Remote data return method and system for automatic weather station in unmanned area

Abstract

The invention discloses a remote data return method and system for an automatic meteorological station in a depopulated area, and relates to the technical field of meteorological data transmission, and the method comprises the steps: collecting temperature, humidity, air pressure and wind speed data monitored by a meteorological station, and generating 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 indexes of all to-be-distributed data through a priority evaluation algorithm, and forming a priority queue; setting a meteorological data hierarchical return strategy, and distributing a transmission period for all the to-be-distributed data according to the priority queue; and uploading the received data to a receiving platform. According to the invention, the problems of limited system resources and easy data loss of meteorological data under the condition of unstable signals or severe environment are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data transmission, and in particular to a remote data return method and system for an automatic weather station in an unmanned area. Background Art

[0002] In uninhabited areas, traditional meteorological data transmission methods have problems such as unstable signals, limited bandwidth, data loss and transmission delay, which 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 a difficult problem that needs to be solved in the field of meteorological monitoring.

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

[0004] However, this method and the prior art generally have the following problems: Instability of data transmission: Existing backhaul technologies are often unable to dynamically adjust communication methods according to different environmental conditions. Especially in harsh environments, communication quality is unstable, resulting in data loss. Insufficient utilization of bandwidth resources: Traditional methods generally use a fixed data transmission cycle, which cannot be dynamically adjusted according to the real-time network bandwidth conditions, resulting in inefficient utilization of bandwidth and affecting the real-time and accuracy of data transmission; Lack of disaster level judgment and emergency data priority transmission mechanism: Most existing technologies use a static feedback mechanism, without automatic judgment and dynamic adjustment of disaster data priority, and cannot meet the demand for rapid feedback of disaster data in emergency situations.

[0005] To this end, the present invention provides a method and system for remote data transmission of an automatic weather station in an unmanned area. Summary of the invention

[0006] The object of the present invention is to provide a method and system for remote data transmission of an automatic weather station in an unmanned area, so as to solve the existing problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for remote data transmission of an automatic weather station in an unmanned area, comprising the following steps: 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; S5. Upload the received data to the receiving platform.

[0008] The present invention is further improved in that the priority evaluation algorithm includes combining all meteorological data with weighted coefficients according to the current disaster level and the meteorological data reliability index to obtain the priority level of all meteorological data and list them in the priority data set. , Indicates the Nth meteorological data, N indicates the number of meteorological data, and all meteorological data are listed in the priority queue in descending order of priority.

[0009] The present invention is further improved in that the priority evaluation algorithm further 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 evaluates 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.

[0010] The present invention is further improved in that the meteorological data hierarchical return strategy specifically comprises the following steps: 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; S43, allocating the number of broadbands 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 period through a period dynamic adjustment algorithm.

[0011] A further improvement of the present invention is that the incremental feedback strategy tracks the absolute value of the change in meteorological data within the time TT, sets a change threshold, extracts meteorological data whose absolute value of the change in meteorological data is greater than the change threshold, and combines the disaster level, meteorological data reliability index and the weighted average of the standardized absolute value of the change in meteorological data to obtain a new priority level; and adds it to the allocation result of step S41.

[0012] The present invention is further improved in that the period dynamic adjustment algorithm includes calculating the transmission evaluation value of the meteorological data, and the transmission evaluation value is obtained by extracting the actual transmission value to generate the transmission success rate tsr, the transmission delay trd and the 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, and represents the adjustment coefficient, .

[0013] A further improvement of the present invention is 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.

[0014] The present invention is further improved 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, and 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.

[0015] On the other hand, the present invention provides a remote data return system for an automatic weather station in an unmanned area, comprising: 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.

[0016] A further improvement of the present invention is that 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 the incremental data; the feedback cycle adjustment unit is used for the receiving platform to generate a feedback report according to the actual transmission value, and to update the feedback cycle through a dynamic cycle adjustment algorithm.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention firstly evaluates the signal quality in real time and dynamically adjusts the data transmission through the signal quality monitoring strategy. Even in the case of unstable signal or bad environment, the system can automatically select high-quality data for transmission, reducing the risk of data loss; 2. Combining the reliability indicators of signal quality, disaster level and meteorological data, the system can adjust the return cycle in real time to ensure that key data is transmitted first, thereby improving the transmission stability of the system and the integrity of the data; 3. Divide meteorological data into different return levels according to priority, and adjust the return cycle according to the real-time environment and bandwidth conditions, so as to ensure the timely return of key data while making reasonable use of limited bandwidth resources; 4. Through the incremental backhaul strategy, the system dynamically updates the priority according to the data changes during each transmission process. This can improve bandwidth utilization, avoid redundant data transmission, and improve overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a remote data return method of an automatic weather station in an unmanned area according to the present invention; Figure 2 It is a flow chart of a meteorological data hierarchical return strategy of a remote data return method of an automatic weather station in an unmanned area according to the present invention; Figure 3 This is a framework diagram of a remote data return system for an automatic weather station in an unmanned area according to the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described in detail below through the accompanying 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. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0020] The term "and / or" is merely a description of the association relationship between associated objects, indicating that three possible relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0021] Figure 1 A flowchart of a method for remote data transmission of an automatic weather station in an unmanned area disclosed in this embodiment is shown, and the steps are as follows: 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 multiple types of meteorological data sets with a signal quality monitoring strategy to obtain a signal quality score; The signal quality monitoring strategy includes calculating the meteorological environment impact value through the multi-type meteorological data set, and obtaining it by weighted summing the real-time signal strength and signal packet loss rate after standardization with the meteorological environment impact value. The meteorological environment impact value is obtained by weighted summing the humidity impact value, the temperature impact value, the air pressure impact value and the wind speed impact value. The change in temperature will affect the propagation of electromagnetic waves. Usually at higher temperatures, the conductivity of the air increases, resulting in increased signal attenuation. To this end, this embodiment uses a function based on temperature difference to represent the impact of temperature on signal quality. This impact usually decreases with increasing temperature, so we use a function containing the square difference of temperature to represent its negative impact on signal quality.

[0022] 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 effect of humidity on signal quality is usually negative, and as humidity increases, signal quality gradually decreases. This embodiment uses an exponential decay function to represent the effect of humidity on signal quality.

[0023] Changes in air pressure affect the density of the air, and thus affect the propagation efficiency of radio waves. When the air pressure is low, the air density is low, and the signal propagation effect is also poor. This embodiment uses a function that is linearly related to the change in air pressure to represent the negative impact of air pressure on signal quality.

[0024] Wind speed affects signal quality mainly by affecting the multipath propagation effect of the signal (such as reflection, refraction, etc.). A large wind speed may cause signal instability and signal attenuation. In order to reflect the impact of wind speed on signal quality, this embodiment uses a linear function to represent the negative impact of wind speed.

[0025] Therefore, the temperature influence 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.

[0026] Set a minimum signal quality value, extract meteorological data whose signal quality score is less than or equal to the minimum signal quality value, list them in the meteorological data to be distributed, and send them to step S3; extract meteorological data whose signal quality score is greater than the minimum signal quality value, list them in the meteorological data to be transmitted, and send them 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; 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. , Indicates the Nth meteorological data, N indicates the number of meteorological data, and all meteorological data are listed in the priority queue in descending order of priority.

[0027] 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.

[0028] 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; Figure 2 A flowchart of a meteorological data hierarchical return strategy for a remote data return method of an automatic weather station in an unmanned area disclosed in this embodiment is shown. The specific steps of the meteorological data hierarchical 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. In order to improve bandwidth utilization and reduce communication costs, an incremental backhaul strategy is set. During each transmission, a new priority level is calculated based on incremental data, and the backhaul level is reallocated. S43, allocating the number of broadbands 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 period through a period dynamic adjustment algorithm.

[0029] During the transmission process, the priority queue changes dynamically. According to the real-time meteorological data changes, the disaster level changes or the actual bandwidth situation, the system should continuously adjust the priority queue. After each data transmission, the priority of all meteorological data is re-evaluated and the priority queue is updated.

[0030] 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 absolute value of meteorological data changes after standardization to obtain a new priority level; adds it to the allocation result of step S41, for example, there is a new priority level greater than the intermediate priority level data. , the data will be changed to high priority data.

[0031] 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. 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, which is the basic cycle set according to the data priority (for example, 1 hour for high priority data and 2 hours for low priority data). and represents the adjustment coefficient, .

[0032] because Reflects the data transmission quality. The higher the value, the better the transmission quality. The smaller it is, the longer the return cycle will be; conversely, if the transmission quality is poor, the return cycle will be shortened to increase the frequency of data transmission and avoid data loss.

[0033] For high priority data, The larger the value, the shorter the return cycle. For low priority data, The smaller the value, the longer the return cycle. The value is in the range of [0, 1], and data with higher priority corresponds to larger values.

[0034] Meteorological data with higher priority (such as disaster data) will be assigned a shorter return cycle to ensure that they are returned as soon as possible. Meteorological data with lower priority (such as ordinary weather data) can have a longer return cycle to avoid occupying too much bandwidth resources.

[0035] Priority data is transmitted first: This formula ensures that high-priority data (such as disaster warning data) can be transmitted quickly, while the transmission cycle of low-priority data will be dynamically adjusted according to bandwidth utilization to avoid wasting bandwidth resources.

[0036] Strong adaptability: Combining the impact of transmission quality assessment and data priority, the system can flexibly adjust the return cycle according to different network environments and bandwidth conditions to improve overall transmission efficiency.

[0037] Improve system reliability: Automatically shorten or extend the return cycle according to the data transmission quality, and increase the data transmission frequency when the transmission quality is poor, ensuring the timely transmission and integrity of the data.

[0038] S5. Upload the received data to the receiving platform.

[0039] The threshold and weight can be set by operators in this field.

[0040] Figure 3 The framework diagram of a remote data return system of an automatic weather station in an unmanned area of ​​the present invention is shown. Based on the same inventive concept as that of Example 1, the present invention provides a remote data return system of an automatic weather station in an unmanned area, comprising: 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 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.

[0041] The data return module uploads the received data to the receiving platform.

[0042] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection 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; 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. , Indicates the Nth meteorological data, N indicates the number of meteorological data, and all meteorological data are listed in the priority queue in descending order of priority.

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 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; S43, allocating the number of broadbands 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 period through a period dynamic adjustment algorithm.

5. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 4, characterized in that: The incremental feedback strategy tracks the absolute value of meteorological data changes within 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 standardized absolute value of meteorological data changes to obtain a new priority level; and adds it to the allocation result of step S41.

6. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 5, characterized in that: 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. 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, and represents the adjustment coefficient, .

7. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 6, 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.

8. The method for remote data transmission of an automatic weather station in an unmanned area according to claim 7, 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.

9. 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 8, 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.

10. The remote data transmission system for automatic weather stations in unmanned areas according to claim 9, characterized in that: 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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