Sports event track design method and system

Abstract

The invention provides a design method and system of a sports event track. The method comprises the following steps: determining that any target bicycle runs in a racing track field, and performing video monitoring on the target bicycle to obtain a real-time running state; the real-time driving state of the target bicycle is converted from a normal state to a collision state, and the initial state position of the target bicycle at the corresponding state conversion moment is determined; controlling the unmanned aerial vehicle to fly to the initial state position to perform data acquisition on the collision acquisition area to obtain collision form data; calling reference site data, and performing data comparison on reference form data obtained based on the reference site data and the collision form data to obtain a form comparison value; calling a historical collision record to determine the collision frequency of the corresponding collision acquisition area, and determining a collision high-frequency value based on the collision frequency; and performing data fusion on the form contrast value and the collision high-frequency value to obtain a region correction value, and determining the region attribute of the collision acquisition region. According to the invention, the monitoring efficiency of the racing track field is at least improved.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a design method and system for a sports event track. Background Art

[0002] BMX is an extreme sport, named for its thick tires and the fact that the race track is very similar to the track used by off-road motorcycles. When a BMX vehicle collides with a track, it may cause some damage to the track. In order to improve safety, it is necessary to promptly correct the track area with greater damage.

[0003] The existing technology is unable to accurately determine the collision area between the BMX vehicle and the track, resulting in low efficiency in monitoring the track, and is unable to inform the relevant managers of the extent of damage caused by the collision and whether it needs to be corrected. Summary of the invention

[0004] Based on the above problems, the present invention is proposed to provide a method and system for designing a sports event track that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for designing a sports event track is provided, comprising the following steps: In response to determining that any target bicycle is traveling on the track, performing video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling state corresponding to the target bicycle; In response to the real-time driving state of the target bicycle changing from a normal state to a collision state at any time, determining the initial state position of the target bicycle at the corresponding state change time; Controlling the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtaining collision shape data corresponding to the collision collection area; Retrieving reference site data corresponding to the track site, and performing a data comparison between reference shape data corresponding to the collision collection area obtained based on the reference site data and the collision shape data to obtain a shape comparison value; Retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining a collision high frequency value corresponding to the collision collection area based on the number of collisions; The morphology contrast value and the collision high frequency value are data-fused, and the regional attributes corresponding to the collision collection area are determined based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

[0006] Optionally, in the method according to the present invention, in response to determining that any target bicycle is traveling on a track, performing video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling state corresponding to the target bicycle includes: In response to the presence of any target bicycle in the track, position detection is performed on the target bicycle based on adjacent detection time points with the same time interval, to obtain target position information corresponding to each detection time point; Determine the driving trend corresponding to the target bicycle based on each target position information, and in response to the driving trend being a dynamic trend, determine the target detection position corresponding to the earliest detection time point as the driving starting point position and determine the target detection position corresponding to the latest detection time point as the driving end point position based on the chronological order; Establishing a track connection line connecting the travel starting point position and the travel end point position to obtain a track connection line corresponding to the target bicycle; Determine the extension direction of the track venue, and determine the line segment angle between the track connection line and the extension direction; Comparing the line segment angle with a preset angle, and in response to the line segment angle being less than or equal to the preset angle, performing image acquisition on the target bicycle to obtain a bicycle image corresponding to the target bicycle; Based on the bicycle image, the usage attribute corresponding to the target bicycle is determined, and in response to the usage attribute being a riding attribute, the target bicycle is subjected to video monitoring based on the driving process to obtain a real-time driving status corresponding to the target bicycle.

[0007] Optionally, in the method according to the present invention, determining the travel trend corresponding to the target bicycle based on each target position information includes: Sort the position information of each target based on the time sequence of each detection time point to obtain a position sequence; When each of the target position information in the position sequence corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend; or, When any two pieces of target position information that are continuously arranged in the position sequence correspond to the same position of the target bicycle, the any two pieces of target position information that are continuously arranged are determined as a trend group to be determined; In response to the trend group to be determined being located at the first part of the position sequence, determining the travel trend corresponding to the target bicycle as a dynamic trend; In response to the trend group to be determined being located at the tail portion of the position sequence, performing position detection on the target bicycle at a preset number of adjacent newly added time points with the same time interval, and obtaining newly added position information corresponding to each newly added time point; When each newly added position information corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend.

[0008] Optionally, in the method according to the present invention, determining the usage attribute corresponding to the target bicycle based on the bicycle image includes: Recognize the bicycle image based on a pre-created image recognition model to determine whether there is a person part corresponding to the rider in the bicycle image; In response to the absence of the human part in the bicycle image, determining the target bicycle as a non-riding attribute; In response to the person portion existing in the bicycle image, determining a foot portion corresponding to the rider based on the person portion, and determining a pedal portion corresponding to the target bicycle based on a bicycle portion corresponding to the target bicycle in the bicycle image; In response to the foot portion and the pedal portion having a close relationship, the target bicycle is determined as having a cycling attribute.

[0009] Optionally, in the method according to the present invention, in response to the real-time driving state of the target bicycle at any time being converted from a normal state to a collision state, determining the initial state position of the target bicycle at the corresponding state conversion moment includes: Determining a bicycle posture corresponding to the target bicycle based on the bicycle image, and determining the bicycle posture as a normal state corresponding to the target bicycle; In response to determining based on the video monitoring that the bicycle posture corresponding to the target bicycle at any moment is different from the bicycle posture corresponding to the normal state, determining the bicycle posture of the target bicycle at that moment as a collision state corresponding to the target bicycle, and determining that moment as a state transition moment; The initial state position of the target bicycle is determined based on the video monitoring corresponding to the state transition moment.

[0010] Optionally, in the method according to the present invention, controlling the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle to obtain collision shape data corresponding to the collision collection area includes: In response to determining based on the video monitoring that at any time after the state transition time, the bicycle posture corresponding to the target bicycle is the same as the bicycle posture corresponding to the normal state, determining the time as the collision end time; Determine the current state position of the target bicycle based on the video monitoring corresponding to the collision end time, and determine the collision impact area corresponding to the target bicycle based on the initial state position and the current state position; Retrieving a collision influence coefficient, and amplifying the collision influence area based on the collision influence coefficient to obtain a collision collection area; Controlling the drone to fly to the initial state position to collect data from the collision collection area corresponding to the target bicycle, and obtaining a top view of the area corresponding to the collision collection area; Performing grid processing on the upper view of the region to obtain monitoring points with the same point spacing; The drone is controlled to measure the elevation of each monitoring point, and the obtained elevation monitoring values ​​corresponding to each monitoring point are determined as collision shape data corresponding to the collision collection area.

[0011] Optionally, in the method according to the present invention, determining the current state position of the target bicycle based on the video monitoring corresponding to the collision end time, and determining the collision impact area corresponding to the target bicycle based on the initial state position and the current state position, includes: In response to the initial state position being the same as the current state position, generating a first planning area with a preset area radius on the track with the initial state position or the current state position as the center, and determining the first planning area as a collision impact area corresponding to the target bicycle; In response to the initial state position being different from the current state position, generating a second planning area and a third planning area with preset area radiuses on the track field with the initial state position and the current state position as circle centers, respectively, and generating a connection planning area connecting the second planning area and the third planning area and having a tangent relationship; Based on the merging of the second planning area, the third planning area and the connection planning area, a collision impact area corresponding to the target bicycle is obtained.

[0012] Optionally, in the method according to the present invention, retrieving reference site data corresponding to the track site, and performing a data comparison between reference morphological data corresponding to the collision collection area obtained based on the reference site data and the collision morphological data to obtain a morphological comparison value, comprises: Determine a current collision position of the collision collection area in the track venue, and retrieve reference venue data corresponding to the track venue; Extracting the reference site data based on the current collision position to obtain reference shape data corresponding to the collision collection area, and acquiring elevation reference values ​​corresponding to each monitoring point based on the reference shape data; Based on the reference morphological data, the elevation monitoring value corresponding to the same monitoring point and the elevation reference value are respectively calculated, and all monitoring points whose corresponding elevation difference is greater than a preset difference are respectively determined as points to be corrected; Connect the points to be corrected at adjacent positions to obtain the areas to be corrected; Determine the point to be corrected corresponding to the minimum elevation value among all the points to be corrected in the same area to be corrected as the target point, and compare the target point with the retrieved preset morphological division value; Determine the regional morphology of each to-be-corrected region whose elevation minimum value is greater than the preset morphology division value as a wear morphology, and determine the regional state of each to-be-corrected region whose elevation minimum value is less than or equal to the preset morphology division value as a bump morphology; Respectively determine the number of wear regions whose regional states of the regions to be corrected are in the form of wear and the number of bump regions whose regional states of the regions to be corrected are in the form of bump; Retrieving a wear shape weight value and a bump shape weight value, wherein the wear shape weight value is greater than the bump shape weight value; The number of wear areas and the wear shape weight value, the number of bump areas and the bump shape weight value are respectively multiplied to obtain a wear area contrast value and a bump area contrast value; The areas of the regions to be corrected are summed up and a morphological comparison value is obtained based on the obtained total area to be corrected and the comparison value of the worn region and the comparison value of the bump region.

[0013] Optionally, in the method according to the present invention, retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining the collision high frequency value corresponding to the collision collection area based on the number of collisions, includes: Retrieving historical collision records, wherein the historical collision records include historical collision positions and historical collision times corresponding to different historical collision areas; Comparing a current collision position corresponding to the collision collection area with historical collision positions corresponding to different historical collision areas; When the position overlap rate between the collision collection area and any historical collision area reaches a preset overlap value based on the comparison result, the historical collision records are traversed to obtain the number of historical collisions corresponding to the historical collision area, and the number of historical collisions is superimposed based on the collision collection area to obtain the current number of collisions; A collision high frequency value corresponding to the collision collection area is determined based on the current collision number.

[0014] Optionally, in the method according to the present invention, data fusion is performed on the morphological contrast value and the collision high-frequency value, and a region attribute corresponding to the collision collection region is determined based on the obtained region correction value corresponding to the collision collection region, wherein the region attribute includes a correction attribute and a maintenance attribute, including: Normalizing the morphological contrast value based on the retrieved contrast normalization value to obtain a contrast coefficient value; Normalizing the collision high frequency value based on the retrieved high frequency normalization value to obtain a high frequency coefficient value; Performing data fusion on the contrast coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision collection region; Retrieving a regional preset value, and comparing the regional correction value with the regional preset value; When the comparison result is that the area correction value is less than the area preset value, the area attribute corresponding to the collision collection area is determined as a maintenance attribute; When the area correction value is greater than or equal to the area preset value, the area attribute corresponding to the collision collection area is determined as the correction attribute.

[0015] Optionally, in the method according to the present invention, the method further comprises: The area correction value is sent to the management end for display processing. If it is determined that the management end performs adjustment processing based on the area correction value, the training adjustment parameter To obtain; In response to the management terminal performing an operation judgment based on the adjustment data inputted by the regional correction value, if it is judged that the inputted adjustment data is configured to amplify the regional correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct enlargement training; If it is determined that the input adjustment data is configured to reduce the area correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct reduction training; Among them, the training adjustment parameters The training can be performed using the following formula: in, Tune parameters for training after training, The adjusted regional correction value corresponding to the adjusted data. is the positive training coefficient, is the negative training coefficient.

[0016] According to another aspect of the present invention, a system for designing a sports event track is provided, comprising: A video monitoring module is configured to, in response to determining that any target bicycle is traveling on the track, perform video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling status corresponding to the target bicycle; a position determination module, configured to determine an initial state position of the target bicycle at a state transition moment in response to the target bicycle's real-time driving state at any moment changing from a normal state to a collision state; A data collection module is configured to control the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtain collision shape data with the collision collection area; a data comparison module configured to retrieve reference site data corresponding to the track site, and compare reference shape data corresponding to the collision collection area obtained based on the reference site data with the collision shape data to obtain a shape comparison value; a record retrieval module configured to retrieve historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine a collision high frequency value corresponding to the collision collection area based on the number of collisions; The data fusion module is configured to perform data fusion on the morphology contrast value and the collision high-frequency value, and determine the regional attributes corresponding to the collision collection area based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

[0017] According to the solution of the present invention, the server will conduct video monitoring of the driving process of the target bicycle traveling on the track field, so as to obtain the real-time driving state corresponding to the target bicycle. When the real-time driving state of the target bicycle at any time is converted from a normal state to a collision state, the server will determine the initial state position of the target bicycle at the state conversion moment. Then, the server will further determine the collision collection area corresponding to the target bicycle, and control the drone to fly to the initial state position to collect data from the collision collection area, so as to obtain the corresponding collision morphological data. Then, the server will retrieve the benchmark site data of the track field, and determine the benchmark morphological data of the collision collection area in the benchmark site data, and then compare the benchmark morphological data with the collision morphological data, so as to obtain the morphological comparison value, so as to determine the specific degree of damage to the track field caused by this collision. Then, the server will retrieve the historical collision records, and determine the number of collisions that occurred in the collision collection area based on the historical collision records, and then determine the collision high-frequency value corresponding to the collision collection area based on the number of collisions. The server will fuse the morphological comparison value with the collision high-frequency value to obtain the regional correction value corresponding to the collision collection area, and then determine the regional correction value as the regional attribute corresponding to the collision collection area, and the regional attribute includes a correction attribute and a maintenance attribute. The present invention can improve the monitoring efficiency of the track venue, and can also more accurately determine the regional attributes corresponding to the collision collection area, so that subsequent management personnel can determine whether the collision collection area needs to be corrected based on the regional attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart showing a method for designing a sports event track according to an embodiment of the present invention is shown; Figure 2 A schematic diagram showing a first planning area according to an embodiment of the present invention is shown; Figure 3 A schematic diagram showing a connection planning area according to an embodiment of the present invention is shown; Figure 4 A structural block diagram of a sports event track design system according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0020] BMX is an extreme sport, named for its thick tires and the fact that the race track is very similar to the track used by off-road motorcycles. When a BMX vehicle collides with a track, it may cause some damage to the track. In order to improve safety, it is necessary to promptly correct the track area with greater damage.

[0021] The existing technology is unable to accurately determine the collision area between the BMX vehicle and the track, resulting in low efficiency in monitoring the track, and is unable to inform the relevant managers of the extent of damage caused by the collision and whether it needs to be corrected.

[0022] In order to solve the above problems in the prior art, the inventor proposes the solution of the present invention. An embodiment of the present invention provides a method for designing a sports event track, which can be executed in a computing device.

[0023] Figure 1 A flow chart of a method for designing a sports event track according to an embodiment of the present invention is shown, and the method is suitable for being executed in a computing device.

[0024] like Figure 1 As shown, the method for designing a sports event track proposed in this embodiment begins with step S102. In step S102, the following contents are included: In response to determining that any target bicycle is traveling on a track, video monitoring of the target bicycle is performed based on the traveling process to obtain a real-time traveling status corresponding to the target bicycle.

[0025] For example, in this embodiment, the target bicycles can be understood as the various bicycles appearing on the track. Since the target bicycles will not cause certain damage to the track when they are parked and stationary on the track, the server will perform video monitoring of the driving process of the target bicycle when it determines that any target bicycle is driving on the track, thereby obtaining the real-time driving status corresponding to the target bicycle.

[0026] Furthermore, the above-mentioned “in response to determining that any target bicycle is traveling on the track, performing video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling state corresponding to the target bicycle” further includes the following steps: In response to the presence of any target bicycle in the track, position detection is performed on the target bicycle based on adjacent detection time points with the same time interval, to obtain target position information corresponding to each detection time point; Determine the driving trend corresponding to the target bicycle based on each target position information, and in response to the driving trend being a dynamic trend, determine the target detection position corresponding to the earliest detection time point as the driving starting point position and determine the target detection position corresponding to the latest detection time point as the driving end point position based on the chronological order; Establishing a track connection line connecting the travel starting point position and the travel end point position to obtain a track connection line corresponding to the target bicycle; Determine the extension direction of the track venue, and determine the line segment angle between the track connection line and the extension direction; Comparing the line segment angle with a preset angle, and in response to the line segment angle being less than or equal to the preset angle, performing image acquisition on the target bicycle to obtain a bicycle image corresponding to the target bicycle; Based on the bicycle image, the usage attribute corresponding to the target bicycle is determined, and in response to the usage attribute being a riding attribute, the target bicycle is subjected to video monitoring based on the driving process to obtain a real-time driving status corresponding to the target bicycle.

[0027] For example, in this embodiment, when there is any target bicycle in the track, the server will perform position detection on the target bicycle at each detection time point with the same interval between adjacent detection time points. For example, if the interval between adjacent detection time points is 3 seconds, the server will perform position detection on the target bicycle every 3 seconds, thereby obtaining each target position information corresponding to each detection time point.

[0028] At this time, the server can determine the driving trend of the target bicycle based on the target position information. When the driving trend of the target bicycle is a dynamic trend, it means that the target bicycle is moving on the track, and the server will further determine the moving direction of the target bicycle.

[0029] First, the server will determine the target detection position corresponding to the earliest detection time point as the starting point of travel, and the target detection position corresponding to the latest detection time point as the end point of travel, according to the chronological order of the detection time points, and then establish a line segment connecting the starting point of travel and the end point of travel, so as to obtain the trajectory connection line corresponding to the target bicycle.

[0030] Next, the server will determine the extension direction of the track, and further determine the line segment angle between the track connection line and the extension direction, and compare the line segment angle with the preset angle. When the line segment angle is greater than the preset angle, it means that the current target bicycle's moving trajectory is inconsistent with the extension direction of the track, and the target bicycle may be pushed across the track instead of traveling along the extension direction of the track. Since the target bicycle is less likely to collide in this moving situation and the degree of damage to the track in the event of a collision may also be low, the server will not further monitor the target bicycle whose corresponding line segment angle is greater than the preset angle.

[0031] When the line segment angle is less than or equal to the preset angle, it means that the current target bicycle's moving trajectory is roughly consistent with the extension direction of the track. The target bicycle may be traveling along the extension direction of the track. Since the target bicycle is more likely to collide in this moving situation and the degree of damage to the track may also be higher when a collision occurs, the server will further collect images of the target bicycle to obtain a bicycle image corresponding to the target bicycle, and then determine the usage attributes of the target bicycle based on the bicycle image.

[0032] When the usage attribute of the target bicycle is a riding attribute, the server will perform video monitoring on the driving process of the target bicycle to obtain a real-time driving status corresponding to the target bicycle.

[0033] Furthermore, the above-mentioned “determining the driving trend corresponding to the target bicycle based on each target position information” further includes the following steps: Sort the position information of each target based on the time sequence of each detection time point to obtain a position sequence; When each of the target position information in the position sequence corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend; or, When any two pieces of target position information that are continuously arranged in the position sequence correspond to the same position of the target bicycle, the any two pieces of target position information that are continuously arranged are determined as a trend group to be determined; In response to the trend group to be determined being located at the first part of the position sequence, determining the travel trend corresponding to the target bicycle as a dynamic trend; In response to the trend group to be determined being located at the tail portion of the position sequence, performing position detection on the target bicycle at a preset number of adjacent newly added time points with the same time interval, and obtaining newly added position information corresponding to each newly added time point; When each newly added position information corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend.

[0034] For example, in this embodiment, the server will sort the position information of each target according to the time sequence of each detection time point, so as to obtain a position sequence.

[0035] When each target position information in the position sequence corresponds to a different position of the target bicycle, it means that the target bicycle is moving in the track, so the server determines the driving trend of the target bicycle as a dynamic trend.

[0036] When any two target position information that are arranged continuously in the position sequence correspond to the same position of the target bicycle, the server cannot immediately determine whether the target bicycle is in a short-term or long-term stationary state. Therefore, the server will first determine these two target position information that are arranged continuously as the trend group to be determined, and further determine the driving trend of the target bicycle.

[0037] When the trend group to be determined is located at the first part of the position sequence, it means that the target bicycle is only stationary for a small part of the time at the beginning, and then starts to move continuously. Therefore, the server will determine the driving trend corresponding to the target bicycle as a dynamic trend.

[0038] When the trend group to be determined is at the tail part of the position sequence, the server will again perform position detection on the target bicycle at a preset number of newly added time points with the same time interval between adjacent ones. For example, the preset number can be 5, then the server will add 5 newly added time points with the same time interval between adjacent ones and perform corresponding position detection, thereby obtaining each newly added position information corresponding to each newly added time point. At this time, when each newly added position information corresponds to a different position of the target bicycle, it means that the target bicycle is only stationary for a short period of time and quickly resumes moving. Therefore, the server will determine the driving trend corresponding to the target bicycle as a dynamic trend.

[0039] This embodiment can determine the driving trend of the target bicycle according to different situations, with a certain degree of accuracy.

[0040] Furthermore, the above-mentioned “determining the usage attribute corresponding to the target bicycle based on the bicycle image” further includes the following steps: Recognize the bicycle image based on a pre-created image recognition model to determine whether there is a person part corresponding to the rider in the bicycle image; In response to the absence of the human part in the bicycle image, determining the target bicycle as a non-riding attribute; In response to the person portion existing in the bicycle image, determining a foot portion corresponding to the rider based on the person portion, and determining a pedal portion corresponding to the target bicycle based on a bicycle portion corresponding to the target bicycle in the bicycle image; In response to the foot portion and the pedal portion having a close relationship, the target bicycle is determined as having a cycling attribute.

[0041] For example, in this embodiment, the server will recognize the bicycle image based on a pre-created image recognition model, so as to determine whether there is a person part corresponding to the rider in the bicycle image.

[0042] When there is no human figure in the bicycle image, it means that the target bicycle is not accompanied by a rider, so the server determines the usage attribute of the target bicycle as a non-riding attribute.

[0043] When there is a person in the bicycle image, the server will first determine the rider's feet based on the person's parts through the image recognition model, and then determine the pedals of the target bicycle based on the bicycle part of the target bicycle in the bicycle image. When the foot part and the pedal part are close to each other, it means that there is a rider riding on the target bicycle, so the server will determine the use attribute of the target bicycle as the riding attribute.

[0044] In step S104, the following contents are included: In response to the real-time driving state of the target bicycle being converted from a normal state to a collision state at any time, an initial state position of the target bicycle corresponding to the state conversion moment is determined.

[0045] For example, in this embodiment, when the real-time driving state of the target bicycle at any time changes from a normal state to a collision state, it means that the rider of the target bicycle may have fallen at the current moment, that is, the target bicycle has collided with the track at the current moment. At this time, the server will determine the position of the target bicycle at the state transition moment, that is, the initial state position, so that the subsequent server can control the drone to collect corresponding data according to the initial state position.

[0046] Furthermore, the above-mentioned “in response to the real-time driving state of the target bicycle at any time being converted from a normal state to a collision state, determining the initial state position of the target bicycle at the corresponding state conversion moment” further includes the following steps: Determining a bicycle posture corresponding to the target bicycle based on the bicycle image, and determining the bicycle posture as a normal state corresponding to the target bicycle; In response to determining based on the video monitoring that the bicycle posture corresponding to the target bicycle at any moment is different from the bicycle posture corresponding to the normal state, determining the bicycle posture of the target bicycle at that moment as a collision state corresponding to the target bicycle, and determining that moment as a state transition moment; The initial state position of the target bicycle is determined based on the video monitoring corresponding to the state transition moment.

[0047] For example, in this embodiment, the server will first determine the bicycle posture of the target bicycle based on the bicycle image. Since the target bicycle in the bicycle image is in the normal moving process on the track, the server will determine the bicycle posture as the normal state of the corresponding target bicycle.

[0048] Then, when the server determines that the bicycle posture of the target bicycle is different from the bicycle posture of the corresponding normal state at any time based on the video monitoring, it means that the target bicycle may have collided with the track at that time. At this time, the server will determine the bicycle posture of the target bicycle at that time as the collision state of the corresponding target bicycle, and determine that time as the state transition time. Then, the initial state position of the target bicycle is determined based on the video monitoring of the corresponding state transition time.

[0049] In step S106, the following contents are included: The unmanned aerial vehicle is controlled to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, so as to obtain collision shape data corresponding to the collision collection area.

[0050] For example, in this embodiment, since the target bicycle may be in the initial state position after the rider falls, or it may slide to a position a certain distance away from the initial state position due to inertia and other factors, the collision ranges brought about by these two situations are different, and either situation may cause corresponding damage to the track within the collision range. Therefore, the server will first determine the collision range corresponding to the target bicycle according to the above different situations, that is, the collision collection area.

[0051] When the server controls the drone to fly to the initial state position and collects data for the collision collection area corresponding to the target bicycle, it can collect the complete collision situation, thereby obtaining the collision shape data corresponding to the collision collection area.

[0052] Furthermore, the above-mentioned “controlling the drone to fly to the initial state position to collect data from the collision collection area corresponding to the target bicycle, and obtaining collision shape data corresponding to the collision collection area” also includes the following steps: In response to determining based on the video monitoring that at any time after the state transition time, the bicycle posture corresponding to the target bicycle is the same as the bicycle posture corresponding to the normal state, determining the time as the collision end time; Determine the current state position of the target bicycle based on the video monitoring corresponding to the collision end time, and determine the collision impact area corresponding to the target bicycle based on the initial state position and the current state position; Retrieving a collision influence coefficient, and amplifying the collision influence area based on the collision influence coefficient to obtain a collision collection area; Controlling the drone to fly to the initial state position to collect data from the collision collection area corresponding to the target bicycle, and obtaining a top view of the area corresponding to the collision collection area; Performing grid processing on the upper view of the region to obtain monitoring points with the same point spacing; The drone is controlled to measure the elevation of each monitoring point, and the obtained elevation monitoring values ​​corresponding to each monitoring point are determined as collision shape data corresponding to the collision collection area.

[0053] For example, in this embodiment, since corresponding personnel will arrive at the position of the target bicycle and lift up the target bicycle after the target bicycle collides with the track, the server can determine based on video monitoring that at any time after the state transition moment, when the bicycle posture of the target bicycle is the same as the bicycle posture of the corresponding normal state, the moment is determined as the collision end time, and the current state position of the target bicycle is determined based on the video monitoring at the collision end time, thereby determining the collision impact area of ​​the target bicycle based on the initial state position and the current state position of the target bicycle.

[0054] In order to be able to more completely collect the collision situation of the target bicycle on the track in the future, the server will retrieve the collision impact coefficient and amplify the collision impact area based on the collision impact coefficient to obtain the collision collection area.

[0055] Next, the server will control the drone to fly to the initial state position and collect data for the collision collection area corresponding to the target bicycle, thereby obtaining a regional top view of the collision collection area. The server will then grid the regional top view to obtain various monitoring points with the same point spacing.

[0056] At this time, the server will control the drone to measure the elevation of each monitoring point, so as to obtain the elevation monitoring values ​​corresponding to each monitoring point, and determine each elevation monitoring value as the collision shape data corresponding to the collision collection area.

[0057] Furthermore, the above-mentioned “determining the current state position of the target bicycle based on the video monitoring corresponding to the end time of the collision, and determining the collision impact area corresponding to the target bicycle based on the initial state position and the current state position” also includes the following steps: In response to the initial state position being the same as the current state position, generating a first planning area with a preset area radius on the track with the initial state position or the current state position as the center, and determining the first planning area as a collision impact area corresponding to the target bicycle; In response to the initial state position being different from the current state position, generating a second planning area and a third planning area with preset area radiuses on the track field with the initial state position and the current state position as circle centers, respectively, and generating a connection planning area connecting the second planning area and the third planning area and having a tangent relationship; Based on the merging of the second planning area, the third planning area and the connection planning area, a collision impact area corresponding to the target bicycle is obtained.

[0058] For example, in this embodiment, the server will first compare the initial state position with the current state position. When the initial state position is the same as the current state position, it means that the target bicycle did not slide after the collision, but stayed at the initial state position. Since the target bicycle will be placed flat on the track after the collision, a certain placement area will be generated. Therefore, the server will generate a circle with a preset area radius on the track with the initial state position or the current state position as the center, that is, the first planning area, such as Figure 2 The preset area radius can be set to be close to the length of the target bicycle, so that the generated first planning area can surround the placement area of ​​the target bicycle, so that the subsequent server can more completely determine the extent of damage caused by the target bicycle to the track due to the collision. Therefore, the server will determine the first planning area as the collision impact area corresponding to the target bicycle.

[0059] When the initial state position is different from the current state position, it means that the target bicycle may have slid after the collision and slid to a current state position that is a certain distance away from the initial state position. Since the target bicycle may also cause damage to the track during the sliding process, the server will generate a second planning area and a third planning area with a preset area radius on the track with the initial state position and the current state position as the center of the circle, thereby generating a connecting planning area that connects the second planning area and the third planning area and has a tangent relationship, such as Figure 3As shown, the generated connection planning area can cover the sliding area of ​​the target bicycle, so that the subsequent server can more completely determine the degree of damage caused by the target bicycle to the track due to the collision. Then, the server will merge the second planning area, the third planning area and the connection planning area to obtain the collision impact area corresponding to the target bicycle.

[0060] In step S108, the following contents are included: The reference site data corresponding to the track site is retrieved, and the reference shape data corresponding to the collision collection area obtained based on the reference site data is compared with the collision shape data to obtain a shape comparison value.

[0061] For example, in this embodiment, the server will first retrieve the site data of the track venue, that is, the benchmark site data, and determine the site data of the collision collection area in the benchmark site data, that is, the benchmark shape data, and then compare the benchmark shape data with the collision shape data to obtain a shape comparison value, so as to determine the specific degree of damage to the track venue caused by this collision.

[0062] Furthermore, the above-mentioned “retrieving the reference site data corresponding to the track site, and comparing the reference shape data corresponding to the collision collection area obtained based on the reference site data with the collision shape data to obtain a shape comparison value” also includes the following steps: Determine a current collision position of the collision collection area in the track venue, and retrieve reference venue data corresponding to the track venue; Extracting the reference site data based on the current collision position to obtain reference shape data corresponding to the collision collection area, and acquiring elevation reference values ​​corresponding to each monitoring point based on the reference shape data; Based on the reference morphological data, the elevation monitoring value corresponding to the same monitoring point and the elevation reference value are respectively calculated, and all monitoring points whose corresponding elevation difference is greater than a preset difference are respectively determined as points to be corrected; Connect the points to be corrected at adjacent positions to obtain the areas to be corrected; Determine the point to be corrected corresponding to the minimum elevation value among all the points to be corrected in the same area to be corrected as the target point, and compare the target point with the retrieved preset morphological division value; Determine the regional morphology of each to-be-corrected region whose elevation minimum value is greater than the preset morphology division value as a wear morphology, and determine the regional state of each to-be-corrected region whose elevation minimum value is less than or equal to the preset morphology division value as a bump morphology; Respectively determine the number of wear regions whose regional states of the regions to be corrected are in the form of wear and the number of bump regions whose regional states of the regions to be corrected are in the form of bump; Retrieving a wear shape weight value and a bump shape weight value, wherein the wear shape weight value is greater than the bump shape weight value; The number of wear areas and the wear shape weight value, the number of bump areas and the bump shape weight value are respectively multiplied to obtain a wear area contrast value and a bump area contrast value; The areas of the regions to be corrected are summed up and a morphological comparison value is obtained based on the obtained total area to be corrected and the comparison value of the worn region and the comparison value of the bump region.

[0063] For example, in this embodiment, the server will first determine the current collision position of the collision collection area in the track venue based on the GPS positioning information, and retrieve the benchmark venue data corresponding to the track venue, and then extract the data corresponding to the current collision position in the benchmark venue data, thereby obtaining the benchmark morphological data corresponding to the collision collection area.

[0064] Next, the server will obtain the elevation value of each monitoring point under normal conditions based on the benchmark shape data, that is, each elevation benchmark value. Then, the elevation monitoring value corresponding to the same monitoring point and the elevation benchmark value are calculated to obtain each elevation difference value.

[0065] When the elevation difference is greater than the preset difference, it means that the difference between the elevation monitoring value of the monitoring point corresponding to the elevation difference and the elevation reference value is too large and needs to be corrected. Therefore, the server will determine all monitoring points whose corresponding elevation differences are greater than the preset difference as points to be corrected, and then connect the points to be corrected in adjacent positions to obtain the areas to be corrected.

[0066] Then, the server will determine the point to be corrected corresponding to the minimum elevation value among all the points to be corrected in the same area to be corrected as the target point, and compare the target point with the retrieved preset morphological division value.

[0067] When the elevation minimum value is greater than the preset morphology division value, it means that the damage degree of the area to be corrected corresponding to the elevation minimum value is relatively large. Therefore, the server will determine the regional morphology of each area to be corrected whose corresponding elevation minimum value is greater than the preset morphology division value as a wear morphology.

[0068] When the elevation minimum value is less than or equal to the preset morphological division value, it indicates that the degree of damage to the area to be corrected corresponding to the elevation minimum value is relatively small. Therefore, the server will determine the area status of each area to be corrected whose corresponding elevation minimum value is less than or equal to the preset morphological division value as a collision morphology.

[0069] Then, the server will respectively determine the number of wear areas whose regional status is wear form and the number of collision areas whose regional status is collision form for each area to be corrected, and then retrieve the wear form weight value and the collision form weight value. Since the degree of damage of the wear form is greater than that of the collision form, the wear form weight value is greater than the collision form weight value.

[0070] Next, the server will multiply the number of wear areas and the wear shape weight value, the number of bump areas and the bump shape weight value, respectively, to obtain the wear area comparison value and the bump area comparison value. Then, the area of ​​each area to be corrected is summed up, and the shape comparison value is obtained based on the total area to be corrected and the wear area comparison value and the bump area comparison value. Furthermore, in this embodiment, in addition to the quantity dimension, the corresponding size dimension can be added to the calculation of the wear area contrast value and the bump area contrast value, that is, the wear area size corresponding to the wear form and the bump area size corresponding to the bump form are obtained, and the number of bump areas and the size of bump areas as well as the number of wear areas and the size of wear areas are normalized respectively, and further multiplied with the retrieved bump form weight value and the wear form weight value, so as to obtain the corresponding wear area contrast value and the bump area contrast value; That is, based on the above content, the accuracy of the obtained comparison value can be further improved through the fusion of multi-dimensional data (including quantity dimension and size dimension).

[0071] This embodiment can determine the regional status of each area to be corrected by comparing the points to be corrected corresponding to the minimum elevation value among all the points to be corrected in the same area to be corrected with the preset morphological division value, with a certain degree of accuracy.

[0072] In step S110, the following contents are included: Retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining a collision high frequency value corresponding to the collision collection area based on the number of collisions.

[0073] For example, in this embodiment, the server retrieves historical collision records, thereby determining the number of collisions occurring in the collision collection area based on the historical collision records, and then determining the collision high frequency value corresponding to the collision collection area based on the number of collisions.

[0074] Furthermore, the above-mentioned “retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining the collision high frequency value corresponding to the collision collection area based on the number of collisions” also includes the following steps: Retrieving historical collision records, wherein the historical collision records include historical collision positions and historical collision times corresponding to different historical collision areas; Comparing a current collision position corresponding to the collision collection area with historical collision positions corresponding to different historical collision areas; When the position overlap rate between the collision collection area and any historical collision area reaches a preset overlap value based on the comparison result, the historical collision records are traversed to obtain the number of historical collisions corresponding to the historical collision area, and the number of historical collisions is superimposed based on the collision collection area to obtain the current number of collisions; A collision high frequency value corresponding to the collision collection area is determined based on the current collision number.

[0075] For example, in this embodiment, the server retrieves historical collision records, which include historical collision positions and historical collision times corresponding to different historical collision areas.

[0076] Next, the server will compare the current collision position corresponding to the collision collection area with the historical collision positions corresponding to different historical collision areas. When the comparison result is that the position overlap rate between the collision collection area and any historical collision area reaches a preset overlap value, it means that the collision collection area and the historical collision area roughly correspond to the same area.

[0077] Then, the server will obtain the historical collision times corresponding to the historical collision area through the historical collision records, and superimpose the historical collision times based on the collision collection area to obtain the current collision times, and then determine the current collision times as the collision high frequency value corresponding to the collision collection area.

[0078] In step S112, the following contents are included: The morphology contrast value and the collision high frequency value are data-fused, and the regional attributes corresponding to the collision collection area are determined based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

[0079] For example, in this embodiment, the server will fuse the morphological contrast value with the collision high-frequency value to obtain a regional correction value corresponding to the collision collection area, and then determine the regional correction value as the regional attribute corresponding to the collision collection area, and the regional attribute includes a correction attribute and a maintenance attribute.

[0080] When the regional attribute corresponding to the collision collection area is a correction attribute, it means that the damage degree of the collision collection area is high and needs to be corrected; when the regional attribute corresponding to the collision collection area is a maintenance attribute, it means that the damage degree of the collision collection area is low and the status quo can be maintained.

[0081] Furthermore, the above-mentioned “performing data fusion on the morphological contrast value and the collision high-frequency value, and determining the regional attribute corresponding to the collision collection area based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attribute includes a correction attribute and a maintenance attribute” also includes the following steps: Normalizing the morphological contrast value based on the retrieved contrast normalization value to obtain a contrast coefficient value; Normalizing the collision high frequency value based on the retrieved high frequency normalization value to obtain a high frequency coefficient value; Performing data fusion on the contrast coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision collection region; Retrieving a regional preset value, and comparing the regional correction value with the regional preset value; When the comparison result is that the area correction value is less than the area preset value, the area attribute corresponding to the collision collection area is determined as a maintenance attribute; When the area correction value is greater than or equal to the area preset value, the area attribute corresponding to the collision collection area is determined as the correction attribute.

[0082] For example, in this embodiment, since the measurement units of the morphological contrast value and the collision high-frequency value are different, the server will respectively call the contrast normalization value and the high-frequency normalization value to normalize the morphological contrast value and the collision high-frequency value to obtain the contrast coefficient value and the high-frequency coefficient value.

[0083] Then, the server will fuse the contrast coefficient value and the high frequency coefficient value to obtain the regional correction value corresponding to the collision collection area, and then retrieve the regional preset value and compare the regional correction value with the regional preset value.

[0084] When the comparison result is that the area correction value is less than the area preset value, it means that the damage degree of the collision collection area corresponding to the area correction value is low and does not need to be corrected. Therefore, the server will determine the area attribute corresponding to the collision collection area as the maintenance attribute.

[0085] When the comparison result is that the area correction value is greater than or equal to the area preset value, it means that the damage degree of the collision collection area corresponding to the area correction value is high and needs to be corrected. Therefore, the server will determine the area attribute corresponding to the collision collection area as the correction attribute.

[0086] Furthermore, the above method further comprises the following steps: The area correction value is sent to the management end for display processing. If it is determined that the management end performs adjustment processing based on the area correction value, the training adjustment parameter To obtain; In response to the management terminal performing an operation judgment based on the adjustment data inputted by the regional correction value, if it is judged that the inputted adjustment data is configured to amplify the regional correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct enlargement training; If it is determined that the input adjustment data is configured to reduce the area correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct reduction training; Among them, the training adjustment parameters The training can be performed using the following formula: in, Tune parameters for training after training, The adjusted regional correction value corresponding to the adjusted data. is the positive training coefficient, is the negative training coefficient.

[0087] For example, in this embodiment, since this method uses a formula to calculate the area correction value corresponding to the collision collection area, it may be different from the actual situation. Therefore, a training adjustment parameter is set in the server, which enables the management end to adjust the area correction value according to the actual correction situation, so as to obtain a more accurate area correction value. Therefore, the server will send the area correction value to the management end for display processing. When it is determined that the management end makes adjustments based on the area correction value, the server will adjust the training adjustment parameter. Obtain and make operational judgments on the adjustment data entered by the management side.

[0088] When the server determines that the adjustment data input by the management end is configured to amplify the regional correction value, it adjusts the training adjustment parameters based on the adjustment data. When the server determines that the adjustment data input by the management end is configured to reduce the area correction value, it will adjust the training parameters based on the adjustment data. Perform reduction training to obtain updated training adjustment parameters.

[0089] According to the solution of the present invention, the server will conduct video monitoring of the driving process of the target bicycle traveling on the track field, so as to obtain the real-time driving state corresponding to the target bicycle. When the real-time driving state of the target bicycle at any time is converted from a normal state to a collision state, the server will determine the initial state position of the target bicycle at the state conversion moment. Then, the server will further determine the collision collection area corresponding to the target bicycle, and control the drone to fly to the initial state position to collect data from the collision collection area, so as to obtain the corresponding collision morphological data. Then, the server will retrieve the benchmark site data of the track field, and determine the benchmark morphological data of the collision collection area in the benchmark site data, and then compare the benchmark morphological data with the collision morphological data, so as to obtain the morphological comparison value, so as to determine the specific degree of damage to the track field caused by this collision. Then, the server will retrieve the historical collision records, and determine the number of collisions that occurred in the collision collection area based on the historical collision records, and then determine the collision high-frequency value corresponding to the collision collection area based on the number of collisions. The server will fuse the morphological comparison value with the collision high-frequency value to obtain the regional correction value corresponding to the collision collection area, and then determine the regional correction value as the regional attribute corresponding to the collision collection area, and the regional attribute includes a correction attribute and a maintenance attribute. The present invention can improve the monitoring efficiency of the track venue, and can also more accurately determine the regional attributes corresponding to the collision collection area, so that subsequent management personnel can determine whether the collision collection area needs to be corrected based on the regional attributes.

[0090] Another embodiment of the present invention provides a system for designing a sports event track. Figure 4 As the corresponding system block diagram, the system includes: A video monitoring module is configured to, in response to determining that any target bicycle is traveling on the track, perform video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling status corresponding to the target bicycle; a position determination module, configured to determine an initial state position of the target bicycle at a state transition moment in response to the target bicycle's real-time driving state at any moment changing from a normal state to a collision state; A data collection module is configured to control the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtain collision shape data with the collision collection area; a data comparison module configured to retrieve reference site data corresponding to the track site, and compare reference shape data corresponding to the collision collection area obtained based on the reference site data with the collision shape data to obtain a shape comparison value; a record retrieval module configured to retrieve historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine a collision high frequency value corresponding to the collision collection area based on the number of collisions; The data fusion module is configured to perform data fusion on the morphology contrast value and the collision high-frequency value, and determine the regional attributes corresponding to the collision collection area based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

[0091] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred embodiment of the present invention.

[0092] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0093] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0094] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0095] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of submodules or subunits or subcomponents.

[0096] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0097] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0098] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0099] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is primarily selected for readability and instructional purposes, rather than for explaining or limiting the subject matter of the present invention.

Claims

1. A method for designing a sports event track, characterized in that: The following steps are involved: In response to determining that any target bicycle is traveling on the track, performing video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling state corresponding to the target bicycle; In response to the real-time driving state of the target bicycle changing from a normal state to a collision state at any time, determining the initial state position of the target bicycle at the corresponding state change time; Controlling the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtaining collision shape data corresponding to the collision collection area; Retrieving reference site data corresponding to the track site, and performing a data comparison between reference shape data corresponding to the collision collection area obtained based on the reference site data and the collision shape data to obtain a shape comparison value; Retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining a collision high frequency value corresponding to the collision collection area based on the number of collisions; The morphology contrast value and the collision high frequency value are data-fused, and the regional attributes corresponding to the collision collection area are determined based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

2. The method for designing a sports event track according to claim 1, characterized in that: In response to determining that any target bicycle is traveling on a track, video monitoring of the target bicycle based on a traveling process is performed to obtain a real-time traveling state corresponding to the target bicycle, including: In response to the presence of any target bicycle in the track, position detection is performed on the target bicycle based on adjacent detection time points with the same time interval, to obtain target position information corresponding to each detection time point; Determine the driving trend corresponding to the target bicycle based on each target position information, and in response to the driving trend being a dynamic trend, determine the target detection position corresponding to the earliest detection time point as the driving starting point position and determine the target detection position corresponding to the latest detection time point as the driving end point position based on the chronological order; Establishing a track connection line connecting the travel starting point position and the travel end point position to obtain a track connection line corresponding to the target bicycle; Determine the extension direction of the track venue, and determine the line segment angle between the track connection line and the extension direction; Comparing the line segment angle with a preset angle, and in response to the line segment angle being less than or equal to the preset angle, performing image acquisition on the target bicycle to obtain a bicycle image corresponding to the target bicycle; Based on the bicycle image, the usage attribute corresponding to the target bicycle is determined, and in response to the usage attribute being a riding attribute, the target bicycle is subjected to video monitoring based on the driving process to obtain a real-time driving status corresponding to the target bicycle.

3. The method for designing a sports event track according to claim 2, characterized in that: Determining a driving trend corresponding to the target bicycle based on each target position information includes: Sort the position information of each target based on the time sequence of each detection time point to obtain a position sequence; When each of the target position information in the position sequence corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend; or When any two pieces of target position information that are continuously arranged in the position sequence correspond to the same position of the target bicycle, the any two pieces of target position information that are continuously arranged are determined as a trend group to be determined; In response to the trend group to be determined being located at the first part of the position sequence, determining the travel trend corresponding to the target bicycle as a dynamic trend; In response to the trend group to be determined being located at the tail portion of the position sequence, performing position detection on the target bicycle at a preset number of adjacent newly added time points with the same time interval, and obtaining newly added position information corresponding to each newly added time point; When each newly added position information corresponds to a different position of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend.

4. The method for designing a sports event track according to claim 2, characterized in that: Determining a usage attribute corresponding to the target bicycle based on the bicycle image includes: Recognize the bicycle image based on a pre-created image recognition model to determine whether there is a person part corresponding to the rider in the bicycle image; In response to the absence of the human part in the bicycle image, determining the target bicycle as a non-riding attribute; In response to the person portion existing in the bicycle image, determining a foot portion corresponding to the rider based on the person portion, and determining a pedal portion corresponding to the target bicycle based on a bicycle portion corresponding to the target bicycle in the bicycle image; In response to the foot portion and the pedal portion having a close relationship, the target bicycle is determined as having a cycling attribute.

5. The method for designing a sports event track according to claim 4, characterized in that: In response to the real-time driving state of the target bicycle being converted from a normal state to a collision state at any time, determining the initial state position of the target bicycle at the corresponding state conversion time, including: Determining a bicycle posture corresponding to the target bicycle based on the bicycle image, and determining the bicycle posture as a normal state corresponding to the target bicycle; In response to determining based on the video monitoring that the bicycle posture corresponding to the target bicycle at any moment is different from the bicycle posture corresponding to the normal state, determining the bicycle posture of the target bicycle at that moment as a collision state corresponding to the target bicycle, and determining that moment as a state transition moment; The initial state position of the target bicycle is determined based on the video monitoring corresponding to the state transition moment.

6. The method for designing a sports event track according to claim 5, characterized in that: Controlling the drone to fly to the initial state position to collect data from the collision collection area corresponding to the target bicycle, and obtaining collision shape data corresponding to the collision collection area, including: In response to determining based on the video monitoring that at any time after the state transition time, the bicycle posture corresponding to the target bicycle is the same as the bicycle posture corresponding to the normal state, determining the time as the collision end time; Determine the current state position of the target bicycle based on the video monitoring corresponding to the collision end time, and determine the collision impact area corresponding to the target bicycle based on the initial state position and the current state position; Retrieving a collision influence coefficient, and amplifying the collision influence area based on the collision influence coefficient to obtain a collision collection area; Controlling the drone to fly to the initial state position to collect data from the collision collection area corresponding to the target bicycle, and obtaining a top view of the area corresponding to the collision collection area; Performing grid processing on the upper view of the region to obtain monitoring points with the same point spacing; The drone is controlled to measure the elevation of each monitoring point, and the obtained elevation monitoring values ​​corresponding to each monitoring point are determined as collision shape data corresponding to the collision collection area.

7. The method for designing a sports event track according to claim 6, characterized in that: Determining the current state position of the target bicycle based on the video monitoring corresponding to the collision end time, and determining the collision impact area corresponding to the target bicycle based on the initial state position and the current state position, including: In response to the initial state position being the same as the current state position, generating a first planning area with a preset area radius on the track with the initial state position or the current state position as the center, and determining the first planning area as a collision impact area corresponding to the target bicycle; In response to the initial state position being different from the current state position, generating a second planning area and a third planning area with preset area radiuses on the track field with the initial state position and the current state position as circle centers, respectively, and generating a connection planning area connecting the second planning area and the third planning area and having a tangent relationship; Based on the merging of the second planning area, the third planning area and the connection planning area, a collision impact area corresponding to the target bicycle is obtained.

8. The method for designing a sports event track according to claim 5, characterized in that: Retrieving reference site data corresponding to the track site, and performing a data comparison between reference shape data corresponding to the collision collection area obtained based on the reference site data and the collision shape data to obtain a shape comparison value, including: Determine a current collision position of the collision collection area in the track venue, and retrieve reference venue data corresponding to the track venue; Extracting the reference site data based on the current collision position to obtain reference shape data corresponding to the collision collection area, and acquiring elevation reference values ​​corresponding to each monitoring point based on the reference shape data; Based on the reference morphological data, the elevation monitoring value corresponding to the same monitoring point and the elevation reference value are respectively calculated, and all monitoring points whose corresponding elevation difference is greater than a preset difference are respectively determined as points to be corrected; Connect the points to be corrected at adjacent positions to obtain the areas to be corrected; Determine the point to be corrected corresponding to the minimum elevation value among all the points to be corrected in the same area to be corrected as the target point, and compare the target point with the retrieved preset morphological division value; Determine the regional morphology of each to-be-corrected region whose elevation minimum value is greater than the preset morphology division value as a wear morphology, and determine the regional state of each to-be-corrected region whose elevation minimum value is less than or equal to the preset morphology division value as a bump morphology; Respectively determine the number of wear regions whose regional states of the regions to be corrected are in the form of wear and the number of bump regions whose regional states of the regions to be corrected are in the form of bump; Retrieving a wear shape weight value and a bump shape weight value, wherein the wear shape weight value is greater than the bump shape weight value; The number of wear areas and the wear shape weight value, the number of bump areas and the bump shape weight value are respectively multiplied to obtain a wear area contrast value and a bump area contrast value; The areas of the regions to be corrected are summed up and a morphological comparison value is obtained based on the obtained total area to be corrected and the comparison value of the worn region and the comparison value of the bump region.

9. The method for designing a sports event track according to claim 1, characterized in that: Retrieving historical collision records, determining the number of collisions corresponding to the collision collection area based on the historical collision records, and determining a collision high frequency value corresponding to the collision collection area based on the number of collisions, including: Retrieving historical collision records, wherein the historical collision records include historical collision positions and historical collision times corresponding to different historical collision areas; Comparing a current collision position corresponding to the collision collection area with historical collision positions corresponding to different historical collision areas; When the position overlap rate between the collision collection area and any historical collision area reaches a preset overlap value based on the comparison result, the historical collision records are traversed to obtain the number of historical collisions corresponding to the historical collision area, and the number of historical collisions is superimposed based on the collision collection area to obtain the current number of collisions; A collision high frequency value corresponding to the collision collection area is determined based on the current collision number.

10. The method for designing a sports event track according to claim 1, characterized in that: The morphology contrast value and the collision high frequency value are data-fused, and the regional attribute corresponding to the collision collection area is determined based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attribute includes a correction attribute and a maintenance attribute, including: Normalizing the morphological contrast value based on the retrieved contrast normalization value to obtain a contrast coefficient value; Normalizing the collision high frequency value based on the retrieved high frequency normalization value to obtain a high frequency coefficient value; Performing data fusion on the contrast coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision collection region; Retrieving a regional preset value, and comparing the regional correction value with the regional preset value; When the comparison result is that the area correction value is less than the area preset value, the area attribute corresponding to the collision collection area is determined as a maintenance attribute; When the area correction value is greater than or equal to the area preset value, the area attribute corresponding to the collision collection area is determined as the correction attribute.

11. The method for designing a sports event track according to claim 10, characterized in that: The method further comprises: The area correction value is sent to the management end for display processing. If it is determined that the management end performs adjustment processing based on the area correction value, the training adjustment parameter To obtain; In response to the management terminal making an operation judgment based on the adjustment data inputted by the regional correction value, if it is judged that the inputted adjustment data is configured to amplify the regional correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct enlargement training; If it is determined that the input adjustment data is configured to reduce the area correction value, the training adjustment parameter is adjusted based on the adjustment data. Conduct reduction training; Among them, the training adjustment parameters The training can be performed using the following formula: in, Tune parameters for training after training, The adjusted regional correction value corresponding to the adjusted data. is the positive training coefficient, is the negative training coefficient.

12. A sports event track design system, characterized in that: include: A video monitoring module is configured to, in response to determining that any target bicycle is traveling on the track, perform video monitoring of the target bicycle based on the traveling process to obtain a real-time traveling status corresponding to the target bicycle; a position determination module, configured to determine an initial state position of the target bicycle at a state transition moment in response to the target bicycle's real-time driving state at any moment changing from a normal state to a collision state; A data collection module is configured to control the drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtain collision shape data with the collision collection area; a data comparison module configured to retrieve reference site data corresponding to the track site, and compare reference shape data corresponding to the collision collection area obtained based on the reference site data with the collision shape data to obtain a shape comparison value; a record retrieval module configured to retrieve historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine a collision high frequency value corresponding to the collision collection area based on the number of collisions; The data fusion module is configured to perform data fusion on the morphology contrast value and the collision high-frequency value, and determine the regional attributes corresponding to the collision collection area based on the obtained regional correction value corresponding to the collision collection area, wherein the regional attributes include correction attributes and maintenance attributes.

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