A simulation experiment method and system based on unmanned sweeper simulation

Abstract

The present invention discloses a simulation experiment method and system based on unmanned road sweeper simulation, which relates to the field of vehicle simulation test technology, including obtaining unmanned road sweeper working data, obtaining working mode information based on the unmanned road sweeper working data, classifying the unmanned road sweeper working data based on the working mode, and obtaining working data classification information. The present invention improves data analysis efficiency by classifying working data of different working modes, facilitates subsequent data processing steps, accurately analyzes the driving conditions of unmanned road sweepers under different road conditions through mobile mode working sub-data groups, and provides a benchmark for subsequent working condition analysis of unmanned road sweepers under other modes. Through the mode feature data corresponding to each working mode, a data basis is provided for unmanned road sweeper simulation, thereby improving the simulation efficiency of unmanned road sweepers and ensuring the stability and reliability of unmanned road sweeper operation.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle simulation testing, and in particular to a simulation experiment method and system based on unmanned sweeper simulation. Background Art

[0002] The application of autonomous driving technology in ground vehicles has gradually matured. Autonomous driving technology is efficient, safe, and can extend vehicle lifespan. Applying autonomous driving technology to street sweepers can replace drivers, significantly increasing their operating hours and ensuring they can consistently maintain a clean environment. Therefore, in the current development of urban environmental maintenance and intelligent transportation, the use of unmanned street sweepers is gradually becoming widespread. As the technology matures, unmanned street sweepers, integrating advanced technologies such as high-definition cameras, laser sensors, and ultrasonic sensors, achieve high-precision positioning and obstacle avoidance, resulting in more stable performance. As a result, unmanned street sweepers are widely used in municipal roads, campuses, communities, parks, scenic spots, and other scenarios. To avoid wasting resources during the R&D process, simulation experiments are often conducted on unmanned street sweepers.

[0003] Current simulation experiments for unmanned road sweepers still face challenges in accurately evaluating the vehicles' daily operating data and constructing simulation scenarios based on operating data that can accurately test the vehicles' performance. Traditional testing methods rely primarily on on-site road testing, which is limited by environmental conditions, is costly, and lacks comprehensive coverage of various operating conditions. During the vehicle design phase, the lack of efficient virtual verification methods makes it difficult to quickly evaluate and optimize different design options. However, directly establishing virtual scenarios based on operating data not only requires a large amount of data processing, but also requires a large amount of test data for simulation experiments. This makes it impossible to promptly identify data representing the performance of the unmanned road sweeper, reducing the efficiency of its development and operational reliability. Summary of the Invention

[0004] In order to solve the above technical problems, a simulation experiment method and system based on the simulation of unmanned sweepers are provided. This technical solution solves the problems raised in the above background technology, such as the inability to accurately evaluate the daily work data of unmanned sweepers and the inability to construct simulation scenarios that can accurately test the performance of unmanned sweepers based on the work data. Traditional testing methods mainly rely on field road tests, but field road tests are limited by environmental conditions, are expensive, and are difficult to fully cover various working conditions. During the vehicle design stage, there is a lack of efficient virtual verification methods, making it difficult to quickly evaluate and optimize different design solutions. However, if a virtual scene is directly established based on the work data, not only will the data processing volume be large, but the test data will also be large when conducting simulation experiments. It is impossible to identify the data representing the performance of the unmanned sweeper in a timely manner, reducing the research and development efficiency and operational reliability of the unmanned sweeper.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A simulation experiment method based on unmanned sweeper simulation, comprising:

[0007] Acquiring unmanned sweeper operating data, wherein the unmanned sweeper operating data includes environmental landscape data, sweeper operating parameters, and map data;

[0008] Acquire working mode information according to the working data of the unmanned sweeper, wherein the working mode includes a cleaning mode, an obstacle avoidance mode, and a moving mode;

[0009] Based on the working mode, the working data of the unmanned sweeper is classified to obtain the working data classification information;

[0010] Obtaining mode feature data corresponding to each working mode according to the working mode information and working data classification information;

[0011] According to the pattern feature data, corresponding environmental landscape data and map data are obtained;

[0012] Based on environmental landscape data and map data, three-dimensional scenes are constructed to obtain simulation experiment scene information;

[0013] Based on the simulated experimental scenario, a simulation experiment was conducted on the unmanned sweeper.

[0014] Preferably, the acquiring of mode feature data corresponding to each working mode according to the working mode information and the working data classification information specifically includes:

[0015] According to the work data classification information, the unmanned sweeper work data corresponding to the same work mode is divided into the same data group, and the mode work data group corresponding to each work mode is obtained;

[0016] Acquire a mobile mode working data group, wherein the mobile mode working data group represents mode working data corresponding to the unmanned sweeper in the mobile mode;

[0017] Based on the road type corresponding to each data in the mobile mode working data group, the mobile mode working data group is further divided to obtain a mobile mode working sub-data group, wherein the road type includes a flat road, a sloped road, and a curved road;

[0018] acquiring movement pattern characteristic data according to the movement pattern working sub-data group;

[0019] Acquiring cleaning mode characteristic data based on the cleaning mode working data group, wherein the cleaning mode working data group represents mode working data corresponding to the unmanned cleaning vehicle in the cleaning mode;

[0020] Obtaining obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group;

[0021] Obtain obstacle avoidance pattern feature data based on obstacle avoidance path information;

[0022] The pattern feature data is acquired based on the movement pattern feature data, the cleaning pattern feature data, and the obstacle avoidance pattern feature data.

[0023] Preferably, obtaining the mobile mode characteristic data according to the mobile mode working sub-data group specifically includes:

[0024] Obtaining performance design parameters of the unmanned sweeper, wherein the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information, and standard cleaning coverage;

[0025] Based on the performance design parameters of the unmanned sweeper, a standard energy consumption ratio coefficient is obtained, where the standard energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper;

[0026] Based on the standard energy consumption ratio coefficient, the weight coefficient of the speed and energy consumption of the unmanned sweeper is obtained;

[0027] According to the mobile mode working sub-data set corresponding to the flat road, the product of the speed of the unmanned sweeper and the corresponding weight coefficient is used as the speed of the unmanned sweeper, and the product of the energy consumption of the unmanned sweeper and the corresponding weight coefficient is used as the energy consumption of the unmanned sweeper;

[0028] The ratio of the speed of the unmanned sweeper to the energy consumption of the unmanned sweeper is used as the flat road calibration coefficient;

[0029] The mobile mode working sub-data set corresponding to the maximum flat road calibration coefficient in the mobile mode working sub-data set corresponding to the flat road is used as the flat road characteristic data;

[0030] Based on the mobile mode working sub-data group corresponding to the flat road, obtaining slope road characteristic data and curved road characteristic data;

[0031] The movement pattern characteristic data is acquired based on the flat road characteristic data, the slope road characteristic data, and the curved road characteristic data.

[0032] Preferably, the step of obtaining slope road characteristic data and curved road characteristic data based on the mobile mode working sub-data set corresponding to the flat road specifically includes:

[0033] Based on the mobile mode working sub-data set corresponding to the flat road, the maximum and minimum speeds of the unmanned road sweeper on the flat road are obtained;

[0034] The maximum and minimum speeds of the unmanned road sweeper on a flat road are used as the benchmark speed thresholds for the unmanned road sweeper;

[0035] Remove the data of the unmanned sweeper speed not exceeding the unmanned sweeper reference speed threshold from the movement mode working sub-data groups corresponding to slope roads and curved roads, and obtain the movement mode feature data group;

[0036] Based on the movement pattern characteristic data set corresponding to the slope road, the speed and energy consumption of the slope unmanned sweeper are obtained;

[0037] The sum of the speed of the slope unmanned sweeper and the energy consumption of the slope unmanned sweeper is used as the slope road calibration coefficient;

[0038] The mobile mode working sub-data group corresponding to the maximum value of the slope road calibration coefficient in the mobile mode working sub-data group corresponding to the slope road is used as the slope road characteristic data;

[0039] Based on the flat road characteristic data, the unmanned road sweeper speed corresponding to the flat road characteristic data is used as the unmanned road sweeper characteristic speed;

[0040] Based on the mobile pattern feature data set corresponding to the curved road, the speed change information of the unmanned sweeper corresponding to each data is obtained, wherein the speed change information of the unmanned sweeper represents the speed change of the unmanned sweeper when passing through the curved road;

[0041] According to the speed change information of the unmanned sweeper, the maximum and minimum speed values ​​of the unmanned sweeper corresponding to each data in the mobile mode feature data group are obtained;

[0042] The sum of the differences between the maximum and minimum speeds of the unmanned sweeper and the characteristic speed of the unmanned sweeper is used as the calibration coefficient for the curved road;

[0043] The movement mode working sub-data set corresponding to the maximum value of the curved road calibration coefficient in the movement mode working sub-data set corresponding to the curved road is used as the curved road characteristic data.

[0044] Preferably, the acquiring of cleaning mode characteristic data based on the cleaning mode working data group specifically includes:

[0045] Based on the cleaning mode working data group, obtain the cleaning coverage rate corresponding to each mode working data;

[0046] According to the mobile mode characteristic data, the mobile energy consumption ratio coefficient corresponding to each mobile mode characteristic data is obtained based on the proportional relationship between the speed and energy consumption of the unmanned sweeper;

[0047] According to the performance design parameters of the unmanned sweeper, the standard cleaning energy consumption information of the unmanned sweeper is obtained;

[0048] Obtain the standard energy consumption ratio coefficient, and use the ratio of the mobile energy consumption ratio coefficient to the standard energy consumption ratio coefficient as the energy consumption difference coefficient;

[0049] The mean value of the energy consumption difference coefficient corresponding to the mobile mode characteristic data is used as the coefficient difference benchmark value;

[0050] The ratio of the cleaning coverage rate corresponding to each mode working data in the cleaning mode working data group to the standard cleaning coverage rate is used as the cleaning difference coefficient;

[0051] According to the cleaning mode working data group, a characteristic energy consumption ratio coefficient corresponding to each mode working data is obtained, wherein the characteristic energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper in the cleaning mode working data group;

[0052] The ratio of the characteristic energy consumption ratio coefficient to the coefficient difference benchmark value is used as the energy consumption difference coefficient;

[0053] The product of the energy consumption difference coefficient and the cleaning difference coefficient is taken as the cleaning characteristic coefficient;

[0054] The mode operation data corresponding to the smallest cleaning characteristic coefficient in the cleaning mode operation data group is used as the cleaning mode characteristic data.

[0055] Preferably, obtaining obstacle avoidance pattern characteristic data based on the obstacle avoidance path information specifically includes:

[0056] Obtain the obstacle avoidance planning path information of the unmanned sweeper corresponding to each mode working data according to the obstacle avoidance mode working data group;

[0057] Match the obstacle avoidance planning path information of the unmanned sweeper with the obstacle avoidance path information to obtain obstacle avoidance path comparison information;

[0058] According to the obstacle avoidance path comparison information, the starting point and end point of the obstacle avoidance planning path and the starting point and end point of the obstacle avoidance path are obtained;

[0059] The sum of the distance between the starting point of the obstacle avoidance path and the starting point of the obstacle avoidance path and the distance between the ending point of the obstacle avoidance path and the ending point of the obstacle avoidance path is used as the reference offset;

[0060] Obtain obstacle avoidance planning path length information and obstacle avoidance path length information;

[0061] The ratio of the baseline offset to the obstacle avoidance planning path length is used as the path base offset coefficient;

[0062] The ratio of the obstacle avoidance path length to the obstacle avoidance planning path length is used as the obstacle avoidance path comparison coefficient;

[0063] The product of the path basic deviation coefficient and the obstacle avoidance path comparison coefficient is used as the obstacle avoidance characteristic coefficient;

[0064] The mode operating data corresponding to the largest obstacle avoidance characteristic coefficient in the obstacle avoidance mode operating data group is used as the obstacle avoidance mode characteristic data.

[0065] Furthermore, a simulation experiment system based on unmanned sweeper simulation is proposed to implement the above-mentioned simulation experiment method, including:

[0066] A main control module, the main control module is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information, obtain corresponding environmental landscape data and map data according to the mode characteristic data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and simulate the experimental scene to conduct a simulation experiment on the unmanned sweeper;

[0067] An information acquisition module, the information acquisition module is used to obtain unmanned sweeper operating data, the unmanned sweeper operating data including environmental landscape data, sweeper operating parameters and map data; based on the unmanned sweeper operating data, obtain operating mode information; obtain unmanned sweeper performance design parameters, the unmanned sweeper performance design parameters including standard speed information, standard energy consumption information and standard sweeping coverage; based on the unmanned sweeper performance design parameters, obtain a standard energy consumption ratio coefficient;

[0068] a data identification module, the data identification module being configured to divide the unmanned sweeper working data corresponding to the same working mode into the same data group based on the working data classification information, obtain a mode working data group corresponding to each working mode, further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group, obtain a mobile mode working sub-data group, obtain a weight coefficient of the unmanned sweeper speed and energy consumption based on the standard energy consumption ratio coefficient, obtain flat road characteristic data based on the weight coefficient of the unmanned sweeper speed and energy consumption, and obtain slope road characteristic data and curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road;

[0069] The display module interacts with the main control module and is used to output and display work data classification information, movement mode feature data, cleaning mode feature data, obstacle avoidance mode feature data and simulation experiment scene information.

[0070] Optionally, the main control module specifically includes:

[0071] A control unit is configured to obtain corresponding environmental landscape data and map data based on the pattern feature data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and perform a simulation experiment on the unmanned sweeper using the simulation experiment scene;

[0072] An information receiving unit, which interacts with the information acquisition module and the data identification module to receive data and transmit it to the data processing unit;

[0073] A data processing unit is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, and obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information.

[0074] Optionally, the information acquisition module specifically includes:

[0075] a first acquisition unit, configured to acquire operating data of the unmanned sweeper, the operating data of the unmanned sweeper including environmental landscape data, operating parameters of the sweeper, and map data, and to acquire operating mode information based on the operating data of the unmanned sweeper;

[0076] The second acquisition unit is used to obtain the performance design parameters of the unmanned sweeper, the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information and standard cleaning coverage rate, and obtain the standard energy consumption ratio coefficient based on the performance design parameters of the unmanned sweeper.

[0077] Optionally, the data identification module specifically includes:

[0078] A first identification unit, wherein the first identification unit is used to divide the unmanned sweeper working data corresponding to the same working mode into the same data group according to the working data classification information, obtain the mode working data group corresponding to each working mode, and further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group to obtain the mobile mode working sub-data group;

[0079] The second identification unit is used to obtain the weight coefficient of the speed and energy consumption of the unmanned sweeper based on the standard energy consumption ratio coefficient, obtain the flat road characteristic data according to the weight coefficient of the speed and energy consumption of the unmanned sweeper, and obtain the slope road characteristic data and the curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] The present invention proposes a simulation experiment method and system based on unmanned sweeper simulation, which improves data analysis efficiency by classifying working data of different working modes and facilitates subsequent data processing steps. By accurately analyzing the driving conditions of unmanned sweepers under different road conditions through the mobile mode working sub-data group, it provides a benchmark for the subsequent working condition analysis of unmanned sweepers under other modes. The mode feature data corresponding to each working mode provides a data basis for the simulation of unmanned sweepers, improves the simulation efficiency of unmanned sweepers, and ensures the stability and reliability of the operation of unmanned sweepers. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of a simulation experiment method based on unmanned sweeper simulation proposed by the present invention;

[0083] Figure 2 This is a flow chart for obtaining pattern feature data in the present invention;

[0084] Figure 3 This is a flow chart for obtaining mobile mode feature data in the present invention;

[0085] Figure 4 This is a flow chart for obtaining characteristic data of the cleaning mode in the present invention;

[0086] Figure 5 This is a structural block diagram of a simulation experiment system based on unmanned sweeper simulation proposed by the present invention. DETAILED DESCRIPTION

[0087] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0088] Reference Figure 1 - Figure 4 As shown, a simulation experiment method based on unmanned sweeper simulation in an embodiment of the present invention includes:

[0089] Acquiring unmanned sweeper operating data, wherein the unmanned sweeper operating data includes environmental landscape data, sweeper operating parameters, and map data;

[0090] Acquire working mode information according to the working data of the unmanned sweeper, wherein the working mode includes a cleaning mode, an obstacle avoidance mode, and a moving mode;

[0091] Based on the working mode, the working data of the unmanned sweeper is classified to obtain the working data classification information;

[0092] Obtaining mode feature data corresponding to each working mode according to the working mode information and working data classification information;

[0093] Specifically, according to the working mode information and working data classification information, the mode feature data corresponding to each working mode is obtained, including:

[0094] According to the work data classification information, the unmanned sweeper work data corresponding to the same work mode is divided into the same data group, and the mode work data group corresponding to each work mode is obtained;

[0095] Acquire a mobile mode working data group, wherein the mobile mode working data group represents mode working data corresponding to the unmanned sweeper in the mobile mode;

[0096] Based on the road type corresponding to each data in the mobile mode working data group, the mobile mode working data group is further divided to obtain a mobile mode working sub-data group, wherein the road type includes a flat road, a sloped road, and a curved road;

[0097] acquiring movement pattern characteristic data according to the movement pattern working sub-data group;

[0098] Acquiring cleaning mode characteristic data based on the cleaning mode working data group, wherein the cleaning mode working data group represents mode working data corresponding to the unmanned cleaning vehicle in the cleaning mode;

[0099] Obtaining obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group;

[0100] Obtain obstacle avoidance pattern feature data based on obstacle avoidance path information;

[0101] The pattern feature data is acquired based on the movement pattern feature data, the cleaning pattern feature data, and the obstacle avoidance pattern feature data.

[0102] In this solution, the working data of the unmanned sweeper corresponding to the same working mode are divided into the same data group to obtain the mode working data group corresponding to each working mode. The mobile mode working data group is further divided based on the road type corresponding to each data in the mobile mode working data group to obtain the mobile mode working sub-data group. According to the mobile mode working sub-data group, the mobile mode characteristic data is obtained. Based on the cleaning mode working data group, the cleaning mode characteristic data is obtained. Based on the obstacle avoidance path information, the obstacle avoidance mode characteristic data is obtained. For the operation parameters, the obstacle avoidance mode focuses on the sensor response time and the path curvature change. The mobile mode separates the motor torque, driving speed and other data according to the road type (flat / slope / curved) to avoid simulation errors caused by the mixing of multi-mode data. For example, in a curved road scenario, the smoothness of the steering algorithm can be evaluated separately to eliminate the interference of the cleaning operation on the mobile performance.

[0103] It is understandable that the daily working data of unmanned sweepers are often multiple data under different working modes, different road conditions and different weather types. It is impossible to directly use a fixed standard or value to accurately analyze the working conditions of the unmanned sweeper. For example, when the unmanned sweeper is in cleaning mode on rainy days, the working parameters of the unmanned sweeper are related to too many variables (road conditions, weather, etc.), and it is impossible to accurately analyze the impact of each variable on the unmanned sweeper. Therefore, based on the mobile mode data of the unmanned sweeper on flat roads, an unmanned sweeper working parameter standard is established, which provides a basis for the subsequent data analysis of other modes.

[0104] Specifically, according to the mobile mode working sub-data group, the mobile mode characteristic data is obtained, which specifically includes:

[0105] Obtaining performance design parameters of the unmanned sweeper, wherein the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information, and standard cleaning coverage;

[0106] Based on the performance design parameters of the unmanned sweeper, a standard energy consumption ratio coefficient is obtained, where the standard energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper;

[0107] Based on the standard energy consumption ratio coefficient, the weight coefficient of the speed and energy consumption of the unmanned sweeper is obtained;

[0108] According to the mobile mode working sub-data set corresponding to the flat road, the product of the speed of the unmanned sweeper and the corresponding weight coefficient is used as the speed of the unmanned sweeper, and the product of the energy consumption of the unmanned sweeper and the corresponding weight coefficient is used as the energy consumption of the unmanned sweeper;

[0109] The ratio of the speed of the unmanned sweeper to the energy consumption of the unmanned sweeper is used as the flat road calibration coefficient;

[0110] The mobile mode working sub-data set corresponding to the maximum flat road calibration coefficient in the mobile mode working sub-data set corresponding to the flat road is used as the flat road characteristic data;

[0111] Based on the mobile mode working sub-data group corresponding to the flat road, obtaining slope road characteristic data and curved road characteristic data;

[0112] The movement pattern characteristic data is acquired based on the flat road characteristic data, the slope road characteristic data, and the curved road characteristic data.

[0113] In this solution, through standardized parameter modeling, dynamic weight allocation and multi-road condition feature extraction, accurate quantification of performance evaluation and targeted design optimization are achieved in the unmanned sweeper simulation experiment. The weight coefficient of the speed and energy consumption of the unmanned sweeper is established through the standard energy consumption ratio coefficient (speed and energy consumption ratio), and the speed and energy consumption are separated from the mixed influencing factors. The evaluation focus is dynamically adjusted through the weight coefficient to avoid the fuzzy evaluation of "speed increase accompanied by energy consumption surge". For example, if the ratio coefficient of the speed and energy consumption of the unmanned sweeper is 1.5, the weight coefficient of the speed of the unmanned sweeper is 0.6, and the weight coefficient of the energy consumption of the unmanned sweeper is 0.4. The standard energy consumption ratio is a core indicator and can be directly mapped to hardware selection (such as motor power and battery capacity). For example, the standard energy consumption ratio of a certain model of sweeper is 0.8km / kWh, and verify the deviation between the actual ratio and the standard value under different road conditions through simulation, quickly locate inefficient links, and lock the "limit operating point" under various road conditions by maximizing the calibration coefficient (for example, the maximum speed-energy consumption ratio on a flat road corresponds to a fully loaded uniform speed condition, and on a sloping road it corresponds to a maximum climbing condition). Simulation can specifically test performance boundaries under extreme conditions (such as motor overload protection and battery life limits), and expose design flaws in advance (such as insufficient battery capacity on a slope leading to operation interruption). This upgrades the mobile performance evaluation of unmanned sweepers from experience-driven to data-driven, significantly improving the accuracy of simulation experiments and R&D efficiency. Especially under complex urban road conditions, its ability to quantitatively analyze multi-dimensional performance indicators provides key technical support for the hardware selection, algorithm optimization, and product implementation of unmanned sweepers.

[0114] Specifically, based on the mobile mode working sub-data set corresponding to the flat road, the slope road characteristic data and the curved road characteristic data are obtained, which specifically includes:

[0115] Based on the mobile mode working sub-data set corresponding to the flat road, the maximum and minimum speeds of the unmanned road sweeper on the flat road are obtained;

[0116] The maximum and minimum speeds of the unmanned road sweeper on a flat road are used as the benchmark speed thresholds for the unmanned road sweeper;

[0117] Remove the data of the unmanned sweeper speed not exceeding the unmanned sweeper reference speed threshold from the movement mode working sub-data groups corresponding to slope roads and curved roads, and obtain the movement mode feature data group;

[0118] Based on the movement pattern characteristic data set corresponding to the slope road, the speed and energy consumption of the slope unmanned sweeper are obtained;

[0119] The sum of the speed of the slope unmanned sweeper and the energy consumption of the slope unmanned sweeper is used as the slope road calibration coefficient;

[0120] The mobile mode working sub-data group corresponding to the maximum value of the slope road calibration coefficient in the mobile mode working sub-data group corresponding to the slope road is used as the slope road characteristic data;

[0121] Based on the flat road characteristic data, the unmanned road sweeper speed corresponding to the flat road characteristic data is used as the unmanned road sweeper characteristic speed;

[0122] Based on the mobile pattern feature data set corresponding to the curved road, the speed change information of the unmanned sweeper corresponding to each data is obtained, wherein the speed change information of the unmanned sweeper represents the speed change of the unmanned sweeper when passing through the curved road;

[0123] According to the speed change information of the unmanned sweeper, the maximum and minimum speed values ​​of the unmanned sweeper corresponding to each data in the mobile mode feature data group are obtained;

[0124] The sum of the differences between the maximum and minimum speeds of the unmanned sweeper and the characteristic speed of the unmanned sweeper is used as the calibration coefficient for the curved road;

[0125] The movement mode working sub-data set corresponding to the maximum value of the curved road calibration coefficient in the movement mode working sub-data set corresponding to the curved road is used as the curved road characteristic data.

[0126] This solution uses a flat road baseline speed threshold to filter out invalid data on sloped and curved roads (such as outliers outside the flat road speed range), avoiding the inefficiency caused by analyzing every piece of data. A slope calibration coefficient is used as a benchmark to identify characteristic data on sloped roads. The sum of "speed" and "energy consumption" (the slope calibration coefficient) is used to simultaneously consider gradeability and energy efficiency, ensuring that the characteristic data set only contains valid samples that meet design constraints, making simulations more targeted. Using the characteristic speed of flat roads as an anchor point (such as a standard speed of 5 km / h), speed changes (acceleration / deceleration amplitude) on curved roads can be quantified. The sum of the difference between the extreme speed and the characteristic speed (the curvature calibration coefficient) is used to quantify speed stability during steering, effectively identifying steering algorithm defects (such as a speed drop of more than 30% during sharp turns, resulting in fluctuating sweeping efficiency). This provides a key basis for optimizing the path planning algorithm, addresses the ambiguity and inefficiency of unmanned sweeper performance evaluation under complex road conditions, and offers a practical, quantitative solution for hardware selection and algorithm optimization.

[0127] Specifically, based on the cleaning mode working data group, the cleaning mode characteristic data is obtained, which specifically includes:

[0128] Based on the cleaning mode working data group, obtain the cleaning coverage rate corresponding to each mode working data;

[0129] According to the mobile mode characteristic data, the mobile energy consumption ratio coefficient corresponding to each mobile mode characteristic data is obtained based on the proportional relationship between the speed and energy consumption of the unmanned sweeper;

[0130] According to the performance design parameters of the unmanned sweeper, the standard cleaning energy consumption information of the unmanned sweeper is obtained;

[0131] Obtain the standard energy consumption ratio coefficient, and use the ratio of the mobile energy consumption ratio coefficient to the standard energy consumption ratio coefficient as the energy consumption difference coefficient;

[0132] The mean value of the energy consumption difference coefficient corresponding to the mobile mode characteristic data is used as the coefficient difference benchmark value;

[0133] The ratio of the cleaning coverage rate corresponding to each mode working data in the cleaning mode working data group to the standard cleaning coverage rate is used as the cleaning difference coefficient;

[0134] According to the cleaning mode working data group, a characteristic energy consumption ratio coefficient corresponding to each mode working data is obtained, wherein the characteristic energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper in the cleaning mode working data group;

[0135] The ratio of the characteristic energy consumption ratio coefficient to the coefficient difference benchmark value is used as the energy consumption difference coefficient;

[0136] The product of the energy consumption difference coefficient and the cleaning difference coefficient is taken as the cleaning characteristic coefficient;

[0137] The mode operation data corresponding to the smallest cleaning characteristic coefficient in the cleaning mode operation data group is used as the cleaning mode characteristic data.

[0138] In this solution, a two-way constraint evaluation of "operation effect-energy consumption" is achieved by multiplying the cleaning difference coefficient (coverage rate) and the energy consumption difference coefficient (energy efficiency) (cleaning characteristic coefficient). This avoids the one-sidedness of traditional simulations that only focus on cleaning coverage rate (such as reaching 95% but energy consumption exceeding the standard by 30%) or simply pursue low energy consumption (such as energy consumption meeting the standard but coverage rate less than 80%). By multiplying the coefficients, the "double-optimal" working mode is forced to be screened to ensure the practicality of the design solution.

[0139] It is understandable that for the cleaning operation of an unmanned sweeper, different road environments and weather environments have completely different effects on the working parameters of the unmanned sweeper. If each data is analyzed one by one to evaluate the working conditions of the unmanned sweeper during cleaning, not only will the data processing volume be large and the analysis efficiency will be low, but the cleaning operation status of the unmanned sweeper cannot be accurately identified. Therefore, the ratio of the characteristic energy consumption ratio coefficient to the coefficient difference baseline value is used as the energy consumption difference coefficient. The smaller the energy consumption difference coefficient, the greater the difference between the energy consumption of the unmanned sweeper and the energy consumption during movement. At this time, the ratio of the cleaning coverage rate corresponding to each mode working data in the cleaning mode working data group and the standard cleaning coverage rate is used as the cleaning difference coefficient. Under normal conditions, the cleaning difference coefficient will also increase with the increase of energy consumption. Therefore, the mode working data corresponding to the smallest cleaning characteristic coefficient in the cleaning mode working data group is used as the cleaning mode characteristic data to accurately identify the characteristic data during the cleaning operation.

[0140] Specifically, based on the obstacle avoidance path information, obstacle avoidance pattern feature data is obtained, including:

[0141] Obtain the obstacle avoidance planning path information of the unmanned sweeper corresponding to each mode working data according to the obstacle avoidance mode working data group;

[0142] Match the obstacle avoidance planning path information of the unmanned sweeper with the obstacle avoidance path information to obtain obstacle avoidance path comparison information;

[0143] According to the obstacle avoidance path comparison information, the starting point and end point of the obstacle avoidance planning path and the starting point and end point of the obstacle avoidance path are obtained;

[0144] The sum of the distance between the starting point of the obstacle avoidance path and the starting point of the obstacle avoidance path and the distance between the ending point of the obstacle avoidance path and the ending point of the obstacle avoidance path is used as the reference offset;

[0145] Obtain obstacle avoidance planning path length information and obstacle avoidance path length information;

[0146] The ratio of the baseline offset to the obstacle avoidance planning path length is used as the path base offset coefficient;

[0147] The ratio of the obstacle avoidance path length to the obstacle avoidance planning path length is used as the obstacle avoidance path comparison coefficient;

[0148] The product of the path basic deviation coefficient and the obstacle avoidance path comparison coefficient is used as the obstacle avoidance characteristic coefficient;

[0149] The mode operating data corresponding to the largest obstacle avoidance characteristic coefficient in the obstacle avoidance mode operating data group is used as the obstacle avoidance mode characteristic data.

[0150] In this solution, the baseline offset directly reflects the starting / end point positioning accuracy of the obstacle avoidance path, which can quickly identify coordinate calibration issues in the sensor fusion algorithm. The obstacle avoidance path comparison coefficient can expose redundant detour problems in the planning algorithm, providing a clear direction for path optimization. This solves the problems of "rough evaluation" and "fuzzy defect positioning" in traditional obstacle avoidance simulation. Especially in dynamic and complex environments, its in-depth analysis of the position accuracy and detour efficiency of the obstacle avoidance path provides key technical support for the safety and reliability design of unmanned sweepers.

[0151] According to the pattern feature data, corresponding environmental landscape data and map data are obtained;

[0152] Based on environmental landscape data and map data, three-dimensional scenes are constructed to obtain simulation experiment scene information;

[0153] Specifically, the environmental landscape data and map data corresponding to the pattern feature data are integrated with high-precision map data in different coordinate systems through a coordinate conversion algorithm. This allows the laser point cloud data in different coordinate systems to be integrated with high-precision map data, accurately reflecting the spatial information of the real environment. Professional 3D modeling software (such as Blender or Maya) is then used to construct a digital twin 3D scene. Based on the terrain, buildings, road facilities, and other elements in the real environment, a precise 3D model is created, and its material, texture, and physical properties are configured. The pattern feature data is then used to configure the road environment and obstacle environment for the unmanned sweeper simulation experiment. Different weather conditions and traffic scenarios are then set to comprehensively test and analyze the unmanned sweeper's operating stability and cleaning performance.

[0154] Based on the simulated experimental scenario, a simulation experiment was conducted on the unmanned sweeper.

[0155] Reference Figure 5 As shown, further, combined with the above-mentioned simulation experiment method based on unmanned sweeper simulation, a simulation experiment system based on unmanned sweeper simulation is proposed, including:

[0156] A main control module, the main control module is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information, obtain corresponding environmental landscape data and map data according to the mode characteristic data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and simulate the experimental scene to conduct a simulation experiment on the unmanned sweeper;

[0157] An information acquisition module, the information acquisition module is used to obtain unmanned sweeper operating data, the unmanned sweeper operating data including environmental landscape data, sweeper operating parameters and map data; based on the unmanned sweeper operating data, obtain operating mode information; obtain unmanned sweeper performance design parameters, the unmanned sweeper performance design parameters including standard speed information, standard energy consumption information and standard sweeping coverage; based on the unmanned sweeper performance design parameters, obtain a standard energy consumption ratio coefficient;

[0158] a data identification module, the data identification module being configured to divide the unmanned sweeper working data corresponding to the same working mode into the same data group based on the working data classification information, obtain a mode working data group corresponding to each working mode, further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group, obtain a mobile mode working sub-data group, obtain a weight coefficient of the unmanned sweeper speed and energy consumption based on the standard energy consumption ratio coefficient, obtain flat road characteristic data based on the weight coefficient of the unmanned sweeper speed and energy consumption, and obtain slope road characteristic data and curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road;

[0159] The display module interacts with the main control module and is used to output and display work data classification information, movement mode feature data, cleaning mode feature data, obstacle avoidance mode feature data and simulation experiment scene information.

[0160] Main control module, specifically including:

[0161] A control unit is configured to obtain corresponding environmental landscape data and map data based on the pattern feature data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and perform a simulation experiment on the unmanned sweeper using the simulation experiment scene;

[0162] An information receiving unit, which interacts with the information acquisition module and the data identification module to receive data and transmit it to the data processing unit;

[0163] A data processing unit is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, and obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information.

[0164] Information acquisition module, specifically including:

[0165] a first acquisition unit, configured to acquire operating data of the unmanned sweeper, the operating data of the unmanned sweeper including environmental landscape data, operating parameters of the sweeper, and map data, and to acquire operating mode information based on the operating data of the unmanned sweeper;

[0166] The second acquisition unit is used to obtain the performance design parameters of the unmanned sweeper, the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information and standard cleaning coverage rate, and obtain the standard energy consumption ratio coefficient based on the performance design parameters of the unmanned sweeper.

[0167] Data identification module, specifically including:

[0168] A first identification unit, wherein the first identification unit is used to divide the unmanned sweeper working data corresponding to the same working mode into the same data group according to the working data classification information, obtain the mode working data group corresponding to each working mode, and further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group to obtain the mobile mode working sub-data group;

[0169] The second identification unit is used to obtain the weight coefficient of the speed and energy consumption of the unmanned sweeper based on the standard energy consumption ratio coefficient, obtain the flat road characteristic data according to the weight coefficient of the speed and energy consumption of the unmanned sweeper, and obtain the slope road characteristic data and the curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road.

[0170] To sum up, the advantages of the present invention are: through the work data classification information, the work data of different working modes are classified, the data analysis efficiency is improved, and the subsequent data processing steps are facilitated; by further dividing the mobile mode work data group, the mobile mode work sub-data group is obtained, and the driving conditions of the unmanned sweeper under different road conditions are accurately analyzed, which provides a benchmark for the subsequent analysis of the working conditions of the unmanned sweeper under other modes; through the mode feature data corresponding to each working mode, with the environmental landscape data and map data as the benchmark, a three-dimensional scene is constructed, which provides a data basis for the simulation of the unmanned sweeper, improves the simulation efficiency of the unmanned sweeper, and ensures the stability and reliability of the unmanned sweeper operation.

[0171] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A simulation experiment method based on unmanned sweeper simulation, characterized in that: include: Acquiring unmanned sweeper operating data, wherein the unmanned sweeper operating data includes environmental landscape data, sweeper operating parameters, and map data; Acquire working mode information according to the working data of the unmanned sweeper, wherein the working mode includes a cleaning mode, an obstacle avoidance mode, and a moving mode; Based on the working mode, the working data of the unmanned sweeper is classified to obtain the working data classification information; Obtaining mode feature data corresponding to each working mode according to the working mode information and working data classification information; According to the pattern feature data, corresponding environmental landscape data and map data are obtained; Based on environmental landscape data and map data, three-dimensional scenes are constructed to obtain simulation experiment scene information; Based on the simulation experiment scenario, a simulation experiment was conducted on the unmanned sweeper; The obtaining of mode feature data corresponding to each working mode according to the working mode information and the working data classification information specifically includes: According to the work data classification information, the unmanned sweeper work data corresponding to the same work mode is divided into the same data group, and the mode work data group corresponding to each work mode is obtained; Acquire a mobile mode working data group, wherein the mobile mode working data group represents mode working data corresponding to the unmanned sweeper in the mobile mode; Based on the road type corresponding to each data in the mobile mode working data group, the mobile mode working data group is further divided to obtain a mobile mode working sub-data group, wherein the road type includes a flat road, a sloped road, and a curved road; acquiring movement pattern characteristic data according to the movement pattern working sub-data group; Acquiring cleaning mode characteristic data based on the cleaning mode working data group, wherein the cleaning mode working data group represents mode working data corresponding to the unmanned cleaning vehicle in the cleaning mode; Obtaining obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group; Obtain obstacle avoidance pattern feature data based on obstacle avoidance path information; The pattern feature data is acquired based on the movement pattern feature data, the cleaning pattern feature data, and the obstacle avoidance pattern feature data.

2. The simulation experiment method based on unmanned sweeper simulation according to claim 1 is characterized in that: The obtaining of the mobile mode characteristic data according to the mobile mode working sub-data group specifically includes: Obtaining performance design parameters of the unmanned sweeper, wherein the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information, and standard cleaning coverage; Based on the performance design parameters of the unmanned sweeper, a standard energy consumption ratio coefficient is obtained, where the standard energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper; Based on the standard energy consumption ratio coefficient, the weight coefficient of the speed and energy consumption of the unmanned sweeper is obtained; According to the mobile mode working sub-data set corresponding to the flat road, the product of the speed of the unmanned sweeper and the corresponding weight coefficient is used as the speed of the unmanned sweeper, and the product of the energy consumption of the unmanned sweeper and the corresponding weight coefficient is used as the energy consumption of the unmanned sweeper; The ratio of the speed of the unmanned sweeper to the energy consumption of the unmanned sweeper is used as the flat road calibration coefficient; The mobile mode working sub-data set corresponding to the maximum flat road calibration coefficient in the mobile mode working sub-data set corresponding to the flat road is used as the flat road characteristic data; Based on the mobile mode working sub-data group corresponding to the flat road, obtaining slope road characteristic data and curved road characteristic data; The movement pattern characteristic data is acquired based on the flat road characteristic data, the slope road characteristic data, and the curved road characteristic data.

3. The simulation experiment method based on unmanned sweeper simulation according to claim 2 is characterized in that: The step of obtaining slope road characteristic data and curved road characteristic data based on the mobile mode working sub-data set corresponding to the flat road specifically includes: Based on the mobile mode working sub-data set corresponding to the flat road, the maximum and minimum speeds of the unmanned road sweeper on the flat road are obtained; The maximum and minimum speeds of the unmanned road sweeper on a flat road are used as the benchmark speed thresholds for the unmanned road sweeper; Remove the data of the unmanned sweeper speed not exceeding the unmanned sweeper reference speed threshold from the movement mode working sub-data groups corresponding to slope roads and curved roads, and obtain the movement mode feature data group; Based on the movement pattern characteristic data set corresponding to the slope road, the speed and energy consumption of the slope unmanned sweeper are obtained; The sum of the speed of the slope unmanned sweeper and the energy consumption of the slope unmanned sweeper is used as the slope road calibration coefficient; The mobile mode working sub-data group corresponding to the maximum value of the slope road calibration coefficient in the mobile mode working sub-data group corresponding to the slope road is used as the slope road characteristic data; Based on the flat road characteristic data, the unmanned road sweeper speed corresponding to the flat road characteristic data is used as the unmanned road sweeper characteristic speed; Based on the mobile pattern feature data set corresponding to the curved road, the speed change information of the unmanned sweeper corresponding to each data is obtained, wherein the speed change information of the unmanned sweeper represents the speed change of the unmanned sweeper when passing through the curved road; According to the speed change information of the unmanned sweeper, the maximum and minimum speed values ​​of the unmanned sweeper corresponding to each data in the mobile mode feature data group are obtained; The sum of the differences between the maximum and minimum speeds of the unmanned sweeper and the characteristic speed of the unmanned sweeper is used as the calibration coefficient for the curved road; The movement mode working sub-data set corresponding to the maximum value of the curved road calibration coefficient in the movement mode working sub-data set corresponding to the curved road is used as the curved road characteristic data.

4. The simulation experiment method based on unmanned sweeper simulation according to claim 1 is characterized in that: The acquiring of cleaning mode characteristic data based on the cleaning mode working data group specifically includes: Based on the cleaning mode working data group, obtain the cleaning coverage rate corresponding to each mode working data; According to the mobile mode characteristic data, the mobile energy consumption ratio coefficient corresponding to each mobile mode characteristic data is obtained based on the proportional relationship between the speed and energy consumption of the unmanned sweeper; According to the performance design parameters of the unmanned sweeper, the standard cleaning energy consumption information of the unmanned sweeper is obtained; Obtain the standard energy consumption ratio coefficient, and use the ratio of the mobile energy consumption ratio coefficient to the standard energy consumption ratio coefficient as the energy consumption difference coefficient; The mean value of the energy consumption difference coefficient corresponding to the mobile mode characteristic data is used as the coefficient difference benchmark value; The ratio of the cleaning coverage rate corresponding to each mode working data in the cleaning mode working data group to the standard cleaning coverage rate is used as the cleaning difference coefficient; According to the cleaning mode working data group, a characteristic energy consumption ratio coefficient corresponding to each mode working data is obtained, wherein the characteristic energy consumption ratio represents a proportional coefficient between the speed and energy consumption of the unmanned sweeper in the cleaning mode working data group; The ratio of the characteristic energy consumption ratio coefficient to the coefficient difference benchmark value is used as the energy consumption difference coefficient; The product of the energy consumption difference coefficient and the cleaning difference coefficient is taken as the cleaning characteristic coefficient; The mode operation data corresponding to the smallest cleaning characteristic coefficient in the cleaning mode operation data group is used as the cleaning mode characteristic data.

5. The simulation experiment method based on unmanned sweeper simulation according to claim 1 is characterized in that: Obtaining obstacle avoidance pattern feature data based on the obstacle avoidance path information specifically includes: Obtain the obstacle avoidance planning path information of the unmanned sweeper corresponding to each mode working data according to the obstacle avoidance mode working data group; Match the obstacle avoidance planning path information of the unmanned sweeper with the obstacle avoidance path information to obtain obstacle avoidance path comparison information; According to the obstacle avoidance path comparison information, the starting point and end point of the obstacle avoidance planning path and the starting point and end point of the obstacle avoidance path are obtained; The sum of the distance between the starting point of the obstacle avoidance path and the starting point of the obstacle avoidance path and the distance between the ending point of the obstacle avoidance path and the ending point of the obstacle avoidance path is used as the reference offset; Obtain obstacle avoidance planning path length information and obstacle avoidance path length information; The ratio of the baseline offset to the obstacle avoidance planning path length is used as the path base offset coefficient; The ratio of the obstacle avoidance path length to the obstacle avoidance planning path length is used as the obstacle avoidance path comparison coefficient; The product of the path basic deviation coefficient and the obstacle avoidance path comparison coefficient is used as the obstacle avoidance characteristic coefficient; The mode operation data corresponding to the largest obstacle avoidance characteristic coefficient in the obstacle avoidance mode operation data group is used as the obstacle avoidance mode characteristic data.

6. A simulation experiment system based on unmanned sweeper simulation, used to implement the simulation experiment method according to any one of claims 1 to 5, characterized in that: include: A main control module, the main control module is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information, obtain corresponding environmental landscape data and map data according to the mode characteristic data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and simulate the experimental scene to conduct a simulation experiment on the unmanned sweeper; An information acquisition module, the information acquisition module is used to obtain unmanned sweeper operating data, the unmanned sweeper operating data including environmental landscape data, sweeper operating parameters and map data; based on the unmanned sweeper operating data, obtain operating mode information; obtain unmanned sweeper performance design parameters, the unmanned sweeper performance design parameters including standard speed information, standard energy consumption information and standard sweeping coverage; based on the unmanned sweeper performance design parameters, obtain a standard energy consumption ratio coefficient; a data identification module, the data identification module being configured to divide the unmanned sweeper working data corresponding to the same working mode into the same data group based on the working data classification information, obtain a mode working data group corresponding to each working mode, further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group, obtain a mobile mode working sub-data group, obtain a weight coefficient of the unmanned sweeper speed and energy consumption based on the standard energy consumption ratio coefficient, obtain flat road characteristic data based on the weight coefficient of the unmanned sweeper speed and energy consumption, and obtain slope road characteristic data and curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road; The display module interacts with the main control module and is used to output and display work data classification information, movement mode feature data, cleaning mode feature data, obstacle avoidance mode feature data and simulation experiment scene information.

7. The simulation experiment system based on unmanned sweeper simulation according to claim 6 is characterized in that: The main control module specifically includes: A control unit is configured to obtain corresponding environmental landscape data and map data based on the pattern feature data, construct a three-dimensional scene based on the environmental landscape data and map data, obtain simulation experiment scene information, and perform a simulation experiment on the unmanned sweeper using the simulation experiment scene; An information receiving unit, which interacts with the information acquisition module and the data identification module to receive data and transmit it to the data processing unit; A data processing unit is used to obtain cleaning mode characteristic data based on the cleaning mode working data group, obtain obstacle avoidance path information corresponding to each mode working data according to the obstacle avoidance mode working data group, and obtain obstacle avoidance mode characteristic data based on the obstacle avoidance path information.

8. The simulation experiment system based on unmanned sweeper simulation according to claim 6 is characterized in that: The information acquisition module specifically includes: a first acquisition unit, configured to acquire operating data of the unmanned sweeper, the operating data of the unmanned sweeper including environmental landscape data, operating parameters of the sweeper, and map data, and to acquire operating mode information based on the operating data of the unmanned sweeper; The second acquisition unit is used to obtain the performance design parameters of the unmanned sweeper, the performance design parameters of the unmanned sweeper include standard speed information, standard energy consumption information and standard cleaning coverage rate, and obtain the standard energy consumption ratio coefficient based on the performance design parameters of the unmanned sweeper.

9. The simulation experiment system based on unmanned sweeper simulation according to claim 6 is characterized in that: The data identification module specifically includes: A first identification unit, wherein the first identification unit is used to divide the unmanned sweeper working data corresponding to the same working mode into the same data group according to the working data classification information, obtain the mode working data group corresponding to each working mode, and further divide the mobile mode working data group based on the road type corresponding to each data in the mobile mode working data group to obtain the mobile mode working sub-data group; The second identification unit is used to obtain the weight coefficient of the speed and energy consumption of the unmanned sweeper based on the standard energy consumption ratio coefficient, obtain the flat road characteristic data according to the weight coefficient of the speed and energy consumption of the unmanned sweeper, and obtain the slope road characteristic data and the curved road characteristic data based on the mobile mode working sub-data group corresponding to the flat road.

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