Collapse risk assessment method and system based on unmanned aerial vehicle

Abstract

According to the unmanned aerial vehicle-based collapse risk assessment method and system provided by the invention, the collapse risk feature value of each preset target collapse risk feature is subjected to distribution statistics, so that the collapse risk feature assessment scores of the collapse risk feature values of different parts are determined, and the accuracy is high; and determining the distribution condition of each collapse risk feature evaluation score based on the collapse risk feature average value, the collapse risk feature standard deviation and the collapse risk feature value of each preset target collapse risk feature, so that the accuracy of the collapse risk feature evaluation scores can be improved. And then the collapse risk characteristic evaluation score of the collapse monitoring data needing to be processed is determined according to the collapse risk characteristic evaluation score of each target part, so that the authenticity and accuracy of the collapse risk characteristic evaluation scores can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of collapse risk assessment, and in particular to a collapse risk assessment method and system based on drones. Background Art

[0002] Collapse (landslide, collapse or landslide) is a geological phenomenon in which the rock and soil on a steep slope suddenly break away from the parent body under the action of gravity and collapse, roll and accumulate at the foot of the slope (or valley).

[0003] Nowadays, the application fields of drones are becoming more and more extensive. When the inventor combines drone technology with geological disaster monitoring technology, drones can replace manual labor to go to places that relevant technicians cannot reach. For geological disasters such as landslides, drones can monitor the area in all directions and transmit the obtained data to the artificial intelligence thread for evaluation to obtain evaluation results. However, how to make the evaluation results more accurate and reliable is a technical problem that is difficult to solve at present. Summary of the invention

[0004] In order to improve the technical problems existing in the relevant technologies, the present application provides a collapse risk assessment method and system based on drones.

[0005] In a first aspect, a method for assessing collapse risk based on a drone is provided, the method comprising: Obtaining collapse monitoring data that needs to be processed, and performing collapse hazard element analysis on the target range collapse monitoring data included in the collapse monitoring data that needs to be processed, to obtain positioning information of several target collapse elements in the target range collapse monitoring data; Based on the positioning information of the plurality of target collapse elements, determining a collapse risk characteristic value of at least one preset target collapse risk characteristic in the collapse monitoring data of the target range; Obtaining a collapse risk characteristic average value and a collapse risk characteristic standard deviation of each preset target collapse risk characteristic, wherein the collapse risk characteristic average value and the collapse risk characteristic standard deviation are obtained by statistically analyzing the collapse risk characteristic value of each preset target collapse risk characteristic of collapse monitoring data of a plurality of different target ranges; Determine the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic assessment score based on the collapse risk characteristic mean value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic; Based on the collapse risk characteristic assessment score of each preset target collapse risk characteristic, a collapse risk assessment result of the collapse monitoring data to be processed is determined.

[0006] In the present application, the method of obtaining the average collapse risk characteristic value and the standard deviation of the collapse risk characteristic value of each preset target collapse risk characteristic includes: Performing collapse element identification on the collapse monitoring data of the target range in the collapse monitoring data to be processed, and obtaining the target collapse element corresponding to the collapse monitoring data to be processed; Based on the target collapse factor, the collapse risk characteristic average value and the collapse risk characteristic standard deviation corresponding to each preset target collapse risk characteristic are obtained.

[0007] It can be understood that the collapse risk characteristic mean value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic can be obtained more accurately.

[0008] In the present application, the method further comprises: Acquire a number of reference collapse monitoring data, and perform collapse hazard factor analysis on the reference range collapse monitoring data included in each reference collapse monitoring data, and obtain reference positioning information of a number of reference target collapse elements in each reference range collapse monitoring data; Determine, based on the reference positioning information of the reference target collapse elements in each reference range collapse monitoring data, a reference collapse risk characteristic value of at least one preset target collapse risk characteristic in each reference range collapse monitoring data; Performing collapse element identification on each reference range collapse monitoring data to obtain reference collapse elements of each reference collapse monitoring data; Based on the reference collapse risk characteristic value of each preset target collapse risk characteristic in the collapse monitoring data of the reference range of the same reference collapse element, collapse risk characteristic distribution information corresponding to the same reference collapse element of each preset target collapse risk characteristic is determined.

[0009] It can be understood that the collapse risk feature distribution information of each preset target collapse risk feature corresponding to the same reference collapse feature can be determined more accurately through the reference positioning information of several reference target collapse features.

[0010] In the present application, the collapse risk characteristic evaluation score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic evaluation score are determined based on the collapse risk characteristic average value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic, including: Based on the collapse risk characteristic average value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic, normalizing the collapse risk characteristic value of each preset target collapse risk characteristic to obtain a normalized collapse risk characteristic value of each preset target collapse risk characteristic; Obtaining a collapse hazard analysis thread corresponding to each of the preset target collapse risk characteristics; Determine a collapse risk characteristic assessment score of each preset target collapse risk characteristic by using the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic; The distribution of each collapse risk feature assessment score is determined based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature and the normalized collapse risk characteristic value of each preset target collapse risk feature; or, the distribution of each collapse risk feature assessment score is determined based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the collapse risk characteristic average value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk feature.

[0011] It can be understood that based on the collapse risk characteristic mean value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic, the problem of inaccurate evaluation scores is avoided, so that the collapse risk characteristic evaluation score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic evaluation score can be accurately determined.

[0012] In the present application, when the target range collapse monitoring data is collapse hazard data, the collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic are used to determine the collapse risk characteristic assessment score of each preset target collapse risk characteristic, including: When the preset target collapse risk feature is a hidden danger data volume or a hidden danger description attribute, based on the first collapse hidden danger analysis thread and the normalized collapse risk characteristic value of the preset target collapse risk feature, a collapse risk characteristic assessment score of the normalized collapse risk characteristic value of the preset target collapse risk feature is determined, wherein the first collapse hidden danger analysis thread constrains that the collapse risk characteristic assessment score is related to the normalized collapse risk characteristic value; When the collapse risk characteristic value of the preset target collapse risk characteristic is a human factor, based on the second collapse hazard analysis thread and the normalized collapse risk characteristic value in the preset target collapse risk characteristic, a collapse risk characteristic assessment score of the normalized collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the second collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the normalized collapse risk characteristic value; When the collapse risk characteristic value of the preset target collapse risk characteristic is one of rainfall, soil dryness, vegetation coverage, rock stability, slope inclination data, soil looseness and large equipment working condition, based on the third collapse hazard analysis thread and the conventional collapse risk characteristic value of the preset target collapse risk characteristic, the collapse risk characteristic assessment score of the conventional collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the third collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the collapse risk characteristic comparison result, and the collapse risk characteristic comparison result is a comparison value of the conventional collapse risk characteristic value and the standard graphic data distribution.

[0013] It can be understood that when the target range collapse monitoring data is collapse hazard data, the collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic are used, thereby avoiding the problem of characteristic value calculation errors, thereby being able to more accurately determine the collapse risk characteristic assessment score of each preset target collapse risk characteristic.

[0014] In the present application, the distribution of each collapse risk feature assessment score is determined based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature and the normalized collapse risk feature value of each preset target collapse risk feature, including: Obtain standard graphical data distribution statistical model; Based on the normalized collapse risk characteristic value of each preset target collapse risk characteristic, the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic performs model processing on the standard graphic data distribution statistical model to obtain a risk description result corresponding to each preset target collapse risk characteristic; Based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution situation corresponding to each preset target collapse risk feature is determined.

[0015] It can be understood that, based on the corresponding collapse hazard analysis thread of each preset target collapse risk feature and the normalized collapse risk characteristic value of each preset target collapse risk feature, the problem of calculation errors can be avoided, thereby accurately determining the distribution of each collapse risk feature assessment score.

[0016] In the present application, the corresponding collapse hazard analysis thread based on each preset target collapse risk feature, the collapse risk feature average value, collapse risk feature standard deviation and collapse risk feature value of each preset target collapse risk feature, determines the distribution of each collapse risk feature assessment score, including: Determine a function calculation model corresponding to each preset target collapse risk feature based on a collapse risk feature average value and a collapse risk feature standard deviation of each preset target collapse risk feature; Based on the collapse risk characteristic value of each preset target collapse risk characteristic and the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic, the function calculation model is subjected to model processing to obtain a risk description result corresponding to each preset target collapse risk characteristic; Based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution situation corresponding to each preset target collapse risk feature is determined.

[0017] It can be understood that, based on the corresponding collapse hazard analysis thread of each preset target collapse risk feature, the collapse risk feature average value, the collapse risk feature standard deviation and the collapse risk feature value of each preset target collapse risk feature, the problem of calculation errors can be effectively avoided, thereby accurately determining the distribution of each collapse risk feature assessment score.

[0018] In the present application, the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature are used to determine the distribution of each preset target collapse risk feature, including: When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the risk description result is used as the distribution situation corresponding to the preset target collapse risk feature; When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, a comparison result between the risk description standard result and the risk description result is used as the distribution situation corresponding to the preset target collapse risk feature; When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the third collapse hazard analysis thread, the product of the preset value and the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

[0019] It can be understood that, based on the risk description results corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the working ability of the thread can be improved, so that the distribution of each preset target collapse risk feature can be determined more accurately.

[0020] In the present application, the collapse monitoring data of the target range included in the collapse monitoring data to be processed is subjected to collapse hazard element analysis to obtain positioning information of several target collapse elements in the collapse monitoring data of the target range, including: Performing collapse monitoring data identification on the collapse monitoring data to be processed to obtain collapse monitoring data of an initial target range in the collapse monitoring data to be processed; When it is determined that the collapse monitoring data of the initial target range needs to be verified, a collapse data queue of the collapse monitoring data of the initial target range is determined; Performing verification processing on the initial target range collapse monitoring data based on the collapse data queue to obtain verified target range collapse monitoring data; The verified target range collapse monitoring data is subjected to collapse hazard factor analysis to obtain positioning information of several target collapse factors.

[0021] It can be understood that when analyzing the collapse hazard elements of the target range collapse monitoring data included in the collapse monitoring data to be processed, the problem of inaccurate analysis is improved, so that the positioning information of several target collapse elements in the target range collapse monitoring data can be obtained more accurately.

[0022] In a second aspect, a UAV-based collapse risk assessment system is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0023] A method and system for assessing collapse risk based on a drone provided in an embodiment of the present application, after collapse monitoring data to be processed are obtained, collapse hazard elements are first analyzed for collapse monitoring data of a target range included in the collapse monitoring data to be processed, and positioning information of several target collapse elements in the collapse monitoring data of the target range is obtained. Then, based on the positioning information of the several target collapse elements, collapse risk characteristic values ​​of no less than one preset target collapse risk characteristic in the collapse monitoring data of the target range are determined. Then, the collapse risk characteristic mean value and collapse risk characteristic standard deviation of each preset target collapse risk characteristic are obtained. Then, based on the collapse risk characteristic mean value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic, the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic assessment score are determined. Finally, based on the collapse risk characteristic mean value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic, the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic assessment score are determined. The collapse risk characteristic assessment score of the characteristic is used to determine the collapse risk assessment result of the collapse monitoring data that needs to be processed; since the collapse risk characteristic mean value and the collapse risk characteristic standard deviation are obtained by statistically analyzing the collapse risk characteristic values ​​of preset target collapse risk characteristics of collapse monitoring data of several target ranges, the collapse risk characteristic assessment scores of the collapse risk characteristic values ​​of different parts can be determined by performing distribution statistics on the collapse risk characteristic values ​​of each preset target collapse risk characteristic, which has high accuracy, and the distribution of each collapse risk characteristic assessment score is determined based on the collapse risk characteristic mean value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic, which can improve the accuracy of the collapse risk characteristic assessment score, and then the collapse risk characteristic assessment score of the collapse monitoring data that needs to be processed is determined according to the collapse risk characteristic assessment score of each target part, which can ensure the authenticity and accuracy of the collapse risk characteristic assessment score. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a method for assessing collapse risk based on a drone is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0027] See also Figure 1 , shows a method for assessing collapse risk based on a drone, which may include the technical solutions described in the following steps 101 to 105.

[0028] In step 101, collapse monitoring data to be processed is obtained, and collapse hazard element analysis is performed on the target range collapse monitoring data included in the collapse monitoring data to be processed to obtain positioning information of several target collapse elements in the target range collapse monitoring data.

[0029] For example, the collapse monitoring data that needs to be processed is obtained by flying a drone and collecting data. The specific data collection methods include: image shooting and ultrasonic reflection, etc., which can obtain the corresponding situation. For example, the crack situation on the surface of the rock soil can be obtained through pictures, and the structure inside can be obtained through ultrasound. I will not go into details one by one here.

[0030] Among them, collapse risk factors can be understood as factors that cause collapse, such as micro-seismic conditions, stability conditions, etc.

[0031] In a possible implementation example, step 101 may be implemented through steps 1011 to 1016, which are described in detail below.

[0032] In step 1011, collapse monitoring data identification is performed on the collapse monitoring data to be processed to obtain collapse monitoring data within an initial target range in the collapse monitoring data to be processed.

[0033] In a possible implementation, a trained neural network model can be used to identify the collapse monitoring data to be processed, and the location information of the collapse monitoring data in the target range in the collapse monitoring data to be processed can be obtained, so as to determine the initial target range collapse monitoring data based on the location information of the collapse monitoring data in the target range. The neural network model can be a convolutional neural network model, a recurrent neural network model, a deep learning neural network model, etc.

[0034] In step 1012, it is determined whether the initial target range collapse monitoring data needs to be verified.

[0035] In step 1013, a collapse data queue of collapse monitoring data in the initial target range is determined.

[0036] In step 1014, the initial target range collapse monitoring data is verified based on the collapse data queue to obtain verified target range collapse monitoring data.

[0037] In a possible implementation example, the collapse data queue is used to rotate and translate the collapse monitoring data of the initial target range, so as to verify the collapse monitoring data of the initial target range and obtain the verified collapse monitoring data of the target range.

[0038] In step 1015, the verified target range collapse monitoring data is subjected to collapse hazard element analysis to obtain location information of a number of target collapse elements.

[0039] In a possible implementation example, a target collapse hazard factor analysis algorithm may be used to perform collapse hazard factor analysis on the verified target range collapse monitoring data to obtain initial coordinate information of several target collapse factors.

[0040] In step 1016, the collapse monitoring data in the initial target range is analyzed for collapse hazard factors to obtain the location information of several target collapse factors.

[0041] In a possible implementation example, the implementation process of analyzing the collapse hazard elements of the initial target range collapse monitoring data to obtain the positioning information of several target collapse elements is similar to the implementation process of step 1015, and the implementation process of step 1015 may be referred to.

[0042] In step 102, based on the positioning information of the target collapse elements, a collapse risk characteristic value of at least one preset target collapse risk characteristic is determined.

[0043] Exemplarily, collapse factors are factors that affect collapse, such as crack levels.

[0044] When the target range collapse monitoring data is collapse hazard data, the preset target collapse risk characteristics include: rainfall, soil dryness, vegetation coverage, rock stability, soil looseness, large equipment working conditions, amount of hazard data, and at least one of human factors. In the embodiment of the present application, unlike the related art in which the entire collapse hazard data is directly evaluated, a collapse risk characteristic evaluation score is performed on a more detailed, preset target collapse risk characteristic, thereby improving the accuracy of the collapse risk characteristic evaluation score.

[0045] Correspondingly, when step 102 is implemented, the collapse risk characteristic value of the preset target collapse risk characteristic is determined based on at least one of the following implementation processes: 1. Based on the positioning information of several first collapse elements, determine the collapse risk characteristic value of at least one of the following: rainfall, soil dryness, vegetation coverage, and rock stability.

[0046] In step 103, the collapse risk characteristic average value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic are obtained.

[0047] For example, the average value of the collapse risk characteristic can be understood as the average wind direction level of the collapse risk, and the standard deviation of the collapse risk characteristic can be understood as the difference between the possibility of collapse in the implementation area and the possibility of collapse in the sample database. The larger the difference, the smaller the possibility of collapse.

[0048] In a possible implementation embodiment, when step 103 is implemented, first, collapse element detection is performed on the target range collapse monitoring data in the collapse monitoring data that needs to be processed to obtain the target collapse element corresponding to the collapse monitoring data that needs to be processed; then, based on the target collapse element, the collapse risk characteristic average value and the collapse risk characteristic standard deviation corresponding to each preset target collapse risk characteristic are obtained.

[0049] In one possible implementation, a trained deep learning classification model can be used to identify collapse elements in the target range collapse monitoring data in the collapse monitoring data that needs to be processed, thereby determining the target collapse elements corresponding to the collapse monitoring data that needs to be processed. Then, based on the target collapse elements, the collapse risk feature average value and collapse risk feature standard deviation corresponding to each preset target collapse risk feature are obtained. In this way, when obtaining the collapse risk feature average value and collapse risk feature standard deviation corresponding to the preset target collapse risk feature, the target collapse element corresponding to the collapse monitoring data that needs to be processed is referenced, thereby ensuring the accuracy of the facial collapse risk feature assessment score based on the collapse risk feature average value and collapse risk feature standard deviation.

[0050] In a possible implementation example, before step 103, the collapse risk characteristic average value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic may be determined through steps 201 to 205, which are described in detail below.

[0051] In step 201, a number of reference collapse monitoring data are obtained.

[0052] In a possible implementation example, the reference collapse monitoring data includes target range collapse monitoring data.

[0053] In step 202, a collapse hazard element analysis is performed on the reference range collapse monitoring data included in each reference collapse monitoring data to obtain reference positioning information of a plurality of reference target collapse elements in each reference range collapse monitoring data.

[0054] In step 203, based on the reference positioning information of the reference target collapse elements in each reference range collapse monitoring data, a reference collapse risk characteristic value of at least one preset target collapse risk characteristic in each reference range collapse monitoring data is determined.

[0055] It can be understood that the implementation process of step 202 and step 203 is similar to the implementation process of step 101 and step 102 described above. In actual application, the implementation process of step 101 and step 102 can be referred to.

[0056] In step 204, collapse element identification is performed on each reference range collapse monitoring data to obtain a reference collapse element of each reference collapse monitoring data.

[0057] Exemplarily, the reference collapse factor can be understood as In a possible implementation example, a trained deep learning classification model may be used to identify collapse elements of the reference range collapse monitoring data, thereby determining reference collapse elements corresponding to the reference range collapse monitoring data.

[0058] In step 205, based on the reference collapse risk characteristic value of each preset target collapse risk characteristic in the reference range collapse monitoring data of the same reference collapse element, the collapse risk characteristic distribution information corresponding to each preset target collapse risk characteristic in the same reference collapse element is determined.

[0059] The collapse risk characteristic distribution information includes the collapse risk characteristic mean value and the collapse risk characteristic standard deviation. In this step, based on the reference collapse risk characteristic value of each preset target collapse risk characteristic in the reference range collapse monitoring data of the same reference collapse element, the collapse risk characteristic mean value and the collapse risk characteristic standard deviation corresponding to each predicted face collapse risk characteristic in the same reference collapse element are determined.

[0060] Through the above steps 201 to 205, a large amount of reference collapse monitoring data is first obtained, collapse monitoring data identification is performed on the reference collapse monitoring data to determine the reference range collapse monitoring data, and at least one reference collapse risk characteristic value of a preset target collapse risk characteristic in each reference range collapse monitoring data is determined, and then collapse element identification is performed on each reference range collapse monitoring data to determine the reference collapse element of each reference range collapse monitoring data. When determining the collapse risk characteristic mean value and collapse risk characteristic standard deviation corresponding to the preset target collapse risk characteristic, they are determined based on the reference collapse risk characteristic value of each preset target collapse risk characteristic in the reference range collapse monitoring data of the same reference collapse element, thereby improving the accuracy of the collapse risk characteristic distribution information.

[0061] In step 104, a collapse risk characteristic assessment score of each preset target collapse risk characteristic and a distribution of each collapse risk characteristic assessment score are determined based on the collapse risk characteristic mean value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic.

[0062] For example, In a possible implementation example, step 104 may be implemented through the following steps 1041 to 1044A, or through steps 1041 to 1044B, as described in detail below.

[0063] In step 1041, the collapse risk characteristic value of each preset target collapse risk characteristic is normalized based on the collapse risk characteristic mean value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic to obtain the normalized collapse risk characteristic value of each preset target collapse risk characteristic.

[0064] Among them, regular processing can be understood as standardized processing, which is to process data directly according to specifications.

[0065] In step 1042, a collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic is obtained.

[0066] Since the standards corresponding to different preset target collapse risk characteristics are different, the standards corresponding to some preset target collapse risk characteristics are that the larger the collapse risk characteristic value, the higher the aesthetics, such as rainfall, soil dryness, vegetation coverage, rock stability, slope inclination data, soil looseness, and large equipment working conditions. Therefore, for preset target collapse risk characteristics with different standards, different collapse hazard analysis threads are set. For preset target collapse risk characteristics whose standard is that the larger the collapse risk characteristic value, the higher the accuracy, the first collapse hazard analysis thread that can constrain the collapse risk characteristic evaluation score to be related to the collapse risk characteristic value is adopted.

[0067] In step 1043, the collapse risk characteristic assessment score of each preset target collapse risk characteristic is determined by using the collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic.

[0068] When the collapse risk characteristic value of the preset target collapse risk characteristic is the amount of hidden danger data or the hidden danger description attribute, the collapse risk characteristic assessment score of the conventional collapse risk characteristic value of the preset target collapse risk characteristic is determined based on the first collapse hidden danger analysis thread and the conventional collapse risk characteristic value of the preset target collapse risk characteristic, wherein the first collapse hidden danger analysis thread constrains the collapse risk characteristic assessment score to be related to the conventional collapse risk characteristic value, that is, the higher the conventional collapse risk characteristic value, the higher the collapse risk characteristic assessment score.

[0069] When the collapse risk characteristic value of the preset target collapse risk characteristic is a human factor, based on the second collapse hazard analysis thread and the conventional collapse risk characteristic value in the preset target collapse risk characteristic, the collapse risk characteristic assessment score of the conventional collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the second collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the conventional collapse risk characteristic value, that is, the lower the conventional collapse risk characteristic value, the higher the collapse risk characteristic assessment score.

[0070] When the collapse risk characteristic value of the preset target collapse risk characteristic is one of rainfall, soil dryness, vegetation coverage, rock stability, slope inclination data, soil looseness and large equipment working condition, based on the third collapse hazard analysis thread and the conventional collapse risk characteristic value of the preset target collapse risk characteristic, the collapse risk characteristic assessment score of the conventional collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the third collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the collapse risk characteristic comparison result, and the collapse risk characteristic comparison result is a comparison value of the conventional collapse risk characteristic value and the standard graphic data distribution, that is, the lower the collapse risk characteristic comparison result, the higher the collapse risk characteristic assessment score.

[0071] Step 1044A, determining the distribution of each collapse risk feature assessment score based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature and the normalized collapse risk feature value of each preset target collapse risk feature.

[0072] In one possible implementation, step 1044A may be implemented through steps 441A to 443A, which are described in detail below.

[0073] In step 441A, a standard graphical data distribution statistical model is obtained.

[0074] In step 442A, based on the normalized collapse risk characteristic value of each preset target collapse risk characteristic and the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic, the standard graphic data distribution statistical model is model processed to obtain a risk description result corresponding to each preset target collapse risk characteristic.

[0075] In step 443A, based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution corresponding to each preset target collapse risk feature is determined.

[0076] In a possible implementation embodiment, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the higher the normalized collapse risk characteristic value of the preset target collapse risk feature, the higher the collapse risk characteristic score of the preset target collapse risk feature. Since the higher the normalized collapse risk characteristic value of the preset target collapse risk feature, the higher the risk description result, therefore, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

[0077] When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, the higher the conventional collapse risk characteristic value of the preset target collapse risk feature, the lower the collapse risk characteristic score of the preset target collapse risk feature. Since the higher the conventional collapse risk characteristic value of the preset target collapse risk feature, the higher the risk description result, therefore, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, the comparison result of the risk description standard result and the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

[0078] When the collapse hazard analysis thread corresponding to the preset target collapse risk characteristic is the third collapse hazard analysis thread, the closer the normalized collapse risk characteristic value of the preset target collapse risk characteristic is to 0, the higher the collapse risk characteristic assessment score is, and the risk description result corresponding to the preset target collapse risk characteristic represents the probability that the collapse risk characteristic assessment score is less than half of the standard collapse risk characteristic value corresponding to the preset target collapse risk characteristic. Therefore, the product of 2 and the risk description result is taken as the distribution corresponding to the preset target collapse risk characteristic.

[0079] Step 1044B, based on the corresponding collapse hazard analysis thread of each preset target collapse risk feature, the collapse risk feature average value, the collapse risk feature standard deviation and the collapse risk feature value of each preset target collapse risk feature, determine the distribution of each collapse risk feature assessment score.

[0080] In one possible implementation, step 1044B may be implemented through steps 441B to 443B, which are described in detail below.

[0081] In step 441B, a function calculation model corresponding to each preset target collapse risk feature is determined based on the collapse risk feature average value and the collapse risk feature standard deviation of each preset target collapse risk feature.

[0082] In step 442B, based on the collapse risk characteristic value of each preset target collapse risk characteristic and the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic, the function calculation model is model processed to obtain the corresponding risk description result of each preset target collapse risk characteristic.

[0083] In step 443B, based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution corresponding to each preset target collapse risk feature is determined.

[0084] In a possible implementation, similar to step 108A, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the higher the collapse risk characteristic value of the preset target collapse risk feature, the higher the collapse risk characteristic score of the preset target collapse risk feature. Since the higher the collapse risk characteristic value of the preset target collapse risk feature, the higher the risk description result, therefore, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

[0085] When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, the higher the collapse risk characteristic value of the preset target collapse risk feature, the lower the collapse risk characteristic score of the preset target collapse risk feature. Since the higher the collapse risk characteristic value of the preset target collapse risk feature, the higher the risk description result, therefore, when the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, the comparison result of the risk description standard result and the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

[0086] When the collapse hazard analysis thread corresponding to the preset target collapse risk characteristic is the third collapse hazard analysis thread, the closer the collapse risk characteristic value of the preset target collapse risk characteristic is to the average collapse risk characteristic value, the higher the collapse risk characteristic assessment score is, and the risk description result corresponding to the preset target collapse risk characteristic represents the probability that the collapse risk characteristic assessment score is less than half of the collapse risk characteristic value corresponding to the preset target collapse risk characteristic. Therefore, the product of 2 and the risk description result is taken as the distribution corresponding to the preset target collapse risk characteristic.

[0087] In the above steps 1041 to 1043, the collapse risk characteristic value of each preset target collapse risk characteristic is first normalized based on the collapse risk characteristic mean value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic to obtain the normalized collapse risk characteristic value of each preset target collapse risk characteristic, and then the collapse risk characteristic assessment score of each preset target collapse risk characteristic is determined using the collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic. Since the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic is set using the standard of each preset target collapse risk characteristic, the collapse risk characteristic assessment score of the preset target collapse risk characteristic meets the standard, thereby improving the accuracy of the collapse risk characteristic assessment score.

[0088] In step 105, based on the collapse risk characteristic assessment score of each preset target collapse risk characteristic, a collapse risk assessment result of the collapse monitoring data to be processed is determined.

[0089] For example, the collapse risk assessment result can be understood as evaluating the possibility of collapse and the time and place of collapse through various factors.

[0090] In a possible implementation example, the collapse risk characteristic assessment score of each preset target collapse risk characteristic may be averaged to obtain a comprehensive assessment score of the collapse monitoring data in the target range.

[0091] In a possible implementation, after determining the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the comprehensive assessment score of the collapse monitoring data of the target range, the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the comprehensive assessment score of the collapse monitoring data of the target range can be output. When the UAV-based collapse risk assessment method provided in the embodiment of the present application is executed by a server, outputting the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the comprehensive assessment score of the collapse monitoring data of the target range can be that the server sends the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the comprehensive assessment score of the collapse monitoring data of the target range to the terminal requesting collapse monitoring data processing, so as to output the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the comprehensive assessment score of the collapse monitoring data of the target range through the display device of the terminal; when the UAV-based collapse risk assessment method provided in the embodiment of the present application is executed by a terminal, outputting the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the collapse risk assessment result of the collapse monitoring data to be processed can be that the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the collapse risk assessment result of the collapse monitoring data to be processed are displayed on the display device of the terminal.

[0092] In the collapse risk assessment method based on drone provided in the embodiment of the present application, after the collapse monitoring data to be processed is obtained, the collapse hazard factor analysis is first performed on the collapse monitoring data of the target range included in the collapse monitoring data to be processed, and the positioning information of several target collapse factors in the collapse monitoring data of the target range is obtained, and then based on the positioning information of the several target collapse factors, the collapse risk characteristic value of no less than one preset target collapse risk characteristic is determined, and then the collapse risk characteristic average value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic calculated according to the function are obtained, and the collapse risk characteristic of each preset target collapse risk characteristic is determined based on the collapse risk characteristic average value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic. The collapse risk characteristic assessment score of the collapse risk characteristic is finally determined based on the collapse risk characteristic assessment score of each preset target collapse risk characteristic, and the collapse risk assessment result of the collapse monitoring data to be processed is determined; since the collapse risk characteristic mean value and the collapse risk characteristic standard deviation are obtained by statistically analyzing the collapse risk characteristic values ​​of the preset target collapse risk characteristics of several target range collapse monitoring data, the collapse risk characteristic assessment scores of the collapse risk characteristic values ​​of different facial parts can be determined by distributing and statistically analyzing the collapse risk characteristic values ​​of each preset target collapse risk characteristic, which has high accuracy, and then the collapse risk characteristic assessment scores of the collapse monitoring data of the target range are determined according to the collapse risk characteristic assessment scores of each facial part, which can ensure the authenticity and accuracy of the collapse risk characteristic assessment scores. Moreover, when determining the collapse risk characteristic assessment scores of the preset target collapse risk characteristics, different collapse hazard analysis threads are set for the preset target collapse risk characteristics with different standards, so as to ensure that the collapse risk characteristic assessment scores of the preset target collapse risk characteristics meet the standards and improve the accuracy of the collapse risk characteristic assessment scores.

[0093] On the basis of the above, a collapse risk assessment device based on a drone is provided, the device comprising: An information acquisition module is used to obtain collapse monitoring data that needs to be processed, and to analyze collapse hazard elements of target range collapse monitoring data included in the collapse monitoring data that needs to be processed, so as to obtain positioning information of several target collapse elements in the collapse monitoring data of the target range; A characteristic value determination module, used to determine the collapse risk characteristic value of at least one preset target collapse risk characteristic in the collapse monitoring data of the target range based on the positioning information of the plurality of target collapse elements; A feature acquisition module, used to obtain a collapse risk feature average value and a collapse risk feature standard deviation of each preset target collapse risk feature, wherein the collapse risk feature average value and the collapse risk feature standard deviation are obtained by statistically analyzing the collapse risk feature value of each preset target collapse risk feature of collapse monitoring data of a plurality of different target ranges; A distribution determination module, for determining a collapse risk characteristic assessment score of each preset target collapse risk characteristic and a distribution of each collapse risk characteristic assessment score based on a collapse risk characteristic mean value, a collapse risk characteristic standard deviation and a collapse risk characteristic value of each preset target collapse risk characteristic; The result evaluation module is used to determine the collapse risk evaluation result of the collapse monitoring data to be processed based on the collapse risk characteristic evaluation score of each preset target collapse risk characteristic.

[0094] On the basis of the above, a collapse risk assessment system based on a drone is shown, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0095] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0096] In summary, based on the above scheme, after the collapse monitoring data that needs to be processed is obtained, the collapse hazard factor analysis is first performed on the target range collapse monitoring data included in the collapse monitoring data that needs to be processed to obtain the positioning information of several target collapse factors in the collapse monitoring data of the target range, and then based on the positioning information of the several target collapse factors, the collapse risk characteristic value of no less than one preset target collapse risk characteristic in the collapse monitoring data of the target range is determined, and then the collapse risk characteristic mean value and collapse risk characteristic standard deviation of each preset target collapse risk characteristic are obtained, and the collapse risk characteristic evaluation score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic evaluation score are determined based on the collapse risk characteristic mean value, collapse risk characteristic standard deviation and collapse risk characteristic value of each preset target collapse risk characteristic, and finally the collapse risk characteristic evaluation score of each preset target collapse risk characteristic is determined based on the collapse risk characteristic standard deviation of the collapse risk characteristic of each preset target. Score, determine the collapse risk assessment result of the collapse monitoring data that needs to be processed; since the collapse risk characteristic mean value and the collapse risk characteristic standard deviation are obtained by statistically analyzing the collapse risk characteristic values ​​of preset target collapse risk characteristics of collapse monitoring data of several target ranges, it is possible to determine the collapse risk characteristic assessment scores of the collapse risk characteristic values ​​of different parts by distributing and statistically analyzing the collapse risk characteristic values ​​of each preset target collapse risk characteristic, with high accuracy, and also determine the distribution of each collapse risk characteristic assessment score based on the collapse risk characteristic mean value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic, which can improve the accuracy of the collapse risk characteristic assessment score, and then determine the collapse risk characteristic assessment score of the collapse monitoring data that needs to be processed according to the collapse risk characteristic assessment score of each target part, which can ensure the authenticity and accuracy of the collapse risk characteristic assessment score.

[0097] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0098] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

Claims

1. A method for assessing collapse risk based on drones, characterized in that: The method comprises: Obtaining collapse monitoring data that needs to be processed, and performing collapse hazard element analysis on the target range collapse monitoring data included in the collapse monitoring data that needs to be processed, to obtain positioning information of several target collapse elements in the target range collapse monitoring data; Based on the positioning information of the plurality of target collapse elements, determining a collapse risk characteristic value of at least one preset target collapse risk characteristic in the collapse monitoring data of the target range; Obtaining a collapse risk characteristic average value and a collapse risk characteristic standard deviation of each preset target collapse risk characteristic, wherein the collapse risk characteristic average value and the collapse risk characteristic standard deviation are obtained by statistically analyzing the collapse risk characteristic value of each preset target collapse risk characteristic of collapse monitoring data of a plurality of different target ranges; Determine the collapse risk characteristic assessment score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic assessment score based on the collapse risk characteristic mean value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic; Based on the collapse risk characteristic assessment score of each preset target collapse risk characteristic, a collapse risk assessment result of the collapse monitoring data to be processed is determined.

2. The method according to claim 1, characterized in that The method of obtaining the average collapse risk characteristic value and the standard deviation of the collapse risk characteristic of each preset target collapse risk characteristic includes: Performing collapse element identification on the collapse monitoring data in the target range among the collapse monitoring data to be processed, and obtaining the target collapse element corresponding to the collapse monitoring data to be processed; Based on the target collapse factor, the collapse risk characteristic average value and the collapse risk characteristic standard deviation corresponding to each preset target collapse risk characteristic are obtained.

3. The method according to claim 2, characterized in that The method further comprises: Acquire a number of reference collapse monitoring data, and perform collapse hazard factor analysis on the reference range collapse monitoring data included in each reference collapse monitoring data, and obtain reference positioning information of a number of reference target collapse elements in each reference range collapse monitoring data; Determine, based on the reference positioning information of the reference target collapse elements in each reference range collapse monitoring data, a reference collapse risk characteristic value of at least one preset target collapse risk characteristic in each reference range collapse monitoring data; Performing collapse element identification on each reference range collapse monitoring data to obtain reference collapse elements of each reference collapse monitoring data; Based on the reference collapse risk characteristic value of each preset target collapse risk characteristic in the collapse monitoring data of the reference range of the same reference collapse element, collapse risk characteristic distribution information corresponding to the same reference collapse element of each preset target collapse risk characteristic is determined.

4. The method according to claim 1, characterized in that: The method of determining the collapse risk characteristic evaluation score of each preset target collapse risk characteristic and the distribution of each collapse risk characteristic evaluation score based on the collapse risk characteristic average value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk characteristic comprises: Based on the collapse risk characteristic average value and the collapse risk characteristic standard deviation of each preset target collapse risk characteristic, normalizing the collapse risk characteristic value of each preset target collapse risk characteristic to obtain a normalized collapse risk characteristic value of each preset target collapse risk characteristic; Obtaining a collapse hazard analysis thread corresponding to each of the preset target collapse risk characteristics; Determine a collapse risk characteristic assessment score of each preset target collapse risk characteristic by using the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic; The distribution of each collapse risk feature assessment score is determined based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature and the normalized collapse risk characteristic value of each preset target collapse risk feature; or, the distribution of each collapse risk feature assessment score is determined based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the collapse risk characteristic average value, the collapse risk characteristic standard deviation and the collapse risk characteristic value of each preset target collapse risk feature.

5. The method according to claim 4, characterized in that When the target range collapse monitoring data is collapse hazard data, the collapse hazard analysis thread corresponding to the collapse risk characteristic value of each preset target collapse risk characteristic and the normalized collapse risk characteristic value of each preset target collapse risk characteristic are used to determine the collapse risk characteristic assessment score of each preset target collapse risk characteristic, including: When the preset target collapse risk feature is a hidden danger data volume or a hidden danger description attribute, based on the first collapse hidden danger analysis thread and the normalized collapse risk characteristic value of the preset target collapse risk feature, a collapse risk characteristic assessment score of the normalized collapse risk characteristic value of the preset target collapse risk feature is determined, wherein the first collapse hidden danger analysis thread constrains that the collapse risk characteristic assessment score is related to the normalized collapse risk characteristic value; When the collapse risk characteristic value of the preset target collapse risk characteristic is a human factor, based on the second collapse hazard analysis thread and the normalized collapse risk characteristic value in the preset target collapse risk characteristic, a collapse risk characteristic assessment score of the normalized collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the second collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the normalized collapse risk characteristic value; When the collapse risk characteristic value of the preset target collapse risk characteristic is one of rainfall, soil dryness, vegetation coverage, rock stability, slope inclination data, soil looseness and large equipment working condition, based on the third collapse hazard analysis thread and the conventional collapse risk characteristic value of the preset target collapse risk characteristic, the collapse risk characteristic assessment score of the conventional collapse risk characteristic value of the preset target collapse risk characteristic is determined, wherein the third collapse hazard analysis thread constrains that the collapse risk characteristic assessment score has no connection with the collapse risk characteristic comparison result, and the collapse risk characteristic comparison result is a comparison value of the conventional collapse risk characteristic value and the standard graphic data distribution.

6. The method according to claim 4, characterized in that The determining of the distribution of each collapse risk feature assessment score based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature and the normalized collapse risk feature value of each preset target collapse risk feature includes: Obtain standard graphical data distribution statistical model; Based on the normalized collapse risk characteristic value of each preset target collapse risk characteristic, the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic performs model processing on the standard graphic data distribution statistical model to obtain a risk description result corresponding to each preset target collapse risk characteristic; Based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution situation corresponding to each preset target collapse risk feature is determined.

7. The method according to claim 4, characterized in that The method of determining the distribution of each collapse risk feature assessment score based on the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the collapse risk feature average value, the collapse risk feature standard deviation and the collapse risk feature value of each preset target collapse risk feature comprises: Determine a function calculation model corresponding to each preset target collapse risk feature based on a collapse risk feature average value and a collapse risk feature standard deviation of each preset target collapse risk feature; Based on the collapse risk characteristic value of each preset target collapse risk characteristic and the collapse hazard analysis thread corresponding to each preset target collapse risk characteristic, the function calculation model is subjected to model processing to obtain a risk description result corresponding to each preset target collapse risk characteristic; Based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution situation corresponding to each preset target collapse risk feature is determined.

8. The method according to claim 6 or 7, characterized in that The determining, based on the risk description result corresponding to each preset target collapse risk feature and the collapse hazard analysis thread corresponding to each preset target collapse risk feature, the distribution of each preset target collapse risk feature comprises: When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the first collapse hazard analysis thread, the risk description result is used as the distribution situation corresponding to the preset target collapse risk feature; When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the second collapse hazard analysis thread, a comparison result between the risk description standard result and the risk description result is used as the distribution situation corresponding to the preset target collapse risk feature; When the collapse hazard analysis thread corresponding to the preset target collapse risk feature is the third collapse hazard analysis thread, the product of the preset value and the risk description result is used as the distribution corresponding to the preset target collapse risk feature.

9. The method according to claim 1, characterized in that: The collapse monitoring data in the target range included in the collapse monitoring data to be processed are analyzed for collapse hazard elements to obtain positioning information of several target collapse elements in the collapse monitoring data in the target range, including: Performing collapse monitoring data identification on the collapse monitoring data to be processed to obtain collapse monitoring data of an initial target range in the collapse monitoring data to be processed; When it is determined that the collapse monitoring data of the initial target range needs to be verified, a collapse data queue of the collapse monitoring data of the initial target range is determined; Performing verification processing on the initial target range collapse monitoring data based on the collapse data queue to obtain verified target range collapse monitoring data; The verified target range collapse monitoring data is subjected to collapse hazard factor analysis to obtain positioning information of several target collapse factors.

10. A collapse risk assessment system based on drones, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 9.

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