A power optimization system and optimization method for all-terrain aerial work vehicle

Abstract

The present invention discloses a power optimization system for an all-terrain aerial work vehicle and an optimization method thereof, and relates to the technical field of aerial work vehicles. The system comprises a terrain recognition module, an operation demand analysis module, a central processing unit module, a power source switching control module and a display and early warning module. The present invention greatly improves the adaptability and operation efficiency of the all-terrain aerial work vehicle through an intelligent multi-mode power source automatic switching system. When faced with different terrains and operation requirements, the system can automatically select the most suitable power source to ensure that the operation vehicle has sufficient power output. This intelligent switching avoids operation interruptions and delays caused by insufficient or mismatched power, significantly improves the continuity and overall efficiency of the operation, reduces the operation cost, and also ensures that the operators can complete the task safely in various environments.

Description

Technical Field

[0001] The invention relates to the technical field of aerial work vehicles, and in particular to a power optimization system and an optimization method for an all-terrain aerial work vehicle. Background Art

[0002] With the rapid development of modern industry and urbanization, the demand for aerial work in many fields such as construction, power maintenance, communication facility installation and maintenance, bridge construction, etc. is growing. As a special vehicle capable of performing aerial work under various complex terrain conditions, the importance of all-terrain aerial work vehicles is becoming increasingly prominent. Different operating scenes may include narrow urban streets, rugged mountain terrain, wetlands, snowfields and other diverse terrain environments, and the operating height and load requirements are also different. This puts high demands on the power system of the aerial work vehicle, which needs to be able to operate stably under different working conditions to ensure the smooth progress of the operation and the safety of personnel.

[0003] However, the existing power optimization system for aerial work vehicles switches between power sources based only on simple manual operations, and switches only according to whether the work vehicle is in a driving or working state, without considering the impact of terrain changes on power requirements. This rough switching method cannot adjust the power source in real time according to actual terrain conditions and work requirements, resulting in insufficient power when working in complex terrain, affecting work efficiency and even failing to complete the work task.

[0004] In summary, existing patents have obvious defects in the automatic switching of multi-mode power sources of all-terrain aerial work vehicles and cannot meet the complex and diverse actual work needs. Therefore, there is an urgent need for an all-terrain aerial work vehicle power optimization system to improve the overall performance of the all-terrain aerial work vehicle. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide an all-terrain aerial work vehicle power optimization system and an optimization method thereof, which can automatically switch between multiple power sources according to different working scenarios, terrain conditions and working requirements of the all-terrain aerial work vehicle, which can not only maximize the efficiency of the power system, but also significantly reduce energy consumption and environmental pollution. Through the smooth transition mechanism, the stability and safety of the vehicle during the switching process are ensured, and the operation interruption caused by improper power source switching is avoided.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a power optimization system for an all-terrain aerial work vehicle comprises a terrain recognition module, an operation demand analysis module, a central processing unit module, a power source switching control module and a display and warning module;

[0007] The terrain recognition module is composed of a terrain sensor unit and a data processing unit, which performs preliminary processing and feature extraction on the original terrain data collected by the terrain sensor unit, generates terrain feature data, and transmits the terrain feature data to the central processing unit module, wherein:

[0008] The terrain sensor unit collects the terrain data of the working vehicle in real time through a laser radar sensor, a gyroscope and an accelerometer. The laser radar sensor performs a full range of three-dimensional scanning of the terrain around the working vehicle to obtain detailed terrain geometry information. The gyroscope and accelerometer monitor the attitude change information of the working vehicle during driving and operation in real time.

[0009] The data processing unit includes a preprocessing subunit, a feature extraction subunit and a terrain classification subunit. The preprocessing subunit performs filtering processing on the original terrain geometry information collected by the laser radar sensor to remove noise points and abnormal points caused by environmental factors. At the same time, the posture change information of the gyroscope and the accelerometer is calibrated to compensate for the deviation caused by sensor errors. The feature extraction subunit correlates the terrain geometry information with the posture change information to form terrain point cloud data, and divides the terrain point cloud data into different areas, each area representing a terrain feature. The terrain classification subunit classifies the extracted terrain features, identifies different types of terrain, and transmits the classification results to the central processing unit module.

[0010] The operation demand analysis module is composed of a working arm sensor unit and an operation analysis unit. The working arm of the operation vehicle is equipped with a sensor to measure the actual kinematic data of the working arm during the operation and compare it with the results of the working arm mechanical model to identify the operation type and quantify the operation demand, wherein:

[0011] The working arm sensor unit includes a force sensor, an angle sensor and a displacement sensor, which monitor the actual kinematic data of the working arm during the working process in real time. The force sensor measures the force data of the working arm in all directions, and the force sensor data at different positions are integrated to obtain the load situation of the working arm during the operation. The angle sensor monitors the angle changes of the working arm during pitch, rotation and extension in real time, and records the motion trajectory of the working arm. The displacement sensor is combined with the data of the angle sensor to describe the motion state of the working arm in detail, and provide kinematic data for the type identification and complexity analysis of the operation.

[0012] The job analysis unit includes a mechanical model building subunit, a job type identification subunit and a job demand quantification subunit. The mechanical model building subunit builds a mechanical model of the working arm according to the force data measured by the force sensor and the deadweight, load force and joint friction force of the working arm, and detects the force distribution and deformation of the working arm under different working postures; the job type identification subunit compares the kinematic data of the working arm with the mechanical model analysis results to identify the job type; the job demand quantification subunit further quantifies the job demand according to the job type identification results, and divides the job load into three levels of light load, medium load and heavy load; divides the job complexity into three levels of simple, medium and complex, and transmits the quantitative information of the job load level and the job complexity in data format to the central processing unit module;

[0013] The central processing unit module is used to receive and process data from the terrain recognition module and the operation demand analysis module, send control instructions to the power source switching control module, and store historical instruction data and analysis results. The control instructions are deeply integrated with the classification results of terrain feature data, the operation load level and the operation complexity, and generate power source switching control instructions according to the integrated data. The power source includes electric mode, fuel mode and hybrid power mode;

[0014] The power source switching control module includes a motor controller, a fuel engine controller and a hybrid power controller, which are used to receive instructions from the intelligent control module and perform corresponding control operations. The motor controller is used to control the start, stop and power output of the motor, the fuel engine controller is used to control the start, stop and power output of the fuel engine, and the hybrid power controller is used to control the coordinated work of the motor and the fuel engine, wherein:

[0015] The motor controller includes a drive circuit, a protection circuit and a communication interface, wherein the drive circuit is used to control the start and stop and power output of the motor, the protection circuit is used to prevent the motor from overloading and short circuiting, and the communication interface is used to exchange data with the central processing unit module;

[0016] The fuel engine controller includes an ignition unit, a fuel supply unit and a cooling unit, wherein the ignition unit is used to control the ignition timing of the fuel engine, the fuel supply unit is used to adjust the fuel supply amount, and the cooling unit is used to maintain the operating temperature of the fuel engine;

[0017] The hybrid power controller includes an energy management unit and a power distribution unit, wherein the energy management unit is used to coordinate the energy supply of the electric motor and the fuel engine, and the power distribution unit is used to distribute the power output of the electric motor and the fuel engine according to the operation requirements;

[0018] The display and warning module receives data from the central processing unit module and is used to display the system's operating status information to the operator, including the currently identified terrain type, the analyzed operating requirements, the switched power source type, and the operating parameters of the power source. At the same time, it monitors the operating status of the entire system in real time. When an abnormal situation occurs, an alarm is immediately sounded through a sound alarm, and detailed warning information is displayed on the display screen to ensure operational safety.

[0019] Furthermore, in the mechanical model construction subunit of the work demand analysis module, according to the kinematic parameters of the working arm and the external force parameters of the working arm, including the force data in various directions measured by the force sensor, the Lagrange equation is used to establish the dynamic model of the working arm, and the kinetic energy T and potential energy V of the working arm are calculated. m i is the mass of the i-th joint, n is the number of working arm joints, is the velocity component of the i-th joint, I i is the moment of inertia of the i-th joint, ω i is the angular velocity of the i-th joint, g is the gravitational acceleration, z i is the vertical height of the i-th joint, τ i is the external torque of the i-th joint, θ i is the angle of the i-th joint, using the Lagrange equation Establish the dynamic equation, where L = TV is the Lagrangian function, q i are generalized coordinates, is the generalized velocity, Q i is a generalized external force, The partial derivative of the Lagrangian function L is expressed to obtain the force distribution and deformation of each joint, form a mechanical model of the working arm, detect the force distribution and deformation of the working arm under different working postures, and provide basic data for job type identification and job demand quantification.

[0020] Furthermore, when the operation type identification subunit identifies the operation type, it calculates the motion trajectory complexity C of the working arm through the angle sensor and displacement sensor data. m ,Right now Among them, Δθ i is the angular change of the i-th time, Δd j is the jth displacement change, n is the number of angle changes, and m is the number of displacement changes. The load stability of the working arm is calculated by the force sensor data as S l ,Right now where F k is the load force measured at the kth time, is the average load force, p is the number of measurements, when C m <C m1 And S l >S l1 When it is judged as a simple operation type, that is, the operation complexity C is judged to be simple, and C = ω1C m +ω2S l ω1 and ω2 are weight coefficients determined by actual operation data. m1 ≤C m <C m2 And S l2 l ≤S l1 When C m ≥C m2 And S l ≤S l2 When the operation is judged as a complex operation type, the operation complexity C is judged to be complex, where C m1 , C m2 , S l1 , S l2 It is the operation type threshold of the operation vehicle.

[0021] Furthermore, in the feature extraction subunit of the terrain recognition module, the terrain point cloud data is defined as P = {(x i ,y i ,z i )}, where (x i ,y i ,z i ) represents the coordinates of the ith point, clusters the terrain point cloud data, and divides the terrain point cloud data into multiple regions, each region represents a terrain feature, and for each region R j , its terrain feature vector F j ={f j1 ,f j2 ,…,f jn}, where f jk represents the kth eigenvalue of the jth region, k = 1, 2, ..., n, and the terrain feature vector F extracted by the terrain classification subunit j For classification, its terrain feature vector F j The classification result is D t =(d t1 ,d t2 ,…,d tn ), the operation demand quantification subunit of the operation demand analysis module is based on the terrain feature vector F j The classification result D t , define the workload L as where F i ​denotes the force value measured by the i-th force sensor. According to the value of L, the operation load is divided into three levels: light load, medium load, and heavy load, that is, light load: 0 < L ≤ L1, medium load: L1 < L ≤ L2, heavy load: L > L2, where L1 and L2 are the mechanical load thresholds of the work vehicle.

[0022] Furthermore, in the central processing unit module, by using the terrain feature vector F j the classification result D t , the operation load level L, and the operation complexity C in the operation type recognition sub-unit are fused into a feature vector T, that is, T = αD t + βC + γL, where α, β, and γ are weight coefficients. According to the power source and the feature vector T, the final power source switching control instruction I is generated, that is, I =

[0023] F1 and F2 are the power switching thresholds.

[0024] Furthermore, in the energy management unit of the hybrid power controller in the power source switching control module, when coordinating the energy supply of the electric motor and the fuel engine, the output power of the electric motor is defined as P e , the output power of the fuel engine is defined as P f , the total power demand during the operation of the work vehicle is P total , the battery charging power is P charge , then there is P total = P e + P f + P charge .

[0025] Furthermore, when the power distribution unit in the hybrid power controller coordinates the power output of the electric motor and the fuel engine, the driving speed of the work vehicle is defined as v, the maximum power of the electric motor is P emax , the maximum power of the fuel engine is P fmax , the power ratio allocated to the electric motor is k, then the output power of the electric motor is P e = k × P total , the output power of the fuel engine P f = (1 - k) × P total , P total is the total power demand during the operation of the work vehicle.

[0026] Furthermore, the power source switching logic module realizes a smooth transition by gradually adjusting the power output of the electric motor and the fuel engine according to the power source switching control instruction of the central processing unit module. During the switching process, when switching from the electric mode to the fuel mode, the fuel engine is first started to preheat, and after the engine reaches a stable operating state, the load is gradually transferred from the electric motor to the fuel engine. Conversely, when switching to the electric mode, the load of the fuel engine is first reduced, the engine is smoothly shut down, and then the electric motor is started and the load is loaded;

[0027] In hybrid power mode, based on the ratio range in the power source switching decision instruction, combined with the current operating speed of the work vehicle, load changes and the remaining battery power, a dynamic programming algorithm or a fuzzy control algorithm is used to accurately adjust the output ratio of electric and fuel power.

[0028] On the other hand, a method for optimizing power of an all-terrain aerial work vehicle is provided, wherein the specific steps of the method are as follows:

[0029] Terrain recognition: collect terrain data and identify terrain types through a variety of sensors;

[0030] Operation demand analysis: Analyze the operation load and type through the sensors on the working arm of the operation vehicle to determine the power demand for the operation;

[0031] Central Processing Unit: Receives and processes data from terrain identification and work demand analysis, and sends control instructions to the power source switching control based on the mechanical model;

[0032] Power source switching control: switches between electric, fuel and hybrid power sources according to instructions, and adjusts the power ratio in hybrid mode;

[0033] Display and warning: used to display the system operation status and issue alarms in abnormal situations.

[0034] Compared with the prior art, the power optimization system and optimization method of the all-terrain aerial work vehicle have the following beneficial effects:

[0035] 1. The present invention greatly improves the adaptability and operating efficiency of all-terrain aerial work vehicles through an intelligent multi-mode power source automatic switching system. When facing different terrains and operating requirements, the system can automatically select the most suitable power source to ensure that the work vehicle has sufficient power output. This intelligent switching avoids operation interruptions and delays caused by insufficient or mismatched power, significantly improves the continuity and overall efficiency of operations, reduces operating costs, and also ensures that operators can complete tasks safely in various environments.

[0036] 2. The present invention effectively improves the environmental performance and system stability of the all-terrain aerial work vehicle. The power source switching is determined by precise terrain recognition and work demand analysis. During the switching process, the power source switching control module ensures the smoothness of power transmission, avoids vehicle bumps and impacts caused by traditional switching methods, and reduces damage to vehicle parts. This intelligent power management can also monitor and adjust the working status of the power source in real time, further optimize energy utilization efficiency, and make the entire work vehicle system more stable and reliable during long-term use.

[0037] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 It is a flow chart of a power optimization system for an all-terrain aerial work vehicle;

[0040] Figure 2 This is an operational flow chart of a work demand analysis model in a power optimization system for an all-terrain aerial work vehicle. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment 1

[0043] This embodiment describes in detail the working principle and specific implementation process of a power optimization system for an all-terrain aerial work vehicle switching from electric mode to hybrid mode during urban road operations. Through the coordinated work of the terrain recognition module, the operation demand analysis module, the central processing unit module, the power source switching control module and the display and warning module, intelligent switching of the power source is achieved, ensuring that the work vehicle can operate stably under different working conditions, thereby improving operation efficiency and vehicle performance.

[0044] In the specific implementation, first, each module of the system starts to initialize. The lidar sensor, gyroscope and accelerometer in the terrain recognition module are started to collect data on the terrain around the work vehicle. The lidar sensor performs a full-range three-dimensional scan of the surrounding terrain to obtain detailed terrain geometry information; the gyroscope and accelerometer monitor the posture change information of the work vehicle in real time, and the force sensor, angle sensor and displacement sensor of the work demand analysis module also start working at the same time, ready to monitor the movement and force of the working arm.

[0045] Since the work site is located on an urban road with flat terrain, the work demand analysis module measures the force data of the working arm in all directions through force sensors. After data fusion, it is found that the load on the working arm is small. The angle sensor and displacement sensor monitor that the working arm has a simple motion trajectory and a small motion amplitude. The calculation formula for identifying the subunit according to the work type is Calculate the complexity C of the working arm motion trajectory m Lower, load stability S l The terrain recognition module determines that the current terrain is flat after preprocessing, feature extraction and terrain classification by the data processing unit. The central processing unit module calculates the terrain characteristics data (classification result is flat terrain), the operation load level (light load) and the operation complexity (simple) according to the fusion formula (T = αD t +βC+γL), where α, β, and γ are weight coefficients. In electric mode, the central processing unit module sends a control instruction to the power source switching control module, and the motor controller of the power source switching control module controls the motor to start, and the work vehicle starts working in electric mode.

[0046] After completing the operation, the work vehicle receives a new task instruction. The force sensor of the work demand analysis module detects that the load on the working arm has increased significantly. By calculating the load level formula It is found that the load level has changed from medium load to heavy load. The angle sensor and displacement sensor also detect that the motion trajectory of the working arm has become complicated, and the motion amplitude and frequency have increased. The complexity of the motion trajectory of the working arm is C m The terrain recognition module continues to monitor the terrain, which is still flat, but the central processing unit module considers the changes in workload and complexity and switches the power source according to the decision instruction formula. Determine switching to hybrid mode, where F1 and F2 are power switching thresholds, and the currently calculated F value exceeds F1.

[0047] The central processing unit module sends a command to switch to the hybrid mode to the power source switching control module, and the hybrid controller of the power source switching control module starts to work. The energy management unit calculates the energy balance formula (Ptotal = P e + P f + P charge ) Coordinate the energy supply preparation of the electric motor and the fuel engine. Meanwhile, the power distribution unit determines the power ratio k allocated to the electric motor according to the current driving speed v of the work vehicle (at this time, the speed is relatively low, i.e., v < v1, where v1 is a threshold), the load change situation (heavy load), and the remaining battery power (the remaining power is SOC). The electric motor controller and the fuel engine controller gradually adjust the power output of the electric motor and the fuel engine according to the instructions of the hybrid controller. When switching from the electric mode to the hybrid mode, first start the fuel engine for preheating. After the engine reaches a stable operating state, gradually transfer the load from the electric drive motor to the fuel engine to achieve a smooth transition of the power source, and the work vehicle smoothly switches to the hybrid mode to continue working.

[0048] In summary, this embodiment demonstrates the complete process of the all-terrain aerial work vehicle intelligently switching from the electric mode to the hybrid mode during the actual operation process. Through the close cooperation of each module and precise data processing, calculation, and control, the work vehicle can flexibly adjust the power source under different working conditions, ensuring the progress of the operation.

[0049] Embodiment 2

[0050] This embodiment focuses on describing the detailed operation process of the all-terrain aerial work vehicle switching from the hybrid mode to the fuel mode in the mountain operation scenario, reflecting the adaptability of the system to complex working conditions and the power optimization ability, ensuring that the work vehicle can maintain stable operation under different terrains and operation tasks.

[0051] The work vehicle travels to the communication base station maintenance operation area in the mountain area. The terrain of this area is complex, with large slopes and rough roads. On the way to the operation point, the system has entered the hybrid mode operation according to the previous terrain and operation demand analysis. At this time, the terrain recognition module continuously collects terrain data through lidar sensors, gyroscopes, and accelerometers. The lidar sensor accurately scans the surrounding terrain to obtain detailed terrain undulation and slope information. The gyroscope and accelerometer real-time feedback the severe attitude changes of the work vehicle when driving on complex terrain. The operation demand analysis module is also closely monitoring the state of the working arm, and the force sensor, angle sensor, and displacement sensor are working normally.

[0052] After arriving at the work site, the work task is determined to be replacing a large communication device, which needs to be lifted from the ground to a higher position and installed. The work demand analysis module immediately detects that the load on the working arm has increased significantly. The load level is calculated through the force sensor data and it is found that it has reached the heavy load level. At the same time, the angle sensor and displacement sensor record that the working arm has a complex motion trajectory and a large motion range. The complexity of the working arm motion trajectory is C m Very high (through Calculated, where Δθ i is the ith angle change, Δd j is the jth displacement change, n is the number of angle changes, and m is the number of displacement changes). After data processing, the terrain recognition module confirms that the current terrain is steep slope or complex and rugged terrain. After receiving this information, the central processing unit module combines the terrain feature data (steep slope, complex and rugged terrain classification results), the operation load level (heavy load) and the operation complexity (complex) according to the fusion formula (T = αD t +βC+γL) is calculated and analyzed, combined with the power source switching decision instruction formula It is judged that the current operation demand exceeds the optimal efficiency range of the hybrid mode and needs to be switched to the fuel mode. At this time, the calculated F value is greater than F2. The central processor module issues an instruction to switch to the fuel mode to the power source switching control module. The fuel engine controller of the power source switching control module responds quickly, the ignition unit prepares the ignition time, the fuel supply unit adjusts the fuel supply, and the cooling unit ensures the stability of the engine operating temperature. During the switching process, the power source switching logic module operates strictly according to the set process. First, the hybrid controller gradually reduces the power output of the motor, while steadily increasing the load of the fuel engine, and gradually transfers the power to the fuel engine. When switching from the hybrid mode to the fuel mode, the participation of the motor is gradually reduced on the premise of ensuring the smooth operation of the work vehicle until the motor stops working completely, and the fuel engine assumes all the power output. During the entire switching process, the smooth transition of the power source is achieved by accurately controlling the ignition, fuel supply and load adjustment of the fuel engine, as well as the reasonable control of the motor. The work vehicle successfully switches to the fuel mode and starts the replacement of the communication equipment. With the powerful power output of the fuel engine, the heavy-loaded and complex operation tasks are successfully completed.

[0053] To sum up, this embodiment demonstrates in detail the whole process of switching from hybrid power mode to fuel mode when the all-terrain aerial work vehicle performs heavy-load and complex operations in complex mountainous terrain. This process fully reflects the intelligence and adaptability of the system of the present invention. The modules work together to make accurate judgments and execute corresponding power source switching operations in a timely manner according to changes in terrain and operation requirements, thereby ensuring operation safety and providing strong technical support for the application of all-terrain aerial work vehicles in complex environments.

[0054] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A power optimization system for an all-terrain aerial work vehicle, characterized in that: The system includes a terrain recognition module, an operation demand analysis module, a central processing unit module, a power source switching control module and a display and warning module; The terrain recognition module is composed of a terrain sensor unit and a data processing unit, performs preliminary processing and feature extraction on the original terrain data collected by the terrain sensor unit, generates terrain feature data, and transmits the terrain feature data to the central processing unit module; The terrain sensor unit collects the terrain data of the working vehicle in real time through a laser radar sensor, a gyroscope and an accelerometer. The laser radar sensor performs a full-scale three-dimensional scan of the terrain around the working vehicle to obtain detailed terrain geometry information. The gyroscope and accelerometer monitor the attitude change information of the working vehicle during driving and operation in real time. The data processing unit includes a preprocessing subunit, a feature extraction subunit and a terrain classification subunit. The preprocessing subunit performs filtering processing on the original terrain geometry information collected by the laser radar sensor to remove noise points and abnormal points caused by environmental factors. At the same time, the posture change information of the gyroscope and the accelerometer is calibrated to compensate for the deviation caused by sensor errors. The feature extraction subunit correlates the terrain geometry information with the posture change information to form terrain point cloud data, and divides the terrain point cloud data into different areas, each area representing a terrain feature. The terrain classification subunit classifies the extracted terrain features, identifies different types of terrain, and transmits the classification results to the central processing unit module. The operation demand analysis module is composed of a working arm sensor unit and an operation analysis unit. The working arm of the operation vehicle is equipped with a sensor to measure the actual kinematic data of the working arm during the operation and compare it with the results of the working arm mechanical model to identify the operation type and quantify the operation demand. The working arm sensor unit includes a force sensor, an angle sensor and a displacement sensor, which monitor the actual kinematic data of the working arm during the working process in real time. The force sensor measures the force data of the working arm in all directions, and fuses the force sensor data at different positions to obtain the load situation of the working arm during the operation. The angle sensor monitors the angle changes of the working arm during pitch, rotation and extension in real time, and records the motion trajectory of the working arm. The displacement sensor is combined with the data of the angle sensor to describe the motion state of the working arm in detail, and provide kinematic data for the type identification and complexity analysis of the operation. The job analysis unit includes a mechanical model building subunit, a job type identification subunit and a job demand quantification subunit. The mechanical model building subunit builds a mechanical model of the working arm according to the force data measured by the force sensor and the deadweight, load force and joint friction force of the working arm, and detects the force distribution and deformation of the working arm under different working postures; the job type identification subunit compares the kinematic data of the working arm with the mechanical model analysis results to identify the job type; the job demand quantification subunit further quantifies the job demand according to the job type identification results, and divides the job load into three levels of light load, medium load and heavy load; divides the job complexity into three levels of simple, medium and complex, and transmits the quantitative information of the job load level and the job complexity in data format to the central processing unit module; The central processing unit module is used to receive and process data from the terrain recognition module and the operation demand analysis module, send control instructions to the power source switching control module, and store historical instruction data and analysis results. The control instructions are deeply integrated with the classification results of terrain feature data, the operation load level and the operation complexity, and generate power source switching control instructions according to the integrated data. The power source includes electric mode, fuel mode and hybrid power mode; The power source switching control module includes a motor controller, a fuel engine controller and a hybrid power controller, which are used to receive instructions from the intelligent control module and perform corresponding control operations. The motor controller is used to control the start, stop and power output of the motor, the fuel engine controller is used to control the start, stop and power output of the fuel engine, and the hybrid power controller is used to control the coordinated work of the motor and the fuel engine; The motor controller includes a drive circuit, a protection circuit and a communication interface. The drive circuit is used to control the start and stop and power output of the motor. The protection circuit is used to prevent the motor from overloading and short circuiting. The communication interface is used to exchange data with the central processing unit module. The fuel engine controller includes an ignition unit, a fuel supply unit and a cooling unit, wherein the ignition unit is used to control the ignition timing of the fuel engine, the fuel supply unit is used to adjust the fuel supply amount, and the cooling unit is used to maintain the operating temperature of the fuel engine; The hybrid power controller includes an energy management unit and a power distribution unit, wherein the energy management unit is used to coordinate the energy supply of the electric motor and the fuel engine, and the power distribution unit is used to distribute the power output of the electric motor and the fuel engine according to the operation requirements; The display and warning module receives data from the central processing unit module and is used to display the system's operating status information to the operator, including the currently identified terrain type, the analyzed operating requirements, the switched power source type, and the operating parameters of the power source. At the same time, it monitors the operating status of the entire system in real time. When an abnormal situation occurs, an alarm is immediately sounded through a sound alarm, and detailed warning information is displayed on the display screen to ensure operational safety.

2. The all-terrain aerial work vehicle power optimization system according to claim 1, characterized in that: In the mechanical model building subunit of the work demand analysis module, the dynamic model of the working arm is established using the Lagrange equation according to the kinematic parameters of the working arm and the external force parameters of the working arm, including the force data in various directions measured by the force sensor, and the kinetic energy T and potential energy V of the working arm are calculated. The calculation method of the kinetic energy T and potential energy V is: Among them, m i is the mass of the i-th joint, n is the number of working arm joints, is the velocity component of the i-th joint, I i is the moment of inertia of the ith joint, ω i is the angular velocity of the i-th joint, g is the gravitational acceleration, z i is the vertical height of the i-th joint, τ i is the external torque of the i-th joint, θ i is the angle of the i-th joint; Using Lagrange equations Establish the dynamic equation, where L = TV is the Lagrangian function, q i are generalized coordinates, is the generalized velocity, Q i is a generalized external force, The partial derivative of the Lagrangian function L is expressed to obtain the force distribution and deformation of each joint, form a mechanical model of the working arm, detect the force distribution and deformation of the working arm under different working postures, and provide basic data for job type identification and job demand quantification.

3. The all-terrain aerial work vehicle power optimization system according to claim 1, characterized in that: When the operation type identification subunit identifies the operation type, it calculates the motion trajectory complexity C of the working arm through the angle sensor and displacement sensor data. m , the motion trajectory complexity C of the working arm m The calculation method is: Among them, Δθ i is the angular change of the i-th time, Δd j is the jth displacement change, n is the number of angle changes, and m is the number of displacement changes; The load stability of the working arm is calculated by the force sensor data as S l , the load stability of the working arm is S l The calculation method is: Among them, F k is the load force measured at the kth time, is the average load force, p is the number of measurements, when C m <C m1 And S l >S l1 When the operation is judged as a simple operation type, the operation complexity C is judged to be simple, and C = ω1C m +ω2S l ω1 and ω2 are weight coefficients determined by actual operation data. m1 ≤C m <C m2 And S l2 l ≤S l1 When C m ≥C m2 And S l ≤S l2 When the operation is judged as a complex operation type, the operation complexity C is judged to be complex, where C m1 , C m2 , S l1 , S l2 It is the operation type threshold of the operation vehicle.​ 4. The all-terrain aerial work vehicle power optimization system according to claim 3, characterized in that: In the feature extraction subunit of the terrain recognition module, the terrain point cloud data is defined as P = {(x i ,y i ,z i )}, where (x i ,y i ,z i ) represents the coordinates of the ith point; cluster the terrain point cloud data and divide the terrain point cloud data into multiple regions, each region represents a terrain feature, and for each region R j , whose terrain feature vector F j ={f j1 ,f j2 ,…,f jn }, where f jk represents the kth eigenvalue of the jth region, k = 1, 2, ..., n, and the terrain feature vector F extracted by the terrain classification subunit j For classification, its terrain feature vector F j The classification result is D t =(d t1 ,d t2 ,…,d tn ); The job requirement quantification subunit of the job requirement analysis module defines the job load L according to the classification result D j of the terrain feature vector F t . The job load where F i represents the force value measured by the i-th force sensor. The job load is divided into three levels: light load, medium load, and heavy load according to the L value, that is, light load: 0 < L ≤ L1, medium load: L1 < L ≤ L2, heavy load: L > L2, where L1 and L2 are the mechanical load thresholds of the work vehicle.

5. The all-terrain aerial work vehicle power optimization system according to claim 4, characterized in that: The CPU module converts the terrain feature vector F j The classification result D t , the workload level L and the job complexity C in the job type identification subunit are fused into the feature vector T, that is, T = αD t +βC+γL, α, β and γ are weight coefficients. According to the power source and the characteristic vector T, the final power source switching control instruction I is generated, that is, F1 and F2 are power switching thresholds.

6. The all-terrain aerial work vehicle power optimization system according to claim 1, characterized in that: The energy management unit of the hybrid power controller in the power source switching control module defines the motor output power as P when coordinating the energy supply of the motor and the fuel engine. e , the fuel engine output power is P f , the total power demand of the operating vehicle during the operation is P total , the battery charging power is P charge , then P total =P e +P f +P charge .

7. The all-terrain aerial work vehicle power optimization system according to claim 6, characterized in that: The power distribution unit in the hybrid power controller defines the travel speed of the working vehicle as v and the maximum power of the motor as P when coordinating the power output of the motor and the fuel engine. emax , the maximum power of the fuel engine is P fmax , the power ratio allocated to the motor is k, then the motor output power is P e =k×P total , fuel engine output power P f =(1-k)×P total , P total It is the total power requirement of the work vehicle during the operation.

8. The all-terrain aerial work vehicle power optimization system according to claim 1, characterized in that: The power source switching logic module realizes a smooth transition by gradually adjusting the power output of the electric motor and the fuel engine according to the power source switching control instruction of the central processing unit module. During the switching process, when switching from the electric mode to the fuel mode, the fuel engine is first started to preheat. After the engine reaches a stable operating state, the load is gradually transferred from the electric motor to the fuel engine. Conversely, when switching to the electric mode, the load of the fuel engine is first reduced, the engine is smoothly shut down, and then the electric motor is started and the load is loaded; In hybrid power mode, based on the ratio range in the power source switching control command, combined with the current operating speed of the work vehicle, load changes and the remaining battery power, a dynamic programming algorithm or a fuzzy control algorithm is used to accurately adjust the output ratio of electric and fuel power.

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