Design Method and System for Sports Event Tracks

Abstract

The present invention provides a design method and system for a sports event track. Among them, the method includes: determining that any target bicycle travels on the track site, performing video monitoring on the target bicycle to obtain the real-time driving state; when the real-time driving state of the target bicycle changes from the normal state to the collision state, determining the initial state position at the moment of the corresponding state conversion of the target bicycle; controlling the drone to fly to the initial state position to collect data on the collision collection area to obtain collision form data; retrieving the reference site data, comparing the reference form data obtained based on the reference site data with the collision form data to obtain a form comparison value; retrieving the historical collision records to determine the number of collisions in the corresponding collision collection area, and determining the collision high-frequency value based on the number of collisions; performing data fusion on the form comparison value and the collision high-frequency value to obtain a regional correction value, and determining the regional attribute of the collision collection area. The present invention at least improves the monitoring efficiency of the track site.

Description

Technical Field

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

[0002] BMX is an extreme sport, so named because its tires are relatively thick and the tracks used in the races are very similar to those used by off-road motorcycles. When a BMX collides with the track site, it may cause certain damage to the track site. To improve safety, it is necessary to promptly correct the track areas with relatively large damage.

[0003] The prior art cannot accurately determine the collision area between a BMX and the track site, resulting in low monitoring efficiency of the track site and being unable to prompt relevant managers of the damage degree 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 track for a sports event that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, there is provided a method for designing a track for a sports event, including the following steps:

[0006] In response to determining that any target bicycle is traveling on a track site, perform video monitoring on the target bicycle based on its traveling process to obtain a real-time traveling state corresponding to the target bicycle;

[0007] In response to the real-time traveling state of the target bicycle changing from a normal state to a collision state at any moment, determine the initial state position where the target bicycle is located at the moment of state conversion;

[0008] Control a drone to fly to the initial state position to collect data from a collision collection area corresponding to the target bicycle, and obtain collision form data corresponding to the collision collection area;

[0009] Retrieve reference site data corresponding to the track site, and perform data comparison between the reference form data corresponding to the collision collection area obtained based on the reference site data and the collision form data to obtain a form comparison value;

[0010] 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;

[0011] Perform data fusion on the morphological comparison value and the collision high-frequency value, and determine the area attribute corresponding to the collision acquisition area based on the obtained area correction value corresponding to the collision acquisition area, where the area attribute includes a correction attribute and a maintenance attribute.

[0012] Optionally, in the method according to the present invention, in response to determining that any target bicycle is traveling on a track site, perform video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle, including:

[0013] In response to the existence of any target bicycle in the track site, perform position detection on the target bicycle respectively at each detection time point with the same time interval between adjacent ones, and obtain each target position information corresponding to each detection time point;

[0014] 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 at the detection time point corresponding to the earliest time as the driving start position and the target detection position at the detection time point corresponding to the latest time as the driving end position based on the chronological order;

[0015] Establish a trajectory connection line connecting the driving start position and the driving end position to obtain the trajectory connection line corresponding to the target bicycle;

[0016] Determine the extension direction of the track site and determine the line segment angle between the trajectory connection line and the extension direction;

[0017] Compare 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, perform image acquisition on the target bicycle to obtain the bicycle image corresponding to the target bicycle;

[0018] Determine the usage attribute corresponding to the target bicycle based on the bicycle image, and in response to the usage attribute being a riding attribute, perform video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle.

[0019] Optionally, in the method according to the present invention, determining the driving trend corresponding to the target bicycle based on each target position information includes:

[0020] Perform position sorting on each target position information according to the chronological order of each detection time point to obtain a position sequence;

[0021] When each of the target position information in the position sequence corresponds to a different position of the target bicycle, determine the driving trend corresponding to the target bicycle as a dynamic trend; or,

[0022] When any two target position information that are continuously arranged in the position sequence correspond to the same position of the target bicycle, determine the any two target position information that are continuously arranged as a to-be-determined trend group;

[0023] In response to the to-be-determined trend group being located in the first part of the position sequence, determine the driving trend corresponding to the target bicycle as a dynamic trend;

[0024] In response to the to-be-determined trend group being located in the tail part of the position sequence, perform position detection on each new time point with the same time interval between adjacent ones for the target bicycle for a preset number of times, and obtain each new position information corresponding to each new time point respectively;

[0025] When each new position information corresponds to a different position of the target bicycle respectively, determine the driving trend corresponding to the target bicycle as a dynamic trend.

[0026] Optionally, in the method according to the present invention, determining the usage attribute corresponding to the target bicycle based on the bicycle image includes:

[0027] Recognize the bicycle image based on a pre-created image recognition model, and determine whether there is a human part corresponding to the rider in the bicycle image;

[0028] In response to the absence of the human part in the bicycle image, determine the target bicycle as a non-riding attribute;

[0029] In response to the presence of the human part in the bicycle image, determine the foot part corresponding to the rider based on the human part, and determine the pedal part corresponding to the target bicycle based on the bicycle part corresponding to the target bicycle in the bicycle image;

[0030] In response to there being a proximity relationship between the foot part and the pedal part, determine the target bicycle as a riding attribute.

[0031] Optionally, in the method according to the present invention, in response to the real-time driving state of the target bicycle being converted from a normal state to a collision state at any moment, determining the initial state position where the state conversion moment of the target bicycle is located includes:

[0032] Determine the bicycle posture corresponding to the target bicycle based on the bicycle image, and determine the bicycle posture as the normal state corresponding to the target bicycle;

[0033] When it is determined, based on the video monitoring, that the bicycle attitude of the target bicycle at any moment is different from the bicycle attitude corresponding to the normal state, the bicycle attitude of the target bicycle at this moment is determined as the collision state corresponding to the target bicycle, and this moment is determined as the state conversion moment;

[0034] Based on the video monitoring corresponding to the state conversion moment, determine the initial state position where the target bicycle is located.

[0035] Optionally, in the method according to the present invention, control the drone to fly to the initial state position to collect data for the collision collection area corresponding to the target bicycle, and obtain the collision form data corresponding to the collision collection area, including:

[0036] When it is determined, based on the video monitoring, that the bicycle attitude of the target bicycle at any moment after the state conversion moment is the same as the bicycle attitude corresponding to the normal state, this moment is determined as the collision end moment;

[0037] Based on the video monitoring corresponding to the collision end moment, determine the current state position where the target bicycle is located, and determine the collision impact area corresponding to the target bicycle based on the initial state position and the current state position;

[0038] Retrieve the collision impact coefficient, and magnify the collision impact area based on the collision impact coefficient to obtain the collision collection area;

[0039] Control the drone to fly to the initial state position to collect data for the collision collection area corresponding to the target bicycle, and obtain the top view of the area corresponding to the collision collection area;

[0040] Perform grid processing on the top view of the area to obtain each monitoring point with the same point spacing;

[0041] Control the drone to measure the point elevation of each monitoring point, and determine each elevation monitoring value corresponding to each monitoring point as the collision form data corresponding to the collision collection area.

[0042] Optionally, in the method according to the present invention, based on the video monitoring corresponding to the collision end moment, determine the current state position where the target bicycle is located, and determine the collision impact area corresponding to the target bicycle based on the initial state position and the current state position, including:

[0043] In response to the initial state position being the same as the current state position, a first planned area with a preset area radius is generated on the track ground with the initial state position or the current state position as the center, and the first planned area is determined as the collision impact area corresponding to the target bicycle;

[0044] In response to the initial state position being different from the current state position, a second planned area and a third planned area with a preset area radius are respectively generated on the track ground with the initial state position and the current state position as the centers, and a connection planned area connecting the second planned area and the third planned area and having a tangent relationship is generated;

[0045] Based on the merger of the second planned area, the third planned area, and the connection planned area, the collision impact area corresponding to the target bicycle is obtained.

[0046] Optionally, in the method according to the present invention, the reference site data corresponding to the track ground is retrieved, and data comparison is performed between the reference form data corresponding to the collision collection area based on the reference site data and the collision form data to obtain a form comparison value, including:

[0047] Determine the current collision position of the collision collection area on the track ground, and retrieve the reference site data corresponding to the track ground;

[0048] Based on the current collision position, data extraction is performed on the reference site data to obtain the reference form data corresponding to the collision collection area, and respective elevation reference values corresponding to each monitoring point are obtained based on the reference form data;

[0049] Based on the reference form data, difference calculations are respectively performed on the elevation monitoring values and the elevation reference values corresponding to the same monitoring point, and all monitoring points with an elevation difference greater than a preset difference are respectively determined as points to be corrected;

[0050] Connect the points to be corrected at adjacent positions to obtain respective areas to be corrected;

[0051] Among all the points to be corrected located in the same area to be corrected, the point to be corrected corresponding to the minimum elevation value is determined as the target point, and the target point is compared with the retrieved preset form division value;

[0052] The area forms of the respective areas to be corrected corresponding to the minimum elevation value greater than the preset form division value are respectively determined as wear forms, and the area states of the respective areas to be corrected corresponding to the minimum elevation value less than or equal to the preset form division value are respectively determined as bump forms;

[0053] Determine the number of wear areas with the area state of the wear form and the number of collision areas with the area state of the collision form for each area to be corrected respectively;

[0054] Retrieve the wear form weight value and the collision form weight value, where the wear form weight value is greater than the collision form weight value;

[0055] Multiply the number of wear areas and the wear form weight value, and the number of collision areas and the collision form weight value respectively to obtain a wear area comparison value and a collision area comparison value;

[0056] Sum up the areas of each area to be corrected, and obtain a form comparison value based on the total area to be corrected obtained and the wear area comparison value and the collision area comparison value.

[0057] Optionally, in the method according to the present invention, retrieve the historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine the collision high-frequency value corresponding to the collision collection area based on the number of collisions, including:

[0058] Retrieve the historical collision records, where the historical collision records include the historical collision positions and the number of historical collisions corresponding to different historical collision areas;

[0059] Compare the current collision position corresponding to the collision collection area with the historical collision positions corresponding to different historical collision areas;

[0060] When the position coincidence rate between the collision collection area and any historical collision area reaches a preset coincidence value based on the comparison result, traverse the historical collision records to obtain the number of historical collisions corresponding to this historical collision area, and add the number of collisions based on the collision collection area to obtain the current number of collisions;

[0061] Determine the collision high-frequency value corresponding to the collision collection area based on the current number of collisions.

[0062] Optionally, in the method according to the present invention, perform data fusion on the form comparison value and the collision high-frequency value, and determine the area attribute corresponding to the collision collection area based on the obtained area correction value corresponding to the collision collection area, where the area attribute includes a correction attribute and a maintenance attribute, including:

[0063] Normalize the form comparison value based on the retrieved comparison normalization value to obtain a comparison coefficient value;

[0064] Normalize the collision high-frequency value based on the retrieved high-frequency normalization value to obtain a high-frequency coefficient value;

[0065] Perform data fusion on the comparison coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision acquisition region;

[0066] Retrieve the region preset value and compare the region correction value with the region preset value;

[0067] When the comparison result is that the region correction value is less than the region preset value, determine the region attribute corresponding to the collision acquisition region as the maintenance attribute;

[0068] When the region correction value is greater than or equal to the region preset value, determine the region attribute corresponding to the collision acquisition region as the correction attribute.

[0069] Optionally, in the method according to the present invention, the method further includes:

[0070] Send the region correction value to the management end for display processing. If it is determined that the management end performs adjustment processing based on the region correction value, then obtain the training adjustment parameter for acquisition;

[0071] Respond to the operation judgment based on the adjustment data input by the management end based on the region correction value. If it is determined that the input adjustment data is configured to amplify the region correction value, then increase the training of the training adjustment parameter based on the adjustment data for increased training;

[0072] If it is determined that the input adjustment data is configured to reduce the region correction value, then reduce the training of the training adjustment parameter based on the adjustment data for reduced training;

[0073] Among them, the training of the training adjustment parameter can be performed through the following formula:

[0074] Among them, is the trained training adjustment parameter, is the adjusted region correction value corresponding to the adjustment data, is the positive training coefficient, is the negative training coefficient.

[0075] According to another aspect of the present invention, there is provided a design system for a sports event track, including:

[0076] A video monitoring module, configured to perform video monitoring based on the driving process on a target bicycle in response to determining that any target bicycle is traveling on a track site, and obtain a real-time driving state corresponding to the target bicycle;

[0077] A position determination module, configured to determine an initial state position where the target bicycle is located at the moment of state conversion in response to the real-time driving state of the target bicycle changing from a normal state to a collision state at any moment;

[0078] A data acquisition module, configured to control a drone to fly to the initial state position to perform data acquisition on a collision acquisition area corresponding to the target bicycle, and obtain collision form data of the collision acquisition area;

[0079] A data comparison module, configured to retrieve reference site data corresponding to the track site, and perform data comparison between reference form data corresponding to the collision acquisition area obtained based on the reference site data and the collision form data to obtain a form comparison value;

[0080] A record retrieval module, configured to retrieve historical collision records, determine the number of collisions corresponding to the collision acquisition area based on the historical collision records, and determine a collision high-frequency value corresponding to the collision acquisition area based on the number of collisions;

[0081] A data fusion module, configured to perform data fusion on the form comparison value and the collision high-frequency value, and determine a regional attribute corresponding to the collision acquisition area based on the obtained regional correction value corresponding to the collision acquisition area, where the regional attribute includes a correction attribute and a maintenance attribute.

[0082] According to the solution of the present invention, the server will conduct video monitoring on the driving process of the target bicycle traveling on the track venue, so as to obtain the real-time driving state corresponding to the target bicycle. When the real-time driving state of the target bicycle changes from the normal state to the collision state at any moment, the server will determine the initial state position of the target bicycle at the moment of state conversion. 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 on the collision collection area, so as to obtain the corresponding collision form data. Then, the server will retrieve the reference venue data of the track venue, determine the reference form data of the collision collection area in the reference venue data, and then compare the reference form data with the collision form data to obtain a form comparison value, so as to determine the specific damage degree of the track venue caused by this collision. Then, the server will retrieve the historical collision records, determine the number of collisions that occurred in the collision collection area according to the historical collision records, and then determine the collision high-frequency value corresponding to the collision collection area according to the number of collisions. The server will fuse the form comparison value and 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. 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 more accurately determine the regional attribute corresponding to the collision collection area, which is convenient for subsequent management personnel to determine whether it is necessary to correct the collision collection area according to the regional attribute. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 FIG. shows a flowchart of a design method for a sports event track according to an embodiment of the present invention;

[0084] Figure 2 FIG. shows a schematic diagram of a first planning area according to an embodiment of the present invention;

[0085] Figure 3 FIG. shows a schematic diagram of a connected planning area according to an embodiment of the present invention;

[0086] Figure 4 FIG. shows a block diagram of a design system for a sports event track according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the 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 so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0088] BMX is an extreme sport, named because its tires are relatively thick and the race tracks are very similar to those used by off-road motorcycles. When a BMX collides with the track venue, it may cause certain damage to the track venue. To improve safety, it is necessary to promptly correct the track areas with relatively large damage.

[0089] The prior art cannot accurately determine the collision area between a BMX and the track venue, resulting in low monitoring efficiency of the track venue, and unable to prompt relevant managers about the damage degree caused by the collision and whether it needs to be corrected.

[0090] To solve the problems existing in the above prior art, the inventor proposed the solution of the present invention. An embodiment of the present invention provides a design method for a sports event track, which can be executed in a computing device.

[0091] Figure 1 The flowchart of the design method for a sports event track according to an embodiment of the present invention is shown, and this method is suitable for execution in a computing device.

[0092] As Figure 1 shown, the design method for a sports event track proposed in this embodiment starts from step S102, and in step S102, it includes the following content:

[0093] In response to determining that any target bicycle is traveling on the track venue, perform video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle.

[0094] For example, in this embodiment, the target bicycle can be understood as each bicycle appearing on the track venue. Since a target bicycle will not cause certain damage to the track venue when it is parked and stationary on the track venue, the server will perform video monitoring on the driving process of the target bicycle when it determines that any target bicycle is traveling on the track venue, so as to obtain the real-time driving state corresponding to the target bicycle.

[0095] Further, the above "In response to determining that any target bicycle is traveling on the track venue, perform video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle" further includes the following steps:

[0096] In response to the existence of any target bicycle in the track venue, perform position detection on the target bicycle respectively based on each detection time point with the same time interval between adjacent ones to obtain each target position information corresponding to each detection time point;

[0097] 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 at the detection time point corresponding to the earliest time as the driving start position and the target detection position at the detection time point corresponding to the latest time as the driving end position based on the chronological order;

[0098] Establish a trajectory connection line connecting the driving start position and the driving end position to obtain a trajectory connection line corresponding to the target bicycle;

[0099] Determine the extension direction of the track venue and determine the line segment angle between the trajectory connection line and the extension direction;

[0100] Compare 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, perform image acquisition on the target bicycle to obtain a bicycle image corresponding to the target bicycle;

[0101] Determine the usage attribute corresponding to the target bicycle based on the bicycle image, and in response to the usage attribute being a riding attribute, perform video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle.

[0102] For example, in this embodiment, when there is any target bicycle in the track venue, the server will perform position detection on the target bicycle at each detection time point with the same time interval between adjacent ones. For example, the time intervals between adjacent detection time points are 3 seconds each, then the server will perform position detection on the target bicycle every 3 seconds, so as to obtain each target position information corresponding to each detection time point.

[0103] At this time, the server can determine the driving trend of the target bicycle according to each 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 venue, and the server will further determine the moving direction of the target bicycle.

[0104] First, the server will determine the target detection position at the detection time point corresponding to the earliest time as the driving start position and the target detection position at the detection time point corresponding to the latest time as the driving end position according to the chronological order of the detection time points, and then establish a line segment connecting the driving start position and the driving end position, so as to obtain the trajectory connection line corresponding to the target bicycle.

[0105] Next, the server will determine the extension direction of the track venue, further determine the included angle between the trajectory connection line and the extension direction, and compare the included angle with a preset included angle. When the included angle is greater than the preset included angle, it indicates that the moving trajectory of the current target bicycle is inconsistent with the extension direction of the track venue. Then, the target bicycle may be in a situation of being pushed across the track venue, rather than moving along the extension direction of the track venue. Since the possibility of collision of the target bicycle in this moving situation is relatively low and the damage degree to the track venue during a collision may also be relatively low, the server will not further monitor the target bicycle with the corresponding included angle greater than the preset included angle.

[0106] When the included angle is less than or equal to the preset included angle, it indicates that the moving trajectory of the current target bicycle is roughly consistent with the extension direction of the track venue. The target bicycle may be moving along the extension direction of the track venue. Since the possibility of collision of the target bicycle in this moving situation is relatively high and the damage degree to the track venue during a collision may also be relatively high, the server will further collect an image of the target bicycle to obtain a bicycle image corresponding to the target bicycle, and then determine the usage attribute of the target bicycle based on the bicycle image.

[0107] When the usage attribute of the target bicycle is the riding attribute, the server will perform video monitoring on the driving process of the target bicycle to obtain the corresponding real-time driving state of the target bicycle.

[0108] Furthermore, the above "determining the driving trend corresponding to the target bicycle based on each target position information" further includes the following steps:

[0109] Sort the target position information based on the time sequence of each detection time point to obtain a position sequence;

[0110] When each of the target position information in the position sequence corresponds to a different position of the target bicycle, determine the driving trend corresponding to the target bicycle as a dynamic trend; or,

[0111] When any two consecutively arranged target position information in the position sequence correspond to the same position of the target bicycle, determine the any two consecutively arranged target position information as a to-be-determined trend group;

[0112] In response to the to-be-determined trend group being located in the first part of the position sequence, determine the driving trend corresponding to the target bicycle as a dynamic trend;

[0113] When it is determined that the to-be-determined trend group is located in the tail part of the position sequence, position detections are performed on the target bicycle at each new time point with a preset number of adjacent time intervals being the same, so as to obtain respective new position information corresponding to each new time point;

[0114] When the respective new position information corresponds to different positions of the target bicycle, the driving trend corresponding to the target bicycle is determined as a dynamic trend.

[0115] For example, in this embodiment, the server sorts the respective target position information in the order of time of each detection time point, so as to obtain a position sequence.

[0116] When the respective target position information located in the position sequence corresponds to different positions of the target bicycle, it indicates that the target bicycle is moving in the track venue. Therefore, the server determines the driving trend of the target bicycle as a dynamic trend.

[0117] When any two consecutively arranged target position information in the position sequence corresponds to the same position of the target bicycle, the server cannot immediately determine whether the target bicycle is in a short-term stationary state or a long-term stationary state. Therefore, the server first determines these two consecutively arranged target position information as a to-be-determined trend group, and further determines the driving trend of the target bicycle.

[0118] When the to-be-determined trend group is located in the head part of the position sequence, it indicates that the target bicycle is only in a stationary state in the first small part of time, and then starts continuous movement. Therefore, the server determines the driving trend corresponding to the target bicycle as a dynamic trend.

[0119] When the to-be-determined trend group is located in the tail part of the position sequence, the server performs position detections on the target bicycle again at each new time point with a preset number of adjacent time intervals being the same. For example, the preset number can be 5. Then the server adds 5 new time points with adjacent time intervals being the same and performs corresponding position detections, so as to obtain respective new position information corresponding to each new time point. At this time, when the respective new position information corresponds to different positions of the target bicycle, it indicates that the target bicycle is only in a stationary state in a small part of time and quickly resumes movement. Therefore, the server determines the driving trend corresponding to the target bicycle as a dynamic trend.

[0120] This embodiment can correspondingly determine the driving trend of the target bicycle for different situations, and has a certain degree of accuracy.

[0121] Furthermore, the above "determining the usage attribute corresponding to the target bicycle based on the bicycle image" further includes the following steps:

[0122] Identify the bicycle image based on a pre-created image recognition model to determine whether there is a human part corresponding to the rider in the bicycle image;

[0123] In response to the absence of the human part in the bicycle image, determine that the target bicycle has a non-riding attribute;

[0124] In response to the presence of the human part in the bicycle image, determine the foot part corresponding to the rider based on the human part, and determine the pedal part corresponding to the target bicycle based on the bicycle part corresponding to the target bicycle in the bicycle image;

[0125] In response to a proximity relationship between the foot part and the pedal part, determine that the target bicycle has a riding attribute.

[0126] For example, in this embodiment, the server will identify the bicycle image based on a pre-created image recognition model to determine whether there is a human part corresponding to the rider in the bicycle image.

[0127] When there is no human part in the bicycle image, it means that the target bicycle does not have a accompanying rider. Therefore, the server will determine the usage attribute of the target bicycle as a non-riding attribute.

[0128] When there is a human part in the bicycle image, the server will first determine the foot part of the rider based on the human part through the image recognition model, and then determine the pedal part of the target bicycle based on the bicycle part of the target bicycle in the bicycle image. When there is a proximity relationship between the foot part and the pedal part, it means that there is a rider riding on the target bicycle. Therefore, the server will determine the usage attribute of the target bicycle as a riding attribute.

[0129] In step S104, the following content is included:

[0130] In response to the real-time driving state of the target bicycle changing from a normal state to a collision state at any moment, determine the initial state position of the target bicycle at the moment of state conversion.

[0131] For example, in this embodiment, when the real-time driving state of the target bicycle changes from a normal state to a collision state at any moment, 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 site at the current moment. At this time, the server will determine the position of the target bicycle at the moment of state conversion, that is, the initial state position, so that the server can control the drone to perform corresponding data collection according to the initial state position later.

[0132] Further, the step of "determining the initial state position of the target bicycle at the moment when the state transition occurs in response to the real-time driving state of the target bicycle changing from the normal state to the collision state at any moment" further includes the following steps:

[0133] Determine the bicycle attitude corresponding to the target bicycle based on the bicycle image, and determine the bicycle attitude as the normal state corresponding to the target bicycle;

[0134] In response to the bicycle attitude corresponding to the target bicycle at any moment being different from the bicycle attitude corresponding to the normal state determined based on the video monitoring, determine the bicycle attitude of the target bicycle at this moment as the collision state corresponding to the target bicycle, and determine this moment as the state transition moment;

[0135] Determine the initial state position where the target bicycle is located based on the video monitoring corresponding to the state transition moment.

[0136] For example, in this embodiment, the server will first determine the bicycle attitude of the target bicycle according to the bicycle image. Since the target bicycle in the bicycle image is in the process of moving normally on the track field, the server will determine this bicycle attitude as the normal state corresponding to the target bicycle.

[0137] Then, when the server determines based on the video monitoring that the bicycle attitude of the target bicycle is different from the bicycle attitude corresponding to the normal state at any moment, it means that the target bicycle may have collided with the track field at this moment. At this time, the server will determine the bicycle attitude of the target bicycle at this moment as the collision state corresponding to the target bicycle, and determine this moment as the state transition moment. Then, determine the initial state position where the target bicycle is located according to the video monitoring corresponding to the state transition moment.

[0138] In step S106, the following content is included:

[0139] Control the drone to fly to the initial state position to collect data on the collision collection area corresponding to the target bicycle, and obtain the collision form data corresponding to the collision collection area.

[0140] For example, in this embodiment, since the target bicycle may be at the initial state position after the rider falls, or may slide to a position at a certain distance from the initial state position due to factors such as inertia, the collision ranges brought about by these two situations are different, and in either case, it is possible to cause corresponding damage to the track field within the collision range. Therefore, the server will first determine the collision range corresponding to the target bicycle, that is, the collision collection area, according to the above different situations.

[0141] When the server controls the drone to fly to the initial state position and collect data on the collision collection area corresponding to the target bicycle, it can collect the complete collision situation, thereby obtaining the collision form data corresponding to the collision collection area.

[0142] Further, the above-mentioned "controlling the drone to fly to the initial state position to collect data on the collision collection area corresponding to the target bicycle, and obtaining the collision form data corresponding to the collision collection area" further includes the following steps:

[0143] In response to determining that the bicycle attitude corresponding to the target bicycle at any moment after the state transition moment is the same as the bicycle attitude corresponding to the normal state based on the video monitoring, determining this moment as the collision end moment;

[0144] Based on the video monitoring corresponding to the collision end moment, determining the current state position of the target bicycle, and based on the initial state position and the current state position, determining the collision impact area corresponding to the target bicycle;

[0145] Retrieving the collision impact coefficient, and based on the collision impact coefficient, magnifying the collision impact area to obtain the collision collection area;

[0146] Controlling the drone to fly to the initial state position to collect data on the collision collection area corresponding to the target bicycle, and obtaining an aerial view of the area corresponding to the collision collection area;

[0147] Performing rasterization processing on the aerial view of the area to obtain each monitoring point with the same point spacing;

[0148] Controlling the drone to measure the elevation of each monitoring point, and determining each elevation monitoring value corresponding to each monitoring point as the collision form data corresponding to the collision collection area.

[0149] For example, in this embodiment, since there will be corresponding personnel arriving at the position of the target bicycle to lift the target bicycle after the target bicycle collides with the track site, the server can determine that when the bicycle attitude of the target bicycle at any moment after the state transition moment is the same as the bicycle attitude corresponding to the normal state based on the video monitoring, determining this moment as the collision end moment, and determining the current state position of the target bicycle based on the video monitoring at the collision end moment, so as to determine the collision impact area of the target bicycle according to the initial state position and the current state position of the target bicycle.

[0150] In order to more completely collect the collision situation of the target bicycle with the track venue subsequently, the server will retrieve the collision impact coefficient and magnify the collision impact area based on the collision impact coefficient to obtain the collision collection area.

[0151] Next, the server will control the drone to fly to the initial state position and collect data on the collision collection area corresponding to the target bicycle, so as to obtain the top view of the area of the collision collection area. Then, the server will perform a grid processing on the top view of the area to obtain each monitoring point with the same point spacing.

[0152] At this time, the server will control the drone to measure the point elevation of each monitoring point, so as to obtain each elevation monitoring value corresponding to each monitoring point, and determine each elevation monitoring value as the collision form data corresponding to the collision collection area.

[0153] Furthermore, the above "determining the current state position of the target bicycle based on the video monitoring corresponding to the collision end moment, and determining the collision impact area corresponding to the target bicycle based on the initial state position and the current state position" further includes the following steps:

[0154] In response to the initial state position being the same as the current state position, generate a first planning area with a preset area radius centered at the initial state position or the current state position on the track venue, and determine the first planning area as the collision impact area corresponding to the target bicycle;

[0155] In response to the initial state position being different from the current state position, generate a second planning area and a third planning area with a preset area radius centered at the initial state position and the current state position respectively on the track venue, and generate a connection planning area connecting the second planning area and the third planning area and having a tangent relationship;

[0156] Based on the merger of the second planning area, the third planning area and the connection planning area, obtain the collision impact area corresponding to the target bicycle.

[0157] 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 in a state of lying flat on the track venue after the collision, a certain placement area will be generated. Therefore, the server will generate a circle with a preset area radius centered at the initial state position or the current state position on the track venue, that is, the first planning area, as Figure 2As shown in the figure. 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, facilitating the subsequent server to more completely determine the damage degree caused by the collision of the target bicycle to the track site. Therefore, the server will determine the first planning area as the collision impact area corresponding to the target bicycle.

[0158] When the initial state position is different from the current state position, it indicates that the target bicycle may have skidded after the collision and skidded to a current state position that is at a certain distance from the initial state position. Since the target bicycle may also cause damage to the track site during the skidding process, the server will generate a second planning area and a third planning area with a preset area radius centered on the initial state position and the current state position respectively on the track site, so as to generate a connection planning area that connects the second planning area and the third planning area and has a tangent relationship, as Figure 3 shown, so that the generated connection planning area can cover the skidding area of the target bicycle, facilitating the subsequent server to more completely determine the damage degree caused by the collision of the target bicycle to the track site. 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.

[0159] In step S108, it includes the following content:

[0160] Retrieve the reference site data corresponding to the track site, and perform a data comparison between the reference form data corresponding to the collision collection area obtained based on the reference site data and the collision form data to obtain a form comparison value.

[0161] For example, in this embodiment, the server will first retrieve the site data of the track site, that is, the reference site data, and determine the site data of the collision collection area in the reference site data, that is, the reference form data, and then perform a data comparison between the reference form data and the collision form data to obtain a form comparison value, so as to determine the specific damage degree of the track site caused by this collision.

[0162] Furthermore, the above-mentioned "retrieve the reference site data corresponding to the track site, and perform a data comparison between the reference form data corresponding to the collision collection area obtained based on the reference site data and the collision form data to obtain a form comparison value" further includes the following steps:

[0163] Determine the current collision position of the collision collection area in the track site, and retrieve the reference site data corresponding to the track site;

[0164] Extract data from the reference site data based on the current collision position to obtain reference shape data corresponding to the collision collection area, and obtain respective elevation reference values corresponding to each monitoring point based on the reference shape data;

[0165] Based on the reference shape data, calculate the difference between the elevation monitoring value and the elevation reference value corresponding to the same monitoring point respectively, and determine all monitoring points with the corresponding elevation difference greater than the preset difference as the points to be corrected;

[0166] Connect the points to be corrected at adjacent positions to obtain respective areas to be corrected;

[0167] Among all the points to be corrected located in the same area to be corrected, determine the point to be corrected corresponding to the minimum elevation value as the target point, and compare the target point with the preset shape division value retrieved;

[0168] Determine the area shape of each area to be corrected corresponding to the minimum elevation value greater than the preset shape division value as the wear shape, and determine the area state of each area to be corrected corresponding to the minimum elevation value less than or equal to the preset shape division value as the knock shape;

[0169] Determine the number of wear areas where the area state of each area to be corrected is the wear shape and the number of knock areas where the area state of each area to be corrected is the knock shape respectively;

[0170] Retrieve the wear shape weight value and the knock shape weight value, wherein the wear shape weight value is greater than the knock shape weight value;

[0171] Multiply the number of wear areas and the wear shape weight value, and the number of knock areas and the knock shape weight value respectively to obtain the wear area comparison value and the knock area comparison value;

[0172] Sum up the areas of each area to be corrected, and obtain the shape comparison value based on the total area to be corrected obtained and the wear area comparison value and the knock area comparison value.

[0173] For example, in this embodiment, the server will first determine the current collision position of the collision collection area in the track site according to the GPS positioning information, retrieve the reference site data corresponding to the track site, and then extract the data corresponding to the current collision position from the reference site data, so as to obtain the reference shape data corresponding to the collision collection area.

[0174] Next, the server will obtain the elevation values of each monitoring point in the normal state based on the reference form data, that is, each elevation reference value. Then, the elevation monitoring value and the elevation reference value corresponding to the same monitoring point are respectively subjected to a difference calculation to obtain each elevation difference.

[0175] When the elevation difference is greater than the preset difference, it indicates that the difference between the elevation monitoring value and the elevation reference value of the monitoring point corresponding to the elevation difference is too large and needs to be corrected. Therefore, the server will respectively determine all the monitoring points with the corresponding elevation difference greater than the preset difference as the points to be corrected, and then connect the points to be corrected at adjacent positions to obtain each area to be corrected.

[0176] 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 preset form division value retrieved.

[0177] When the minimum elevation value is greater than the preset form division value, it indicates that the damage degree of the area to be corrected corresponding to the minimum elevation value is relatively large. Therefore, the server will respectively determine the area forms of each area to be corrected with the corresponding minimum elevation value greater than the preset form division value as the wear form.

[0178] When the minimum elevation value is less than or equal to the preset form division value, it indicates that the damage degree of the area to be corrected corresponding to the minimum elevation value is relatively small. Therefore, the server will respectively determine the area states of each area to be corrected with the corresponding minimum elevation value less than or equal to the preset form division value as the bump form.

[0179] Then, the server will respectively determine the number of wear areas with the area state of the wear form in each area to be corrected and the number of bump areas with the area state of the bump form in each area to be corrected, and then retrieve the wear form weight value and the bump form weight value. Since the damage degree of the wear form is greater than that of the bump form, the wear form weight value is greater than the bump form weight value.

[0180] Next, the server will respectively perform a product calculation on the number of wear areas and the wear form weight value, and the number of bump areas and the bump form weight value to obtain the wear area comparison value and the bump area comparison value. Then, the sum of the areas of each area of each area to be corrected is calculated, and the form comparison value is obtained based on the total area to be corrected obtained and the wear area comparison value and the bump area comparison value;

[0181] Further, in this embodiment, for the calculation of the wear area comparison value and the bump area comparison value, in addition to the quantity dimension, a corresponding size dimension can also be added, that is, obtain the wear area size corresponding to the wear form and the bump area size corresponding to the bump form. By normalizing the bump area quantity, bump area size, wear area quantity, and wear area size respectively, and further multiplying them by the retrieved bump form weight value and wear form weight value, the corresponding wear area comparison value and bump area comparison value can be obtained;

[0182] That is to say, based on the above content, through the fusion of multi-dimensional data (including quantity dimension and size dimension), the accuracy of the obtained comparison value can be further improved.

[0183] This embodiment can compare the to-be-corrected point corresponding to the minimum elevation value among all the to-be-corrected points in the same to-be-corrected area with the preset form division value to determine the area state of each to-be-corrected area, with a certain degree of accuracy.

[0184] In step S110, it includes the following content:

[0185] Retrieve the historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine the collision high-frequency value corresponding to the collision collection area based on the number of collisions.

[0186] For example, in this embodiment, the server will retrieve the historical collision records, thereby determining the number of collisions that occurred in the collision collection area according to the historical collision records, and then determining the collision high-frequency value corresponding to the collision collection area according to the number of collisions.

[0187] Further, the above "retrieve the historical collision records, determine the number of collisions corresponding to the collision collection area based on the historical collision records, and determine the collision high-frequency value corresponding to the collision collection area based on the number of collisions" also includes the following steps:

[0188] Retrieve the historical collision records, which include the historical collision positions and historical collision times corresponding to different historical collision areas;

[0189] Compare the current collision position corresponding to the collision collection area with the historical collision positions corresponding to different historical collision areas;

[0190] When the position coincidence rate between the collision collection area and any historical collision area reaches the preset coincidence value based on the comparison result, traverse the historical collision records to obtain the historical collision times corresponding to the historical collision area, and perform a number superposition on the historical collision times based on the collision collection area to obtain the current collision times;

[0191] Determine a collision high-frequency value corresponding to the collision acquisition area based on the current number of collisions.

[0192] 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.

[0193] Next, the server compares the current collision position corresponding to the collision acquisition area with the historical collision positions corresponding to different historical collision areas. When the comparison result shows that the position coincidence rate between the collision acquisition area and any one of the historical collision areas reaches a preset coincidence value, it indicates that the collision acquisition area and the historical collision area roughly correspond to the same area.

[0194] Then, the server obtains the historical collision times corresponding to the historical collision area through the historical collision records, and performs a superposition of the number of times on the historical collision times based on the collision acquisition area, so as to obtain the current number of collisions. Then, the current number of collisions is determined as the collision high-frequency value corresponding to the collision acquisition area.

[0195] In step S112, the following contents are included:

[0196] Perform data fusion on the form comparison value and the collision high-frequency value, and determine the area attribute corresponding to the collision acquisition area based on the obtained area correction value corresponding to the collision acquisition area, where the area attribute includes a correction attribute and a maintenance attribute.

[0197] For example, in this embodiment, the server performs data fusion on the form comparison value and the collision high-frequency value to obtain an area correction value corresponding to the collision acquisition area, and then determines the area correction value as the area attribute corresponding to the collision acquisition area. The area attribute includes a correction attribute and a maintenance attribute.

[0198] When the area attribute corresponding to the collision acquisition area is a correction attribute, it indicates that the damage degree of the collision acquisition area is relatively high and needs to be corrected; when the area attribute corresponding to the collision acquisition area is a maintenance attribute, it indicates that the damage degree of the collision acquisition area is relatively low and the status quo can be maintained.

[0199] Further, the above "perform data fusion on the form comparison value and the collision high-frequency value, and determine the area attribute corresponding to the collision acquisition area based on the obtained area correction value corresponding to the collision acquisition area, where the area attribute includes a correction attribute and a maintenance attribute" further includes the following steps:

[0200] Perform normalization processing on the form comparison value based on the retrieved comparison normalization value to obtain a comparison coefficient value;

[0201] Normalize the collision high-frequency value based on the retrieved high-frequency normalization value to obtain a high-frequency coefficient value;

[0202] Perform data fusion on the comparison coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision acquisition region;

[0203] Retrieve the region preset value and compare the region correction value with the region preset value;

[0204] When the comparison result is that the region correction value is less than the region preset value, determine the region attribute corresponding to the collision acquisition region as the maintenance attribute;

[0205] When the region correction value is greater than or equal to the region preset value, determine the region attribute corresponding to the collision acquisition region as the correction attribute.

[0206] For example, in this embodiment, since the measurement units of the shape comparison value and the collision high-frequency value are different, the server will retrieve the comparison normalization value and the high-frequency normalization value respectively to normalize the shape comparison value and the collision high-frequency value, so as to obtain the comparison coefficient value and the high-frequency coefficient value.

[0207] Then, the server will perform data fusion on the comparison coefficient value and the high-frequency coefficient value to obtain a region correction value corresponding to the collision acquisition region. Then retrieve the region preset value and compare the region correction value with the region preset value.

[0208] When the comparison result is that the region correction value is less than the region preset value, it indicates that the damage degree of the collision acquisition region corresponding to this region correction value is relatively low and does not need to be corrected. Therefore, the server will determine the region attribute corresponding to this collision acquisition region as the maintenance attribute.

[0209] When the comparison result is that the region correction value is greater than or equal to the region preset value, it indicates that the damage degree of the collision acquisition region corresponding to this region correction value is relatively high and needs to be corrected. Therefore, the server will determine the region attribute corresponding to this collision acquisition region as the correction attribute.

[0210] Further, the above method further includes the following steps:

[0211] Send the region correction value to the management terminal for display processing. If it is determined that the management terminal performs adjustment processing based on the region correction value, then obtain the training adjustment parameter for acquisition;

[0212] In response to the operation judgment of the management terminal based on the adjustment data input for the regional correction value, if it is judged that the input adjustment data is configured to amplify the regional correction value, the training adjustment parameter is adjusted based on the adjustment data. Perform increased training;

[0213] If it is judged that the input adjustment data is configured to reduce the regional correction value, the training adjustment parameter is adjusted based on the adjustment data. Perform decreased training;

[0214] Among them, the training of the training adjustment parameter can be carried out through the following formula:

[0215] Among them, is the trained training adjustment parameter, is the adjusted regional correction value corresponding to the adjustment data, is the positive training coefficient, is the negative training coefficient.

[0216] For example, in this embodiment, since this method calculates the regional correction value corresponding to the collision acquisition area through a formula, there may be a difference from the actual situation. Therefore, a training adjustment parameter is set in the server, which enables the management terminal to adjust the regional correction value according to the actual correction situation, so as to obtain a more accurate regional correction value. Therefore, the server will send the regional correction value to the management terminal for display processing. When it is judged that the management terminal performs adjustment processing based on the regional correction value, the server will obtain the training adjustment parameter and perform an operation judgment on the adjustment data input by the management terminal.

[0217] When the server judges that the adjustment data input by the management terminal is configured to amplify the regional correction value, it will perform increased training on the training adjustment parameter based on the adjustment data ; when the server judges that the adjustment data input by the management terminal is configured to reduce the regional correction value, it will perform decreased training on the training adjustment parameter based on the adjustment data to obtain an updated training adjustment parameter.

[0218] According to the solution of the present invention, the server will conduct video monitoring on 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 changes from the normal state to the collision state at any moment, the server will determine the initial state position where the target bicycle is located at the moment of state conversion. 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 on the collision collection area, so as to obtain the corresponding collision form data. Then, the server will retrieve the reference field data of the track field, and determine the reference form data of the collision collection area in the reference field data, and then compare the reference form data with the collision form data to obtain a form comparison value, so as to determine the specific damage degree of the track field caused by this collision. Then, the server will retrieve the historical collision records, so as to determine the number of collisions that occurred in the collision collection area according to the historical collision records, and then determine the collision high-frequency value corresponding to the collision collection area according to the number of collisions. The server will fuse the form comparison value and 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. The regional attribute includes a correction attribute and a maintenance attribute. The present invention can improve the monitoring efficiency of the track field, and can more accurately determine the regional attribute corresponding to the collision collection area, which is convenient for subsequent management personnel to determine whether it is necessary to correct the collision collection area according to the regional attribute.

[0219] Another embodiment of the present invention provides a design system for a sports event track. Figure 4 For its corresponding system block diagram, the system includes:

[0220] A video monitoring module, configured to respond to determining that any target bicycle is traveling on the track field, and conduct video monitoring on the target bicycle based on the driving process to obtain the real-time driving state corresponding to the target bicycle;

[0221] A position determination module, configured to respond to the real-time driving state of the target bicycle changing from the normal state to the collision state at any moment, and determine the initial state position where the target bicycle is located at the moment of state conversion;

[0222] A data collection module, configured to control the drone to fly to the initial state position to collect data on the collision collection area corresponding to the target bicycle to obtain the collision form data of the collision collection area;

[0223] A data comparison module, configured to retrieve reference site data corresponding to the track site, and perform data comparison between the reference form data corresponding to the collision collection area obtained based on the reference site data and the collision form data to obtain a form comparison value;

[0224] 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;

[0225] A data fusion module, configured to perform data fusion on the form comparison value and the collision high-frequency value, and determine a regional attribute corresponding to the collision collection area based on the obtained regional correction value corresponding to the collision collection area, where the regional attribute includes a correction attribute and a maintenance attribute.

[0226] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the examples of the present invention. Based on the above description, the structure required to construct such a system is obvious. In addition, the present invention is not directed to any particular programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of the specific language above is for the purpose of disclosing the preferred embodiments of the present invention.

[0227] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0228] Similarly, it should be understood that, in order to streamline the present disclosure and assist in understanding one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0229] Those skilled in the art should understand that the modules or units or components of the devices in the examples disclosed herein can be arranged in the devices as described in the embodiments, or alternatively can be located in one or more devices different from the devices in the examples. The modules in the foregoing examples can be combined into one module or further divided into multiple sub-modules.

[0230] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components.

[0231] In addition, those skilled in the art can understand that although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of the present invention and forms different embodiments.

[0232] In addition, some of the embodiments herein are described as a combination of methods or method elements that can be implemented by a processor of a computer system or by other devices performing the functions. Therefore, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. In addition, the elements described herein in the device embodiments are examples of the following devices: the device is used to implement the functions performed by the elements for the purpose of implementing the present invention.

[0233] As used herein, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe ordinary objects only represents different instances of similar objects, and does not intend to imply that the objects so described must have a given order in terms of time, space, sorting, or in any other way.

[0234] Although the present invention is described in terms of a limited number of embodiments, those skilled in the art in this technical field will understand, based on the above description, that other embodiments can be conceived within the scope of the present invention thus described. In addition, it should be noted that the language used in this specification is mainly selected for readability and teaching purposes, rather than for the purpose of interpreting or limiting the subject matter of the present invention.

Claims

1. A method for designing a sports event track, characterized in that: include: In response to the target bicycle driving on the track, the target bicycle is monitored by video to obtain the real-time driving status; In response to the real-time driving state changing from a normal state to a collision state, determining an initial state position corresponding to the state change time; Control the UAV to fly to the initial state position to collect data in the collision collection area, and obtain an upper view of the area including monitoring points with the same point spacing; Determine the current collision position in the collision collection area and retrieve the benchmark site data; Extracting the benchmark site data based on the current collision position, and obtaining the elevation benchmark value corresponding to the monitoring point based on the obtained benchmark shape data; Subtract the elevation monitoring value of the same monitoring point from the elevation reference value, and determine the point to be corrected if the corresponding elevation difference is greater than the preset difference; Connect the points to be corrected to obtain the areas to be corrected; In the same area to be corrected, the point to be corrected corresponding to the minimum elevation value is determined as the target point, and the target point is compared with the preset morphological division value; The regional morphology of the area to be corrected whose corresponding elevation minimum value is greater than the preset morphology division value is determined as a wear morphology, otherwise it is determined as a bump morphology; Determine the number of worn areas for the wear pattern and the number of bumped areas for the bump pattern; Retrieve the wear shape weight value and the collision shape weight value that is less than the wear shape weight value; The number of wear areas and the weight value of wear shape are multiplied, and the number of bump areas and the weight value of bump shape are multiplied to obtain the wear area contrast value and the bump area contrast value; The area of ​​the region to be corrected is summed, and a morphological comparison value is obtained based on the obtained total area to be corrected and the comparison value of the worn area and the comparison value of the bumped area; Determine the number of collisions in the corresponding collision collection area based on historical collision records, and determine the collision high frequency value based on the number of collisions; The morphology contrast value and the collision high frequency value are fused to obtain a region correction value, and the region attribute is determined based on the obtained region correction value, wherein the region attribute includes a correction attribute and a maintenance attribute.

2. The method for designing a sports event track according to claim 1, characterized in that: In response to the target bicycle driving on the track, the target bicycle is monitored by video to obtain the real-time driving status, 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 changing from a normal state to a collision state, determining the initial state position corresponding to the state change 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: Control the drone to fly to the initial state position to collect the collision collection area, and obtain the top view of the area including the monitoring points with the same point spacing, 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 1, characterized in that: The number of collisions in the corresponding collision collection area is determined based on the historical collision records, and the collision high frequency value is determined 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.

9. The method for designing a sports event track according to claim 1, characterized in that: The morphological contrast value and the collision high frequency value are fused, and the regional attributes are determined based on the obtained regional correction value, wherein the regional attributes include correction attributes and maintenance attributes, 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.

10. A sports event track design system, characterized in that: include: The video monitoring module responds to the target bicycle running on the track and performs video monitoring on the target bicycle to obtain the real-time driving status; A position determination module, in response to the real-time driving state changing from a normal state to a collision state, determines an initial state position corresponding to the state change moment; The data acquisition module controls the UAV to fly to the initial state position to collect data in the collision collection area, and obtains a top view of the area including monitoring points with the same point spacing; The data comparison module determines the current collision position in the collision collection area and retrieves the benchmark site data; Extracting the benchmark site data based on the current collision position, and obtaining the elevation benchmark value corresponding to the monitoring point based on the obtained benchmark shape data; Subtract the elevation monitoring value of the same monitoring point from the elevation reference value, and determine the point to be corrected if the corresponding elevation difference is greater than the preset difference; Connect the points to be corrected to obtain the areas to be corrected; In the same area to be corrected, the point to be corrected corresponding to the minimum elevation value is determined as the target point, and the target point is compared with the preset morphological division value; The regional morphology of the area to be corrected whose corresponding elevation minimum value is greater than the preset morphology division value is determined as a wear morphology, otherwise it is determined as a bump morphology; Determine the number of worn areas for the wear pattern and the number of bumped areas for the bump pattern; Retrieve the wear shape weight value and the collision shape weight value that is less than the wear shape weight value; The number of wear areas and the weight value of wear shape are multiplied, and the number of bump areas and the weight value of bump shape are multiplied to obtain the wear area contrast value and the bump area contrast value; The area of ​​the region to be corrected is summed, and a morphological comparison value is obtained based on the obtained total area to be corrected and the comparison value of the worn area and the comparison value of the bumped area; A record retrieval module determines the number of collisions in the corresponding collision collection area based on historical collision records, and determines the collision high frequency value based on the number of collisions; The data fusion module fuses the morphological contrast value and the collision high-frequency value to obtain a regional correction value, and determines the regional attributes based on the obtained regional correction value, wherein the regional attributes include correction attributes and maintenance attributes.

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