Municipal intelligent drawing scribing device

Abstract

The invention discloses a municipal intelligent drawing lineation device, and relates to the technical field of municipal lineation, the device comprises a lineation trolley, a sensing module, a path planning module, a mechanical arm control module and an intelligent decision and parameter self-adaptive control module; advanced sensing technologies such as radar, inertial navigation and machine vision, a path planning algorithm based on geometric modeling and an intelligent decision-making and parameter self-adaptive control module are integrated in the line drawing device, full-automatic operation is achieved, the road line drawing efficiency is greatly improved, the problem that traditional manual line drawing efficiency is low is effectively solved, and meanwhile, the line drawing efficiency is greatly improved. Based on a multi-sensor fusion technology and a path planning module, the device can adapt to various marking modes, and by combining with a mechanical arm control module capable of flexibly adjusting parameters, the device has high flexibility and adaptability and can complete personalized marking tasks in complex terrains and multiple scenes; the limitation that an existing automatic lineation device is poor in scene adaptability and single in function is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal road marking, and specifically provides a municipal intelligent road marking device. Background Art

[0002] With the continuous acceleration of the urbanization process, the urban traffic flow is increasing day by day, and the demand for traffic management is becoming more and more diverse. In the process of road construction and maintenance, road markings, as an important part of traffic management, the quality and efficiency of their drawing directly affect traffic safety and smoothness. Road markings, as an important part of the road traffic system, are traffic safety facilities composed of various lines, arrows, texts, elevation markings, raised road signs, and contour markers marked on the road surface. These markings convey traffic management instructions such as guidance, restriction, and warning to road users with intuitive and clear visual information. Road marking is a concrete manifestation of urban traffic planning, which helps to achieve scientific allocation and effective management of traffic resources. It can flexibly set the number of lanes, directions, and parking areas according to the traffic flow and functional requirements of different regions, and improve the overall operation efficiency of the urban traffic system.

[0003] However, the current road marking technology still has certain defects. The current road marking technology mainly relies on manual operation, and its main limitations are low efficiency and high cost. In addition, although the existing automatic road marking devices have been improved in some aspects, they are mostly limited to specific scenarios (such as highways) or single functions (such as lane line marking), lacking comprehensive intelligence and adaptability, and it is difficult to meet the diverse needs in urban traffic management. For example, the existing technology is difficult to flexibly handle the marking tasks in special environments such as complex terrains and sharp turn sections, and the setting method of unified marking specifications cannot meet personalized needs. Therefore, it is of great significance to develop a municipal intelligent road marking device. Summary of the Invention

[0004] The purpose of the present invention is to make up for the deficiencies of the existing technology, and provides a municipal intelligent road marking device, which can solve the problems of low efficiency and high cost of traditional manual road marking, and at the same time overcome the limitations of the existing automatic road marking devices in terms of scene adaptability and function diversity, realize the precise drawing of various lines such as lane lines, indication lines, and parking space lines, significantly improve the road marking efficiency and cost-effectiveness, and meet the diverse and personalized needs of urban traffic management.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A municipal intelligent road marking device, which includes: a marking trolley, a perception module, a path planning module, a robotic arm control module, and an intelligent decision-making and parameter adaptive control module;

[0006] The marking trolley, as the device carrier, is equipped with a four-wheel drive system and a large-capacity lithium battery pack;

[0007] The perception module is installed on the line marking vehicle and includes a radar, an inertial navigation system, and a machine vision system for collecting road surface environment information;

[0008] The path planning module is integrated in the control center of the line marking vehicle, integrates high-precision map data, combines the information of the perception module, and uses a path planning algorithm based on geometric modeling to calculate the line marking path. The path planning algorithm is based on the formula P = f(S, M, T), where P is the planned path, S is the data collected by the sensor, M is the high-precision map data, T is the preset marking type parameter, and f is a custom function for generating the planned path according to the relationship among the three;

[0009] The robotic arm control module is installed at the rear of the line marking vehicle and controls a high-performance robotic arm through a servo drive system for line marking operations;

[0010] The intelligent decision-making and parameter adaptive control module is integrated in the control center of the line marking vehicle and adjusts the line marking parameters according to the real-time environment data according to the formula V = g(E, L), where V is the line marking speed adjustment value, E is the environment data, L is the current marking length parameter, and g is a custom function for determining the speed adjustment value according to the environment and the marking length.

[0011] Furthermore, the environment data E is obtained through E = h(R data , I data , N data ), where R data is the road surface obstacle position and distance data collected by the radar, I data is the device position, attitude, and motion state data obtained by the inertial navigation system, N data is the image information of the road surface material, flatness, and marking condition recognized by the machine vision system, and h is a data fusion function for fusing R data , I data , N data to obtain the environment data E.

[0012] Even further, the custom function g is obtained by performing machine learning training on experimental data. The experimental data includes different environment data E, marking lengths L, and the corresponding optimal line marking speed adjustment values V. The training process uses an algorithm to process the data, constructs a relationship model between E, L, and V, and finally determines the expression of the function g, that is, g = G({E i , L i , V i}), where {E i , L i , V i} represents a data set of environment data, marking lengths, and optimal line marking speed adjustment values obtained from multiple groups of experiments.

[0013] Further, in the perception module, a frequency modulated continuous wave radar is used to obtain the distance information of road obstacles. The inertial navigation system uses a strapdown inertial measurement device to measure acceleration and angular velocity to calculate position and attitude changes. The machine vision system uses a convolutional neural network to identify the characteristics of road markings.

[0014] Further, when planning the path, the path planning module takes into account the road curvature factor and calculates the safe turning radius through the formula where R is the turning radius, v is the driving speed of the line marking vehicle, and a is the maximum allowable acceleration. The maximum allowable acceleration a is determined through comprehensive tests of the mechanical properties of the device and the friction between the tires and the ground.

[0015] Further, the servo drive system of the robotic arm control module uses vector control technology to achieve precise positioning of the robotic arm. During the system debugging stage, the impedance Z of the motor is obtained by testing the impedance characteristics of the motor under different loads and operating speeds. The specific test method is to set different operating speeds under various load conditions, measure the voltage and current of the motor, and use the formula to calculate the impedance values under different working conditions, and then select the impedance value as the control parameter according to the actual operating conditions.

[0016] Further, the intelligent decision-making and parameter adaptive control module has the function of adjusting the coating spraying pressure. When adjusting the coating spraying pressure, the formula P spray = k × Rough + b is used, where P spray is the coating spraying pressure and Rough is the road roughness. The coefficients k and b in the formula are determined by performing multiple groups of spraying experiments on road samples with different roughness levels and fitting the experimental data using the least squares method.

[0017] Further, the device also includes a data storage module and a wireless communication module. The data storage module adopts a distributed storage architecture and is installed in the line marking vehicle. It classifies and stores data according to the type and time of the data, and establishes an indexing mechanism based on data characteristics. The wireless communication module is installed on the line marking vehicle, supports the 5G communication protocol, adopts adaptive modulation and coding technology, dynamically adjusts the modulation and coding method according to the signal strength and interference situation, and switches the modulation method according to the set threshold by real-time monitoring the signal-to-noise ratio of the received signal.

[0018] Compared with the prior art, the municipal intelligent line marking device has the following beneficial effects:

[0019] By integrating advanced sensing technologies such as radar, inertial navigation, and machine vision, as well as path planning algorithms based on geometric modeling and intelligent decision-making and parameter adaptive control modules in the line marking device, the present invention realizes fully automated operation, greatly improves the road line marking efficiency, and effectively solves the problem of low efficiency of traditional manual line marking. At the same time, based on the multi-sensor fusion technology of the sensing module and the algorithms of the path planning module, it can adapt to various marking methods. Combined with the robotic arm control module with flexible parameter adjustment, the device has high flexibility and adaptability, and can complete personalized line marking tasks in complex terrains and multiple scenarios, overcoming the limitations of poor scene adaptability and single function of existing automatic line marking devices.

[0020] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. Brief Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic structural diagram of a municipal intelligent line marking device;

[0023] Figure 2 It is a schematic working process diagram of a municipal intelligent line marking device. Detailed Embodiments

[0024] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific embodiments, structures, features, and their effects of the present invention as follows.

[0025] Embodiment 1

[0026] Refer to Figure 1 and Figure 2 , in a new urban area of a rapidly developing mountain city, large-scale road construction and optimization projects are underway. The terrain in this area is complex and changeable, including many curves, steep slopes, and the road surface materials are diverse, such as cement roads and asphalt roads. At the same time, there are multiple under-construction roads under construction in the surrounding area, resulting in a large traffic flow and complex traffic directions, which pose extremely high requirements for traffic guidance and the accuracy and efficiency of line marking. In this context, this municipal intelligent line marking device plays a key role.

[0027] The construction team transports the municipal intelligent line marking device to the designated area at the construction site. First, the technicians conduct a comprehensive inspection of the line marking cart to ensure that all components of the four-wheel drive system are firmly connected, the tire pressure is normal, and the power output is stable; the large-capacity lithium battery pack has sufficient power, and the charging and power supply lines are free of faults. Then, the perception module, path planning module, robotic arm control module, and intelligent decision-making and parameter adaptive control module are powered on for testing to check whether the data transmission lines between the modules are properly connected to ensure that the modules can work together. At the same time, within the device control center, the technicians input the preset marking type parameters T required for this construction, including the specific specifications and style information of lane lines, guiding lines, parking space lines, etc.

[0028] After the device is started, the perception module begins to work. The frequency-modulated continuous-wave radar installed on the line marking cart continuously emits electromagnetic waves, and precisely collects the position and distance data R of road obstacles by receiving the reflected signals. data , These data can provide real-time feedback on whether there are construction equipment, roadblocks and other objects ahead, providing key information for subsequent path planning. The inertial navigation system uses the internal gyroscope and accelerometer to continuously measure the position, attitude and motion state data I of the device. data , Precisely record the real-time position changes, driving directions and accelerations of the line marking cart in the construction area. The machine vision system uses multiple high-definition cameras to comprehensively collect road surface images, and uses the powerful image recognition ability of the convolutional neural network to accurately identify the image information N of the road surface material, flatness and marking conditions. data , Such as judging whether the road surface is a rough cement road surface or a relatively smooth asphalt road surface, and whether the existing markings need to be repaired or covered, etc. These data are fused through the data fusion function E = h(R data ,I data ,N data ) for fusion processing to generate comprehensive and accurate environmental data E, providing a reliable basis for subsequent decision-making and operations.

[0029] The path planning module integrates high-precision map data M, combines the sensor data S collected by the perception module (including the key information in the environmental data E) and the preset marking type parameters T, and performs complex calculations using the path planning algorithm based on geometric modeling. The optimal marking path P is calculated through the formula P = f(S, M, T). This path not only considers the current road surface conditions and obstacle distributions, but also combines the requirements of different marking types to ensure that the marking operation can be carried out efficiently and safely.

[0030] During the planning process, in view of the characteristics of many curves in this area, the path planning module passes the formula Calculate the safe turning radius R, where v is the driving speed of the line marking vehicle, and a is the maximum allowable acceleration. The value of a was determined by the previous technicians through a large number of comprehensive tests on factors such as the mechanical performance of the device and the friction between the tires and different road surface materials, so as to ensure the stability and safety of the device when marking at a bend and avoid situations where the equipment gets out of control or the marking is inaccurate due to too small a turning radius.

[0031] After the robotic arm control module receives the line marking path P generated by the path planning module, it starts to control the high-performance robotic arm for line marking operations. The servo drive system of the robotic arm adopts vector control technology. By precisely controlling the voltage and current of the motor, precise positioning and motion control of the robotic arm are achieved. During the system debugging stage, the technicians set different operating speeds under various load conditions, measure the voltage U and current I of the motor, and use the formula to obtain the impedance value Z of the motor under different working conditions, and select a suitable impedance value as the control parameter according to the actual operating conditions to ensure that the robotic arm can accurately move along the preset path during the line marking process, with the error controlled within a very small range. The line marking spray gun at the end of the robotic arm sprays the paint evenly on the road surface according to the set parameters to start drawing various markings.

[0032] During the line marking operation, the intelligent decision-making and parameter adaptive control module keeps working. According to the real-time environmental data E and the current marking length parameter L, it dynamically adjusts the line marking speed adjustment value V according to the formula V = g(E, L). The custom function q is obtained through machine learning training on a large number of experimental data containing different environmental data E, marking length L, and the corresponding optimal line marking speed adjustment value V. It can intelligently adjust the line marking speed according to the actual situation. For example, it appropriately reduces the speed when the road surface is uneven or when encountering a complex bend to ensure the quality and coherence of the markings; it increases the speed when the road conditions are good to improve the construction efficiency.

[0033] At the same time, this module will also adopt the formula P spray = k × Rough + b to adjust the paint spraying pressure P spray . The coefficients k and b are determined by fitting the experimental data using the least squares method through multiple groups of spraying experiments on road surface samples with different roughness levels. This can ensure that the paint can be evenly attached on different road surface materials to form clear and firm markings. In addition, the intelligent decision-making and parameter adaptive control module will also monitor various parameters during the line marking process in real time, such as the paint flow rate, spray gun temperature, etc. Once an abnormality is detected, it will immediately make adjustments or issue an alarm to ensure the smooth progress of the line marking operation.

[0034] The data storage module adopts a distributed storage architecture and is installed in the line marking trolley. During the construction process, it will collect and store in real time the data collected by the sensing module, the path data generated by the path planning module, and various parameter data during the line marking process, etc. These data are classified and stored according to the type and time of the data. For example, the road surface environment data collected in different time periods are stored in different folders, and at the same time, an indexing mechanism based on data characteristics is established, such as establishing indexes according to key information such as line marking type, construction area, and time, to facilitate subsequent rapid query and analysis.

[0035] The wireless communication module is installed in the line marking trolley, supports the 5G communication protocol, and adopts adaptive modulation and coding technology. It transmits the working state data of the device, including position information, line marking progress, equipment operation parameters, etc., to the remote monitoring center in real time. During the communication process, according to the signal strength and interference situation, by real-time monitoring the signal-to-noise ratio of the received signal, the modulation method is switched according to the set threshold. When the signal strength is good and the interference is small, a high-order modulation method is adopted to improve the data transmission rate and ensure that the remote monitoring center can obtain detailed construction information in time; when the signal is interfered or the strength is weak, it automatically switches to a low-order modulation method to ensure the stability of data transmission, avoid data loss or errors, so that construction management personnel can grasp the construction site situation in real time and make decisions and adjustments in time.

[0036] In summary, through the application in the scenario of this embodiment, the municipal intelligent line marking device has shown significant advantages. Compared with traditional manual line marking, the construction efficiency has been greatly improved. The complex road line marking work that originally required a large amount of manpower and time can now be completed with the help of this device in a shorter time, greatly shortening the construction period and reducing the impact on the surrounding traffic. Since the device adopts advanced sensing technology, accurate path planning algorithms and intelligent parameter adjustment mechanisms, it can automatically optimize the line marking operation according to complex terrain and road surface conditions, ensuring high-quality drawing of the line markings, and greatly improving the accuracy, clarity and coherence of the line markings, effectively enhancing the road traffic safety guarantee.

[0037] Embodiment 2

[0038] See Figure 1 and Figure 2 In a road renovation project around a large commercial center, the traffic flow in this area is extremely large, especially during peak hours, and the traffic congestion is serious. Moreover, special activities are often held in the commercial center, and it is necessary to temporarily adjust the road markings to meet different traffic control requirements, with extremely high requirements for the flexibility and efficiency of road marking drawing. In such a scenario, the working process of this municipal intelligent line marking device is as follows.

[0039] During the period when the night business center is closed and the traffic flow is small, the construction team quickly transports the municipal intelligent painting line device to the construction site. After arriving at the site, the technicians quickly conduct a comprehensive inspection of the painting line cart, confirm that the four-wheel drive system is operating normally, and ensure that it can respond flexibly to frequent starts, stops, and turning operations on urban roads; check the power and charging status of the large-capacity lithium battery pack to ensure that the device has a stable power supply throughout the construction process.

[0040] At the same time, conduct functional tests on the perception module, path planning module, robotic arm control module, and intelligent decision-making and parameter adaptive control module to ensure smooth data transmission between modules. According to the traffic control requirements of the business center and the road renovation plan, the technicians input the preset marking type parameter T required for this construction, such as relevant specification information for temporarily added tidal lane markings, dedicated activity passage markings, etc., into the device control center.

[0041] After the device is started, the perception module begins to work. The frequency-modulated continuous wave radar is installed at the front end of the painting line cart to monitor road obstacles in real time and collect the position and distance data R of road obstacles data , including information such as parked vehicles on the roadside and construction material stacking points. The inertial navigation system obtains the position, attitude, and motion state data I of the device in real time data , and accurately records the changes in the driving trajectory of the painting line cart under the complex road conditions of urban roads.

[0042] The machine vision system uses multiple high-definition cameras to collect road surface images from different angles, and identifies the image information N of road surface materials, flatness, and existing marking conditions through convolutional neural networks data , judges whether there are wear, cracks, etc. on the road surface, and whether the existing markings need to be removed or covered. These data are fused through the data fusion function E = h(R data ,I data ,N data for fusion processing to generate comprehensive and accurate environmental data E, providing a reliable basis for subsequent path planning and parameter adjustment.

[0043] The path planning module integrates high-precision map data M, combines the sensor data S (including environmental data E) collected by the perception module and the preset marking type parameters T, and performs operations using a path planning algorithm based on geometric modeling. It calculates the painting path P that meets the construction requirements through the formula P = f(S, M, T). Due to the complex road conditions around commercial centers, with many intersections and changing traffic flows, the path planning module fully considers these factors during the planning process. For example, when planning the tidal lane markings, it combines the traffic flow statistics data at different times and the road capacity to plan a reasonable lane change path; when planning the markings for dedicated activity channels, it designs a safe and efficient guiding path based on the location of the activity venue and the expected directions of pedestrian and vehicle flows.

[0044] Meanwhile, considering the safety issues when the vehicle turns at intersections, the path planning module calculates the safe turning radius R through the formula where v is the driving speed of the painting trolley, and a is the maximum allowable acceleration. The value of a is determined through comprehensive tests considering factors such as the mechanical performance of the device under urban road driving conditions and the friction between the tires and the ground, ensuring that the painting path complies with traffic safety specifications.

[0045] The robotic arm control module controls the high-performance robotic arm to perform painting operations based on the painting path P generated by the path planning module. The servo drive system of the robotic arm uses vector control technology. During the system debugging phase, technicians set multiple operating speeds under different load conditions simulating urban road construction, measure the voltage U and current I of the motor, and use the formula to obtain the impedance value Z of the motor under different working conditions, and select a suitable impedance value as the control parameter according to the actual construction situation to achieve precise positioning and motion control of the robotic arm.

[0046] During the painting process, the robotic arm accurately guides the paint spraying gun to perform coating spraying operations according to the preset marking parameters, such as the marking width, length, shape, etc., ensuring that the drawing accuracy of the markings meets the construction requirements.

[0047] During the painting operation, the intelligent decision-making and parameter adaptive control module plays a key role. It dynamically adjusts the painting speed adjustment value V according to the real-time environmental data E and the current marking length parameter L according to the formula V = g(E, L). The custom function g is obtained through machine learning training on a large amount of experimental data in urban road construction scenarios. These data include different environmental data E, marking length L, and the corresponding optimal painting speed adjustment value V. For example, when encountering uneven road surfaces or temporary vehicle parking in front that affects construction, the intelligent decision-making and parameter adaptive control module will automatically reduce the painting speed to ensure the quality of the markings; when the traffic flow in the construction area is small and the road conditions are good, it will appropriately increase the painting speed to improve construction efficiency.

[0048] Meanwhile, the module will also adopt the formula P spray = k × Rough + b to adjust the paint spraying pressure P spray . The coefficients k and b are determined by performing multiple groups of spraying experiments on urban road surface samples with different roughness levels and fitting the experimental data using the least squares method. This can ensure that the paint can be evenly attached to the road surface under different road conditions, forming clear and firm markings. In addition, the intelligent decision-making and parameter adaptive control module will also monitor various parameters during the line marking process in real time, such as the remaining amount of paint, the working status of the spray gun, etc. When parameter anomalies are detected, adjustments will be made in a timely manner or an alarm will be issued to ensure the smooth progress of the line marking operation.

[0049] The data storage module adopts a distributed storage architecture and is installed in the line marking vehicle. During the construction process, it will collect and store in real time the data collected by the sensing module, the path data generated by the path planning module, and various parameter data during the line marking process, etc. These data are classified and stored according to the type and time of the data, and at the same time, an indexing mechanism based on data characteristics is established to facilitate subsequent query and analysis.

[0050] The wireless communication module is installed in the line marking vehicle, supports the 5G communication protocol, and adopts adaptive modulation and coding technology. It transmits the working status data of the device, including real-time position, line marking progress, equipment operation parameters, etc., to the remote monitoring center in real time. Construction management personnel can view the construction situation in real time at the remote monitoring center, remotely adjust construction parameters or issue new construction tasks according to actual needs. During the communication process, the wireless communication module switches the modulation mode according to the signal strength and interference situation by real-time monitoring the signal-to-noise ratio of the received signal and based on the set threshold to ensure the stability and timeliness of data transmission, and achieve efficient remote collaboration and construction management.

[0051] In summary, in the scenario of this embodiment, the municipal intelligent line marking device demonstrates excellent performance. Compared with the traditional road line marking method, its construction efficiency has been significantly improved, and it can complete complex marking tasks within the limited night construction period, minimizing the impact on daytime traffic to the greatest extent. The flexibility of the device enables it to quickly adapt to the changing traffic control requirements of the roads around commercial centers and accurately draw various special markings. Through advanced sensing technology, intelligent path planning and parameter adjustment mechanisms, the high-quality drawing of markings is guaranteed, improving the traffic safety and traffic management efficiency of the road.

[0052] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A municipal intelligent drawing and marking device, characterized in that, The device includes: a scribing cart, a sensing module, a path planning module, a robotic arm control module, and an intelligent decision-making and parameter adaptive control module; The scribing cart serves as the device carrier and is equipped with a four-wheel drive system and a large-capacity lithium battery pack; The sensing module is installed on the scribing cart and includes a radar, an inertial navigation system, and a machine vision system for collecting road surface environment information; The path planning module is integrated into the control center of the scribing cart, integrates high-precision map data, combines the information of the sensing module, and uses a path planning algorithm based on geometric modeling to calculate the scribing path. The path planning algorithm is based on the formula P = f(S, M, T), where P is the planned path, S is the data collected by the sensor, M is the high-precision map data, T is the preset marking type parameter, and f is a custom function that generates the planned path according to the relationship between the three; The robotic arm control module is installed at the rear of the scribing cart and controls a high-performance robotic arm to perform scribing operations through a servo drive system; The intelligent decision-making and parameter adaptive control module is integrated into the control center of the scribing cart and adjusts the scribing parameters according to the real-time environment data according to the formula V = g(E, L), where V is the scribing speed adjustment value, W is the environment data, L is the current marking length parameter, and g is a custom function that determines the speed adjustment value according to the environment and the marking length.

2. The municipal intelligent line drawing device according to claim 1, wherein, The environmental data E is obtained through E = h(R data , I data , N data ), where R data is the road surface obstacle position and distance data collected by the radar, I data is the device position, attitude and motion state data obtained by the inertial navigation system, N data is the image information of the road surface material, flatness and marking condition recognized by the machine vision system, and h is a data fusion function that fuses R data , I data , N data to obtain the environmental data E.

3. A municipal intelligent drawing and marking device according to claim 1, characterized in that, The custom function g is obtained by performing machine learning training on experimental data. The experimental data includes different environmental data E, marking line lengths L, and corresponding optimal marking speed adjustment values V. During the training process, an algorithm is used to process the data to construct a relationship model between E, L, and V, and finally the expression of the function g is determined, that is, g = G({E i ,L i ,V i}), where {E i ,L i ,V i} represents a data set of environmental data, marking line lengths, and optimal marking speed adjustment values obtained from multiple experiments.

4. A municipal intelligent drawing and marking device according to claim 1, characterized in that, In the sensing module, the radar uses a frequency-modulated continuous-wave radar to obtain the distance information of road surface obstacles. The inertial navigation system uses a strapdown inertial measurement device to measure acceleration and angular velocity to calculate the position and attitude changes. The machine vision system uses a convolutional neural network to identify the characteristics of road surface markings.

5. A municipal intelligent drawing and marking device according to claim 1, characterized in that, When planning the path, the path planning module takes into account the road curvature factor and calculates the safe turning radius through the formula where R is the turning radius, v is the driving speed of the line marking trolley, and a is the maximum allowable acceleration. The maximum allowable acceleration a is determined by comprehensively testing the mechanical performance of the device and the friction between the tire and the ground.

6. The municipal intelligent drawing and marking device according to claim 1, characterized in that, The servo drive system of the robotic arm control module uses vector control technology to achieve precise positioning of the robotic arm. During the system debugging phase, the impedance Z of the motor is obtained by testing the impedance characteristics of the motor under different loads and operating speeds. The specific testing method is to set different operating speeds under various load conditions, measure the voltage and current of the motor, and use the formula to calculate the impedance values under different working conditions, and then select the impedance value as the control parameter according to the actual operating conditions.

7. The municipal intelligent line drawing device according to claim 1, characterized in that, The intelligent decision-making and parameter adaptive control module has the function of adjusting the paint spraying pressure. When adjusting the paint spraying pressure, the formula P spray = k × Rough + b is adopted, where P spray is the paint spraying pressure and Rough is the road surface roughness. The coefficients k and b in the formula are determined by performing multiple groups of spraying experiments on road surface samples with different roughness levels and fitting the experimental data using the least squares method.

8. The municipal intelligent line drawing device according to claim 1, wherein, The device also includes a data storage module and a wireless communication module. The data storage module adopts a distributed storage architecture, is installed in the scribing cart, classifies and stores data according to the type and time of the data, and establishes an indexing mechanism based on data characteristics. The wireless communication module is installed on the scribing cart, supports the 5G communication protocol, uses adaptive modulation and coding technology, dynamically adjusts the modulation and coding method according to the signal strength and interference situation, and switches the modulation method according to the set threshold by real-time monitoring the signal-to-noise ratio of the received signal.

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