Smart park multi-dimensional linkage management system based on urban information model
Through the use of the technical means of multi -dimensional linkage management system in the smart park management system, the multi -dimensional monitoring and analysis of patrol robots is realized, the problem that traditional systems cannot accurately track and manage the status of patrol robots have been solved, and the level of security management and management efficiency are improved. Essence
Patent Information
- Application Number
- CN202411338321.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2025-01-28
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional smart park management system cannot accurately track and manage the location and status of the patrol robot, and cannot respond to potential safety problems or abnormal situations in a timely manner, resulting in the impact of safety hazards and management effectiveness.
Adopt a multi -dimensional linkage management system based on the city information model, obtain modules, outline analysis modules, standard movement rate obtain modules, tilt identification modules, and abnormal dump signal generating modules through patrol robot outline, to achieve multi -dimensional monitoring and analysis of patrol robots , Timely discover the tilt and abnormal state, generate the corresponding identification and signal.
The accuracy of the status monitoring of patrol robotics has been improved, the data analysis and prediction capabilities have been enhanced, the safety hazards are reduced, and the safety management level and management efficiency of the park are improved.
Smart Images
Figure CN119380265A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of park management, and in particular to a multi-dimensional linkage management system for a smart park based on a city information model. Background Art
[0002] With the acceleration of urbanization and the development of science and technology, more and more parks have begun to introduce intelligent management systems to improve management efficiency and safety. In smart parks, patrol robots are an important force to ensure the safety of the park. In smart parks, patrol robots, as an important security equipment, are widely used in patrol and monitoring tasks in the park. Through patrol robots, real-time monitoring and precise management of the status of smart parks can be achieved to ensure the smooth execution of patrol tasks. As an important part of smart parks, patrol robots have a direct impact on the performance and reliability of the entire system.
[0003] Traditional smart park multi-dimensional linkage management systems based on urban information models may have some problems, such as the inability to accurately track and manage the location and status of patrol robots, and the inability to respond to potential safety issues or abnormal situations in a timely manner. These problems may lead to potential safety hazards and affect the management efficiency and security of smart parks.
[0004] However, when patrol robots are patrolling and monitoring smart parks, the traditional monitoring method of patrol robots only judges the status of patrol robots through sensor data or simple image analysis, which is easily affected by environmental interference and errors. It is unable to detect abnormal situations of patrol robots in time, such as tipping over, and take corresponding measures, resulting in safety hazards and affecting the management efficiency and safety of smart parks. Based on this, a multi-dimensional linkage management system for smart parks based on urban information models is proposed. Summary of the invention
[0005] The purpose of the present invention is to provide an information interaction method for power field operations, which solves the technical problem that the status of a patrol robot is judged only by sensor data or simple image analysis, which is easily affected by environmental interference and errors and cannot detect abnormal conditions of the patrol robot in time.
[0006] A multi-dimensional linkage management system for smart parks based on the city information model, including: A patrol robot contour acquisition module is used to acquire the contour edge of the patrol robot; A contour analysis module obtains a reference lateral width and a reference longitudinal height corresponding to the patrol robot according to the contour edge line analysis of the patrol robot; The standard moving rate acquisition module acquires the historical patrol data of the patrol robot and analyzes it to obtain the standard moving rate of the patrol robot's tracking point; The tilt identification module analyzes the real-time image of the patrol robot to obtain its real-time horizontal width and real-time vertical height, analyzes the real-time horizontal width and real-time vertical height with the reference horizontal width and reference vertical height respectively, obtains the real-time deviation value, and generates a tilt identification according to the deviation value; The abnormal dumping signal generation module obtains the real-time center coordinates of the patrol robot from the real-time image when a tilt mark is generated, and calculates the distance M between it and the tracking point. The distance M is analyzed with the standard moving rate E to obtain a calibration image, and the distance J between the center point of the calibration image and the real-time center point is obtained. The distance J is analyzed to determine whether an abnormal dumping signal is generated.
[0007] As a further solution of the present invention: the specific method of obtaining the reference lateral width corresponding to the patrol robot is: The coordinates of the center point of the patrol robot's contour edge are taken as the coordinates D (Dx, Dy) of the patrol robot's tracking point D. A horizontal line with the tracking point D as the midpoint is defined as the lateral calibration line LA. A vertical line with the tracking point as the center point is defined as the longitudinal calibration line Va. The lateral calibration line LA and the longitudinal calibration line Va are perpendicular to each other. The patrol robot's contour edge, the tracking point D, the lateral calibration line LA and the longitudinal calibration line Va are placed in a two-dimensional plane coordinate system at the same time, and the coordinates of each intersection between the lateral calibration line LA and the patrol robot's contour edge are obtained. The intersection with the largest and smallest lateral coordinates among the intersection coordinates is taken as the lateral calculation points BA and BB of the patrol robot. The distance between the lateral calculation points BA and BB is defined as the corresponding reference lateral width HA of the patrol robot.
[0008] As a further solution of the present invention: the specific method of obtaining the reference longitudinal height corresponding to the patrol robot is: Obtain the coordinates of each intersection point between the longitudinal calibration line Va and the contour edge line of the patrol robot, take the intersection point with the largest and smallest ordinates among the intersection points as the longitudinal calculation points EA and EB of the patrol robot, obtain the distance between the longitudinal calculation points EA and EB, and define it as the reference longitudinal height HB corresponding to the patrol robot.
[0009] As a further solution of the present invention: the specific method of obtaining the standard moving speed of the patrol robot tracking point is: Obtain the patrol route length Ln and the corresponding patrol time Tn of the patrol robot within the preset number of days n, and calculate the corresponding movement rate Vn of the patrol robot for each day within the preset number of days n through Vn=Ln / Tn, where n refers to the preset number of days, and n is a positive integer, here n=66 days, and obtain the value Va of the movement rate Vn that meets the preset screening condition |Va-Vp|≥Y2, where a is the number of values in Vn that meet the preset screening condition Q, n≥a≥1, when the number a is greater than the preset threshold value Y1, define the mean Vp of Vn as the calculated movement rate W corresponding to the patrol robot, when the number a is less than or equal to the preset threshold value Y1, define the mean of the maximum and minimum values in Vn as the calculated movement rate W corresponding to the patrol robot, and define the sum of the calculated movement rate W and the preset value Y4 as the quasi-movement rate E; where Y2 is the preset value.
[0010] As a further solution of the present invention: the specific method of obtaining the real-time deviation value is: The acquired real-time image of the patrol robot is input into the contour analysis module, and the real-time image of the patrol robot is analyzed in the same way as the reference lateral width and reference longitudinal height corresponding to the patrol robot, so as to obtain the real-time lateral width RA and real-time longitudinal height RB corresponding to the patrol robot, and the sum of the absolute values of the differences between the real-time lateral width RA and the real-time longitudinal height RB and the reference lateral width HA and the reference longitudinal height HB, respectively, and the products of the preset coefficients Q1 and Q2, respectively, is marked as the real-time deviation value P.
[0011] As a further solution of the present invention, the specific method of generating the tilt mark according to the deviation value is: When the real-time deviation value P is greater than the preset value Y3, a tilt mark is generated, otherwise, no processing is performed.
[0012] As a further solution of the present invention: the specific method of obtaining the calibration image is: When generating a tilt mark, first obtain the real-time center point coordinates K (Kx, Ky) of the patrol robot from the real-time image of the patrol robot in the same way as obtaining the tracking point D of the patrol robot. Then, calculate the distance M between the real-time center point coordinates K and the tracking point D through the distance calculation formula. Next, define the ratio of the distance M to the standard moving rate E as the tilt duration T. According to the acquisition time of the patrol robot's real-time image, acquire the corresponding image of the patrol robot at the moment 0.5T forward of the acquisition time, and use it as the calibration image of the patrol robot.
[0013] As a further solution of the present invention: the specific method of determining the generation of an abnormal dumping signal is: First, the coordinates U (Ux, Uy) of the center point of the calibration image are obtained from the calibration image in the same way as the tracking point D of the patrol robot. Then, the distance J between the center point U of the calibration image and the real-time center point K is calculated, and the ratio between the distance J and 0.5T is used as the tilt rate Z. When the tilt rate Z is greater than the standard moving speed E, an abnormal dumping signal is generated. Otherwise, no processing is performed.
[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention, through image processing technology and contour analysis module, accurately obtains the contour edge line of the patrol robot, calculates the reference horizontal width and reference vertical height, compares the horizontal width and vertical height in the real-time image in real time, timely detects the tilt situation and generates a tilt mark, greatly improves the accuracy of state monitoring, enhances data analysis and prediction capabilities, and after detecting the tilt mark, quickly determines whether the robot is in an abnormal dumping state by calculating the tilt duration and tilt rate, and generates an abnormal dumping signal, which helps to reduce safety hazards and improve the overall safety management level of the park. Through multi-dimensional monitoring and analysis of the patrol robot, the abnormal state of the patrol robot can be detected in time, and corresponding marks and signals are generated, which helps to improve the reliability of the multi-dimensional linkage management system of the smart park, ensure that the patrol robot can operate normally, and improve the safety and management efficiency of the park. When an abnormal dumping signal is generated, the management personnel can take measures in time to repair or adjust the patrol robot to avoid affecting the safety management of the park due to the failure of the patrol robot, and provide strong support for the intelligent management and security of the park. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of the system framework structure of the present invention; Figure 2 It is a schematic diagram of the framework structure of the method of the present invention. DETAILED DESCRIPTION
[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0017] Embodiment 1 See also Figure 1-Figure 2 ,This application provides a smart park multi-dimensional linkage management system based on the city information model, including; The patrol robot contour acquisition module is used to obtain the contour edge of the patrol robot. The specific method is: When the patrol robot enters the monitoring area, an image of the patrol robot is obtained, and the image of the patrol robot is processed and analyzed by image processing technology to obtain the contour edge of the patrol robot; It should be noted that the way to process and analyze the patrol robot image is: remove the noise in the image through Gaussian filtering, median filtering and other methods to avoid the image being affected by noise during the acquisition and transmission process. It is more accurate to use edge detection. Use image processing technology (such as edge detection algorithm) to pre-process the patrol robot image, analyze and extract the contour edge of the patrol robot. At the same time, in order to optimize the contour edge, some morphological operations can be performed, such as expansion and corrosion, to help remove small noise points and fill small gaps in the contour. The algorithms and image processing methods involved above are existing and mature technologies, so they will not be described here. The contour analysis module obtains the reference horizontal width and reference vertical height of the patrol robot according to the contour edge analysis of the patrol robot. The specific method is as follows: The center point of the contour edge of the patrol robot is taken as the tracking point D of the patrol robot, and the coordinates of the tracking point D are marked as D (Dx, Dy). A horizontal line with the tracking point D as the center point is defined as the horizontal calibration line LA, and a vertical line with the tracking point as the center point is defined as the longitudinal calibration line Va. The horizontal calibration line LA and the longitudinal calibration line Va are perpendicular to each other. The contour edge line, tracking point D, lateral calibration line LA and longitudinal calibration line Va of the patrol robot are placed in a two-dimensional plane coordinate system at the same time, and the coordinates of each intersection point between the lateral calibration line LA and the contour edge line of the patrol robot are obtained. The intersection point with the largest horizontal coordinate and the smallest horizontal coordinate in the coordinates of each intersection point is used as the lateral calculation points BA and BB of the patrol robot, and the coordinates BA (BAx, BAy) and BB (BBx, BBy) of the lateral calculation points BA and BB are obtained at the same time, where BA (BAx, BAy) is the coordinate of the intersection point with the largest horizontal coordinate in the coordinates of each intersection point, and BB (BBx, BBy) is the coordinate of the intersection point with the smallest horizontal coordinate in the coordinates of each intersection point; According to the distance calculation formula, , calculate the distance between the lateral calculation points BA (BAx, BAy) and BB (BBx, BBy), and define it as the reference lateral width HA corresponding to the patrol robot; Obtain the coordinates of each intersection point between the longitudinal calibration line Va and the contour edge line of the patrol robot, and use the intersection point with the largest longitudinal coordinate and the smallest longitudinal coordinate among the coordinates of each intersection point as the longitudinal calculation points EA and EB of the patrol robot. At the same time, obtain the coordinates EA (EAx, EAy) and EB (EBx, EBy) of the longitudinal calculation points EA and EB, where EA (EAx, EAy) is the coordinate of the intersection point with the largest longitudinal coordinate among the coordinates of each intersection point, and EB (EBx, EBy) is the coordinate of the intersection point with the smallest longitudinal coordinate among the coordinates of each intersection point; According to the distance calculation formula, , calculate and obtain the distance between the lateral calculation points EA (EAx, EAy) and EB (EBx, EBy), and define it as the reference longitudinal height HB corresponding to the patrol robot; By acquiring and analyzing the contour edges of the patrol robot, the baseline lateral width HA and baseline longitudinal height HB of the patrol robot can be accurately determined. Compared with traditional monitoring methods, this method based on image processing and precise calculation can more accurately judge the status of the patrol robot and avoid management errors caused by misjudgment.
[0018] The standard movement rate acquisition module acquires the historical patrol data of the patrol robot and analyzes it to obtain the standard movement rate of the patrol robot's tracking point. The historical patrol data includes the patrol route length and the corresponding patrol duration. The specific method is as follows: Obtain the patrol route length Ln and the corresponding patrol duration Tn of the patrol robot within the preset number of days n, and calculate the corresponding movement rate Vn of the patrol robot for each day within the preset number of days n by Vn=Ln / Tn, where n refers to the preset number of days and n is a positive integer, here n=66 days; Obtain the value Va of the mobile rate Vn that meets the preset screening condition Q, where a is the number of values in Vn that meet the preset screening condition Q, n≥a≥1, compare the number a with the preset threshold value Y1, and when the number a is greater than the preset threshold value Y1, define the mean value Vp of Vn as the calculated mobile rate W corresponding to the patrol robot; when the number a is less than or equal to the preset threshold value Y1, define the mean of the maximum and minimum values in Vn as the calculated mobile rate W corresponding to the patrol robot, that is, W=(Vmin+Vmax) / 2, where Vmax and Vmin are the maximum and minimum values in Vn, respectively, and define the sum of the calculated mobile rate W and the preset value Y4 as the quasi-mobile rate E; it should be noted that, here, the preset condition Q is specifically: |Va-Vp|≥Y2, where Y2 is the preset value, and the specific values of Y1, Y2 and Y4 are all formulated by relevant personnel according to actual needs; The tilt identification module acquires the real-time image of the patrol robot and analyzes it to obtain the real-time horizontal width and real-time vertical height corresponding to the patrol robot, compares it with the reference horizontal width and reference vertical height corresponding to the patrol robot, and determines and generates a tilt identification. The specific method is as follows: The specific method for obtaining the real-time horizontal width and real-time vertical height of the patrol robot is: The acquired real-time image of the patrol robot is input into the profile analysis module, and the real-time image of the patrol robot is analyzed in the same manner as the reference lateral width and reference longitudinal height corresponding to the patrol robot, thereby obtaining the real-time lateral width RA and real-time longitudinal height RB corresponding to the patrol robot; The sum of the absolute values of the differences between the real-time lateral width RA and the real-time longitudinal height RB and the reference lateral width HA and the reference longitudinal height HB respectively and the products of the preset coefficients Q1 and Q2 are marked as the real-time deviation value P; When the real-time deviation value P is greater than the preset value Y3, a tilt mark is generated. Otherwise, no processing is performed. The specific value of the preset value Y3 is formulated by relevant personnel according to actual needs. Through precise image processing and calculation methods, the contour edges of the patrol robot are analyzed to obtain parameters such as the baseline lateral width, baseline longitudinal height, real-time lateral width and real-time longitudinal height. This can more accurately judge the status of the patrol robot and solve the problem of inaccurate status monitoring.
[0019] Embodiment 2 As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes an abnormal dumping signal generating module; The abnormal tipping signal generation module, when generating a tilt mark, obtains the real-time center point coordinates K (Kx, Ky) of the patrol robot from the real-time image of the patrol robot in the same way as obtaining the tracking point D of the patrol robot, and uses the distance calculation formula to calculate the center point of the patrol robot. , calculate the distance M between the real-time center point coordinate K and the tracking point D, and define the ratio between the distance M and the standard moving speed E as the tilt duration T; According to the acquisition time of the patrol robot's real-time image, the corresponding image of the patrol robot at the moment 0.5T ahead of the acquisition time is acquired, and it is used as the calibration image of the patrol robot. In the same way as the tracking point D of the patrol robot, the coordinates U (Ux, Uy) of the center point of the calibration image are obtained from the calibration image of the patrol robot, and the distance J between the center point U of the calibration image and the real-time center point K is calculated. The ratio between the distance J and 0.5T is used as the tilt rate Z; When the tilt rate Z is greater than the standard moving rate E, an abnormal dumping signal is generated, otherwise no processing is performed; When judging whether the tilt rate Z is greater than the standard moving rate E, it is possible to more accurately determine whether the patrol robot has fallen abnormally, thus avoiding false alarms or missed alarms caused by misjudgment; Through image processing technology and contour analysis module, the contour edge of the patrol robot is accurately obtained, and the benchmark horizontal width and benchmark vertical height are calculated. The horizontal width and vertical height in the real-time image are compared in real time, the tilt situation is discovered in time and a tilt mark is generated, which greatly improves the accuracy of state monitoring and enhances data analysis and prediction capabilities. After detecting the tilt mark, by calculating the tilt duration and tilt rate, it is quickly determined whether the robot is in an abnormal dumping state, and an abnormal dumping signal is generated, which helps to reduce safety hazards and improve the overall safety management level of the park. Through multi-dimensional monitoring and analysis of the patrol robot, the abnormal state of the patrol robot can be discovered in time, and corresponding marks and signals can be generated, which helps to improve the reliability of the multi-dimensional linkage management system of the smart park, ensure that the patrol robot can operate normally, and improve the safety and management efficiency of the park. When an abnormal dumping signal is generated, the management personnel can take timely measures to repair or adjust the patrol robot to avoid affecting the safety management of the park due to the failure of the patrol robot, providing strong support for the intelligent management and security of the park.
[0020] Embodiment 3 As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.
[0021] Embodiment 5 As the fifth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second, third and fourth embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.
[0022] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.
[0023] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. A multi-dimensional linkage management system for smart parks based on the city information model, characterized by: include; A patrol robot contour acquisition module is used to acquire the contour edge of the patrol robot; A contour analysis module obtains a reference lateral width and a reference longitudinal height corresponding to the patrol robot according to the contour edge line analysis of the patrol robot; The standard moving rate acquisition module acquires the historical patrol data of the patrol robot and analyzes it to obtain the standard moving rate of the patrol robot's tracking point; The tilt identification module analyzes the real-time image of the patrol robot to obtain its real-time horizontal width and real-time vertical height, analyzes the real-time horizontal width and real-time vertical height with the reference horizontal width and reference vertical height respectively, obtains the real-time deviation value, and generates a tilt identification according to the deviation value; The abnormal dumping signal generation module obtains the real-time center coordinates of the patrol robot from the real-time image when a tilt mark is generated, and calculates the distance M between it and the tracking point. The distance M is analyzed with the standard moving rate E to obtain a calibration image, and the distance J between the center point of the calibration image and the real-time center point is obtained. The distance J is analyzed to determine whether an abnormal dumping signal is generated.
2. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 1 is characterized in that: The specific method for obtaining the reference lateral width corresponding to the patrol robot is: The coordinates of the center point of the patrol robot's contour edge are taken as the coordinates D (Dx, Dy) of the patrol robot's tracking point D. A horizontal line with the tracking point D as the midpoint is defined as the lateral calibration line LA. A vertical line with the tracking point as the center point is defined as the longitudinal calibration line Va. The lateral calibration line LA and the longitudinal calibration line Va are perpendicular to each other. The patrol robot's contour edge, the tracking point D, the lateral calibration line LA and the longitudinal calibration line Va are placed in a two-dimensional plane coordinate system at the same time, and the coordinates of each intersection between the lateral calibration line LA and the patrol robot's contour edge are obtained. The intersection with the largest and smallest lateral coordinates among the intersection coordinates is taken as the lateral calculation points BA and BB of the patrol robot. The distance between the lateral calculation points BA and BB is defined as the corresponding reference lateral width HA of the patrol robot.
3. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 2 is characterized in that: The specific method for obtaining the reference longitudinal height corresponding to the patrol robot is: Obtain the coordinates of each intersection point between the longitudinal calibration line Va and the contour edge line of the patrol robot, take the intersection point with the largest and smallest ordinates among the intersection points as the longitudinal calculation points EA and EB of the patrol robot, obtain the distance between the longitudinal calculation points EA and EB, and define it as the reference longitudinal height HB corresponding to the patrol robot.
4. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 3 is characterized in that: The specific method to obtain the standard movement rate of the patrol robot tracking point is: Obtain the patrol route length Ln and the corresponding patrol time Tn of the patrol robot within the preset number of days n, and calculate the corresponding movement rate Vn of the patrol robot for each day within the preset number of days n through Vn=Ln / Tn, where n refers to the preset number of days, and n is a positive integer, here n=66 days, and obtain the value Va of the movement rate Vn that meets the preset screening condition |Va-Vp|≥Y2, where a is the number of values in Vn that meet the preset screening condition Q, n≥a≥1, when the number a is greater than the preset threshold value Y1, define the mean Vp of Vn as the calculated movement rate W corresponding to the patrol robot, when the number a is less than or equal to the preset threshold value Y1, define the mean of the maximum and minimum values in Vn as the calculated movement rate W corresponding to the patrol robot, and define the sum of the calculated movement rate W and the preset value Y4 as the quasi-movement rate E; where Y2 is the preset value.
5. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 4 is characterized in that: The specific method of obtaining the real-time deviation value is: The acquired real-time image of the patrol robot is input into the contour analysis module, and the real-time image of the patrol robot is analyzed in the same way as the reference lateral width and reference longitudinal height corresponding to the patrol robot, so as to obtain the real-time lateral width RA and real-time longitudinal height RB corresponding to the patrol robot, and the sum of the absolute values of the differences between the real-time lateral width RA and the real-time longitudinal height RB and the reference lateral width HA and the reference longitudinal height HB, respectively, and the products of the preset coefficients Q1 and Q2, respectively, is marked as the real-time deviation value P.
6. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 5 is characterized in that: The specific method of generating the tilt mark according to the deviation value is as follows: When the real-time deviation value P is greater than the preset value Y3, a tilt mark is generated, otherwise, no processing is performed.
7. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 6 is characterized in that: The specific method of obtaining the calibration image is: When generating a tilt mark, first obtain the real-time center point coordinates K (Kx, Ky) of the patrol robot from the real-time image of the patrol robot in the same way as obtaining the tracking point D of the patrol robot. Then, calculate the distance M between the real-time center point coordinates K and the tracking point D through the distance calculation formula. Next, define the ratio of the distance M to the standard moving rate E as the tilt duration T. According to the acquisition time of the patrol robot's real-time image, acquire the corresponding image of the patrol robot at the moment 0.5T forward of the acquisition time, and use it as the calibration image of the patrol robot.
8. The multi-dimensional linkage management system of a smart park based on a city information model according to claim 7 is characterized in that: The specific method for determining the generation of abnormal dumping signal is: First, the coordinates U (Ux, Uy) of the center point of the calibration image are obtained from the calibration image in the same way as the tracking point D of the patrol robot. Then, the distance J between the center point U of the calibration image and the real-time center point K is calculated, and the ratio between the distance J and 0.5T is used as the tilt rate Z. When the tilt rate Z is greater than the standard moving speed E, an abnormal dumping signal is generated. Otherwise, no processing is performed.