A meteorological detection method and system for human shadow detection aircraft

Abstract

The invention discloses a meteorological detection method and system for a shadow detection aircraft, and relates to the technical field of meteorological detection. The method discloses the following steps: step one: after the shadow detection aircraft takes off, based on a preset cycle, at each cycle node, multiple environmental parameters are obtained, and multiple parameter generation logs are synchronously generated; step two: the parameter generation logs are deleted or retained; step three: after the shadow detection aircraft enters the cloud layer, a cloud particle spectrum is generated, cloud microphysical parameters are obtained according to the cloud particle spectrum, and based on the M cycle, at each cycle node, the cloud microphysical parameters are updated to obtain update records; step four: different cloud microparameter types are updated in association. The meteorological detection method for the shadow detection aircraft of the invention can optimize the measurement accuracy and rationality of meteorological detection parameters, thereby ensuring the accuracy of subsequent assessments of flight safety and shadow effects by the shadow detection aircraft.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological detection, and more particularly to a meteorological detection method and system for a human shadow detection aircraft. Background Art

[0002] In order to reduce the impact of meteorological disasters on the environment, human shadow detection aircraft are needed to perform corresponding rain and snow enhancement after meteorological disasters. Because meteorological disasters occur suddenly, current human shadow detection aircraft need to collect and predict environmental parameters. The sensors on current human shadow detection aircraft collect a large number of environmental parameters, but there are some abnormal data in these environmental parameters, and the environmental parameters of the sensors are also updated regularly. How to dynamically update environmental parameters and ensure the measurement accuracy of environmental parameters is a problem that needs to be solved urgently. Summary of the invention

[0003] In view of the deficiencies in the prior art, an object of the present invention is to provide a meteorological detection method and system for a human shadow detection aircraft.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A meteorological detection method for a human shadow detection aircraft comprises the following steps:

[0006] Step 1: After the human shadow detection aircraft takes off, based on the preset cycle, at each cycle node, multiple environmental parameters are obtained and multiple parameter generation logs are generated synchronously;

[0007] Step 2: Delete or retain the parameter generation log;

[0008] Step 3: Generate a cloud particle spectrum after the shadow detection aircraft enters the cloud layer, obtain cloud microphysical parameters based on the cloud particle spectrum, and update the cloud microphysical parameters at each cycle node based on the M cycle to obtain an update record;

[0009] Step 4: Update the association between different cloud micro-parameter types.

[0010] Further, a meteorological detection system for a human shadow detection aircraft includes an environment detection module, a log selection module, a cloud detection module, and a parameter update module;

[0011] After the human shadow detection aircraft takes off, the environment detection module obtains multiple environmental parameters based on a preset cycle and at each cycle node, and synchronously generates multiple parameter generation logs;

[0012] The log selection module deletes or retains the parameter generation log;

[0013] The cloud detection module generates a cloud particle spectrum after the shadow detection aircraft enters the cloud layer, obtains cloud microphysical parameters according to the cloud particle spectrum, and updates the cloud microphysical parameters at each cycle node based on the M cycle to obtain an update record;

[0014] The parameter updating module is used to update the association of different cloud micro-parameter types.

[0015] Furthermore, the parameter generation log includes parameter type, actual parameter value, and parameter acquisition time.

[0016] Furthermore, the log selection module deletes or retains the parameter generation log, specifically:

[0017] Get m parameter generation logs of the same parameter type before the current system time, get the number of parameter generation logs marked as parameter deviation logs, and mark them as log deviation number, set the high value of log deviation number and the low value of log deviation number, when the log deviation number is greater than or equal to the high value of log deviation number, retain the parameter deviation log, when the log deviation number is less than or equal to the low value of log deviation number, delete the parameter deviation log.

[0018] Furthermore, the parameter deviation log is obtained in the following manner: obtain the standard parameter value of the parameter type, compare the actual parameter value of the parameter generation log with the standard parameter value, calculate the difference between the actual parameter value and the standard parameter value and take the absolute value to obtain the parameter deviation value, set the parameter deviation threshold, and when the parameter deviation value is greater than or equal to the parameter deviation threshold, mark the parameter generation log as a parameter deviation log.

[0019] Further, when the number of log deviations is between a high value of the number of log deviations and a low value of the number of log deviations, a comparative value is obtained, and a comparative value threshold is set;

[0020] When the comparison value is greater than or equal to the comparison value threshold, the increase value and the decrease value are compared. When the increase value is greater than the decrease value, the log with a tendency to decrease is deleted and the log with a tendency to increase is retained. When the increase value is less than the decrease value, the log with a tendency to increase is deleted and the log with a tendency to decrease is retained.

[0021] When the comparison value is less than the comparison value threshold, the logs with an increasing or decreasing value are deleted.

[0022] Furthermore, the comparative value is obtained by the following steps: obtaining the deviation analysis value of the parameter deviation log, setting the deviation analysis threshold, when the deviation analysis value is greater than or equal to the deviation analysis threshold, marking the parameter deviation log as a deviation increase log, when the deviation analysis value is less than the deviation analysis threshold, marking the parameter deviation log as a deviation decrease log, sorting all the deviation increase logs in order of numbering, performing difference calculation on the numbers of two adjacent deviation increase logs after sorting, obtaining the deviation increase number difference, summing up all the deviation increase number differences and taking the average, obtaining the deviation increase number average difference, and marking it as ZX, obtaining the number of deviation increase logs, and marking it as Fd, using the formula Get the increase analysis value NK of the skewed increasing log, where b1 is the coefficient of the mean difference of the skewed increasing number. Sort all the skewed decreasing logs in the order of numbers, calculate the difference between the numbers of the two adjacent skewed decreasing logs after sorting, and get the skewed decreasing number difference. Sum all the skewed decreasing number differences and take the average to get the mean difference of the skewed decreasing number, and mark it as KL. Get the number of skewed decreasing logs and mark it as Tp. Use the formula Get the drop value YQ of the partial drop log, where c1 is the mean difference coefficient of the partial drop number, using the formula The ratio value BTR is obtained, where b1 is the increase coefficient and b2 is the decrease coefficient.

[0023] Furthermore, the deviation analysis value of the parameter deviation log is obtained by the following steps:

[0024] Sort all parameter deviation logs in the order of parameter acquisition time, mark the first parameter deviation log after sorting as the first deviation log, mark the parameter deviation log after the first deviation log as SGi, i is the sequence number of the parameter deviation log, calculate the difference between the actual parameter value of the parameter deviation log and the actual parameter value of the first deviation log, take the absolute value, get the deviation value, and mark it as CSL, calculate the difference between the parameter acquisition time of the parameter deviation log and the parameter acquisition time of the first deviation log, get the parameter deviation interval, and mark it as HQJ, use the formula Get the deviation analysis value KC of the parameter deviation log i , where a1 is the deviation coefficient and a2 is the parameter deviation interval coefficient.

[0025] Furthermore, the parameter update module is used to update the association of different cloud micro-parameter types, specifically:

[0026] Get the standard update value of the cloud micro parameter type, compare the parameter update value of the update record with the standard update value, and when the parameter update value is greater than the standard update value, mark the parameter update value as PCZ and the standard update value as YEP. The next update of the cloud micro parameter type is performed. When the parameter update value is less than or equal to the standard update value, no corresponding processing is performed;

[0027] Get the average update interval and update times of each cloud micro-parameter type, compare each cloud micro-parameter type in pairs, use the cosine similarity calculation formula to calculate the update similarity of the two cloud micro-parameter types in terms of average update interval and update times, and mark them as GWS, set the update similarity threshold, and when the update similarity is greater than or equal to the update similarity threshold, mark the two cloud micro-parameter types as parameter association types, and then update the parameter association types based on the same cycle.

[0028] Furthermore, the update record includes a cloud micro-parameter type and a parameter update value. The average update interval of the cloud micro-parameter type is obtained in the following manner: based on the update record of the cloud micro-parameter type before the current system time, the update time of all update records is sorted in chronological order, and the time difference between the two adjacent update times after sorting is calculated to obtain the update interval. All update intervals are summed and the average is taken to obtain the average update interval. The number of updates of the cloud micro-parameter type is the total number of updates of the cloud micro-parameter type before the current system time.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The meteorological detection method of the human shadow detection aircraft of the present invention can optimize the measurement accuracy and rationality of meteorological detection parameters, thereby ensuring the accuracy of subsequent human shadow detection aircraft's assessment of flight safety and human shadow effects;

[0031] 2. Setting up the environment detection module and the log selection module can conduct intelligent comprehensive analysis on the parameters generated by the shadow detection aircraft and optimize the measurement accuracy of the environmental parameters by the shadow detection aircraft. Setting up the cloud detection module and the parameter update module can intelligently optimize the update cycle of the cloud microphysical parameters according to the cloud particle spectrum, thus ensuring the rationality of the update of the cloud microphysical parameters while avoiding omissions in the update of the cloud microphysical parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a weather detection method for a human shadow detection aircraft;

[0033] Figure 2 This is a principle block diagram of the log selection module of the present invention;

[0034] Figure 3 It is a principle block diagram of the parameter updating module of the present invention. DETAILED DESCRIPTION

[0035] Example 1

[0036] Reference Figure 1

[0037] A meteorological detection method for a human shadow detection aircraft comprises the following steps:

[0038] Step 1: After the human shadow detection aircraft takes off, based on the preset cycle, at each cycle node, multiple environmental parameters are obtained and multiple parameter generation logs are generated synchronously.

[0039] Step 2: Delete or retain the parameter generation log.

[0040] Step 3: After the shadow detection aircraft enters the cloud layer, a cloud particle spectrum is generated, and cloud microphysical parameters are obtained based on the cloud particle spectrum. Based on the M cycle, the cloud microphysical parameters are updated at each cycle node to obtain an update record.

[0041] Step 4: Update the association between different cloud micro-parameter types.

[0042] This method can optimize the measurement accuracy and rationality of meteorological detection parameters, thereby ensuring the accuracy of subsequent assessments of flight safety and shadow effects by shadow detection aircraft.

[0043] Example 2

[0044] Reference Figure 2-Figure 3 , including environment detection module, log selection module, cloud detection module, and parameter update module;

[0045] Environmental detection module: After the human shadow detection aircraft takes off, based on the preset cycle, at each cycle node, multiple environmental parameters are obtained and multiple parameter generation logs are generated synchronously;

[0046] There are multiple sensors arranged at key parts of the shadow detection aircraft. These sensors detect ambient temperature, relative humidity, atmospheric pressure, wind speed, wind force, visibility, and rainfall in real time. The above data are all environmental parameters. There are also sensors for detecting cloud particle spectrum. The sensors for detecting cloud particle spectrum are activated when the shadow detection aircraft enters the cloud layer. The cloud particle spectrum can be processed to obtain cloud microphysical parameter information while it is being generated.

[0047] The parameter generation log includes parameter type (relative humidity, atmospheric pressure, wind speed, wind force, visibility, and rainfall are all different parameter types), actual parameter value (for example, the actual parameter value of relative humidity is the relative humidity value measured by the sensor), and parameter acquisition time.

[0048] At each periodic node, a parameter generation log of relative humidity, atmospheric pressure, wind speed, wind force, visibility, and rainfall is generated synchronously.

[0049] For example, the parameter type of the relative humidity parameter generation log is relative humidity, the actual parameter value is 60%, and the parameter acquisition time is XXXX-XX-XX-DD XX:XX:XX.

[0050] Log selection module: obtain the standard parameter value of the parameter type (for example, the standard parameter value of relative humidity is the relative humidity value under standard environmental conditions obtained through experimental analysis or comprehensive analysis of previous data), compare the actual parameter value with the standard parameter value, calculate the difference between the actual parameter value and the standard parameter value and take the absolute value to obtain the parameter deviation value, set the parameter deviation threshold, when the parameter deviation value is greater than or equal to the parameter deviation threshold, mark the log generated by the parameter as a parameter deviation log, and when the parameter deviation value is less than the parameter deviation threshold, no corresponding processing is performed.

[0051] Get m parameter generation logs of the same parameter type before the current system time, get the number of parameter generation logs marked as parameter deviation logs, and mark them as log deviation number, set the high value of log deviation number and the low value of log deviation number, where the high value of log deviation number is greater than the low value of log deviation number, the high value of log deviation number and the low value of log deviation number are obtained through experimental analysis or comprehensive analysis of previous data, when the log deviation number is greater than or equal to the high value of log deviation number, the parameter deviation log is retained, when the log deviation number is less than or equal to the low value of log deviation number, the parameter deviation log is deleted;

[0052] When the number of log deviations is between the high value of the number of log deviations and the low value of the number of log deviations, all parameter deviation logs are sorted in the order of the time when the parameters were obtained, and the parameter deviation log that is ranked first after sorting is marked as the first deviation log, and the parameter deviation logs after the first deviation log are marked as SG i, where i is the sequence number of the parameter deviation logs, i = 1, 2, ..., n, and the actual parameter value of the parameter deviation log is calculated by difference and the absolute value is taken to obtain the deviation value, which is marked as CSL. The parameter acquisition time of the parameter deviation log is calculated by difference with the parameter acquisition time of the first deviation log to obtain the parameter deviation interval, which is marked as HQJ. The formula is used Get the deviation analysis value KC of the parameter deviation log i , where a1 is the deviation coefficient, a2 is the parameter deviation interval coefficient, the value of a1 is 0.83, and the value of a2 is 0.62;

[0053] Set the deviation analysis threshold. When the deviation analysis value is greater than or equal to the deviation analysis threshold, mark the parameter deviation log as a deviation increase log. When the deviation analysis value is less than the deviation analysis threshold, mark the parameter deviation log as a deviation decrease log. Sort all the deviation increase logs in order of number. Calculate the difference between the numbers of the two adjacent deviation increase logs after sorting to get the deviation increase number difference. Sum all the deviation increase number differences and take the average to get the deviation increase number average difference, which is marked as ZX. Get the number of deviation increase logs and mark it as Fd. Use the formula Get the increase analysis value NK of the skewed increasing log, where b1 is the mean difference coefficient of the skewed increasing number, and the value of b1 is 0.97. Sort all the skewed decreasing logs in the order of numbers, calculate the difference between the numbers of the two adjacent skewed decreasing logs after sorting, and get the skewed decreasing number difference. Sum all the skewed decreasing number differences and take the average to get the mean difference of the skewed decreasing number, and mark it as KL. Get the number of skewed decreasing logs and mark it as Tp. Use the formula The drop value YQ of the partial drop log is obtained, where c1 is the coefficient of the mean difference of the partial drop number, and the value of c1 is 0.96. Using the formula The comparative analysis value BTR is obtained, where b1 is the increase analysis value coefficient, b2 is the decrease analysis value coefficient, and the comparative analysis value threshold is set, d1 is 1.09, and d2 is 1.08;

[0054] When the comparison value is greater than or equal to the comparison value threshold, the increase value and the decrease value are compared. When the increase value is greater than the decrease value, the log with a tendency to decrease is deleted and the log with a tendency to increase is retained. When the increase value is less than the decrease value, the log with a tendency to increase is deleted and the log with a tendency to decrease is retained.

[0055] When the comparison value is less than the comparison value threshold, the logs with an increasing or decreasing value are deleted.

[0056] Setting up the environment detection module and the log selection module can perform intelligent comprehensive analysis on the parameters generated by the shadow detection aircraft, optimize the measurement accuracy of the environmental parameters by the shadow detection aircraft, and thereby improve the accuracy of subsequent shadow detection aircraft's assessment of flight safety and shadow effects.

[0057] Cloud detection module: After entering the cloud layer, the human shadow detection aircraft generates a cloud particle spectrum and obtains cloud microphysical parameters based on the cloud particle spectrum (cloud microphysical parameter types include cloud water content, cloud particle concentration, etc.). Based on the M cycle, the cloud microphysical parameters are updated at each cycle node to obtain an update record. The update record includes the cloud microparameter type and parameter update value (taking cloud water content as an example, the parameter update value is the difference between the cloud water content before and after the update).

[0058] For example, when the shadow detection aircraft is at an altitude between 500m and 1500m, a cloud particle spectrum at an altitude of 500m-1500m is generated.

[0059] Parameter update module: obtain the standard update value of the cloud micro-parameter type (for example, the standard update value of cloud water content is the difference between the cloud water content before and after the update under the standard environmental state obtained through experimental analysis or comprehensive analysis of previous data), compare the parameter update value with the standard update value, and when the parameter update value is greater than the standard update value, mark the parameter update value as PCZ and the standard update value as YEP.

[0060] In cycle M PCZ The next update of the cloud micro parameter type, when the parameter update value is less than or equal to the standard update value, no corresponding processing is performed.

[0061] Get the average update interval and update times of each cloud micro-parameter type, compare each cloud micro-parameter type in pairs, use the cosine similarity calculation formula to calculate the update similarity of the two cloud micro-parameter types in terms of average update interval and update times, and mark them as GWS, set the update similarity threshold, and when the update similarity is greater than or equal to the update similarity threshold, mark the two cloud micro-parameter types as parameter association types, and then update the parameter association types based on the same cycle. When the update similarity is less than the update similarity threshold, no corresponding processing is performed;

[0062] For example: when the cloud water content and cloud particle concentration are updated simultaneously in all subsequent periods, if the update period of the cloud water content in the next update is shortened, the update period of the cloud particle concentration in the next update is also shortened;

[0063] The average update interval of the cloud micro-parameter type is obtained in the following way: according to the update records of the cloud micro-parameter type before the current time of the system, the update time of all update records is sorted in chronological order, the time difference between the two adjacent update times after sorting is calculated to obtain the update interval, all update intervals are summed up and the average is taken to obtain the average update interval.

[0064] The number of updates of a cloud micro parameter type is the total number of updates of the cloud micro parameter type before the current system time.

[0065] By setting up a cloud detection module and a parameter updating module, the update cycle of cloud microphysical parameters can be intelligently optimized according to the cloud particle spectrum, thereby ensuring the rationality of the update of cloud microphysical parameters while avoiding omissions in the update of cloud microphysical parameters.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0067] 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 the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application 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. The 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, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access 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.

[0068] 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.

[0069] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein 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 this application.

[0070] 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.

[0071] In the several embodiments provided in the present application, 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 a logical function division. 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.

[0072] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0073] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A meteorological detection method for a human shadow detection aircraft, characterized in that: The steps include: Step 1: After the human shadow detection aircraft takes off, based on the preset cycle, at each cycle node, multiple environmental parameters are obtained and multiple parameter generation logs are generated synchronously; Step 2: Delete or retain the parameter generation log; Step 3: Generate a cloud particle spectrum after the shadow detection aircraft enters the cloud layer, obtain cloud microphysical parameters based on the cloud particle spectrum, and update the cloud microphysical parameters at each cycle node based on the M cycle to obtain an update record; Step 4: Update the association of different cloud micro-parameter types; The log selection module deletes or retains the parameter generation logs, specifically: Get m parameter generation logs of the same parameter type before the current system time, get the number of parameter generation logs marked as parameter deviation logs, and mark them as log deviation number, set the high value of log deviation number and the low value of log deviation number, when the log deviation number is greater than or equal to the high value of log deviation number, retain the parameter deviation log, when the log deviation number is less than or equal to the low value of log deviation number, delete the parameter deviation log; The parameter update module is used to update the association of different cloud micro-parameter types, specifically: Get the standard update value of the cloud micro parameter type, compare the parameter update value of the update record with the standard update value, and when the parameter update value is greater than the standard update value, mark the parameter update value as PCZ and set the standard update value as The new value is marked as YEP, based on the cycle The next update of the cloud micro parameter type is performed. When the parameter update value is less than or equal to the standard update value, no corresponding processing is performed; Get the average update interval and update times of each cloud micro-parameter type, compare each cloud micro-parameter type in pairs, use the cosine similarity calculation formula to calculate the update similarity of the two cloud micro-parameter types in terms of average update interval and update times, and mark them as GWS, set the update similarity threshold, and when the update similarity is greater than or equal to the update similarity threshold, mark the two cloud micro-parameter types as parameter association types, and then update the parameter association types based on the same cycle.

2. A meteorological detection system for a human shadow detection aircraft, applied to a meteorological detection method for a human shadow detection aircraft as claimed in claim 1, characterized in that: It includes environment detection module, log selection module, cloud detection module and parameter update module; After the human shadow detection aircraft takes off, the environment detection module obtains multiple environmental parameters based on a preset cycle and at each cycle node, and synchronously generates multiple parameter generation logs; The log selection module deletes or retains the parameter generation log; The cloud detection module generates a cloud particle spectrum after the shadow detection aircraft enters the cloud layer, obtains cloud microphysical parameters according to the cloud particle spectrum, and updates the cloud microphysical parameters at each cycle node based on the M cycle to obtain an update record; The parameter updating module is used to update the association of different cloud micro-parameter types.

3. The meteorological detection system of a human shadow detection aircraft according to claim 2, characterized in that: The parameter generation log includes parameter type, actual parameter value, and parameter acquisition time.

4. The weather detection system for a human shadow detection aircraft according to claim 2, characterized in that: The parameter deviation log is obtained in the following way: obtain the standard parameter value of the parameter type, compare the actual parameter value of the parameter generation log with the standard parameter value, calculate the difference between the actual parameter value and the standard parameter value and take the absolute value to obtain the parameter deviation value, set the parameter deviation threshold, and when the parameter deviation value is greater than or equal to the parameter deviation threshold, mark the parameter generation log as a parameter deviation log.

5. The weather detection system for a human shadow detection aircraft according to claim 2, characterized in that: When the number of log deviations is between the high value of the number of log deviations and the low value of the number of log deviations, a comparative value is obtained, and a comparative value threshold is set; When the comparison value is greater than or equal to the comparison value threshold, the increase value and the decrease value are compared. When the increase value is greater than the decrease value, the log with a tendency to decrease is deleted and the log with a tendency to increase is retained. When the increase value is less than the decrease value, the log with a tendency to increase is deleted and the log with a tendency to decrease is retained. When the comparison value is less than the comparison value threshold, the logs with an increasing or decreasing value are deleted.

6. The weather detection system for a human shadow detection aircraft according to claim 5, characterized in that: The comparative value is obtained by the following steps: obtaining the deviation analysis value of the parameter deviation log, setting the deviation analysis threshold, when the deviation analysis value is greater than or equal to the deviation analysis threshold, marking the parameter deviation log as a deviation increase log, when the deviation analysis value is less than the deviation analysis threshold, marking the parameter deviation log as a deviation decrease log, sorting all the deviation increase logs in order of numbering, calculating the difference between the numbers of the two adjacent deviation increase logs after sorting, obtaining the deviation increase number difference, summing up all the deviation increase number differences and taking the average, obtaining the deviation increase number average difference, and marking it as ZX, obtaining the number of deviation increase logs, and marking it as Fd, using the formula Get the increase analysis value NK of the skewed increasing log, where b1 is the coefficient of the mean difference of the skewed increasing number. Sort all the skewed decreasing logs in the order of numbers, calculate the difference between the numbers of the two adjacent skewed decreasing logs after sorting, and get the skewed decreasing number difference. Sum all the skewed decreasing number differences and take the average to get the mean difference of the skewed decreasing number, and mark it as KL. Get the number of skewed decreasing logs and mark it as Tp. Use the formula Get the drop value YQ of the partial drop log, where c1 is the mean difference coefficient of the partial drop number, using the formula The ratio value BTR is obtained, where b1 is the increase coefficient and b2 is the decrease coefficient.

7. The weather detection system for a human shadow detection aircraft according to claim 6, characterized in that: The deviation analysis value of the parameter deviation log is obtained by the following steps: Sort all parameter deviation logs in the order of parameter acquisition time, mark the first parameter deviation log after sorting as the first deviation log, mark the parameter deviation log after the first deviation log as SGi, i is the sequence number of the parameter deviation log, calculate the difference between the actual parameter value of the parameter deviation log and the actual parameter value of the first deviation log, take the absolute value, get the deviation value, and mark it as CSL, calculate the difference between the parameter acquisition time of the parameter deviation log and the parameter acquisition time of the first deviation log, get the parameter deviation interval, and mark it as HQJ, use the formula Get the deviation analysis value KC of the parameter deviation log i , where a1 is the deviation coefficient and a2 is the parameter deviation interval coefficient.

8. The weather detection system for a human shadow detection aircraft according to claim 2, characterized in that: The update record includes the cloud micro-parameter type and the parameter update value. The average update interval of the cloud micro-parameter type is obtained in the following way: according to the update record of the cloud micro-parameter type before the current time of the system, the update time of all update records is sorted in chronological order, and the time difference between the two adjacent update times after sorting is calculated to obtain the update interval, and all update intervals are summed and averaged to obtain the average update interval. The number of updates of the cloud micro-parameter type is the total number of updates of the cloud micro-parameter type before the current time of the system.

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