Unmanned intelligent compaction method, equipment, medium and product

Abstract

The invention relates to the technical field of unmanned driving, in particular to an unmanned intelligent compaction method and device, a medium and a product, and the method comprises the following steps: carrying out parameter analysis based on compaction area information and road roller information, and determining road roller parameters. And then, acquiring spatial position and attitude information of the road roller in real time, performing unmanned driving analysis based on the spatial position, the attitude information and the parameters of the road roller, generating a compaction control instruction, and controlling the road roller to perform compaction operation by using the compaction control instruction. And finally, in the process that the road roller executes the compaction operation, obtaining multi-dimensional compaction data in real time, and storing the multi-dimensional compaction data to the compaction operation database. In the process of setting the parameters of the road roller, the information of the compaction area and the information of the road roller are comprehensively considered so as to ensure that the parameters of the road roller can be highly matched with the type and the characteristic of the compaction material in the compaction area, so that the compaction effect of the compaction machine can be fully exerted, and the stability of the compaction quality is improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned driving, and in particular to an unmanned driving intelligent compaction method, equipment, medium and product. Background Art

[0002] With the continuous development of modern engineering technology and the widespread application of intelligent technology, unmanned driving technology has shown great application potential and value in various engineering fields. Especially in the fields of road construction, civil engineering, mining, etc., the emergence of unmanned rollers has not only greatly improved construction efficiency, but also reduced the risks and costs of manual operation.

[0003] In recent years, many unmanned rollers and intelligent compaction systems have appeared at home and abroad. Most of the unmanned rollers in related technologies can only realize basic unmanned driving functions and lack the organic combination with the digitalization of compaction quality. However, in actual application, unmanned rollers are affected by many factors, such as changes in soil properties, performance differences of compaction equipment, complexity of construction environment, etc., resulting in certain fluctuations in the stability of compaction quality and the defect of poor compaction quality stability.

[0004] Therefore, how to improve the stability of compaction quality is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0005] The purpose of this application is to provide an unmanned intelligent compaction method, equipment, medium and product to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In the first aspect, the present application provides an unmanned intelligent compaction method, which adopts the following technical solutions: An unmanned intelligent compaction method, comprising: Acquire compaction area information and roller information, perform parameter analysis based on the compaction area information and the roller information, and determine roller parameters, wherein the roller parameters include: path data, vibration state, and travel speed; Acquire the spatial position and posture information of the roller in real time, perform unmanned driving analysis based on the spatial position, the posture information and the roller parameters, generate compaction control instructions, and use the compaction control instructions to control the roller to perform compaction operations; During the compaction operation performed by the roller, multi-dimensional compaction data is acquired in real time and stored in a compaction operation database, wherein the multi-dimensional compaction data includes: real-time driving parameters, real-time environmental perception data, compaction operation parameters and equipment status information.

[0007] By adopting the above technical solution, the compaction area information and roller information are obtained, and parameter analysis is performed based on the compaction area information and roller information to determine the roller parameters. Then, the spatial position and posture information of the roller are obtained in real time, and unmanned driving analysis is performed based on the spatial position, posture information and roller parameters to generate compaction control instructions, and the compaction control instructions are used to control the roller to perform compaction operations. Finally, in the process of the roller performing compaction operations, multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting the roller parameters, the compaction area information and roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the roller parameters based on the compaction area information can achieve the expected compaction effect, so as to ensure that the compactor can give full play to its compaction effect, reduce the fluctuation of compaction quality, and improve the stability of compaction quality.

[0008] In a preferred example, the present application may be further configured as follows: after storing the multi-dimensional compaction data in the compaction operation database, the present application may further include: When it is detected that a compaction operation is completed on the compaction area, the whole process data of the compaction operation is extracted from the compaction operation database, wherein the whole process data of the compaction operation includes: the driving track corresponding to the roller, the land environment data corresponding to the driving track, and the compaction evaluation index corresponding to the driving track; Based on the compaction evaluation index in the whole compaction operation data, the compaction area is divided into compaction sections to determine a plurality of compaction sections; For a target compaction section, parameter optimization is performed based on the land environment data, the compaction evaluation index and the roller information corresponding to the target compaction section to determine optimized roller parameters, wherein the target compaction section is any one of the multiple compaction sections.

[0009] In a preferred example, the present application may be further configured as follows: in the process of the roller performing the compaction operation, after obtaining multi-dimensional compaction data in real time, the present application may further include: Perform obstacle identification based on the real-time environment perception data to determine obstacle information, wherein the obstacle information includes: obstacle type, obstacle location and obstacle size; The current spatial position and current posture information corresponding to the roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, the current posture information, the roller parameters and the obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes: obstacle avoidance type and obstacle avoidance operation.

[0010] In a preferred example, the present application may be further configured as follows: after performing autonomous obstacle avoidance planning based on the current spatial position, the current posture information, the roller parameters and the obstacle information, determining the autonomous obstacle avoidance information, further comprising: When the obstacle avoidance type is trajectory change obstacle avoidance, performing obstacle avoidance control analysis based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, generating a trajectory change obstacle avoidance instruction, and sending the trajectory change obstacle avoidance instruction to the roller, so that the roller avoids obstacles during the compaction operation; When the obstacle avoidance type is emergency braking, performing braking distance calculation based on the roller parameters to determine the emergency braking distance; Performing a warning feasibility analysis based on the current spatial position, the obstacle position and the emergency braking distance, and determining a warning feasibility analysis result; When the feasibility analysis result of the early warning is that the early warning is feasible, an emergency braking early warning is generated; when the feasibility analysis result of the early warning is that the early warning is not feasible, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller performs emergency braking to avoid obstacles during the compaction operation.

[0011] In a preferred example, the present application may be further configured as follows: performing parameter analysis based on the compaction area information and the roller information to determine roller parameters includes: Performing a driving path analysis based on the compaction area size and compaction area shape in the compaction area information to determine path data; Perform vibration analysis based on the compacted material type and compacted material characteristics in the compacted area information to determine the vibration state; Performing a travel analysis based on the compacted material type in the compacted area information and the equipment weight in the roller information to determine a travel speed; The roller parameters are determined by integrating the path data, the vibration state and the travel speed.

[0012] In a preferred example, the present application may be further configured as follows: after storing the multi-dimensional compaction data in the compaction operation database, the present application may further include: Performing operation data screening based on the compaction operation database to determine a target compaction operation data set, wherein the target compaction operation data set includes: multiple passes of road compaction data, each pass of the road compaction data is multi-dimensional compaction data corresponding to the same compaction area; An engineering construction map is obtained, and compaction visualization is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization display map, wherein the compaction visualization display map displays each pass of the road compaction data in layers.

[0013] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned unmanned intelligent compaction method.

[0014] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the unmanned driving intelligent compaction method described above.

[0015] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program, which implements the above-mentioned unmanned intelligent compaction method when executed by a processor.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The compaction area information and roller information are obtained, and parameter analysis is performed based on the compaction area information and roller information to determine the roller parameters. Then, the spatial position and posture information of the roller are obtained in real time, and unmanned driving analysis is performed based on the spatial position, posture information and roller parameters to generate compaction control instructions, and the compaction control instructions are used to control the roller to perform compaction operations. Finally, in the process of the roller performing compaction operations, multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting the roller parameters, the compaction area information and roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the use of roller parameters based on the compaction area information can achieve the expected compaction effect, so as to ensure that the compactor can give full play to its compaction effect, reduce the fluctuation of compaction quality, and improve the stability of compaction quality.

[0017] When it is detected that one compaction operation has been completed in the compaction area, the whole process data of the compaction operation is extracted from the compaction operation database, and based on the compaction evaluation index in the whole process data of the compaction operation, the compaction area is divided into compaction sections to determine multiple compaction sections. After the roller completes one compaction operation, an in-depth analysis is conducted based on the whole process data of the compaction operation to timely discover potential quality problems, and by accurately adjusting the working parameters of the roller, ensure that the compaction effect of subsequent compaction operations can meet the preset requirements, thereby improving the overall compaction quality. Then, for the target compaction section, parameter optimization is performed based on the land environment data, compaction evaluation index and roller information corresponding to the target compaction section, and the optimized roller parameters are determined. Executing parameter optimization to obtain the optimized roller parameters will help the roller achieve the best compaction effect in the next compaction operation, improve the density and strength of the road surface, and thus improve the overall quality of the road surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of an unmanned intelligent compaction method according to one embodiment of the present application; Figure 2 It is a structural schematic diagram of an unmanned intelligent compaction system according to one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides an unmanned driving intelligent compaction method, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Figure 1 As shown, the method includes step S101, step S102 and step S103, wherein: Step S101: Acquire compaction area information and roller information, perform parameter analysis based on the compaction area information and roller information, and determine roller parameters, wherein the roller parameters include: path data, vibration state, and travel speed.

[0025] For the embodiment of the present application, the electronic device pre-stores the compaction area information and the roller information that performs the compaction operation. The compaction area information includes but is not limited to: the size of the compaction area, the shape of the compaction area, the type of compacted material (e.g., soil, asphalt, cement, etc.) and the properties of the compacted material (e.g., the moisture content, density, particle size, etc. of the material); the roller information includes but is not limited to: the roller weight, the vibration system performance (e.g., key parameters such as vibration frequency and amplitude) and the travel system performance (e.g., key parameters such as travel speed, steering ability, and climbing ability). Furthermore, parameter analysis is performed based on the compaction area information and the roller information to determine the roller parameters, wherein the roller parameters include: path data, vibration state, and travel speed. There are many specific implementation methods for parameter analysis, which are no longer limited in the embodiments of the present application. In one feasible method, a driving path analysis is performed based on the compaction area size and compaction area shape in the compaction area information to determine the path data; a vibration analysis is performed based on the compaction material type and compaction material characteristics in the compaction area information to determine the vibration state; a travel analysis is performed based on the compaction material type in the compaction area information and the equipment weight in the roller information to determine the travel speed; and the roller parameters are determined by comprehensively considering the path data, vibration state and travel speed.

[0026] Most of the unmanned rollers in the related art can only realize basic unmanned driving functions, that is, perform compaction operations according to fixed driving paths and fixed working parameters. However, in the actual compaction operation process, the different soil properties, the equipment performance differences of the compactor and the complexity of the construction environment will affect the compaction quality of the roller after work, that is, the compaction operation with fixed working parameters cannot meet the requirements of high compaction quality in complex compaction areas. Therefore, in order to solve the above technical defects, in the embodiment of the present application, in the process of setting the roller parameters, the compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the use of roller parameters based on the compaction area information can achieve the expected compaction effect. At the same time, different roller equipment can work under the best parameters to ensure that the compactor can give full play to its compaction effect. The comprehensive parameter analysis in the embodiment of the present application enables the roller to more accurately adapt to the compaction needs of the current compaction area, reduce the fluctuation of the compaction quality, and improve the stability of the compaction quality.

[0027] Step S102: Acquire the spatial position and posture information of the roller in real time, perform unmanned driving analysis based on the spatial position, posture information and roller parameters, generate compaction control instructions, and use the compaction control instructions to control the roller to perform compaction operations.

[0028] For the embodiment of the present application, a high-precision GPS receiver and an attitude sensing sensor are provided on the roller. The high-precision GPS receiver can receive and process GPS signals in real time to obtain the precise latitude and longitude information of the roller as the spatial position of the roller; the attitude sensing sensor (for example, an angular displacement sensor, a gyroscope and other sensors) is used to measure the attitude information of the roller in real time, including but not limited to: pitch angle, yaw angle, roll angle, etc. Furthermore, the high-precision GPS receiver and the attitude sensing sensor are connected to the electronic device by wireless means, so that the electronic device can obtain the spatial position collected by the high-precision GPS receiver and the attitude information collected by the attitude sensing sensor in real time by wireless transmission.

[0029] Then, an unmanned driving analysis is performed based on the spatial position, posture information and roller parameters, and a compaction control instruction is generated. The compaction control instruction is used to control the roller to perform compaction operations, wherein the compaction control instruction is used to control the compactor to perform compaction operations according to the path data, vibration state and travel speed in the roller parameters. The process of generating compaction control instructions for unmanned driving analysis is as follows: based on the path data, spatial position and posture information in the roller parameters, a driving path corresponding to the roller is generated, that is, the driving path is the walking path from the current spatial position and posture information state to the end point in the path data; based on the vibration state and travel speed in the roller parameters, the vibration frequency, amplitude, travel speed, steering ability and climbing ability corresponding to the roller are determined, and the driving path, vibration frequency, amplitude, travel speed, steering ability and climbing ability are integrated to generate instructions to obtain compaction control instructions, that is, the compaction control instructions are encoded in a format and protocol that the roller can understand, so as to ensure that the roller can accurately and safely perform compaction operations.

[0030] Step S103: During the compaction operation performed by the roller, multi-dimensional compaction data is acquired in real time, and the multi-dimensional compaction data is stored in a compaction operation database, wherein the multi-dimensional compaction data includes: real-time driving parameters, real-time environmental perception data, compaction operation parameters and equipment status information.

[0031] For the embodiments of the present application, in order to help construction personnel monitor the compaction process more accurately, multi-dimensional compaction data is acquired and stored in real time, so as to understand the compaction effect of the roller and optimize subsequent compaction strategies. At the same time, potential safety hazards can be discovered and warned in a timely manner, thereby enhancing the safety of construction.

[0032] Specifically, the real-time driving parameters are obtained through the high-precision GPS receiver and sensors on the roller, that is, the real-time position, speed, driving direction and other parameters are obtained through the high-precision GPS receiver of the roller, and the dynamic driving parameters such as acceleration, deceleration, steering angle and so on are obtained through the sensors of the roller; of course, the actual running trajectory of the roller is also included. Various sensors on the roller are used to obtain real-time environmental perception data, that is, sensors such as laser radar and cameras are used to capture road and obstacle information in real time, such as road width, obstacle distance, road curvature, temperature, humidity, soil conditions, etc.; the temperature of the asphalt mixture can also be measured in real time through infrared temperature sensors to monitor the temperature state of the asphalt paved on the road surface. The compaction operation parameters are obtained by using the pressure sensor and vibration sensor on the roller, that is, the vibration frequency, amplitude and other key parameters of the compaction operation are obtained according to the vibration sensor of the roller; and the compaction degree, flatness, uniformity and other parameters are obtained through the pressure sensor. Use multiple types of sensors to obtain equipment status information that characterizes the working status of the roller, such as engine speed, engine oil temperature, engine water temperature, engine oil pressure, hydraulic system pressure, hydraulic oil temperature, etc. Finally, store the multi-dimensional compaction data in the compaction operation database, which stores the compaction data corresponding to different compaction areas, so that construction personnel can analyze the compaction operation later and find problems and optimization points in the compaction process in time.

[0033] It can be seen that in the embodiment of the present application, the compaction area information and the roller information are obtained, and parameter analysis is performed based on the compaction area information and the roller information to determine the roller parameters. Then, the spatial position and posture information of the roller are obtained in real time, and unmanned driving analysis is performed based on the spatial position, posture information and roller parameters, compaction control instructions are generated, and the roller is controlled to perform compaction operations using the compaction control instructions. Finally, in the process of the roller performing the compaction operation, multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting the roller parameters, the compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the roller parameters can achieve the expected compaction effect based on the compaction area information, so as to ensure that the compaction machine can give full play to its compaction effect, reduce the fluctuation of the compaction quality, and improve the stability of the compaction quality.

[0034] Furthermore, in order to ensure that the compaction effects of subsequent compaction operations can meet the preset requirements and improve the overall compaction quality, in the embodiment of the present application, after storing the multi-dimensional compaction data in the compaction operation database, it also includes: When it is detected that one compaction operation is completed on the compaction area, the whole compaction operation data is extracted from the compaction operation database, wherein the whole compaction operation data includes: the driving track corresponding to the roller, the land environment data corresponding to the driving track, and the compaction evaluation index corresponding to the driving track; Based on the compaction evaluation index in the whole process data of compaction operation, the compaction area is divided into compaction sections and multiple compaction sections are determined; For a target compaction section, parameter optimization is performed based on land environment data, compaction evaluation indicators and roller information corresponding to the target compaction section to determine optimized roller parameters, wherein the target compaction section is any one of multiple compaction sections.

[0035] For the embodiments of the present application, compaction operation is a key environment in road construction, and its compaction quality directly affects the durability of the road and driving safety. During the compaction work, the working parameters of the roller directly affect the compaction quality. Therefore, after the roller completes a compaction operation, an in-depth analysis is performed based on the full data of the compaction operation to facilitate timely discovery of potential quality problems, and by accurately adjusting the working parameters of the roller, it is ensured that the compaction effects of subsequent compaction operations can meet the preset requirements, thereby improving the overall compaction quality.

[0036] Specifically, since the compaction operation database stores the compaction data of each compaction operation corresponding to different compaction areas, the amount of stored data is huge and the data is comprehensive. In order to facilitate the optimization of subsequent compaction operations in a certain compaction area, when it is detected that a compaction operation is completed in the compaction area, the full compaction operation data is extracted from the compaction operation database. The full compaction operation data includes: the driving trajectory corresponding to the roller, the land environment data corresponding to the driving trajectory, and the compaction evaluation index corresponding to the driving trajectory. The full compaction operation data is the data information generated by the roller performing compaction operations in the compaction area. For the driving trajectory corresponding to the roller, it is used to characterize the actual driving trajectory of the roller from the compaction starting point to the compaction ending point when performing the compaction operation; for the land environment data corresponding to the driving trajectory, it is used to characterize the land environment data collected when the roller is moving along the driving trajectory, including but not limited to: temperature, humidity, soil conditions, etc.; for the compaction evaluation index corresponding to the driving trajectory, it is used to characterize the compaction degree, flatness, uniformity and other indicators corresponding to the road collected when the roller is moving along the driving trajectory.

[0037] Furthermore, based on the compaction evaluation index in the whole data of the compaction operation, the driving track in the compaction area is divided into compaction sections, and multiple compaction sections are determined. That is, the electronic device pre-stores the correspondence between the compaction evaluation index and the road division, and the correspondence divides the overall numerical range of the compaction evaluation index into multiple interval ranges with non-overlapping data sizes, so that the roads with the compaction evaluation index in the same interval range can be divided into the same compaction section, wherein the numerical range in the correspondence is the division of the interval range of a single-dimensional indicator. Then, for any target compaction section among the multiple compaction sections, parameter optimization is performed based on the land environment data, compaction evaluation index and roller information corresponding to the target compaction section, and the optimized roller parameters are determined. Executing parameter optimization to obtain the optimized roller parameters will help the roller achieve the best compaction effect in the next rolling operation, improve the density and strength of the road surface, and thus improve the overall quality of the road surface. The specific implementation process of parameter optimization is as follows: adjust the amplitude and frequency of the roller according to the soil type and water content. Generally speaking, clay soil requires a larger amplitude and a lower frequency, while sandy soil may require a smaller amplitude and a higher frequency; adjust the roller's travel speed according to the current compaction situation and land environment in the compaction evaluation index. A slower travel speed usually provides better compaction effect.

[0038] It can be seen that in the embodiment of the present application, after it is detected that a compaction operation is completed on the compaction area, the full data of the compaction operation is extracted from the compaction operation database, and based on the compaction evaluation index in the full data of the compaction operation, the compaction area is divided into compaction sections to determine multiple compaction sections. After the roller completes a compaction operation, an in-depth analysis is performed based on the full data of the compaction operation to facilitate timely discovery of potential quality problems, and by accurately adjusting the working parameters of the roller, it is ensured that the compaction effects of subsequent compaction operations can meet the preset requirements, thereby improving the overall compaction quality. Then, for the target compaction section, parameter optimization is performed based on the land environment data, compaction evaluation index and roller information corresponding to the target compaction section, and the optimized roller parameters are determined. Executing parameter optimization to obtain the optimized roller parameters helps the roller achieve the best compaction effect in the next compaction operation, improve the density and strength of the road surface, and thus improve the overall quality of the road surface.

[0039] Furthermore, in order to ensure the safety of compaction operations and enable the roller to more flexibly cope with various complex construction environments, in the embodiment of the present application, during the compaction operation performed by the roller, after obtaining multi-dimensional compaction data in real time, the following steps are also included: Perform obstacle identification based on real-time environmental perception data to determine obstacle information, where the obstacle information includes: obstacle type, obstacle location, and obstacle size; The current spatial position and current posture information corresponding to the roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, current posture information, roller parameters and obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes: obstacle avoidance type and obstacle avoidance operation.

[0040] In the embodiment of the present application, the roller may encounter various obstacles during operation, such as pedestrians, vehicles, road cones, ground potholes, etc. These obstacles may become safety hazards and pose a threat to the safety of the roller. In order to ensure the safety of compaction operations, obstacle recognition and autonomous obstacle avoidance operations are implemented during the compaction operation to significantly reduce the collision risk of the roller during operation, so that the roller can more flexibly cope with various complex construction environments.

[0041] Specifically, various sensors on the roller are used to obtain real-time environmental perception data, that is, sensors such as laser radar and cameras are used to capture road and obstacle information in real time, and obstacle identification is performed based on the real-time environmental perception data to determine obstacle information, where the obstacle information includes: obstacle type, obstacle position and obstacle size. For obstacle identification, image processing and point cloud processing are performed based on the data collected by the camera and laser radar to extract potential obstacle contours, and obstacle features such as shape, size, color, texture, speed, etc. are extracted from the processed data. At the same time, machine learning or deep learning algorithms are used to classify and identify the extracted features to determine the type of obstacle (for example, people, vehicles, stones, trees, etc.); obstacle position calculation is performed based on real-time environmental perception data to determine the absolute position of the obstacle and its relative position relative to the roller, and the obstacle size (such as length, width, height) is calculated based on the obstacle contour, distance information and possible shape model.

[0042] Then, the current spatial position and current posture information corresponding to the roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, current posture information, roller parameters and obstacle information to determine the autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes: obstacle avoidance type and obstacle avoidance operation. The specific implementation process of autonomous obstacle avoidance planning is as follows: According to the current spatial position and current posture information of the roller, as well as the position of the obstacle, the relative distance and orientation between the obstacle and the roller are calculated to determine whether the obstacle is on the driving path of the roller and whether it will pose a threat to the driving of the roller. If the obstacle is on the driving path of the roller, the driving distance between the obstacle and the roller is calculated, and the distance threshold of the trajectory change obstacle avoidance is obtained. The distance threshold can be the effective minimum distance of the trajectory change obstacle avoidance. When the driving distance between the two is less than the distance threshold, the obstacle avoidance type is determined to be emergency braking; otherwise, the obstacle avoidance type is determined to be trajectory change obstacle avoidance. At the same time, the obstacle avoidance operation is the operation information corresponding to the obstacle avoidance type, so that the roller can complete autonomous obstacle avoidance according to the obstacle avoidance operation to ensure the safety of the compaction operation. For example, when the obstacle avoidance type is trajectory change obstacle avoidance, the obstacle avoidance operation includes but is not limited to: obstacle avoidance path and obstacle avoidance driving information.

[0043] It can be seen that in the embodiment of the present application, in order to ensure the safety of compaction operations, obstacle identification is performed based on real-time environmental perception data to determine obstacle information, and then autonomous obstacle avoidance planning is performed based on the current spatial position of the roller, current posture information, roller parameters and obstacle information to determine autonomous obstacle avoidance information. Obstacle identification and autonomous obstacle avoidance operations are implemented to significantly reduce the collision risk of the roller during operation, so that the roller can more flexibly cope with various complex construction environments.

[0044] Furthermore, in order to improve the safety and stability of the roller during the compaction operation, optimize the working efficiency of the compaction operation, and reduce the risk of accidents, in the embodiment of the present application, autonomous obstacle avoidance planning is performed based on the current spatial position, current posture information, roller parameters and obstacle information. After determining the autonomous obstacle avoidance information, it also includes: When the obstacle avoidance type is trajectory change obstacle avoidance, an obstacle avoidance control analysis is performed based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, a trajectory change obstacle avoidance instruction is generated, and the trajectory change obstacle avoidance instruction is sent to the roller, so that the roller avoids obstacles during the compaction operation; When the obstacle avoidance type is emergency braking, the braking distance is calculated based on the roller parameters to determine the emergency braking distance; Conduct early warning feasibility analysis based on current spatial position, obstacle position and emergency braking distance, and determine the early warning feasibility analysis results; When the feasibility analysis result of the early warning is that the early warning is feasible, an emergency braking early warning is generated; when the feasibility analysis result of the early warning is that the early warning is not feasible, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller can perform emergency braking to avoid obstacles during the compaction operation.

[0045] For the embodiments of the present application, accurate and efficient obstacle avoidance control strategies are provided to meet the obstacle avoidance requirements of different obstacle avoidance types, which not only improves the safety and stability of the roller during the compaction operation, but also optimizes the work efficiency of the compaction operation and reduces the risk of accidents.

[0046] Specifically, the obstacle avoidance path in the obstacle avoidance operation specifies an optimal path to avoid obstacles from the current spatial position, and the obstacle avoidance driving information specifies the parameters such as the steering angle, acceleration and speed required for the roller to travel on the obstacle avoidance path. Therefore, when the obstacle avoidance type is trajectory change obstacle avoidance, the obstacle avoidance control analysis is performed based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, and a trajectory change obstacle avoidance instruction is generated. The trajectory change obstacle avoidance instruction is encoded in a format and protocol that the roller can understand. Then, the trajectory change obstacle avoidance instruction is sent to the roller through the wireless communication network, so that the roller avoids obstacles during the compaction operation.

[0047] When the obstacle avoidance type is emergency braking, you can choose to generate an emergency braking warning or perform an emergency braking operation. That is, when the distance between the roller and the obstacle is much greater than the emergency braking distance, the emergency braking warning can be selected first to remind the operator to take emergency measures in time; when the distance between the roller and the obstacle is close to the emergency braking distance, the emergency braking operation is directly selected to minimize the accident losses caused by improper obstacle avoidance. Therefore, the braking distance is calculated based on the roller parameters to determine the emergency braking distance. The formula for calculating the braking distance is as follows: S=v² / (2μg), where S represents the braking distance, v is the initial speed of the vehicle, g is the acceleration of gravity (about 9.8m / s²), and μ is the friction coefficient between the tire and the ground. The friction coefficient μ is affected by many factors, such as road material, tire type, weather conditions, etc. Users can set the size of the friction coefficient according to actual needs.

[0048] Then, a feasibility analysis of early warning is performed based on the current spatial position, the position of the obstacle and the emergency braking distance, and the result of the feasibility analysis of early warning is determined, that is, the distance between the two points is calculated based on the current spatial position and the position of the obstacle, and the distance between the two points is obtained to obtain the early warning safety threshold. If the difference between the distance between the two points and the emergency braking distance is greater than the early warning safety threshold, indicating that the distance between the roller and the obstacle is much greater than the emergency braking distance, the feasibility analysis result of the early warning is determined to be a feasible early warning; otherwise, the feasibility analysis result of the early warning is determined to be an infeasible early warning. Finally, when the feasibility analysis result of the early warning is a feasible early warning, an emergency braking early warning is generated; when the feasibility analysis result of the early warning is an infeasible early warning, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller can perform emergency braking to avoid obstacles during the compaction operation.

[0049] It can be seen that in the embodiment of the present application, when the obstacle avoidance type is trajectory change obstacle avoidance, obstacle avoidance control analysis is performed based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, a trajectory change obstacle avoidance instruction is generated, and the trajectory change obstacle avoidance instruction is sent to the roller, so that the roller avoids obstacles during the compaction operation. At the same time, when the obstacle avoidance type is emergency braking, the braking distance is calculated based on the roller parameters to determine the emergency braking distance. Then, based on the current spatial position, the obstacle position and the emergency braking distance, an early warning feasibility analysis is performed to determine the early warning feasibility analysis result. Furthermore, when the early warning feasibility analysis result is that the early warning is feasible, an emergency braking early warning is generated; when the early warning feasibility analysis result is that the early warning is not feasible, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller performs emergency braking to avoid obstacles during the compaction operation. According to the obstacle avoidance requirements of different obstacle avoidance types, an accurate and efficient obstacle avoidance control strategy is provided, which not only improves the safety and stability of the roller during the compaction operation, but also optimizes the working efficiency of the compaction operation and reduces the risk of accidents.

[0050] Furthermore, in order to ensure that the roller performs compaction operations under optimal conditions and improve the quality of road compaction, in an embodiment of the present application, parameter analysis is performed based on compaction area information and roller information to determine roller parameters, including: Perform driving path analysis based on the compaction area size and compaction area shape in the compaction area information to determine path data; Perform vibration analysis based on the compacted material type and compacted material characteristics in the compacted area information to determine the vibration state; Performing a travel analysis based on the type of compacted material in the compaction area information and the equipment weight in the roller information to determine the travel speed; The roller parameters are determined by integrating the path data, vibration status and travel speed.

[0051] For the embodiments of the present application, in the actual compaction process, the different soil properties, the performance differences of the compactor and the complexity of the construction environment will affect the compaction quality of the roller after the roller works, that is, the type and characteristics of the compacted material will affect the compaction effect and the required parameters, and different compacted materials require different vibration states to fully exert their compaction effect. Therefore, in order to ensure that the roller performs compaction operations under the best conditions and improve the quality of road compaction, the compaction area information and roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the roller parameters can achieve the expected compaction effect based on the compaction area information, and at the same time, different roller equipment can work under the best parameters to ensure that the compactor can fully exert its compaction effect.

[0052] Specifically, a driving path analysis is performed based on the compaction area size and the compaction area shape in the compaction area information to determine the path data, that is, the starting point and the end point of the roller are determined based on the compaction area size and the compaction area shape, wherein the starting point and the end point are set at positions that are convenient for the roller to enter and exit and operate; then, a path planning algorithm is used to automatically plan a driving path based on the starting point, the end point, and the shape and size of the compaction area. The driving path should be as smooth and continuous as possible, avoiding sharp turns and frequent stops, and the final driving path is recorded as the path data.

[0053] Furthermore, vibration analysis is performed based on the compacted material type and compacted material characteristics in the compacted area information to determine the vibration state, that is, different compacted material types are suitable for different vibration modes. For example, for hard materials such as sand and gravel, a high-frequency and low-amplitude vibration mode is suitable; while for soft materials such as clay and asphalt, a low-frequency and high-amplitude vibration mode is more suitable; on this basis, the correspondence between vibration parameters and compacted material characteristics and the compacted material characteristics are used to determine the specific vibration frequency and amplitude, and recorded as the vibration state, wherein the above correspondence specifies the correspondence between different material characteristics and vibration parameters, for example, the correspondence between water content value and vibration parameters, and the correspondence between particle size and vibration parameters.

[0054] At the same time, for rollers of the same specifications, the compaction effects produced by the rollers on different types of compacted materials are different; for the same type of compacted materials, the compaction effects produced by different rollers on the same type of compacted materials are also different; of course, the driving speed of the roller will also affect the compaction capacity and compaction effect of the roller. Therefore, in order to ensure that the roller performs compaction operations under optimal conditions and improve the quality of road compaction, the travel analysis is performed based on the compaction material type in the compaction area information and the equipment weight in the roller information to determine the travel speed. The electronic device pre-sets the corresponding relationship between the compaction material type, the equipment weight of the roller and the travel speed under the premise of the standard compaction effect, so that the corresponding travel speed of the roller can be quickly determined based on the above corresponding relationship. Finally, the roller parameters are determined based on the comprehensive path data, vibration state and travel speed.

[0055] It can be seen that in the embodiment of the present application, in order to ensure that the roller performs compaction operations under optimal conditions and improve the quality of road compaction, the compaction area information and the roller information are comprehensively considered in the process of performing parameter analysis. Therefore, the driving path analysis is performed based on the compaction area size and the compaction area shape in the compaction area information to determine the path data. Furthermore, a vibration analysis is performed based on the compaction material type and the compaction material characteristics in the compaction area information to determine the vibration state. At the same time, a travel analysis is performed based on the compaction material type in the compaction area information and the equipment weight in the roller information to determine the travel speed. Finally, the roller parameters are determined based on the comprehensive path data, vibration state and travel speed. The compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area.

[0056] Furthermore, in order to enable construction personnel to see the effect of compaction operation at a glance, which is conducive to timely discovering problems in compaction operation, in the embodiment of the present application, after storing the multi-dimensional compaction data in the compaction operation database, it also includes: Performing operation data screening based on the compaction operation database to determine a target compaction operation data set, wherein the target compaction operation data set includes: multiple passes of road compaction data, each pass of road compaction data is multi-dimensional compaction data corresponding to the same compaction area; The engineering construction map is obtained, and compaction visualization is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization map, wherein the compaction visualization map displays each pass of road compaction data in layers.

[0057] For the embodiments of the present application, the compaction data is often in an abstract form. In order to convert the abstract compaction data into intuitive images or graphics so that construction personnel can see the effect of the compaction operation at a glance, a compaction visualization display method is adopted to display the compaction data of each pass of the road in the form of a visualization layer. This helps construction personnel to accurately judge whether the compaction operation meets the design requirements and is conducive to timely discovering problems in the compaction operation, thereby ensuring the quality and progress of the compaction operation.

[0058] Specifically, the compaction area information to be screened is input into the compaction operation database, the compaction number of the compaction area is queried in the compaction operation database, and based on the compaction area information and the compaction number, the operation data is screened in the compaction operation database to determine the target compaction operation data set, wherein the target compaction operation data set includes: multiple road compaction data, each road compaction data is multi-dimensional compaction data corresponding to the same compaction area. Then, the engineering construction map is obtained, which can be obtained from a public database or a professional map service website, and the engineering construction map is formatted so as to be compatible with subsequent visualization tools. Furthermore, multiple layers are created in the visualization tool, each layer corresponds to a road compaction data, and different colors, icons or line styles are set for the layers according to different data items in each road compaction data to distinguish different compaction effects. Of course, the map can also be made more beautiful and easy to understand by adjusting the transparency, color gradient and other parameters of the layer, and interactive elements such as legends, annotations or floating windows are added so that users can more conveniently obtain specific information about the compaction operation. Finally, a compaction visualization display map is obtained, which can realize functions such as real-time viewing of compaction data and user interaction.

[0059] It can be seen that in the embodiment of the present application, in order to be able to convert abstract compaction data into intuitive images or graphics, so that construction personnel can see the effect of the compaction operation at a glance. Therefore, the operation data is screened based on the compaction operation database, and the target compaction operation data set is determined. Then, the compaction visualization display is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization display map. The compaction visualization display helps construction personnel to accurately judge whether the compaction operation meets the design requirements, and is conducive to timely discovering problems in the compaction operation, thereby ensuring the quality and progress of the compaction operation.

[0060] The above-mentioned embodiment introduces an unmanned intelligent compaction method from the perspective of method flow, and the following embodiment introduces an unmanned intelligent compaction system from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiments.

[0061] The present application embodiment provides an unmanned intelligent compaction system, such as Figure 2As shown, the unmanned intelligent compaction system may specifically include: The parameter analysis module 210 is used to obtain compaction area information and roller information, perform parameter analysis based on the compaction area information and roller information, and determine roller parameters, wherein the roller parameters include: path data, vibration state, and travel speed; A control instruction generation module 220 is used to obtain the spatial position and posture information of the roller in real time, perform unmanned driving analysis based on the spatial position, posture information and roller parameters, generate compaction control instructions, and use the compaction control instructions to control the roller to perform compaction operations; The compaction data storage module 230 is used to obtain multi-dimensional compaction data in real time during the compaction operation of the roller, and store the multi-dimensional compaction data in the compaction operation database, wherein the multi-dimensional compaction data includes: real-time driving parameters, real-time environmental perception data, compaction operation parameters and equipment status information.

[0062] For the embodiment of the present application, compaction area information and roller information are obtained, and parameter analysis is performed based on the compaction area information and roller information to determine roller parameters. Then, the spatial position and posture information of the roller are obtained in real time, and unmanned driving analysis is performed based on the spatial position, posture information and roller parameters, compaction control instructions are generated, and the roller is controlled to perform compaction operations using the compaction control instructions. Finally, in the process of the roller performing compaction operations, multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting roller parameters, the compaction area information and roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the use of roller parameters based on the compaction area information can achieve the expected compaction effect, so as to ensure that the compactor can give full play to its compaction effect, reduce the fluctuation of compaction quality, and improve the stability of compaction quality.

[0063] A possible implementation of the embodiment of the present application is an unmanned intelligent compaction system, further comprising: The parameter optimization module is used to extract the whole process data of compaction operation from the compaction operation database after detecting that a compaction operation is completed on the compaction area, wherein the whole process data of compaction operation includes: the driving track corresponding to the roller, the land environment data corresponding to the driving track, and the compaction evaluation index corresponding to the driving track; Based on the compaction evaluation index in the whole process data of compaction operation, the compaction area is divided into compaction sections and multiple compaction sections are determined; For a target compaction section, parameter optimization is performed based on land environment data, compaction evaluation indicators and roller information corresponding to the target compaction section to determine optimized roller parameters, wherein the target compaction section is any one of multiple compaction sections.

[0064] A possible implementation of the embodiment of the present application is an unmanned intelligent compaction system, further comprising: The autonomous obstacle avoidance planning module is used to identify obstacles based on real-time environmental perception data and determine obstacle information, where the obstacle information includes: obstacle type, obstacle location and obstacle size; The current spatial position and current posture information corresponding to the roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, current posture information, roller parameters and obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes: obstacle avoidance type and obstacle avoidance operation.

[0065] A possible implementation of the embodiment of the present application is an unmanned intelligent compaction system, further comprising: An autonomous obstacle avoidance execution module, for performing obstacle avoidance control analysis based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation when the obstacle avoidance type is trajectory change obstacle avoidance, generating a trajectory change obstacle avoidance instruction, and sending the trajectory change obstacle avoidance instruction to the roller, so that the roller avoids obstacles during the compaction operation; When the obstacle avoidance type is emergency braking, the braking distance is calculated based on the roller parameters to determine the emergency braking distance; Conduct early warning feasibility analysis based on current spatial position, obstacle position and emergency braking distance, and determine the early warning feasibility analysis results; When the feasibility analysis result of the early warning is that the early warning is feasible, an emergency braking early warning is generated; when the feasibility analysis result of the early warning is that the early warning is not feasible, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller can perform emergency braking to avoid obstacles during the compaction operation.

[0066] In a possible implementation of the embodiment of the present application, the parameter analysis module 210, when performing parameter analysis based on the compaction area information and the roller information to determine the roller parameters, is used to: Perform driving path analysis based on the compaction area size and compaction area shape in the compaction area information to determine path data; Perform vibration analysis based on the compacted material type and compacted material characteristics in the compacted area information to determine the vibration state; Performing a travel analysis based on the type of compacted material in the compaction area information and the equipment weight in the roller information to determine the travel speed; The roller parameters are determined by integrating the path data, vibration status and travel speed.

[0067] A possible implementation of the embodiment of the present application is an unmanned intelligent compaction system, further comprising: A visualization display module is used to screen operation data based on a compaction operation database and determine a target compaction operation data set, wherein the target compaction operation data set includes: multiple passes of road compaction data, each pass of road compaction data being multi-dimensional compaction data corresponding to the same compaction area; The engineering construction map is obtained, and compaction visualization is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization map, wherein the compaction visualization map displays each pass of road compaction data in layers.

[0068] Technicians in the relevant field can clearly understand that, for the convenience and brevity of description, the specific working process of the unmanned intelligent compaction system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0069] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0070] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0071] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0072] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0073] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0074] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0075] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0076] The embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method in any of the above embodiments is implemented. Compared with the related art, the embodiment of the present application obtains compaction area information and roller information, performs parameter analysis based on the compaction area information and roller information, and determines the roller parameters. Then, the spatial position and posture information of the roller are obtained in real time, and unmanned driving analysis is performed based on the spatial position, posture information and roller parameters, and compaction control instructions are generated, and the roller is controlled to perform compaction operations using the compaction control instructions. Finally, in the process of the roller performing the compaction operation, multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting the roller parameters, the compaction area information and roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, so that the roller parameters can achieve the expected compaction effect based on the compaction area information, so as to ensure that the compactor can give full play to its compaction effect, reduce the fluctuation of compaction quality, and improve the stability of compaction quality.

[0077] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0078] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An unmanned intelligent compaction method, characterized in that: include: Acquire compaction area information and roller information, perform parameter analysis based on the compaction area information and the roller information, and determine roller parameters, wherein the roller parameters include: path data, vibration state, and travel speed; Acquire the spatial position and posture information of the roller in real time, perform unmanned driving analysis based on the spatial position, the posture information and the roller parameters, generate compaction control instructions, and use the compaction control instructions to control the roller to perform compaction operations; During the compaction operation performed by the roller, multi-dimensional compaction data is acquired in real time and stored in a compaction operation database, wherein the multi-dimensional compaction data includes: real-time driving parameters, real-time environmental perception data, compaction operation parameters and equipment status information.

2. The unmanned driving intelligent compaction method according to claim 1, characterized in that: After storing the multi-dimensional compaction data in the compaction operation database, the method further includes: When it is detected that a compaction operation is completed on the compaction area, the whole process data of the compaction operation is extracted from the compaction operation database, wherein the whole process data of the compaction operation includes: the driving track corresponding to the roller, the land environment data corresponding to the driving track, and the compaction evaluation index corresponding to the driving track; Based on the compaction evaluation index in the whole compaction operation data, the compaction area is divided into compaction sections to determine a plurality of compaction sections; For a target compaction section, parameter optimization is performed based on the land environment data, the compaction evaluation index and the roller information corresponding to the target compaction section to determine optimized roller parameters, wherein the target compaction section is any one of the multiple compaction sections.

3. The unmanned driving intelligent compaction method according to claim 1, characterized in that: In the process of the roller performing the compaction operation, after obtaining the multi-dimensional compaction data in real time, the method further includes: Perform obstacle identification based on the real-time environment perception data to determine obstacle information, wherein the obstacle information includes: obstacle type, obstacle location and obstacle size; The current spatial position and current posture information corresponding to the roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, the current posture information, the roller parameters and the obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes: obstacle avoidance type and obstacle avoidance operation.

4. The unmanned driving intelligent compaction method according to claim 3, characterized in that: After performing autonomous obstacle avoidance planning based on the current spatial position, the current posture information, the roller parameters and the obstacle information and determining the autonomous obstacle avoidance information, the method further includes: When the obstacle avoidance type is trajectory change obstacle avoidance, performing obstacle avoidance control analysis based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, generating a trajectory change obstacle avoidance instruction, and sending the trajectory change obstacle avoidance instruction to the roller, so that the roller avoids obstacles during the compaction operation; When the obstacle avoidance type is emergency braking, performing braking distance calculation based on the roller parameters to determine the emergency braking distance; Performing a warning feasibility analysis based on the current spatial position, the obstacle position and the emergency braking distance, and determining a warning feasibility analysis result; When the feasibility analysis result of the early warning is that the early warning is feasible, an emergency braking early warning is generated; when the feasibility analysis result of the early warning is that the early warning is not feasible, the roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the roller performs emergency braking to avoid obstacles during the compaction operation.

5. The unmanned driving intelligent compaction method according to claim 1, characterized in that: The performing parameter analysis based on the compaction area information and the roller information to determine roller parameters includes: Performing a driving path analysis based on the compaction area size and compaction area shape in the compaction area information to determine path data; Perform vibration analysis based on the compacted material type and compacted material characteristics in the compacted area information to determine the vibration state; Performing a travel analysis based on the compacted material type in the compacted area information and the equipment weight in the roller information to determine a travel speed; The roller parameters are determined by integrating the path data, the vibration state and the travel speed.

6. The unmanned driving intelligent compaction method according to claim 1, characterized in that: After storing the multi-dimensional compaction data in the compaction operation database, the method further includes: Performing operation data screening based on the compaction operation database to determine a target compaction operation data set, wherein the target compaction operation data set includes: multiple passes of road compaction data, each pass of the road compaction data is multi-dimensional compaction data corresponding to the same compaction area; An engineering construction map is obtained, and compaction visualization is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization display map, wherein the compaction visualization display map displays each pass of the road compaction data in layers.

7. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the unmanned intelligent compaction method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the unmanned driving intelligent compaction method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It comprises a computer program, wherein the computer program is executed by a processor to implement the unmanned driving intelligent compaction method according to any one of claims 1 to 6.

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