Inspection vehicle based on big data and control system thereof

Abstract

The invention discloses an inspection vehicle based on big data and a control system thereof. The inspection vehicle comprises an inspection vehicle module, a communication module, a solar module, a solar panel, a positioning column, a push cylinder, a positioning clamping block, a vehicle body, moving wheels, a battery, a data processing module, a control module and an auxiliary module. The fire hazard detection system based on image fusion is designed, infrared and visible light images are fused through a double-branch generative adversarial network structure, the problems of halo artifacts and gradient explosion are effectively solved, and the quality of the fused image is improved; the temperature information of the infrared image is reserved, the texture and contour information of the visible light is supplemented, the concept of adjusting the image is introduced, and the adjusted image is obtained through weighted average of the infrared image and the visible light image and used for simulating a real image, so that the fusion result is adjusted, and the consistency of the fused image and the source image in structure, brightness and contrast is improved; therefore, the accuracy of fire hazard detection is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of patrol vehicles, and in particular to a patrol vehicle based on big data and a control system thereof. Background Art

[0002] The inspection vehicle is a special vehicle used for inspection, maintenance, and other tasks. The inspection vehicle can be used in security monitoring, agricultural planting, industrial parks and other fields. It can be customized and modified according to actual needs to meet the needs of different fields. It plays an important role in urban management, environmental monitoring, railway transportation, power facilities and other fields.

[0003] Existing patrol vehicles have the following defects: First, although traditional fire detection technology can play an effective role in most cases, its misjudgment rate increases significantly under extreme weather conditions such as night or fog. These environmental factors greatly interfere with the normal operation of the detection equipment, resulting in frequent false alarms or missed alarms; Second, when performing tasks, patrol vehicles need to autonomously navigate in complex and changing environments. However, existing patrol vehicles generally use a single sensor for positioning. This method often only considers static obstacles, such as buildings, trees, etc., but ignores dynamic obstacles that may appear on the road, such as pedestrians, vehicles or other moving objects. This limitation causes the patrol vehicle's path to be easily affected by control errors during actual driving, resulting in deviations from the preset local path. This not only reduces the accuracy of inspections, but may also cause potential safety risks. Third, battery life is one of the important indicators for measuring the performance of inspection vehicles. However, existing inspection vehicles generally have shortcomings in this regard. Due to limitations of battery capacity, energy management systems and other factors, the battery life of inspection vehicles is often limited, which means that after a single charge, the inspection vehicle may not be able to cover all areas that need to be inspected, thereby affecting the efficiency and comprehensiveness of the inspection. Especially in remote or inaccessible areas, insufficient battery life will bring greater challenges to inspection work. Summary of the invention

[0004] The purpose of the present invention is to provide a patrol vehicle and a control system thereof based on big data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a patrol vehicle based on big data, including a patrol vehicle module, the patrol vehicle module including a vehicle body, a solar module fixedly connected to the vehicle body, the solar module including a first hinge seat, and the first hinge seat fixedly connected to the vehicle body, a second hinge seat hinged on the first hinge seat, a top cylinder fixedly connected to the second hinge seat, a solar panel is arranged on the top of the top cylinder, a ball joint is fixedly connected to one side outer wall of the solar panel, a top column is fixedly connected to the output end of the top cylinder, and the top column is hinged on the ball joint.

[0006] As a further technical solution of the present invention, a positioning column is fixedly connected to the outer wall of the solar panel, a positioning seat is fixedly connected to the position of the positioning column on the vehicle body, and the positioning column is arranged in the positioning seat, a fixing seat is arranged on one side of the positioning seat, and the fixing seat is fixedly connected to the vehicle body.

[0007] As a further technical solution of the present invention, a push cylinder is fixedly connected to the fixed seat, and a positioning block is fixedly connected to the output end of the push cylinder.

[0008] As a further technical solution of the present invention, a battery is fixedly connected to the inner wall of the vehicle body, and the battery is electrically connected to the solar panel.

[0009] As a further technical solution of the present invention, moving wheels are arranged on the vehicle body.

[0010] The control system of the inspection vehicle based on big data includes an inspection vehicle module, the inspection vehicle module is data-connected with a data processing module, the data processing module is data-connected with a control module, and the control module establishes a data connection with the inspection vehicle module. The control module includes a path planning module, an algorithm fusion module, an inertial measurement module, an odometer module, a map construction module, a hidden danger detection module, an image fusion module and a real-time optimization module.

[0011] As a further technical solution of the present invention, the inspection vehicle module includes a communication module, a driving module, a lidar module, an image acquisition module, a solar module and an infrared imaging module, wherein the driving module establishes a data connection with the control module, and the lidar module, the image acquisition module and the infrared imaging module establish a data connection with the data processing module.

[0012] As a further technical solution of the present invention, the data processing module includes a data preprocessing module, a data storage module, a data transmission module and a data normalization module, wherein the data transmission module establishes a data connection with the inspection vehicle module and the control module.

[0013] As a further technical solution of the present invention, the inspection vehicle module is data-connected with an auxiliary module, and the auxiliary module establishes data connections with the data processing module and the control module.

[0014] As a further technical solution of the present invention, the auxiliary module includes a log recording module, a human-computer interaction module, a remote monitoring module and a time synchronization module.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention is designed with a fire hazard detection system based on image fusion, which fuses infrared and visible light images through a dual-branch generative adversarial network structure, effectively solves the problems of halo artifacts and gradient explosion, improves the quality of the fused image, retains the temperature information of the infrared image while supplementing the texture and contour information of the visible light, and introduces the concept of adjusted image, obtains the adjusted image by weighted averaging the infrared and visible light images, and is used to simulate the real image, so as to improve the consistency of the fused image with the source image in structure, brightness and contrast by adjusting the fusion result, thereby enhancing the accuracy of fire hazard detection, and effectively solves the boundary error and noise interference problems existing in the traditional method through the three-frame difference method, improves the accuracy and robustness of fire hazard detection, and through the fusion of lidar, inertial measurement unit and odometer Data from multiple sensors can perceive the environment more comprehensively, reduce the errors and limitations caused by a single sensor, improve the accuracy of positioning and navigation, and effectively integrate the data from the inertial measurement unit and the odometer to estimate the position and posture of the unmanned vehicle. On this basis, lidar data is added to obtain global coordinates through algorithms, and combined with global and local path planning, an overall optimal path is provided and real-time obstacle avoidance is achieved. The combination of the two ensures that the unmanned vehicle can not only find the shortest path, but also flexibly avoid obstacles in complex environments. At the same time, an on-board solar panel structure is designed to convert solar energy into electrical energy to provide auxiliary power supply for the inspection vehicle, and can perform real-time angle adjustment according to the angle of sunlight relative to the inspection vehicle to ensure that the best power generation efficiency is maintained at any time. This design not only improves the energy utilization efficiency, but also provides a reliable energy guarantee for the continuous operation of the inspection vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a three-dimensional structural schematic diagram of the inspection vehicle module of the present invention;

[0017] Figure 2 for Figure 1 Schematic diagram of the enlarged structure of the A area in the middle;

[0018] Figure 3 It is a front view structural schematic diagram of the inspection vehicle module of the present invention;

[0019] Figure 4 It is a schematic diagram of the three-dimensional cross-section structure of the inspection vehicle module of the present invention;

[0020] Figure 5 is a system structure diagram of the present invention;

[0021] Figure 6 is a module architecture diagram of the control module of the present invention;

[0022] Figure 7 It is a system flow chart of the present invention.

[0023] In the figure: 1. Inspection vehicle module; 11. Communication module; 12. Driving module; 13. LiDAR module; 14. Image acquisition module; 15. Solar module; 151. First hinge seat; 152. Second hinge seat; 153. Top cylinder; 154. Top column; 155. Ball joint; 156. Solar panel; 157. Positioning column; 158. Positioning seat; 159. Fixed seat; 1510. Push cylinder; 1511. Positioning block; 16. Infrared imaging module; 17. Vehicle body; 18. Moving wheel; 19. Battery; 2. Digital Data processing module; 21. Data preprocessing module; 22. Data storage module; 23. Data transmission module; 24. Data normalization module; 3. Control module; 31. Path planning module; 32. Algorithm fusion module; 33. Inertial measurement module; 34. Odometer module; 35. Map construction module; 36. Hidden danger detection module; 37. Image fusion module; 38. Real-time optimization module; 4. Auxiliary module; 41. Log recording module; 42. Human-computer interaction module; 43. Remote monitoring module; 44. Time synchronization module. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0025] Please refer to the attached Figure 1 -Attached Figure 4An embodiment of the present invention is as follows: a patrol vehicle based on big data includes a patrol vehicle module 1, the patrol vehicle module 1 includes a vehicle body 17, a solar module 15 is fixedly connected to the vehicle body 17, the solar module 15 includes a first hinge seat 151, and the first hinge seat 151 is fixedly connected to the vehicle body 17, a second hinge seat 152 is hinged on the first hinge seat 151, a top cylinder 153 is fixedly connected to the second hinge seat 152, a solar panel 156 is arranged on the top of the top cylinder 153, a ball joint 155 is fixedly connected to the outer wall of one side of the solar panel 156, a top column 154 is fixedly connected to the output end of the top cylinder 153, and the top column 154 is hinged to the ball joint 155; a positioning column 157 is fixedly connected to the outer wall of the solar panel 156, and a positioning column 157 is fixedly connected to the vehicle body 17. A positioning seat 158 ​​is fixedly connected to the position of the positioning column 157, and the positioning column 157 is arranged in the positioning seat 158. A fixing seat 159 is arranged on one side of the positioning seat 158, and the fixing seat 159 is fixedly connected to the vehicle body 17, and the fixing seat 159 is used to fix the push cylinder 1510; the push cylinder 1510 is fixedly connected to the fixing seat 159, and the output end of the push cylinder 1510 is fixedly connected to the positioning block 1511, and the push cylinder 1510 and the positioning block 1511 are used to cooperate with the positioning seat 158 ​​to fix the positioning column 157; a battery 19 is fixedly connected to the inner wall of the vehicle body 17, and the battery 19 is electrically connected to the solar panel 156, and the battery 19 is used to store electricity and power the system; a moving wheel 18 is arranged on the vehicle body 17, and the moving wheel 18 is used to move the inspection vehicle.

[0026] Please refer to the attached Figure 5 -Attached Figure 7The present invention provides an embodiment: a control system of a patrol vehicle based on big data, including a patrol vehicle module 1, the patrol vehicle module 1 is data-connected with a data processing module 2, the data processing module 2 is data-connected with a control module 3, and the control module 3 establishes a data connection with the patrol vehicle module 1, the control module 3 includes a path planning module 31, an algorithm fusion module 32, an inertial measurement module 33, an odometer module 34, a map construction module 35, a hidden danger detection module 36, an image fusion module 37 and a real-time optimization module 38; the patrol vehicle module 1 includes a communication module 11, a drive module 12, a laser radar module 13, an image acquisition module 14, a laser radar module 15, a laser radar module 16, a laser radar module 17, a laser radar module 18, a laser radar module 19, a laser radar module 20, a laser radar module 21, a laser radar module 22, a laser radar module 23, a laser radar module 24, a laser radar module 25, a laser radar module 26, a laser radar module 27, a laser radar module 28, a laser radar module 29, a laser radar module 30, a laser radar module 31, a laser radar module 32, a laser radar module 33, a laser radar module 34, a laser radar module 35, a laser radar module 36, a laser radar module 37, a laser radar module 38, a laser radar module 38, a laser radar module 39, a laser radar module 31, a laser radar module 31, a laser radar module 32, an image acquisition module 33, a laser radar module 34, a laser radar module 35, a laser radar module 36, a laser radar module 37, a laser radar module 38, a laser radar module 39, a laser radar module 31, a laser radar module 31, a laser radar module 32, an Module 14, solar module 15 and infrared imaging module 16, wherein the driving module 12 establishes a data connection with the control module 3, the laser radar module 13, the image acquisition module 14 and the infrared imaging module 16 establish a data connection with the data processing module 2, and the communication module 11 is used to establish data communication between the inspection vehicle module 1 and the remote monitoring module 43 in the auxiliary module 4; the data processing module 2 includes a data preprocessing module 21, a data storage module 22, a data transmission module 23 and a data normalization module 24, wherein the data transmission module 23 establishes a data connection with the inspection vehicle module 1 and the control module 3, and the data preprocessing module 21 is used to clean, organize and normalize the original data, including removing invalid data, filling missing values, correcting erroneous data, converting data formats, etc. Through preprocessing, the accuracy, completeness and consistency of the data can be ensured. The data storage module 22 is used to store data, the data transmission module 23 is used for data transmission, and the data normalization module 24 is used to convert the data into a unified format or range, so that data from different sources and different dimensions can be compared and analyzed, and these data are converted to the same scale, thereby simplifying the subsequent data processing and analysis work and improving the efficiency of data processing; on the inspection vehicle module 1 The data is connected with an auxiliary module 4, and the auxiliary module 4 establishes a data connection with the data processing module 2 and the control module 3; the auxiliary module 4 includes a log recording module 41, a human-computer interaction module 42, a remote monitoring module 43 and a time synchronization module 44. The log recording module 41 is used to record the operation log of the inspection vehicle, including task execution, fault handling and other information. The human-computer interaction module 42 is used to provide a user interface to facilitate the operator to interact with the inspection vehicle. The remote monitoring module 43 is used to allow remote users to monitor the operating status of the inspection vehicle through the network. The time synchronization module 44 is used to ensure that the internal clock of the inspection vehicle is consistent with the external time.

[0027] Based on the above, the advantages of the present invention are: when the present invention is used for inspection, the inspection vehicle module 1 is used to perform inspections along the inspection route. During the process, the laser radar module 13, the image acquisition module 14, the infrared imaging module 16, the inertial measurement module 33 and the odometer module 34 are used to measure and collect relevant data, and the data transmission module 23 in the data processing module 2 is used for data transmission. Then, the raw data is cleaned, sorted and normalized by the data preprocessing module 21, including removing invalid data, filling missing values, correcting erroneous data, converting data formats and other operations. Through preprocessing, the accuracy, completeness and consistency of the data can be ensured. The data normalization module 24 is used to convert the data into a unified format or range, so that different sources, Data of different dimensions can be compared and analyzed, and these data can be converted to the same scale, thereby simplifying subsequent data processing and analysis work and improving the efficiency of data processing. The data is stored in the data storage module 22, and then the algorithm fusion module 32 in the control module 3 uses the extended Kalman filter algorithm to fuse the data of the inertial measurement module 33 and the odometer module 34 to estimate the posture of the inspection vehicle. Subsequently, the adaptive Monte Carlo positioning algorithm is combined with the data of the laser radar module 13 to achieve accurate acquisition of global coordinates. Then, the grid map is selected as the environmental model through the map construction module 35, and the map is constructed on the ROS platform using the Gmapping algorithm. The map is tested through the Gazebo simulation environment, and then the map is constructed through the Gazebo simulation environment. The inspection path of the inspection vehicle is planned by the path planning module 31. The path planning module 31 includes global path planning and local path planning. The global path planning adopts the A* algorithm. By introducing the heuristic function to reduce the search nodes, the shortest path planning is realized. The local path planning uses the dynamic window method. According to the real-time status and environmental information of the inspection vehicle, the driving speed and direction are dynamically adjusted to achieve obstacle avoidance. The algorithm fusion module 32 combines the global and local path planning algorithms to ensure that the unmanned vehicle can find the optimal and conflict-free path in a complex environment. The image fusion module 37 introduces a dual-branch GAN structure, which consists of a dense residual subnet and an attention subnet. The dense residual subnet extracts robust feature information by introducing dense connection blocks and residual networks to solve the halo problem. Artifacts and gradient explosion problems are solved by using the attention subnet to make up for the lack of spatial information, improve the structural similarity, brightness and contrast between the fused image and the source image, and simulate the real image by weighted averaging infrared and visible light images to adjust the fusion result and enrich the diversity of fusion features. The hidden danger detection module 36 combines the SSD target detection algorithm and the three-frame difference method to process the fused image to detect fire hazards. First, objects without fire hazards are identified through SSD and filled with zero values. Then, the three-frame difference method is used to calculate the average pixel value difference between adjacent frames to determine whether there is a fire hazard. The three-frame difference method separates the hidden danger objects through morphological expansion and AND operation, and corrects the error boundary through difference operation, which effectively reduces noise interference and boundary error.Accurate detection of fire hazards is achieved, wherein the real-time optimization module 38 is used to optimize algorithm parameters, reduce the amount of calculation and parameters, improve the real-time performance of the system, use dense connections and parameter sharing mechanisms to reduce network parameters, and adopt simple weighted average operations to obtain adjustment images to reduce calculation complexity. During the operation of the inspection vehicle, the solar energy can be converted into electrical energy by unfolding the solar module 15 to provide auxiliary power supply for the inspection vehicle, and can be adjusted in real time according to the irradiation angle of sunlight relative to the inspection vehicle. When the solar panel 156 is in a horizontal state, the positioning column 157 thereon is placed on the positioning seat 158, and the push cylinder 1510 on the fixed seat 159 on one side pushes the positioning block 1511 to extend, and cooperates with the positioning seat 158 ​​to fix the solar panel 156. The other positioning columns 157 are in an unlocked state, and the top cylinder 153 pushes the solar panel 156 through the top column 154 to flip upward with the positioning column 157 on the fixed side as the center. Achieve the best lighting angle and maximize the power generation efficiency of the solar module 15. The battery 19 is used to store electricity and power the system. One end of the top column 154 is connected to the solar panel 156 through the ball joint 155. The top cylinder 153 is connected to the vehicle body 17 through the first hinge seat 151 and the second hinge seat 152 to achieve flexible rotation within a certain range. The communication module 11 is used to establish data communication between the inspection vehicle module 1 and the remote monitoring module 43 in the auxiliary module 4. The drive module 12 is used to drive the mobile wheel 18 to move the inspection vehicle. The log recording module 41 is used to record the operation log of the inspection vehicle, including task execution, fault handling and other information. The human-computer interaction module 42 is used to provide a user interface to facilitate the interaction between the operator and the inspection vehicle. The remote monitoring module 43 is used to allow remote users to monitor the operation status of the inspection vehicle through the network. The time synchronization module 44 is used to ensure that the internal clock of the inspection vehicle is consistent with the external time.

[0028] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A patrol vehicle based on big data, comprising a patrol vehicle module (1), characterized in that: The inspection vehicle module (1) comprises a vehicle body (17), a solar module (15) is fixedly connected to the vehicle body (17), the solar module (15) comprises a first hinge seat (151), and the first hinge seat (151) is fixedly connected to the vehicle body (17), a second hinge seat (152) is hinged to the first hinge seat (151), a top cylinder (153) is fixedly connected to the second hinge seat (152), a solar panel (156) is arranged at the top end of the top cylinder (153), a ball joint (155) is fixedly connected to one side outer wall of the solar panel (156), a top column (154) is fixedly connected to the output end of the top cylinder (153), and the top column (154) is hinged to the ball joint (155).

2. The inspection vehicle based on big data according to claim 1 is characterized in that: A positioning column (157) is fixedly connected to the outer wall of the solar panel (156); a positioning seat (158) is fixedly connected to a position on the vehicle body (17) corresponding to the positioning column (157); the positioning column (157) is arranged in the positioning seat (158); a fixing seat (159) is arranged on one side of the positioning seat (158); and the fixing seat (159) is fixedly connected to the vehicle body (17).

3. The inspection vehicle based on big data according to claim 2 is characterized in that: A push cylinder (1510) is fixedly connected to the fixed seat (159), and a positioning block (1511) is fixedly connected to the output end of the push cylinder (1510).

4. The inspection vehicle based on big data according to claim 2 is characterized in that: A battery (19) is fixedly connected to the inner wall of the vehicle body (17), and the battery (19) is electrically connected to the solar panel (156).

5. The inspection vehicle based on big data according to claim 2 is characterized in that: The vehicle body (17) is provided with moving wheels (18).

6. A control system for a patrol vehicle based on big data, comprising a patrol vehicle module (1), characterized in that: The inspection vehicle module (1) is data-connected to a data processing module (2), the data processing module (2) is data-connected to a control module (3), and the control module (3) establishes a data connection with the inspection vehicle module (1), and the control module (3) comprises a path planning module (31), an algorithm fusion module (32), an inertial measurement module (33), an odometer module (34), a map construction module (35), a hidden danger detection module (36), an image fusion module (37) and a real-time optimization module (38).

7. The control system of the inspection vehicle based on big data according to claim 6 is characterized in that: The inspection vehicle module (1) comprises a communication module (11), a driving module (12), a laser radar module (13), an image acquisition module (14), a solar module (15) and an infrared imaging module (16), wherein the driving module (12) establishes a data connection with the control module (3), and the laser radar module (13), the image acquisition module (14) and the infrared imaging module (16) establish a data connection with the data processing module (2).

8. The control system of the inspection vehicle based on big data according to claim 6 is characterized by: The data processing module (2) comprises a data preprocessing module (21), a data storage module (22), a data transmission module (23) and a data normalization module (24), wherein the data transmission module (23) establishes a data connection with the inspection vehicle module (1) and the control module (3).

9. The control system of the inspection vehicle based on big data according to claim 6 is characterized by: The inspection vehicle module (1) is data-connected to an auxiliary module (4), and the auxiliary module (4) establishes data connections with the data processing module (2) and the control module (3).

10. The control system of the inspection vehicle based on big data according to claim 9 is characterized in that: The auxiliary module (4) comprises a log recording module (41), a human-computer interaction module (42), a remote monitoring module (43) and a time synchronization module (44).

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