Unmanned aerial vehicle forced landing control system and method

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle forced landing control system and method.The unmanned aerial vehicle forced landing control system comprises an unmanned aerial vehicle state recognition module, an unmanned aerial vehicle forced landing estimation module, a forced landing processing module and a forced landing evaluation module, and the unmanned aerial vehicle forced landing estimation module comprises a landing point position recognition unit, a landing risk evaluation unit and an early warning execution unit; the forced landing evaluation module comprises a landing self-checking unit and a dispatching recovery unit. The system analyzes the current forced landing state through an unmanned aerial vehicle state recognition module, evaluates the flight distance of the unmanned aerial vehicle in real time, obtains a preliminary landing point after preliminarily screening landing points, judges whether the risk coefficient of the preliminary landing point exceeds a preset warning value after screening out the preliminary landing point, and gives an alarm if the risk coefficient exceeds the preset warning value. The system can give risk early warning in time and implement corresponding strategies, and measures are taken quickly to prevent the unmanned aerial vehicle from landing at a high-risk point location, so that the safety and reliability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle forced landing control system and method. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly entering people's lives. Because unmanned aerial vehicles are available day and night, simple in structure, convenient to use, low in cost, high in efficiency, and do not have to worry about personnel casualties, they are increasingly favored in high-risk environments. They can be used for scene monitoring, weather investigation, highway patrol, exploration and mapping, flood monitoring, aerial photography, traffic management, forest fires, etc., and have extremely broad application prospects.

[0003] Currently, traffic management departments often use remote control unmanned aerial vehicles to manage common violations such as vehicle parking in the jurisdiction. When the unmanned aerial vehicle vision module is disconnected, the operator cannot control the unmanned aerial vehicle remotely based on the surrounding scene. At this time, forced landing is required. Because the unmanned aerial vehicle is already in an emergency state and needs to quickly execute the landing operation, the most suitable landing point needs to be accurately selected at a fast speed during the selection of the landing point. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides an unmanned aerial vehicle forced landing control system and method, which has the advantages of autonomous forced landing control of the unmanned aerial vehicle, and solves the above technical problems.

[0005] To achieve the above purpose, the present application provides the following technical scheme: an unmanned aerial vehicle forced landing control system, comprising:

[0006] An unmanned aerial vehicle state recognition module is used to analyze the current forced landing state of the unmanned aerial vehicle, construct the actual flight distance of the unmanned aerial vehicle, and construct a safe flight area based on the constructed actual flight distance of the unmanned aerial vehicle;

[0007] An unmanned aerial vehicle forced landing estimation module is used to read a pre-stored terrain environment database and a ground camera image, construct a plurality of unmanned aerial vehicle landing points based on the safe flight area in which the unmanned aerial vehicle is currently located, construct a landing risk coefficient for each unmanned aerial vehicle landing point, then select a landing point with the smallest landing risk coefficient as a preliminary landing point, if the landing risk coefficient of the preliminary landing point is greater than a preset risk warning value, a first risk warning signal is sent out and a first warning strategy is executed, if the landing risk coefficient of the preliminary landing point is less than or equal to the preset risk warning value, and the preliminary landing point is taken as a target for forced landing, a forced landing completion instruction is sent out after the forced landing is completed;

[0008] The forced landing processing module is called after the first early warning strategy is executed, and the forced landing risk coefficients of different unmanned aerial vehicle landing points are used to perform secondary evaluation on the unmanned aerial vehicle landing points, so as to obtain forced landing risk coefficients of the unmanned aerial vehicle landing points, and the smallest one is selected as a landing point for forced landing, and a forced landing completion instruction is sent after the forced landing is completed.

[0009] The forced landing evaluation module performs self-checking on the current unmanned aerial vehicle state after the forced landing completion instruction is sent, and personnel are dispatched for recovery.

[0010] As a preferred technical solution of the application, the unmanned aerial vehicle state recognition module is used to analyze the current forced landing state of the unmanned aerial vehicle, and the specific steps of constructing a safe flight area according to the current forced landing state of the unmanned aerial vehicle are as follows:

[0011] Step A1: Obtain the maximum navigation distance of the unmanned aerial vehicle based on the real-time power of the unmanned aerial vehicle;

[0012] Step A2: Construct an environmental deviation coefficient according to the current forced landing state of the unmanned aerial vehicle, specifically including the difference between the standard value corresponding to the unmanned aerial vehicle flight environment influence value and the corresponding unmanned aerial vehicle flight environment influence value, and the ratio of the standard value corresponding to the unmanned aerial vehicle flight environment influence value, and the sum of the environmental deviation coefficient, wherein the standard value corresponding to the unmanned aerial vehicle flight environment influence value is obtained by testing when the unmanned aerial vehicle is produced, and the unmanned aerial vehicle flight environment influence value is obtained by the sensor carried by the unmanned aerial vehicle;

[0013] Step A3: Construct the actual navigation distance of the unmanned aerial vehicle based on the environmental deviation coefficient and the maximum navigation distance of the unmanned aerial vehicle, specifically coupling the environmental deviation coefficient and the maximum navigation distance of the unmanned aerial vehicle, and performing secondary processing on the coupled result by a predetermined correction coefficient to obtain the actual navigation distance of the unmanned aerial vehicle.

[0014] As a preferred technical solution of the application, the unmanned aerial vehicle forced landing estimation module includes a landing point recognition unit, a landing risk evaluation unit and a pre-warning execution unit;

[0015] The landing point recognition unit constructs a plurality of unmanned aerial vehicle landing points based on the safe flight area range where the current unmanned aerial vehicle is located, and the specific steps are as follows:

[0016] Step B1: Read the terrain environment database stored in the offline end of the unmanned aerial vehicle, and remove the pre-marked non-landing area from the safe flight area, while obtaining the aerial coverage radius of the unmanned aerial vehicle, generating the minimum circle of the unmanned aerial vehicle landing, and filling it into the safe flight area according to the map scale to obtain a plurality of preliminary landing points;

[0017] Step B2: evaluate the landing evaluation coefficient of each preliminary landing point, specifically including obtaining the unmanned aerial vehicle landing influence value in the preliminary landing point and the corresponding standard value, and calculating the deviation rate between the corresponding standard value and the unmanned aerial vehicle landing influence value, and obtaining the landing evaluation coefficient after summing all the deviation rates:

[0018] Step B3: remove all points from the safe flight area that do not satisfy the landing evaluation coefficient < preset landing evaluation threshold, and determine whether any two preliminary landing points are adjacent, and merge all adjacent preliminary landing points to obtain a plurality of unmanned aerial vehicle landing points.

[0019] As a preferred technical solution of the present application, the specific steps of the landing risk evaluation unit based on the current safe flight area of the unmanned aerial vehicle to construct a plurality of unmanned aerial vehicle landing points and to construct the landing risk coefficient of the unmanned aerial vehicle landing point are as follows:

[0020] Step C1: obtain the proportion of the number of people in the current unmanned aerial vehicle landing point to the total number of people in all unmanned aerial vehicle landing points, and obtain the proportion of the total number of personnel entering and leaving the current unmanned aerial vehicle landing point to the sum of the total number of personnel entering and leaving all unmanned aerial vehicle landing points, and obtain the dynamic risk coefficient of the unmanned aerial vehicle landing point after averaging the two, the total number of personnel entering and leaving the unmanned aerial vehicle landing point and the number of people in the unmanned aerial vehicle landing point are obtained by reading the camera at the location of the unmanned aerial vehicle landing point;

[0021] Step C2: obtain the area ratio of the unmanned aerial vehicle landing point and the landing deviation ratio of the unmanned aerial vehicle landing point, and obtain the static risk coefficient of the unmanned aerial vehicle landing point after weighting and summing the two, and performing exponential operation, and multiplying the distance ratio between the current position of the unmanned aerial vehicle and the current unmanned aerial vehicle landing point;

[0022] The area ratio of the unmanned aerial vehicle landing point is specifically obtained by taking the ratio of the minimum circular area of the unmanned aerial vehicle landing and the area of the current unmanned aerial vehicle landing point, the landing deviation ratio of the unmanned aerial vehicle landing point is obtained by reading the camera at the location of the current unmanned aerial vehicle landing point, and the distance between the current position of the unmanned aerial vehicle and the current unmanned aerial vehicle landing point is specifically the three-dimensional distance between the current position of the unmanned aerial vehicle and the center of the current unmanned aerial vehicle landing point.

[0023] Step C3: obtain the landing risk coefficient of the unmanned aerial vehicle landing point by weighting and summing the dynamic risk coefficient of the unmanned aerial vehicle landing point and the static risk coefficient of the unmanned aerial vehicle landing point.

[0024] As a preferred technical solution of the present application, the specific steps of the pre-warning execution unit for executing the first pre-warning strategy and issuing the forced landing completion instruction are as follows:

[0025] Step D1: set the unmanned aerial vehicle to hover at 90% to 95% of the current height, and after waiting for t seconds, re-execute the unmanned aerial vehicle state recognition module, and record the first updated actual flight distance of the unmanned aerial vehicle At the same time, re-execute the unmanned aerial vehicle forced landing estimation module to select a standby landing point, and re-calculate the landing risk coefficient of the standby landing point.

[0026] Step D2: if the landing risk coefficient of the standby landing point is less than or equal to the preset risk warning value, continue to execute step D2 to force land with the standby landing point as the target, issue a forced landing completion instruction after the forced landing is completed, and output a signal that the first warning strategy is not executed to the forced landing processing module, otherwise execute step D3.

[0027] Step D3: if the first updated actual flight distance of the unmanned aerial vehicle is less than the alarm value, terminate step D3 and output a signal that the first warning strategy is executed to the forced landing processing module, otherwise jump back to step D1.

[0028] As a preferred technical solution of the present application, the landing deviation ratio of the unmanned aerial vehicle landing point is specifically obtained by averaging the difference area between the current image taken by the camera at the location of the unmanned aerial vehicle landing point and the preset standard image, and then performing ratio operation on the shooting area obtained by the camera at the location of the unmanned aerial vehicle landing point.

[0029] As a preferred technical solution of the present application, the forced landing processing module performs secondary evaluation on a plurality of unmanned aerial vehicle landing points according to the landing risk coefficients of different unmanned aerial vehicle landing points, specifically by multiplying the landing risk coefficient of the unmanned aerial vehicle landing point by the obtained recovery influence coefficient to obtain the forced landing risk coefficient of the unmanned aerial vehicle landing point, and sequentially traversing to obtain the forced landing risk coefficients of a plurality of unmanned aerial vehicle landing points.

[0030] The recovery influence coefficient is specifically obtained by performing ratio operation on the difference area between the color outside the unmanned aerial vehicle and the shooting area obtained by the camera at the location of the unmanned aerial vehicle landing point, and the shooting area obtained by the camera at the location of the unmanned aerial vehicle landing point.

[0031] As a preferred technical solution of the present application, the forced landing estimation module includes a landing self-checking unit and a dispatching recovery unit, the landing self-checking unit is used for evaluating the unmanned aerial vehicle forced landing process, including plane deviation evaluation, landing acceleration evaluation and attitude angle evaluation when landing, and outputting the unmanned aerial vehicle forced landing deviation coefficient after averaging the three evaluation results.

[0032] As the preferred technical solution of the present invention, the dispatch recovery unit calculates the current position of the drone and the deviation coefficient of the drone forced landing. Send to the current dispatcher, the specific steps are:

[0033] Get the time when the current drone operator arrives at the forced landing location , and obtain the time collection of all patrol personnel arriving at the forced landing location , and get the time collection The minimum value in ,when < When, the scheduling time collection The minimum value in The corresponding patrol personnel will recover the drone, otherwise it will be recovered by the current drone operator.

[0034] The present invention also provides a method for controlling a forced landing of a UAV, comprising the following steps:

[0035] Step 1: Analyze the current forced landing status of the drone and construct the actual flight distance of the drone , and build the actual flight distance of the drone based on To build a safe flight area;

[0036] Step 2: Construct several drone landing points based on the safe flight area where the drone is currently located, and construct the first Landing risk factor of each drone landing point , and select the landing point with the lowest landing risk coefficient as the reserve landing point;

[0037] Step 3: Determine the landing point. If the landing risk coefficient of the planned landing point is >Preset risk warning value, then execute step 4. If the landing risk coefficient of the planned landing point is ≤ the preset risk warning value, proceed to step 5;

[0038] Step 4: Issue the first risk warning signal and execute the first warning strategy. After the first warning strategy is executed, Landing risk factor of each drone landing point Perform a secondary evaluation on several drone landing points to obtain the forced landing risk coefficient of the i-th drone landing point , and select the smallest one as the landing point for forced landing, after the forced landing is completed, issue a forced landing completion command and execute step 6;

[0039] Step 5: Make an emergency landing at the planned landing point. After the emergency landing is completed, issue an emergency landing completion command and proceed to step 6.

[0040] Step six: self-check the current unmanned aerial vehicle state after the forced landing instruction is issued, and dispatch personnel for recovery.

[0041] Compared with the prior art, the unmanned aerial vehicle forced landing control system and method have the following beneficial effects:

[0042] 1、The unmanned aerial vehicle state recognition module analyzes the current forced landing state, evaluates the flight distance of the unmanned aerial vehicle in real time, and obtains a preliminary landing point after preliminary screening of the landing point, after the preliminary landing point is screened, the system judges whether the risk coefficient exceeds the preset warning value, if it exceeds, the system will timely issue a risk warning and implement corresponding strategies, take corresponding measures quickly, prevent the unmanned aerial vehicle from forced landing at a high-risk point, thereby enhancing the safety and reliability.

[0043] 2、After the forced landing instruction is issued, the system will self-check the unmanned aerial vehicle to detect possible damage or failure during landing, which provides a basis for subsequent maintenance and maintenance, after the state self-checking is completed, the system automatically dispatches personnel to recover the unmanned aerial vehicle, quickly and effectively organizes resources, reduces the time of unmanned aerial vehicle in the wild without supervision, reduces the further damage risk caused by environmental factors, thereby improving the overall management efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 It is a system framework schematic diagram of the present application;

[0045] Figure 2 It is a flowchart of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Please refer to Figure 1 The application scenarios of the present application include but are not limited to, such as the case of disconnection of the unmanned aerial vehicle vision module, the case that the operator cannot control remotely according to the scene around the unmanned aerial vehicle, at this time forced landing is needed, in the case of disconnection of the GPS of the unmanned aerial vehicle or disconnection of the sensor for automatic landing control, the unmanned aerial vehicle is controlled to hover, and a warning can be sent to the patrol personnel;

[0048] An unmanned aerial vehicle forced landing control system comprises:

[0049] The UAV status recognition module is used to analyze the current forced landing status of the UAV and construct the actual flight distance of the UAV. , and build the actual flight distance of the drone based on To build a safe flight area, the specific steps are as follows:

[0050] Step A1: Obtain the maximum flight distance of the drone based on the drone's real-time power ;

[0051] Step A2: Construct environmental deviation coefficients based on the current forced landing status of the drone , the specific expressions are as follows:

[0052]

[0053] in, It represents the standard value corresponding to the k-th UAV flight environment impact value, which is obtained through the UAV production test. Indicates the kth UAV flight environment impact value, usually In order not to affect the minimum value of the corresponding parameters of the drone flight environment, when an abnormality occurs Usually it is less than 0. The case where the value is greater than 0 generally has no impact or is not considered. It is obtained through the sensors carried by the drone. Represents the sum, K represents the total number of factors affecting the environment; by analyzing the deviation of multiple parameters affecting the drone flight environment and the standard values ​​during the factory test process, the current environment can be compared with the drone flight process to determine the maximum flight distance. Make a bias estimate to measure;

[0054] See Table a below for details:

[0055] Table a

[0056]

[0057] Step A3: Based on the environmental deviation coefficient Maximum flight distance of drones Constructing the actual flight distance of the drone , the specific expression is as follows:

[0058]

[0059] Step A4: Take the current drone positioning point as the center of the circle and the actual flight distance of the drone The radius is matched in the offline map stored in the drone, and the matching area is used as the safe flight area. It represents a correction constant greater than 1, which is preset by the operator before the drone takes off. It is usually used to avoid the overall range being too large. The calculation here is not performed on the drone itself, but by collecting local meteorological data or parameters in the external environment obtained by sensors. It is calculated locally by the traffic management department and then sent to the drone. Subsequent calculations are similar.

[0060] The UAV forced landing prediction module is used to read the pre-stored terrain environment database and the images taken by the ground camera, build several UAV landing points based on the safe flight area where the current UAV is located, and build the first Landing risk factor of each drone landing point , and select a landing point with the smallest landing risk coefficient as the preparatory landing point, and judge the preparatory landing point. If the landing risk coefficient of the preparatory landing point is > the preset risk warning value, the first risk warning signal is issued and the first warning strategy is executed. If the landing risk coefficient of the prepared landing point is ≤ the preset risk warning value, and perform forced landing at the prepared landing point as the target, and issue a forced landing completion command after the forced landing is completed;

[0061] The UAV forced landing prediction module includes a landing point identification unit, a landing risk assessment unit, and a warning execution unit;

[0062] The landing point identification unit constructs several drone landing points based on the safe flight area where the drone is currently located. The specific steps are as follows:

[0063] Step B1: Read the terrain environment database stored offline by the drone and remove pre-marked no-landing areas (including common obstacles within the jurisdiction, such as streetlights and trees, as well as main road lights, vehicle access roads, and water areas) from the safe flight area. At the same time, obtain the drone's bird's-eye view coverage radius, generate a minimum landing circle for the drone, and fill it into the safe flight area based on the map scale to obtain several preliminary landing points (for example, generate the first minimum landing circle with the drone's position on the map as the center, and gradually fill it tangentially, or add minimum landing circles tangential to any intersecting boundary of the map as the starting edge).

[0064] Step B2: Evaluate the landing evaluation coefficient of each preliminary landing point using the following expression: :

[0065]

[0066] in, represents the landing assessment coefficient, Indicates the first landing point The standard value corresponding to the impact value of the drone landing is the same as the above Similarly, Indicates the first landing point The impact value of drone landing, Indicates summation, Indicates the total number of drone landing impact values. For specific landing assessment coefficients, see Table b below;

[0067] Table b

[0068]

[0069] The above table is for reference only. Those skilled in the art can expand or delete the table content. The landing physical parameters are roughly covered by the drone's own sensors and the city's topographic map. When the denominator is 0, the default ;

[0070] Step B3: All the items that do not meet the landing assessment coefficient <Preset landing assessment threshold The points are removed from the safe flight area, and it is determined whether any two preliminary landing points are adjacent, and all adjacent preliminary landing points are merged separately. After the merger, it is necessary to determine whether the preliminary landing point has a camera set up by the traffic management department of the jurisdiction where the drone operator is located. If not, it is removed, thereby ensuring the safety of the area and the availability of data, and obtaining several drone landing points.

[0071] The landing risk assessment unit constructs several drone landing points based on the safe flight area where the drone is currently located, and constructs the first Landing risk factor of each drone landing point The specific steps are as follows:

[0072] Step C1: Build Dynamic risk factor of each drone landing point , the specific expression is as follows:

[0073]

[0074] in, Indicates the The number of people at each drone landing point is read by The camera at the location of the drone landing point is used to obtain the information. Indicates summation, Indicates the total number of drone landing points, Indicates the first The total number of people entering and leaving the drone landing point is read by The camera at the location of the landing point of each drone is used to obtain the information. The specific acquisition process can be to mark the person by a human body recognition model within the same time until the person disappears. At the same time, the human body recognition model is an existing model. Those skilled in the art can arbitrarily select a specific model as long as it can achieve the process of marking the number of people at the location;

[0075] Step C2: Build Static risk factor of each drone landing point , the specific expression is as follows:

[0076]

[0077] in, Indicates the The area ratio of the first UAV landing point is calculated by dividing the minimum circle area of ​​the UAV landing point by the The area of ​​the landing point of each drone is compared to obtain the value. Indicates the The landing deviation ratio of the UAV landing point is read by The camera at the location of the drone landing point is obtained, that is, The camera at the location of the drone landing point (the camera here refers to the camera that can be called within the jurisdiction) and the The distance between the center points of the drone landing points. If there are multiple cameras, it is the distance from the center of the common circle of multiple cameras to the first The distance between the center points of the drone landing points, Indicates the current position of the drone and the The distance between the landing points of the drone, which is the distance between the current position of the drone and the first The three-dimensional distance between the center of the UAV landing point, and They represent weight coefficients that sum to 1 respectively;

[0078] Step C3: Based on Dynamic risk factor of each drone landing point and static risk factor Comprehensive construction Landing risk factor of each drone landing point , the specific expression is as follows:

[0079]

[0080] in, and They represent weight coefficients that sum to 1 respectively;

[0081] No. Landing deviation ratio of the UAV landing point The specific expression is as follows:

[0082]

[0083] in, Indicates the The shooting area captured by the camera at the landing point of the drone, Indicates the The obstacle coverage area of ​​the drone landing point is determined by the image recognition model to identify the preset obstacle types. Indicates the The difference area between the current image and the preset standard image of the location where the UAV lands.

[0084] The early warning execution unit is used to execute the first early warning strategy and issue a forced landing completion instruction. The early warning execution unit performs the following specific steps when executing the first early warning strategy:

[0085] Step D1: Set the drone to hover at 90%~95% of the current altitude, and wait for t seconds, optionally t=10s, re-execute the drone state recognition module, and record the first Actual flight distance of the drone after the update At the same time, re-execute the UAV forced landing prediction module to select the prepared landing point and recalculate the landing risk coefficient of the prepared landing point The purpose of this step is to reduce the drone's hovering height and add voice notification content to evacuate pedestrians, which can effectively reduce dynamic risks and recalculate the landing risk coefficient of the prepared landing point after the waiting period;

[0086] Step D2: If the landing risk coefficient of the planned landing point ≤ the preset risk warning value, proceed to step D2, recalculate the landing risk coefficient of the prepared landing point, and perform a forced landing at the prepared landing point as the target. After the forced landing is completed, issue a forced landing completion command and output a signal that the first warning strategy has not been executed to the forced landing processing module. Otherwise, proceed to step D3;

[0087] Step D3: If Actual flight distance of the drone after the update Less than the alarm value When the first warning strategy is completed, the step D3 is terminated and the signal of the first warning strategy execution completion is output to the forced landing processing module. At this time, it indicates that the UAV has a large range wedge. Otherwise, it jumps back to step D1. .

[0088] The forced landing processing module is called after the first warning strategy is executed. Landing risk factor of each drone landing point Perform a secondary evaluation on several drone landing points to obtain the forced landing risk coefficient of the i-th drone landing point , and select the smallest one as the landing point for forced landing, and issue a forced landing completion command after the forced landing is completed;

[0089] The forced landing processing module is based on Landing risk factor of each drone landing point A secondary evaluation was conducted on several UAV landing points to obtain the The forced landing risk factor of each drone landing point The specific expression is as follows:

[0090]

[0091] in, Represents the recovery impact coefficient, and the specific expression is as follows:

[0092]

[0093] in, Indicates the color of the drone's exterior and the The difference area of ​​the shooting area obtained by the camera at the landing point of the drone is determined in advance, and the main color of the drone body is matched with the color in the environment and all areas that are inconsistent with the main color of the drone are output as the difference area, for example =15, =50, at this time =0.3, Indicates the The shooting area obtained by the camera at the landing point of the first drone. During the second forced landing, due to the attenuation of power, and after a long waiting time, the drone is forced to land. Landing risk factor of each drone landing point A secondary evaluation of several drone landing sites can ensure that the site with the greatest difference between the drone and the environment is chosen for landing, making it easier for staff to identify the drone.

[0094] The forced landing assessment module performs a self-check on the current drone status after the forced landing completion command is issued, and dispatches personnel for recovery;

[0095] The forced landing assessment module includes a landing self-test unit and a dispatch recovery unit. The landing self-test unit is used to evaluate the UAV’s forced landing process and output the UAV’s forced landing deviation coefficient. , the specific expression is as follows:

[0096]

[0097] in, Indicates the preset maximum plane deviation. Indicates the current plane deviation value, represents the landing acceleration, Indicates the preset maximum landing acceleration value. Indicates the preset maximum attitude angle. Indicates the attitude angle at landing. The forced landing deviation coefficient is used to feedback the deviation degree of the drone landing. It is used to select a closer staff member for dispatch during dispatch, thereby achieving rapid recovery and avoiding loss.

[0098] The dispatch recovery unit will calculate the current position of the drone and the deviation coefficient of the drone's forced landing. Send to the current dispatcher, the specific steps are:

[0099] Get the time when the current drone operator arrives at the forced landing location , and obtain the time collection of all patrol personnel arriving at the forced landing location , and get the time collection The minimum value in ,when < hour, It reflects the deviation of the parameters when the drone lands. When it is a negative value, it will reduce , while the time collection The minimum value in Greater than This indicates that the current drone operators are more efficient in recovering the equipment, and the scheduling time is more complete. The minimum value in The corresponding patrol personnel will recover the drone, otherwise the current drone operator will recover it;

[0100] in, Indicates the deviation coefficient of the UAV forced landing;

[0101] Example:

[0102] The actual flight distance of the drone recorded in this embodiment The parameters are as follows Table 1:

[0103] Table 1

[0104]

[0105] =196m, that is, a 196m circumferential area around the UAV is selected, and the circumferential area is divided into 4 UAV landing points according to the landing point identification unit, at this time, the dynamic risk coefficient of the UAV landing point is constructed, see Table 2 below:

[0106] Table 2

[0107]

[0108] The static risk coefficient of the UAV landing point is constructed, see Table 3 below:

[0109] Table 3

[0110]

[0111] At this time, the landing risk coefficient of the UAV landing point is shown in Table 4:

[0112] Table 4

[0113]

[0114] At this time, the landing point with the smallest landing risk coefficient is selected as the standby landing point, that is, As a standby landing point, at this time Less than the preset risk warning value = 0.45, forced landing;

[0115] In the above embodiments, only one or more feasible schemes are given, which do not represent the optimal scheme, and the data and data size recorded in the embodiments are only for the understanding of the technical scheme by those skilled in the art, and do not represent that the scheme in actual application only uses the data recorded in the above embodiments. The size of the threshold is set to facilitate comparison. The size of the threshold depends on the number of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship of the parameters and the quantized values, and the size of the weight can be determined by those skilled in the art according to each sample data and the process of multiple experiments. The above formula is a dimensionless value.

[0116] Please refer to Figure 2 The application also provides a UAV forced landing control method, comprising the following steps:

[0117] Step 1: analyze the current forced landing state of the UAV, and construct the actual flight distance of the UAV , and construct a safe flight area according to the constructed actual flight distance of the UAV

[0118] Step 2: based on the safe flight area where the current UAV is located, construct a plurality of UAV landing points, and construct the first ​The landing risk coefficient of the landing point of the unmanned aerial vehicle The landing risk coefficient of the landing point of the unmanned aerial vehicle

[0119] Step three: judging the pre-landing point, if the landing risk coefficient of the pre-landing point is greater than the preset risk warning value, executing step four, if the landing risk coefficient of the pre-landing point is less than or equal to the preset risk warning value, executing step five;

[0120] Step four: issuing the first risk warning signal and executing the first warning strategy, and after the execution of the first warning strategy is completed, based on the landing risk coefficient of the first pre-landing point The landing risk coefficient of the landing point of the unmanned aerial vehicle The landing risk coefficient of the landing point of the unmanned aerial vehicle The landing risk coefficient of the landing point of the unmanned aerial vehicle

[0121] Step five: landing with the pre-landing point as the target, issuing the landing completion instruction after the landing is completed, and executing step six;

[0122] Step six: self-checking the current state of the unmanned aerial vehicle after the landing completion instruction is issued, and dispatching personnel for recovery.

[0123] Although the embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A UAV forced landing control system, characterized by: include: The drone status recognition module is used to analyze the current forced landing status of the drone, construct the actual flight distance of the drone, and build a safe flight area based on the actual flight distance of the drone; The UAV forced landing prediction module is used to read the pre-stored terrain environment database and images taken by the ground camera, construct several UAV landing points based on the safe flight area where the current UAV is located, and construct a landing risk coefficient for each UAV landing point. Then, the landing point with the smallest landing risk coefficient is selected as the reserve landing point. If the landing risk coefficient of the reserve landing point is greater than the preset risk warning value, a first risk warning signal is issued and the first warning strategy is executed. If the landing risk coefficient of the reserve landing point is less than or equal to the preset risk warning value, a forced landing is performed with the reserve landing point as the target, and a forced landing completion command is issued after the forced landing is completed. The forced landing processing module is called after the first warning strategy is executed. It performs a secondary evaluation on several drone landing points based on the landing risk coefficients of different drone landing points, obtains the forced landing risk coefficients of several drone landing points, and selects the one with the smallest risk coefficient as the landing point for forced landing. After the forced landing is completed, it issues a forced landing completion command. The forced landing assessment module performs a self-check on the current drone status after the forced landing completion command is issued, and dispatches personnel to recover it.

2. The UAV forced landing control system according to claim 1, characterized in that: The drone status recognition module is used to analyze the current forced landing status of the drone and construct a safe flight area based on the current forced landing status of the drone. The specific steps are as follows: Step A1: Obtain the maximum flight distance of the drone based on the real-time power of the drone; Step A2: Constructing environmental deviation coefficients based on the current forced landing status of the UAV, specifically comprising subtracting the standard values ​​corresponding to several UAV flight environmental impact values ​​from their corresponding UAV flight environmental impact values, then comparing the subtractions with the standard values ​​corresponding to the UAV flight environmental impact values, and summing the results to obtain the environmental deviation coefficients. The standard values ​​corresponding to the UAV flight environmental impact values ​​are obtained through testing during UAV production, and the UAV flight environmental impact values ​​are obtained through sensors carried by the UAV. Step A3: Construct the actual flight distance of the UAV based on the environmental deviation coefficient and the maximum flight distance of the UAV. Specifically, the environmental deviation coefficient and the maximum flight distance of the UAV are coupled, and the coupled result is secondary processed using a preset correction constant to obtain the actual flight distance of the UAV.

3. The UAV forced landing control system according to claim 2, characterized in that: The UAV forced landing prediction module includes a landing point identification unit, a landing risk assessment unit and a warning execution unit; The landing point identification unit constructs several drone landing points based on the safe flight area of ​​the current drone. The specific steps are as follows: Step B1: Read the terrain environment database stored offline by the drone and remove pre-marked no-landing areas from the safe flight area. At the same time, obtain the drone's bird's-eye view coverage radius, generate the minimum landing circle for the drone, and fill it into the safe flight area according to the map scale to obtain several preliminary landing points. Step B2: Evaluate the landing assessment coefficient for each preliminary landing point. Specifically, this includes obtaining the drone landing impact value and the corresponding standard value at the preliminary landing point, and calculating the deviation rate between the corresponding standard value and the drone landing impact value. After summing all the deviation rates, the landing assessment coefficient is obtained: Step B3: All points that do not meet the landing assessment coefficient less than the preset landing assessment threshold are removed from the safe flight area, and it is determined whether any two preliminary landing points are adjacent. All adjacent preliminary landing points are merged to obtain several UAV landing points.

4. The UAV forced landing control system according to claim 3, characterized in that: The specific steps of the landing risk assessment unit to construct a number of drone landing points based on the safe flight area where the current drone is located and to construct the landing risk coefficients of the drone landing points are as follows: Step C1: Obtain the ratio of the number of people at the current drone landing site to the total number of people at all drone landing sites. Also, obtain the ratio of the total number of people entering and exiting the current drone landing site to the total number of people entering and exiting all drone landing sites. Average the two and obtain the dynamic risk coefficient for the drone landing site. The total number of people entering and exiting the drone landing site and the number of people at the drone landing site are both obtained by reading the camera at the drone landing site. Step C2: Obtain the area ratio of the drone landing point and the landing deviation ratio of the drone landing point, perform an exponential operation on the weighted sum of the two, and multiply it by the distance ratio between the drone's current position and the current drone landing point to obtain the static risk coefficient of the drone landing point; The drone landing point area ratio is specifically obtained by comparing the area of ​​the drone's minimum landing circle with the area of ​​the current drone landing point. The landing deviation ratio of the drone landing point is obtained by reading the camera at the location of the current drone landing point. The distance between the drone's current position and the current drone landing point is specifically the three-dimensional distance between the drone's current position and the center of the current drone landing point. Step C3: Obtain the landing risk coefficient of the drone landing point based on the weighted sum of the dynamic risk coefficient of the drone landing point and the static risk coefficient of the drone landing point.

5. The UAV forced landing control system according to claim 4, characterized in that: The warning execution unit is used to execute the first warning strategy and issue a forced landing completion instruction. The specific steps of the warning execution unit when executing the first warning strategy are as follows: Step D1: Set the drone to hover at 90%~95% of the current altitude, wait for t seconds, re-execute the drone status recognition module, and record the first Actual flight distance of the drone after the update At the same time, the UAV forced landing prediction module is re-executed to select the prepared landing point and recalculate the landing risk coefficient of the prepared landing point; Step D2: If the landing risk coefficient of the prepared landing point is less than or equal to the preset risk warning value, continue to step D2 and perform a forced landing at the prepared landing point. After the forced landing is completed, a forced landing completion instruction is issued, and a signal indicating that the first warning strategy has not been executed is output to the forced landing processing module. Otherwise, proceed to step D3. Step D3: If Actual flight distance of the drone after the update If it is less than the alarm value, step D3 is terminated and a signal indicating that the first warning strategy is executed is output to the forced landing processing module; otherwise, the process jumps back to step D1.

6. The UAV forced landing control system according to claim 4, characterized in that: The landing deviation ratio of the drone landing point is specifically obtained by averaging the obstacle coverage area of ​​the drone landing point and the difference area between the current shot image of the drone landing point and the preset standard shot image, and then performing a ratio calculation with the shooting area obtained by the camera at the drone landing point.

7. The UAV forced landing control system according to claim 1, characterized in that: The forced landing processing module performs a secondary evaluation on several drone landing points based on the landing risk coefficients of different drone landing points. Specifically, the forced landing risk coefficient of the drone landing point is obtained by multiplying the landing risk coefficient of the drone landing point with the obtained recovery influence coefficient for attenuation, and the forced landing risk coefficients of several drone landing points are obtained by traversing in sequence. The recovery influence coefficient is specifically obtained by performing a ratio calculation between the difference area between the external color of the drone and the shooting area obtained by the camera at the drone landing point and the shooting area obtained by the camera at the drone landing point.

8. The UAV forced landing control system according to claim 1, characterized in that: The forced landing assessment module includes a landing self-test unit and a scheduling recovery unit. The landing self-test unit is used to evaluate the UAV forced landing process, including plane deviation assessment, landing acceleration assessment and attitude angle assessment during landing, and outputs the UAV forced landing deviation coefficient after averaging the three assessment results.

9. The UAV forced landing control system according to claim 8, characterized in that: The dispatch recovery unit calculates the current position of the UAV and the deviation coefficient of the UAV forced landing. Send to the current dispatcher, the specific steps are: Get the time when the current drone operator arrives at the forced landing location , and obtain the time collection of all patrol personnel arriving at the forced landing location , and get the time collection The minimum value in ,when < When, the scheduling time collection The minimum value in The corresponding patrol personnel will recover the drone, otherwise it will be recovered by the current drone operator.

10. A UAV forced landing control method, based on the UAV forced landing control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Analyze the current forced landing status of the drone and construct the actual flight distance of the drone , and build the actual flight distance of the drone based on To build a safe flight area; Step 2: Construct several drone landing points based on the safe flight area where the drone is currently located, and construct the first Landing risk factor of each drone landing point , and select the landing point with the lowest landing risk coefficient as the reserve landing point; Step 3: Determine the landing point. If the landing risk coefficient of the planned landing point is >Preset risk warning value, then execute step 4. If the landing risk coefficient of the planned landing point is ≤ the preset risk warning value, proceed to step 5; Step 4: Issue the first risk warning signal and execute the first warning strategy. After the first warning strategy is executed, Landing risk factor of each drone landing point Perform a secondary evaluation on several drone landing points to obtain the forced landing risk coefficient of the i-th drone landing point , and select the smallest one as the landing point for forced landing, after the forced landing is completed, issue a forced landing completion command and execute step 6; Step 5: Make an emergency landing at the planned landing point. After the emergency landing is completed, issue an emergency landing completion command and proceed to step 6. Step 6: After the forced landing completion command is issued, the current drone status is self-checked and personnel are dispatched for recovery.

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