real-time feature vector and the n historical packet loss feature vectors is greater than the similarity threshold, and calculate the importance of the j-th real-time feature vector. The calculation formula is: ;in, is the importance of the jth real-time feature vector;

[0041] S25. Arrange the real-time feature vectors in descending order according to their importance, and give priority to transmitting data with high importance.

[0042] It should be noted here that packet loss rate prediction is performed through deep learning by combining data from multiple dimensions such as real-time meteorological monitoring information, network information, vehicle status, and transmission information. This method can not only capture the law of network packet loss more comprehensively, but also predict packet loss risks in real time and provide more accurate packet loss rate prediction results; by calculating the importance of real-time data, the data transmission priority is adjusted based on the predicted packet loss rate. When the packet loss rate is high, important data will be transmitted first, thereby ensuring that key meteorological data can be transmitted first when the network condition is poor, improving the reliability and real-time performance of the system. This mechanism greatly optimizes the efficiency of data transmission and ensures the priority transmission of important information; by using historical transmission data and real-time monitoring data for comprehensive analysis, similarity calculation is used to evaluate the correlation between real-time data and historical packet loss data, thereby judging the importance of real-time data. This method can effectively utilize the experience of historical data to improve the intelligence of data transmission, allowing the system to perform more accurate priority scheduling based on past packet loss patterns and the characteristics of real-time data; by optimizing the prediction of packet loss during transmission and the priority transmission of important data, the system can ensure the stability and security of data transmission even under extreme network conditions, avoid data loss or errors caused by packet loss, and thus improve the reliability of on-board meteorological monitoring equipment.

[0043] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0044] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0045] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0046] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0048] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0049] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0051] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0052] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data security transmission system for vehicle-mounted meteorological monitoring equipment, characterized in that: The system includes a database, an information collection port, a data processing port and a data transmission port. The database is used to store historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information; the information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; The data processing port is used to pre-process the information collected by the information collection port, predict the packet loss rate during data transmission and determine the possibility of packet loss during data transmission; The data transmission port is used to calculate the importance of data that may be lost during data transmission, and to give priority to transmitting data with high importance; The packet loss rate prediction module runs a packet loss rate prediction model construction strategy and predicts the packet loss rate during data transmission; the packet loss rate prediction model construction strategy includes the following specific steps: S11, extracting historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information from a database; S12, combining historical meteorological monitoring information, historical network information, historical vehicle status information and historical transmission information into a feature vector, taking the set of all groups of feature vectors as a historical feature vector set, and constructing a packet loss rate prediction model, wherein the packet loss rate prediction model takes the set of all groups of feature vectors as input, takes the predicted packet loss rate of each group of feature vectors as output, takes the actual packet loss rate corresponding to each group of feature vectors as a prediction target, takes minimizing the mean square error between the predicted packet loss rate and the actual packet loss rate as a training target, stops training when the mean square error between the predicted packet loss rate and the actual packet loss rate reaches convergence, and outputs the packet loss rate prediction model; The importance calculation module is used to run the data importance calculation strategy, and the data importance calculation strategy includes the following specific steps: S21. When the predicted packet loss rate is greater than or equal to the packet loss rate threshold, a real-time feature vector set is obtained, and a historical feature vector in which data packet loss occurs after a historical transmission process in the historical feature vector set is marked as a historical packet loss feature vector; S22, importing the real-time feature vector and the historical packet loss feature vector into a cosine similarity calculation formula to perform similarity calculation, and outputting a similarity Xij, where Xij represents the similarity between the j-th real-time feature vector and the i-th historical packet loss feature vector in the real-time feature vector set; S23, calculate the mean similarity between the jth real-time feature vector and all historical packet loss feature vectors ; Where n is the total number of historical packet loss feature vectors; S24, preset a similarity threshold x, extract the number of times Qj when the similarity between the j-th real-time feature vector and the n historical packet loss feature vectors is greater than the similarity threshold, and calculate the importance of the j-th real-time feature vector. The calculation formula is: ;in, is the importance of the jth real-time feature vector; S25. Arrange the real-time feature vectors in descending order according to their importance, and give priority to transmitting data with high importance.

2. The data security transmission system for vehicle-mounted meteorological monitoring equipment according to claim 1 is characterized in that: The information collection port is used to collect real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information within a preset time period; the real-time meteorological monitoring information includes rainfall intensity, snowfall, haze concentration, wind speed, temperature and humidity obtained in real time by vehicle-mounted meteorological monitoring equipment; the real-time network information includes signal strength, network bandwidth, network delay and network switching frequency; the real-time vehicle status information includes vehicle speed, vehicle position and working status of meteorological monitoring equipment; the real-time transmission information includes data packet size, data packet sending frequency and transmission load.

3. The data security transmission system for vehicle-mounted meteorological monitoring equipment according to claim 2 is characterized in that: The data processing port includes a data preprocessing module, a packet loss rate prediction module and a judgment module; the data preprocessing module is used to extract the information collected by the information collection port and perform normalization and formatting in turn; The packet loss rate prediction module is used to run the packet loss rate prediction model construction strategy and predict the packet loss rate during data transmission; the judgment module is used to run the judgment strategy, compare the predicted packet loss rate with the preset packet loss rate threshold, and judge the possibility of packet loss during data transmission.

4. The data security transmission system for vehicle-mounted meteorological monitoring equipment according to claim 3 is characterized in that: The data transmission port includes an importance calculation module and a priority transmission module; the importance calculation module is used to run the data importance calculation strategy and arrange the data to be transmitted in descending order according to importance; the priority transmission module is used to extract the calculation result of the data importance and give priority to the data of importance.

5. The data security transmission system for vehicle-mounted meteorological monitoring equipment according to claim 4 is characterized in that: The judgment module runs a judgment strategy, and the specific content of the judgment strategy is: combining normalized and formatted real-time meteorological monitoring information, real-time network information, real-time vehicle status information and real-time transmission information into a feature vector, taking the set of all feature vectors as a real-time feature vector set, inputting a single feature vector in the real-time feature vector set into a packet loss rate prediction model in the order of time nodes, outputting a predicted packet loss rate, wherein the packet loss rate value is between 0 and 1, comparing the predicted packet loss rate with a preset packet loss rate threshold, and when the predicted packet loss rate is greater than or equal to the packet loss rate threshold, determining that the data at the time node may be lost during transmission.

6. The data security transmission system for vehicle-mounted meteorological monitoring equipment according to claim 5 is characterized in that: The packet loss rate prediction model is any one of a deep neural network model, a random forest model or a deep belief network model.

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