A method and system for remotely transmitting data of an automatic weather station in unmanned areas

Abstract

The present invention discloses a method and system for remotely transmitting data of an unmanned area automatic weather station, which relates to the technical field of meteorological data transmission. The steps of the method include: collecting temperature, humidity, air pressure, and wind speed data monitored by the weather station to generate a multi-type meteorological data set; obtaining a signal quality score through a signal quality monitoring strategy in combination with the multi-type meteorological data set; obtaining meteorological importance indicators of all data to be allocated through a priority evaluation algorithm to form a priority queue; setting a hierarchical data transmission strategy for meteorological data, and allocating a transmission period for all data to be allocated according to the priority queue; and uploading the received data to a receiving platform. The present invention solves the problems of limited system resources and easy data loss in the case of unstable signals or harsh environments for meteorological data.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data transmission, and specifically to a method and system for remotely transmitting data of an automatic meteorological station in uninhabited areas. Background Art

[0002] In uninhabited areas, traditional methods for transmitting meteorological data have problems such as unstable signals, limited bandwidth, data loss, and transmission delays. These problems greatly restrict the effectiveness and reliability of meteorological monitoring systems. Therefore, developing a method that can adapt to harsh environments and ensure reliable data transmission has become an urgent problem to be solved in the field of meteorological monitoring.

[0003] For example, the existing Chinese patent with the publication number CN104986334B discloses a multi-scale aviation meteorological platform, which includes a platform liftoff device, a high-altitude gliding variable-structure aircraft, and a radiosonde. Among them: the platform liftoff device carries the high-altitude gliding variable-structure aircraft and the radiosonde into the air, and collects atmospheric meteorological data during the ascent, and transmits the data back to the ground by radio. After determining that the specified altitude has been reached through an altimeter, the weather balloon stays at high altitude to transmit data, and the platform descends at high speed through the high-altitude gliding variable-structure aircraft. After reaching the predetermined altitude, the rotary tail wing is rotated, and the servo drives the worm and gear to rotate automatically to change the flight mode of the aircraft, using the short wing as the fuselage for gliding. And in the near-ground gliding interval, small-scale meteorological information is collected to achieve the collection of multi-scale meteorological information. Through integrated design, the present invention realizes liftoff, landing, and collection of meteorological information by itself, realizes the synchronization of multi-scale meteorological data, and has great prospects for sharing future meteorological data platforms.

[0004] However, this method and the existing technologies generally have the following problems:

[0005] Instability of data transmission: Existing transmission technologies often cannot dynamically adjust the communication method according to different environmental conditions. Especially in harsh environments, the communication quality is unstable, resulting in data loss;

[0006] Insufficient utilization of bandwidth resources: Traditional methods generally adopt a fixed data transmission cycle and cannot be dynamically adjusted according to the real-time network bandwidth situation, resulting in inefficient use of bandwidth and affecting the real-time and accuracy of data transmission;

[0007] Lack of disaster level judgment and emergency data priority transmission mechanism: Most of the existing technologies adopt static transmission mechanisms, without automatic judgment and dynamic adjustment of the priority of disaster data, and cannot meet the need for rapid transmission of disaster data in emergency situations.

[0008] Therefore, the present invention provides a method and system for remotely transmitting data of an automatic meteorological station in uninhabited areas. Summary of the Invention

[0009] The object of the present invention is to provide a method and system for remotely transmitting data of an automatic weather station in uninhabited areas to solve the existing problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solution: A method for remotely transmitting data of an automatic weather station in uninhabited areas, comprising the following steps:

[0011] S1. Collect temperature, humidity, air pressure and wind speed data monitored by the weather station to generate a multi-type meteorological data set;

[0012] S2. Combine the multi-type meteorological data set to obtain a signal quality score through a signal quality monitoring strategy, set a minimum signal quality value, extract the meteorological data with the signal quality score less than or equal to the minimum signal quality value, list it as the meteorological data to be allocated, send it to step S3, extract the meteorological data with the signal quality score greater than the minimum signal quality value, list it as the meteorological data to be transmitted, and send it to step S5;

[0013] S3. Through a priority evaluation algorithm, obtain the meteorological importance indicators of all the data to be allocated to form a priority queue;

[0014] S4. Set a hierarchical data transmission strategy for meteorological data, and allocate a transmission period for all the data to be allocated according to the priority queue;

[0015] S5. Upload the received data to the receiving platform.

[0016] The further improvement of the present invention lies in that the priority evaluation algorithm includes obtaining the priority levels of all meteorological data by combining the current disaster level and the meteorological data reliability index with a weighting coefficient, and listing them in a priority data set , represents the Nth meteorological data, N represents the number of meteorological data, and all meteorological data are listed in the priority queue in descending order of priority level.

[0017] The further improvement of the present invention lies in that the priority evaluation algorithm further includes judging the current disaster level of the meteorological data through a disaster level prediction model based on the current multi-type meteorological data set. The disaster level prediction model takes the historical meteorological data and the corresponding ratings as model training data, inputs the current multi-type meteorological data set, and obtains the current disaster level; evaluating the reliability index of the current meteorological data based on the deviation between the historical meteorological data and the actual observed value, and the reliability index of the current meteorological data is expressed as: , where represents an adjustment coefficient, represents the root mean square error between the current meteorological data and the historical meteorological data.

[0018] A further improvement of the present invention lies in that the specific steps of the meteorological data hierarchical feedback strategy include:

[0019] S41. Evenly divide the priority queue into 3 feedback levels, including high-priority data, medium-priority data, and low-priority data, and allocate an initial feedback period for each level;

[0020] S42. Set an incremental feedback strategy. During each transmission, calculate a new priority level based on the incremental data and re-allocate the feedback levels;

[0021] S43. Allocate the bandwidth quantity according to the priority ratio of each meteorological data;

[0022] S44. After each feedback ends, the receiving platform generates a feedback report based on the actual transmission value and updates the feedback period through a periodic dynamic adjustment algorithm.

[0023] A further improvement of the present invention lies in that the incremental feedback strategy obtains a new priority level by tracking the absolute value of the change in meteorological data within time TT, setting a change threshold, extracting the meteorological data with the absolute value of the change in meteorological data greater than the change threshold, and combining the disaster level, the meteorological data reliability index, and the weighted average of the standardized absolute value of the change in meteorological data; and adding it to the allocation result in step S41.

[0024] A further improvement of the present invention lies in that the periodic dynamic adjustment algorithm includes calculating the transmission evaluation value of the meteorological data. The transmission evaluation value is obtained by extracting the transmission success rate tsr, transmission delay trd, and bandwidth utilization bau in the feedback report generated from the actual transmission value. Then, the calculation formula for the transmission evaluation value of the i-th data is:

[0025] , where . and represent weight coefficients; then the update formula for the feedback period of the i-th data is: , where, represents the initial feedback period, and represent adjustment coefficients, .

[0026] A further improvement of the present invention lies in that the signal quality monitoring strategy includes calculating the meteorological environment impact value through the multi-type meteorological data set, which is obtained by weighted summing the standardized real-time signal strength and signal packet loss rate with the meteorological environment impact value.

[0027] A further improvement of the present invention lies in that the meteorological environment impact value is obtained by weighted summing the humidity impact value, temperature impact value, air pressure impact value, and wind speed impact value. The calculation formula for the temperature impact value is:

[0028] , the calculation formula for the humidity influence value is: , the calculation formula for the air pressure influence value is: and the calculation formula for the wind speed influence value is: , where represents the temperature influence coefficient, represents the humidity influence coefficient, represents the air pressure influence coefficient, represents the wind speed influence coefficient, represents the reference temperature, represents the standard air pressure, T represents the real-time temperature, H represents the real-time humidity, P represents the real-time air pressure, and W represents the real-time wind speed.

[0029] On the other hand, the present invention provides a remote data transmission system for an automatic weather station in uninhabited areas, including:

[0030] A data acquisition module, which is used to acquire the temperature, humidity, air pressure and wind speed data monitored by the weather station and generate multi-type meteorological data sets;

[0031] A signal quality monitoring module, which is used to obtain a signal quality score through a signal quality monitoring strategy in combination with the multi-type meteorological data sets, set a minimum signal quality value, extract the meteorological data whose signal quality score is less than or equal to the minimum signal quality value, list them as meteorological data to be allocated, send them to the priority queue generation module, extract the meteorological data whose signal quality score is greater than the minimum signal quality value, list them as meteorological data to be transmitted, and send them to the data transmission module;

[0032] A priority queue generation module, which obtains the meteorological importance indicators of all data to be allocated through a priority evaluation algorithm and forms a priority queue;

[0033] A meteorological data hierarchical transmission module, which is used to allocate transmission periods for all data to be allocated according to the priority queue;

[0034] A data transmission module, which uploads the received data to the receiving platform.

[0035] The further improvement of the present invention lies in that the meteorological data hierarchical transmission module includes a transmission layer allocation unit and a transmission period adjustment unit; the transmission layer allocation unit is used to allocate transmission layers according to the priority queue and incremental data; the transmission period adjustment unit is used to generate a feedback report by the receiving platform according to the actual transmission value and update the transmission period through a cycle dynamic adjustment algorithm.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. First, through the signal quality monitoring strategy, the present invention evaluates the signal quality in real time and dynamically adjusts data transmission. Even in the case of unstable signals or harsh environments, the system can automatically select high-quality data for transmission, reducing the risk of data loss;

[0038] 2. By combining the signal quality, disaster level, and reliability index of meteorological data, the system can adjust the feedback period in real time to ensure the priority transmission of key data, thereby improving the transmission stability of the system and the integrity of the data;

[0039] 3. Divide the meteorological data into different feedback levels according to the priority, and adjust the feedback period according to the real-time environment and bandwidth conditions, which can reasonably utilize the limited bandwidth resources while ensuring the timely feedback of key data;

[0040] 4. Through the incremental feedback strategy, the system dynamically updates the priority according to the data changes during each transmission process. This can improve the bandwidth utilization rate, avoid redundant data transmission, and improve the overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of a remote data feedback method for an automatic meteorological station in uninhabited areas according to the present invention;

[0042] Figure 2 It is a flowchart of a meteorological data hierarchical feedback strategy for a remote data feedback method of an automatic meteorological station in uninhabited areas according to the present invention;

[0043] Figure 3 It is a framework diagram of a remote data feedback system for an automatic meteorological station in uninhabited areas according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The technical solution of the present invention will be described in detail below through the drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0045] The term "and / or" is merely a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0046] Embodiment 1

[0047] Figure 1 It shows a flowchart of a remote data feedback method for an automatic meteorological station in uninhabited areas disclosed in this embodiment. The steps are as follows:

[0048] S1. Collect the temperature, humidity, air pressure, and wind speed data monitored by the weather station to generate a multi-type meteorological data set;

[0049] S2. Obtain a signal quality score through a signal quality monitoring strategy in combination with the multi-type meteorological data set;

[0050] The signal quality monitoring strategy includes calculating a meteorological environment impact value through the multi-type meteorological data set, and is obtained by weighted summing the normalized real-time signal strength and signal packet loss rate with the meteorological environment impact value. The meteorological environment impact value is obtained by weighted summing a humidity impact value, a temperature impact value, an air pressure impact value, and a wind speed impact value. The change in temperature affects the propagation of electromagnetic waves. Usually, at higher temperatures, the conductivity of air increases, resulting in increased signal attenuation. Therefore, in this embodiment, a function based on temperature difference is used to represent the impact of temperature on signal quality. This impact generally shows a decreasing trend as the temperature rises. Therefore, we use a function containing the square difference of temperature to represent its negative impact on signal quality.

[0051] In a high-humidity environment, the moisture content in the air increases, which will cause attenuation of radio wave propagation, especially in high-frequency signals. The impact of humidity on signal quality is usually negative, and as the humidity increases, the signal quality will gradually decrease. In this embodiment, a function with exponential decay is used to represent the impact of humidity on signal quality.

[0052] The change in air pressure affects the density of air, and thus affects the propagation efficiency of radio waves. When the air pressure is low, the density of air is low, and the signal propagation effect is also poor. In this embodiment, a function linearly related to the change in air pressure is used to represent the negative impact of air pressure on signal quality.

[0053] Wind speed mainly affects signal quality by influencing the multipath propagation effect of signals (such as reflection, refraction, etc.). A larger wind speed may cause signal instability, resulting in signal attenuation. To reflect the impact of wind speed on signal quality, a linear function is used to represent the negative impact of wind speed in this embodiment.

[0054] Therefore, the calculation formula for the temperature impact value is: , the calculation formula for the humidity impact value is: , the calculation formula for the air pressure impact value is: and the calculation formula for the wind speed impact value is: , where represents the temperature impact coefficient, represents the humidity impact coefficient, represents the air pressure impact coefficient, represents the wind speed impact coefficient, represents the reference temperature, It represents the standard atmospheric pressure, T represents the real-time temperature, H represents the real-time humidity, P represents the real-time atmospheric pressure, and W represents the real-time wind speed.

[0055] Set the minimum signal quality value, extract the meteorological data whose signal quality score is less than or equal to the minimum signal quality value, list them as the meteorological data to be allocated, send them to step S3, extract the meteorological data whose signal quality score is greater than the minimum signal quality value, list them as the meteorological data to be transmitted, and send them to step S5;

[0056] S3. Through the priority evaluation algorithm, obtain the meteorological importance indicators of all the data to be allocated, and form a priority queue;

[0057] The priority evaluation algorithm includes obtaining the priority levels of all meteorological data by combining the current disaster level and the meteorological data reliability index with a weighting coefficient, and listing them in the priority data set

[0058] , represents the Nth meteorological data, N represents the number of meteorological data, and list all meteorological data in the priority queue in descending order of priority level.

[0059] The priority evaluation algorithm further includes, based on the current multi-type meteorological data set, judging the current disaster level of the meteorological data through a disaster level prediction model. The disaster level prediction model uses the collected historical meteorological data and the corresponding ratings as model training data, inputs the current multi-type meteorological data set, and obtains the current disaster level; evaluating the reliability index of the current meteorological data based on the deviation between the historical meteorological data and the actual observed value. The reliability index of the current meteorological data is expressed as: where, represents the adjustment coefficient, represents the root mean square error between the current meteorological data and the historical meteorological data.

[0060] S4. Set the meteorological data hierarchical transmission strategy, and allocate transmission cycles for all the data to be allocated according to the priority queue;

[0061] Figure 2 shows the flowchart of the meteorological data hierarchical transmission strategy of an unmanned area automatic weather station remote data transmission method disclosed in this embodiment. The specific steps of the meteorological data hierarchical transmission strategy include:

[0062] S41. Divide the priority queue into 3 transmission levels on average, including high-priority data, medium-priority data, and low-priority data, and allocate an initial transmission cycle for each level;

[0063] S42. To improve bandwidth utilization and reduce communication costs, an incremental feedback strategy is set. During each transmission, a new priority level is calculated based on the incremental data, and the feedback layer is reallocated.

[0064] S43. Allocate the bandwidth quantity according to the priority ratio of each meteorological data.

[0065] S44. After each feedback ends, the receiving platform generates a feedback report based on the actual transmission value, and updates the feedback period through a periodic dynamic adjustment algorithm.

[0066] During the transmission process, the priority queue is dynamically changing. According to the real-time changes in meteorological data, the changes in disaster levels, or the actual bandwidth situation, the system should continuously adjust the priority queue. After each data transmission, re-evaluate the priorities of all meteorological data and update the priority queue.

[0067] The incremental feedback strategy obtains the new priority level by tracking the absolute value of the change in meteorological data within time TT, setting a change threshold, extracting the meteorological data with the absolute value of the change in meteorological data greater than the change threshold, and combining the disaster level, the meteorological data reliability index, and the weighted average of the standardized absolute value of the change in meteorological data; add it to the allocation result in step S41. For example, if there is a new priority level greater than in the medium-priority data, then this data will be changed to high-priority data.

[0068] The periodic dynamic adjustment algorithm includes calculating the transmission evaluation value of meteorological data. The transmission evaluation value is obtained by extracting the transmission success rate tsr, transmission delay trd, and bandwidth utilization bau in the feedback report generated from the actual transmission value. Then the calculation formula for the transmission evaluation value of the i-th data is:

[0069] , where , and represent the weight coefficients; then the update formula for the feedback period of the i-th data is: , where, represents the initial feedback period, the basic period set according to the data priority (for example, 1 hour for high-priority data and 2 hours for low-priority data), and represent the adjustment coefficients, .

[0070] Since reflects the data transmission quality, the higher this value, the better the transmission quality, the smaller it is, the longer the feedback period; on the contrary, if the transmission quality is poor, the feedback period will be shortened to increase the data transmission frequency and avoid data loss.

[0071] For high-priority data, the larger it is, the shorter the feedback period; for low-priority data, the smaller it is, the longer the feedback period. Usually, takes values in the range of [0, 1], and higher-priority data corresponds to larger values.

[0072] Higher-priority meteorological data (such as disaster data) will be assigned a shorter feedback period to ensure their early feedback. Lower-priority meteorological data (such as ordinary weather data) can appropriately extend the feedback period to avoid occupying too much bandwidth resources.

[0073] Priority data is fed back first: This formula ensures that high-priority data (such as disaster warning data) can be quickly fed back, and the feedback period of low-priority data will be dynamically adjusted according to the bandwidth utilization situation to avoid waste of bandwidth resources.

[0074] Strong adaptability: Combining the transmission quality assessment and the influence of data priority, the system can flexibly adjust the feedback period according to different network environments and bandwidth conditions, improving the overall transmission efficiency.

[0075] Improve system reliability: Automatically shortening or lengthening the feedback period according to the transmission quality of data can increase the transmission frequency of data when the transmission quality is poor, ensuring the timely transmission and integrity of data.

[0076] S5. Upload the received data to the receiving platform.

[0077] The setting of the threshold and weight can be set by the operators in this field themselves.

[0078] Embodiment 2

[0079] Figure 3 shows a framework diagram of a remote data feedback system for an unmanned area automatic weather station according to the present invention. Based on the same inventive concept as Embodiment 1, the present invention provides a remote data feedback system for an unmanned area automatic weather station, including:

[0080] A data acquisition module for collecting temperature, humidity, air pressure, and wind speed data monitored by the weather station and generating multi-type meteorological data sets;

[0081] A signal quality monitoring module for obtaining a signal quality score through a signal quality monitoring strategy in combination with the multi-type meteorological data sets, setting a signal quality minimum value, extracting the meteorological data with the signal quality score less than or equal to the signal quality minimum value, listing it as the meteorological data to be allocated, sending it to the priority queue generation module, extracting the meteorological data with the signal quality score greater than the signal quality minimum value, listing it as the meteorological data to be transmitted, and sending it to the data feedback module;

[0082] A priority queue generation module obtains the meteorological importance indicators of all data to be allocated through a priority evaluation algorithm, and forms a priority queue.

[0083] A meteorological data hierarchical transmission module is used to allocate transmission cycles for all data to be allocated according to the priority queue.

[0084] The meteorological data hierarchical transmission module includes a transmission level allocation unit and a transmission cycle adjustment unit; the transmission level allocation unit is used to allocate transmission levels according to the priority queue and incremental data; the transmission cycle adjustment unit is used to receive a feedback report generated by the platform according to the actual transmission value, and update the transmission cycle through a cycle dynamic adjustment algorithm.

[0085] A data transmission module uploads the received data to a receiving platform.

[0086] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or boxes Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps of the function specified in one box or a plurality of boxes.

[0090] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A method for remote data transmission of an automatic weather station in an unmanned area, characterized in that: The following steps are involved: S1, collect temperature, humidity, air pressure and wind speed data monitored by the weather station to generate multi-type meteorological data sets; S2, combining the multi-type meteorological data sets with the signal quality monitoring strategy to obtain a signal quality score, setting a minimum signal quality value, extracting meteorological data with a signal quality score less than or equal to the minimum signal quality value, including the meteorological data to be allocated, and sending to step S3, extracting meteorological data with a signal quality score greater than the minimum signal quality value, including the meteorological data to be transmitted, and sending to step S5; S3, through the priority evaluation algorithm, obtain the meteorological importance index of all data to be allocated and form a priority queue; S4, setting a hierarchical return strategy for meteorological data, and allocating a transmission period for all data to be allocated according to the priority queue; The specific steps of the meteorological data layered return strategy include: S41, divide the priority queue into three return levels, including high priority data, medium priority data and low priority data, and allocate an initial return cycle for each level; S42, setting an incremental backhaul strategy, calculating a new priority level based on incremental data each time a transmission is performed, and reallocating the backhaul level; The incremental return strategy tracks the absolute value of meteorological data changes within the time TT, sets a change threshold, extracts meteorological data whose absolute value of meteorological data changes is greater than the change threshold, and combines the disaster level, meteorological data reliability index and the weighted average of the normalized absolute value of meteorological data changes to obtain a new priority level; and adds it to the allocation result of step S41; S43, allocating bandwidth according to the priority ratio of each meteorological data; S44. After each return transmission, the receiving platform generates a feedback report based on the actual transmission value and updates the return transmission cycle through a cycle dynamic adjustment algorithm; The period dynamic adjustment algorithm includes calculating the transmission evaluation value of meteorological data, and the transmission evaluation value is obtained by extracting the actual transmission value to generate the transmission success rate tsr, transmission delay trd and bandwidth utilization bau in the feedback report. Then the transmission evaluation value calculation formula of the i-th data is: ,in , and represents the weight coefficient; then the update formula for the i-th data return period is: ,in, Indicates the initial return cycle, represents the i-th meteorological data, and represents the adjustment coefficient, ; S5. Upload the received data to the receiving platform.

2. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 1, characterized in that: The priority evaluation algorithm includes combining all meteorological data with weighted coefficients according to the current disaster level and meteorological data reliability index to obtain the priority level of all meteorological data and list them in the priority data set. , It represents the Nth meteorological data, N represents the number of meteorological data, and all meteorological data are listed in the priority queue in descending order according to the priority level.

3. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 2, characterized in that: The priority evaluation algorithm also includes judging the disaster level of the current meteorological data through a disaster level prediction model based on the current multi-type meteorological data set, wherein the disaster level prediction model collects historical meteorological data and corresponding ratings as model training data, inputs the current multi-type meteorological data set, and obtains the current disaster level; and evaluating the reliability index of the current meteorological data based on the deviation between the historical meteorological data and the actual observation value, wherein the reliability index of the current meteorological data is expressed as: ,in, represents the adjustment coefficient, Represents the root mean square error between current meteorological data and historical meteorological data.

4. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 3, characterized in that: The signal quality monitoring strategy includes calculating a meteorological environment impact value through the multi-type meteorological data set, and obtaining the value by weighted summing the normalized real-time signal strength and signal packet loss rate with the meteorological environment impact value.

5. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 4, characterized in that: The meteorological environment impact value is obtained by weighted summation of humidity impact value, temperature impact value, air pressure impact value and wind speed impact value. The temperature impact value calculation formula is: , the calculation formula of humidity impact value is: 、The calculation formula of air pressure influence value is: The calculation formula of wind speed influence value is: ,in, represents the temperature influence coefficient, represents the humidity influence coefficient, represents the air pressure influence coefficient, represents the wind speed influence coefficient, represents the reference temperature, represents standard air pressure, T represents real-time temperature, H represents real-time humidity, P represents real-time air pressure, and W represents real-time wind speed.

6. A remote data transmission system for an automatic weather station in an unmanned area, used to execute a remote data transmission method for an automatic weather station in an unmanned area as claimed in any one of claims 1 to 5, characterized in that: include: The data acquisition module is used to collect temperature, humidity, air pressure and wind speed data monitored by the weather station and generate multi-type meteorological data sets; A signal quality monitoring module, used to obtain a signal quality score by combining the multi-type meteorological data sets with a signal quality monitoring strategy, set a minimum signal quality value, extract meteorological data with a signal quality score less than or equal to the minimum signal quality value, list the meteorological data in the to-be-allocated meteorological data, and send the meteorological data to a priority queue generation module; extract meteorological data with a signal quality score greater than the minimum signal quality value, list the meteorological data in the to-be-transmitted meteorological data, and send the meteorological data to a data return module; The priority queue generation module obtains the meteorological importance index of all data to be allocated through the priority evaluation algorithm to form a priority queue; A meteorological data hierarchical return module, used to allocate transmission cycles for all data to be allocated according to the priority queue; The data return module uploads the received data to the receiving platform.

7. The remote data transmission system for automatic weather stations in unmanned areas according to claim 6 is characterized by: The meteorological data hierarchical feedback module includes a feedback level allocation unit and a feedback cycle adjustment unit; the feedback level allocation unit is used to allocate the feedback level according to the priority queue and incremental data; the feedback cycle adjustment unit is used to generate a feedback report based on the actual transmission value of the receiving platform, and update the feedback cycle through a dynamic cycle adjustment algorithm.

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