Variable amplitude and telescopic speed optimization control system for cross-country forklift boom

Abstract

The invention provides a cross-country type forklift boom amplitude variation and telescopic speed optimization control system, and relates to the technical field of speed control, and the cross-country type forklift boom amplitude variation and telescopic speed optimization control system comprises the steps that an accessory self-adaptive recognition strategy is formulated, and a corresponding accessory type is selected; collecting a working posture and pressure data; according to the working posture, the pressure data and the accessory type, a load torque dynamic resolving algorithm is constructed, and real-time load torque is obtained; constructing a safety boundary constraint condition according to the real-time load moment, and obtaining a safety constraint speed instruction; obtaining an optimal speed instruction according to the security constraint speed instruction and the accessory type; on the basis of a self-adaptive anti-interference algorithm, according to the optimal speed instruction, a motor rotating speed closed-loop control algorithm is constructed, and then motor driving voltage is obtained; on the basis of the neural network inverse model and according to the optimal speed instruction, an electromagnetic valve opening instruction is obtained; and according to the motor driving voltage and the electromagnetic valve opening degree instruction, the speed of the cross-country type forklift boom of the corresponding accessory type is controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of speed control, and more specifically, to an optimized control system for the boom luffing and telescopic speeds of an off-road forklift. Background Art

[0002] With the continuous expansion of industrial production scale and the increasing complexity of the operating environment, higher requirements are put forward for the performance and safety of off-road forklifts.

[0003] In actual operations, an off-road forklift needs to frequently perform boom luffing and telescopic actions, and its speed control directly affects the operation efficiency and safety. There are many limitations in the traditional forklift speed control system: on the one hand, its adaptability to different attachments is poor, and it cannot automatically adjust the control strategy according to the attachment type. For example, when replacing different attachments such as lifting hooks and fork teeth, it is difficult to accurately match the corresponding operation requirements, resulting in low operation efficiency and even possible safety accidents. On the other hand, its response ability to load changes is insufficient. When the load torque changes dynamically, it cannot adjust the speed in a timely and accurate manner, easily causing problems such as boom swaying and cargo dropping.

[0004] Therefore, an optimized control system for the boom luffing and telescopic speeds of an off-road forklift is provided herein. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an optimized control system for the boom luffing and telescopic speeds of an off-road forklift.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: An optimized control system for the boom luffing and telescopic speeds of an off-road forklift, including the following steps: Formulate an attachment adaptive recognition strategy, and select the corresponding attachment type according to the attachment adaptive recognition strategy; collect the working postures of the boom of the off-road forklift with the corresponding attachment type and the pressure data of the off-road forklift cylinder; Construct a dynamic load torque calculation algorithm based on the working posture, pressure data, and attachment type, and obtain the real-time load torque of the boom of the off-road forklift with the corresponding attachment type according to the dynamic load torque calculation algorithm; Construct safety boundary constraint conditions according to the real-time load torque; obtain the safety constraint speed command of the boom of the off-road forklift with the corresponding attachment type according to the safety boundary constraint conditions; Obtain the optimal speed command of the boom of the off-road forklift with the corresponding attachment type according to the safety constraint speed command and the attachment type; Based on the adaptive anti-interference algorithm, and according to the optimal speed command, construct a closed-loop control algorithm for the motor speed, and then obtain the motor drive voltage of the off-road forklift with the corresponding attachment type; Based on the inverse model of the neural network and according to the optimal speed command, the solenoid valve opening command for the off-road forklift of the corresponding attachment type is obtained; according to the motor drive voltage and the solenoid valve opening command, the speed of the boom of the off-road forklift of the corresponding attachment type is controlled.

[0007] According to one preferred embodiment of the present invention, an attachment adaptive recognition strategy is formulated. The process of selecting the corresponding attachment type according to the attachment adaptive recognition strategy includes: An attachment feature database is established through the hard-wired signals of the off-road forklift. The attachment feature database includes: attachments , attachment type names, initial attachment arm lengths, and safety thresholds; According to the attachment feature database, an attachment adaptive recognition strategy is formulated. The attachment adaptive recognition strategy is: ; where is the attachment feature database; is the attachment .

[0008] According to one preferred embodiment of the present invention, the process of collecting the working postures of the boom of the off-road forklift of the corresponding attachment type and the pressure data of the off-road forklift cylinder includes: According to the wire-pulling sensor, angle sensor, and pressure sensor set in the off-road forklift, and setting the acquisition period, the boom telescopic displacement, boom luffing angle of the off-road forklift of the corresponding attachment type, and the pressure data of the off-road forklift cylinder are respectively collected according to the acquisition period; the boom telescopic displacement and boom luffing angle are uniformly recorded as the working posture.

[0009] According to one preferred embodiment of the present invention, the process of constructing a load torque dynamic calculation algorithm and obtaining the real-time load torque of the off-road forklift boom according to the load torque dynamic calculation algorithm includes: The collected boom telescopic displacement, boom luffing angle, and pressure data are respectively recorded as , and ; According to the boom telescopic displacement , boom luffing angle , pressure data and attachment type , a load torque dynamic calculation algorithm is constructed; the constructed load torque dynamic calculation algorithm is: ; where is the real-time load torque; is the real-time cross-sectional area of the off-road forklift cylinder; is the efficiency coefficient of the hydraulic system, obtained by relevant technical personnel by looking up the table; is the force arm function of the off-road forklift; the force arm function is: ; where is the initial force arm length corresponding to the attachment type .

[0010] According to one preferred embodiment of the present invention, a safety boundary constraint condition is constructed; the process of obtaining the safety constraint speed command for the boom of the off-road forklift corresponding to the attachment type according to the safety boundary constraint condition includes: Obtain the real-time load torque and the boom luffing angle ; Preset the safety load torque threshold and the safety boom luffing angle threshold ; According to the real-time load torque , the boom luffing angle , the safety load torque threshold and the safety boom luffing angle threshold , construct a safety boundary constraint condition; the safety boundary constraint condition is: ; where is the safety constraint speed command; is the original speed command.

[0011] According to one preferred embodiment of the present invention, the process of obtaining the optimal speed command for the boom of the off-road forklift corresponding to the attachment type according to the safety constraint speed command and the attachment type includes: Obtain the safety constraint speed command ; According to the safety constraint speed command , construct a multi-objective optimization control algorithm; the construction of the multi-objective optimization control algorithm is: ; where is the optimal speed command; is the multi-objective particle swarm optimization algorithm; is to minimize the boom movement time; is to minimize the standard deviation of speed fluctuation; is the preset mechanical limit speed of the boom movement.

[0012] According to one preferred embodiment of the present invention, based on the adaptive anti-interference algorithm and according to the optimal speed command, construct a closed-loop control algorithm for the motor speed, and then obtain the motor drive voltage of the off-road forklift corresponding to the attachment type, the process includes: Obtain the optimal speed command ; Based on the adaptive anti-interference algorithm, construct a closed-loop control algorithm for the motor speed; the closed-loop control algorithm for the motor speed is as follows: ; where is the motor drive voltage; is the power amplification factor; is the dynamic correction of the proportional coefficient; is the speed error; is the integral coefficient; is the differential coefficient.

[0013] According to one preferred embodiment of the present invention, based on the neural network inverse model and according to the optimal speed command, further obtain the solenoid valve opening command for the off-road forklift of the corresponding attachment type; the process of controlling the speed of the boom of the off-road forklift of the corresponding attachment type according to the motor drive voltage and the solenoid valve opening command includes: Based on the neural network inverse model and according to the optimal speed command , construct a solenoid valve decoupling control model: the solenoid valve decoupling control model is as follows: ; where , are the solenoid valve opening commands; is the luffing speed; is the telescopic speed; is the inverse model obtained by neural network training.

[0014] An off-road forklift boom luffing and telescopic speed optimization control system includes: a forklift data acquisition module, an attachment identification module, a load torque calculation module, a torque control module, a main control module, and an execution control module; The attachment identification module is used to formulate an attachment adaptive identification strategy and select the corresponding attachment type according to the attachment adaptive identification strategy; The forklift data acquisition module is used to collect the working posture of the boom of the off-road forklift of the corresponding attachment type and the pressure data of the off-road forklift cylinder; The load torque calculation module constructs a load torque dynamic calculation algorithm according to the working posture, pressure data, and attachment type, and obtains the real-time load torque of the off-road forklift boom according to the load torque dynamic calculation algorithm; The torque control module constructs a safety boundary constraint condition according to the real-time load torque; according to the safety boundary constraint condition, obtain the safety constraint speed command of the off-road forklift boom; The master control module constructs a multi-objective optimization control algorithm based on the safety constraint speed instruction and the attachment type, and then obtains the optimal speed instruction for the boom of the off-road forklift. The execution control module constructs a closed-loop control algorithm for the motor speed based on the adaptive anti-interference algorithm and according to the optimal speed instruction, and then obtains the motor drive voltage of the off-road forklift; based on the neural network inverse model and according to the optimal speed instruction, it further obtains the solenoid valve opening instruction of the off-road forklift; according to the motor drive voltage and the solenoid valve opening instruction, it controls the speed of the boom of the off-road forklift.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Through the collaborative work of multiple modules such as the attachment recognition module and the load torque calculation module, the safety of the off-road forklift operation is significantly improved. The attachment recognition module can adaptively recognize the attachment type, providing a basis for subsequent precise control; the load torque calculation module calculates the load torque in real time based on the working posture, pressure data and attachment type, and the torque control module constructs safety boundary constraint conditions accordingly to obtain the safety constraint speed instruction, which can effectively avoid safety accidents caused by overloading or improper speed, ensure the stable and safe operation of the forklift under complex working conditions, and reduce the risk of equipment damage and potential safety hazards for operators.

[0016] 2. The master control module combines the safety constraint speed instruction and the attachment type to construct a multi-objective optimization control algorithm to obtain the optimal speed instruction. The execution control module constructs a closed-loop control algorithm for the motor speed and a solenoid valve opening control algorithm based on the adaptive anti-interference algorithm and the neural network inverse model respectively, accurately controls the motor drive voltage and the solenoid valve opening, realizes the precise regulation of the boom luffing and telescopic speeds, reduces the action response time, improves the accuracy and smoothness of cargo handling, meets the high-efficiency and precise operation requirements of different working scenarios, and helps enterprises improve production efficiency. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0018] Figure 1 It is a step schematic diagram of an off-road forklift boom luffing and telescopic speed optimization control system.

[0019] Figure 2 It is a module schematic diagram of an off-road forklift boom luffing and telescopic speed optimization control system. Detailed Embodiments

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other implementation manners obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope protected by the present invention.

[0021] As Figure 1 shown, an optimization control system for the boom luffing and telescopic speeds of an off-road forklift includes the following steps: Formulate an adaptive attachment recognition strategy. According to the adaptive attachment recognition strategy, select the corresponding attachment type; collect the working postures of the boom of the off-road forklift with the corresponding attachment type and the pressure data of the cylinders of the off-road forklift. According to the working postures, pressure data, and attachment type, construct a dynamic load torque calculation algorithm. According to the dynamic load torque calculation algorithm, obtain the real-time load torque of the boom of the off-road forklift with the corresponding attachment type. According to the real-time load torque, construct safety boundary constraint conditions; according to the safety boundary constraint conditions, obtain the safety constraint speed commands for the boom of the off-road forklift with the corresponding attachment type. According to the safety constraint speed commands and the attachment type, obtain the optimal speed commands for the boom of the off-road forklift with the corresponding attachment type. Based on the adaptive anti-interference algorithm and according to the optimal speed commands, construct a closed-loop motor speed control algorithm, and then obtain the motor drive voltage of the off-road forklift with the corresponding attachment type. Based on the neural network inverse model and according to the optimal speed commands, further obtain the solenoid valve opening commands for the off-road forklift with the corresponding attachment type; control the speed of the boom of the off-road forklift with the corresponding attachment type according to the motor drive voltage and the solenoid valve opening commands.

[0022] It should be further noted that, in the specific implementation process, the specific process of formulating the adaptive attachment recognition strategy and selecting the corresponding attachment type according to the adaptive attachment recognition strategy includes: Establish an attachment feature database through the hard-wired signals of the off-road forklift. The attachment feature database includes: attachments , attachment type names, initial attachment arm lengths, and safety thresholds; it should be further noted that the initial attachment arm lengths and safety thresholds of different attachments are different; the safety thresholds include: the safety load torque threshold and the safety boom luffing angle threshold for the corresponding attachment.

[0023] According to the attachment feature database, formulate an adaptive attachment recognition strategy. The adaptive attachment recognition strategy is: ; where is an attachment feature database; is an attachment .

[0024] For example, the attachment type names are: slewing fork, timber grapple, and square bale clamp; define the corresponding attachments for the slewing fork, timber grapple, and square bale clamp , respectively and , by traversing the attachment feature database inside, the corresponding attachment , such as , if the attachment : is not defined in the attachment feature database , it means that the attachment feature database does not contain the corresponding attachment.

[0025] It should be further noted that in the specific implementation process, the specific process of collecting the working postures of the off-road forklift boom of the corresponding attachment type and the pressure data of the off-road forklift cylinder includes: According to the wire rope sensor, angle sensor, and pressure sensor set in the off-road forklift, and set the acquisition period, and respectively collect the boom telescopic displacement, boom luffing angle of the off-road forklift boom of the corresponding attachment type and the pressure data of the off-road forklift cylinder according to the acquisition period; it should be further noted that the boom telescopic displacement and boom luffing angle are uniformly recorded as the working posture.

[0026] It should be further noted that in the specific implementation process, the specific process of constructing a load moment dynamic calculation algorithm and obtaining the real-time load moment of the off-road forklift boom according to the load moment dynamic calculation algorithm includes: Record the collected boom telescopic displacement, boom luffing angle, and pressure data respectively as , and ; it should be further noted that the boom telescopic displacement , boom luffing angle , pressure data are data collected according to the real-time acquisition period, so as to facilitate the real-time optimal control of the off-road forklift boom.

[0027] According to the boom telescopic displacement , boom luffing angle , pressure data and the attachment type , construct a load moment dynamic calculation algorithm; the construction of the load moment dynamic calculation algorithm is: ; among them, is the real-time load torque; is the real-time cross-sectional area of the off-road forklift cylinder; is the efficiency coefficient of the hydraulic system, obtained by relevant technicians looking up the table; is the force arm function of the off-road forklift; It should be further noted that the force arm function is: ; where, is the initial force arm length corresponding to the attachment type .

[0028] For example, if the attachment type is a rotary fork, the corresponding initial force arm length of the attachment is meters; the telescopic displacement of the boom is meters; the luffing angle of the boom is ; the pressure data of the cylinder is ; the real-time cross-sectional area of the cylinder is ; the efficiency coefficient of the hydraulic system is ; the force arm function is ; the real-time load torque is .

[0029] It should be further noted that in the specific implementation process, safety boundary constraint conditions are constructed; the specific process of obtaining the safety constraint speed command for the boom of the off-road forklift corresponding to the attachment type according to the safety boundary constraint conditions includes: Obtain the real-time load torque and the luffing angle of the boom ; Preset the safety load torque threshold and the safety luffing angle threshold of the boom ; According to the real-time load torque , the luffing angle of the boom , the safety load torque threshold and the safety luffing angle threshold of the boom , construct the safety boundary constraint conditions; the safety boundary constraint conditions are: ; where, is the safety constraint speed command; is the original speed command; It should be further noted that the original speed command is the target speed without safety threshold verification, and the original boom speed command generated according to the actual operation requirements of the off-road forklift.

[0030] For example, if the attachment type is a rotary fork, the real-time load torque is ; the corresponding safety load torque threshold is ; If the original speed command is ; The safety constraint speed command is decelerated to .

[0031] It should be further noted that in the specific implementation process, according to the safety constraint speed command and the type of attachment, the specific process of obtaining the optimal speed command for the off-road forklift boom of the corresponding attachment type includes: Obtain the safety constraint speed command ; According to the safety constraint speed command , construct a multi-objective optimization control algorithm; the construction of the multi-objective optimization control algorithm is: ; Among them, is the optimal speed command; is the multi-objective particle swarm optimization algorithm; is to minimize the boom movement time; is to minimize the standard deviation of speed fluctuation; is the preset mechanical limit speed of the boom movement.

[0032] For example, if the attachment type is a rotary fork, the safety constraint speed command is , the preset mechanical limit speed of the boom movement is ; The real-time load torque is ; The target displacement is ; Weight allocation: , ; In the particle swarm parameters based on the multi-objective particle swarm optimization algorithm, the number of particles is set to pieces, the number of iterations is set to times, the inertia weight decreases from to , the learning factor , reference value setting: ; ; Randomly generate initial speed values, such as: 45, 38, 50, 42, 30, 48, 41, 35, 46, 43, 39, 47, 37, 44, 36, 49, 34, 40, 50, 45 , and generate initial speeds that are all less than or equal to the safety constraint speed command ; The boom movement time ; The standard deviation of speed fluctuation ; Fitness ; Update the particle speed ; Among them, is the best particle velocity in history; is the best particle velocity; Iteration times: The inertia weight is , random number , ; Particle 1 velocity , , standard deviation of velocity fluctuation , fitness ; Particle 2 velocity , , standard deviation of velocity fluctuation , fitness ; Particle 2 velocity is the best, fitness is the best; By extrapolating in turn, the fitness of all particle velocities is calculated.

[0033] Update particle 1 velocity ; By extrapolating in turn, the corresponding particle velocities are updated.

[0034] Iteration times: The inertia weight is , random number , ; By extrapolating in turn, the fitness of all particle velocities is calculated and the corresponding particle velocities are updated.

[0035] Iteration times: The inertia weight is , random number , ; By extrapolating in turn, the fitness of all particle velocities is calculated and the corresponding particle velocities are updated. After 50 iterations, the optimal speed command is .

[0036] It should be further noted that in the specific implementation process, based on the adaptive anti-interference algorithm and according to the optimal speed command, the specific process of constructing the motor speed closed-loop control algorithm and then obtaining the motor drive voltage of the corresponding attachment type off-road forklift includes:

[0037] Obtain the optimal speed command ; Based on the adaptive anti-interference algorithm, construct the motor speed closed-loop control algorithm; The motor speed closed-loop control algorithm is: ; Among them, is the motor drive voltage; is the power amplification factor; is the dynamic correction of the proportional coefficient; is the speed error; is the integral coefficient; is the differential coefficient; It should be further noted that the speed error is: ; where is the actual speed fed back by the motor encoder; The dynamic correction of the proportional coefficient is: ; where is the basic proportional coefficient; is the disturbance amplitude; is the disturbance frequency; is the disturbance phase.

[0038] For example, if the attachment type is a rotary fork, the acquisition period is ; the current time is ; the optimal speed command ; the actual speed fed back by the motor encoder ; based on the parameters of the adaptive anti-disturbance algorithm: the basic proportional coefficient is , the disturbance amplitude is , the disturbance frequency is 50 , the disturbance phase is , , ; the power amplification factor is ; the preset previous period speed errors are respectively ; the speed error ; the dynamic correction of the proportional coefficient is the motor drive voltage .

[0039] It should be further noted that in the specific implementation process, based on the neural network inverse model, and according to the optimal speed command, the solenoid valve opening command for the off-road forklift of the corresponding attachment type is obtained; The specific process of controlling the speed of the boom of the off-road forklift of the corresponding attachment type according to the motor drive voltage and the solenoid valve opening command includes: Based on the neural network inverse model, and according to the optimal speed command , a solenoid valve decoupling control model is constructed: The solenoid valve decoupling control model is: ; where , are the solenoid valve opening commands; is the luffing speed; is the telescopic speed; is the inverse model obtained by neural network training; It should be further noted that is the solenoid valve opening command for luffing operation; is the solenoid valve opening command for telescopic operation; Luffing speed is: ; Telescopic speed is: ; Among them, is the adaptive weight factor.

[0040] For example, if the attachment type is a slewing fork, the luffing angle is ; The current angle is ; The telescopic distance is ; The current distance is ; The action time constraint is ; The acting radius of the luffing cylinder is ; The optimal speed command ; Luffing speed ; Telescopic speed ; The decoupling matrix output by the neural network inverse model ; The solenoid valve opening command ; Converted to the solenoid valve opening command for percentage luffing operation ; The solenoid valve opening command for percentage telescopic operation ; However, the solenoid valve opening cannot be negative. The solenoid valve opening command for telescopic operation The minimum is .

[0041] According to the motor drive voltage , the solenoid valve opening command , , control the luffing speed and telescopic speed of the boom of the off-road forklift with the corresponding attachment type.

[0042] As Figure 2 shown, an off-road forklift boom luffing and telescopic speed optimization control system includes: a forklift data acquisition module, an attachment identification module, a load torque calculation module, a torque control module, a main control module, and an execution control module; The attachment identification module is used to formulate an attachment adaptive identification strategy and select the corresponding attachment type according to the attachment adaptive identification strategy; The forklift data acquisition module is used to collect the working postures of the boom of the off-road forklift with the corresponding attachment type and the pressure data of the off-road forklift cylinder; The load torque calculation module constructs a dynamic load torque calculation algorithm based on the working posture, pressure data, and attachment type, and obtains the real-time load torque of the boom of the rough-terrain forklift according to the dynamic load torque calculation algorithm. The torque control module constructs safety boundary constraint conditions based on the real-time load torque, and obtains a safety constraint speed command for the boom of the rough-terrain forklift according to the safety boundary constraint conditions. The main control module constructs a multi-objective optimization control algorithm based on the safety constraint speed command and the attachment type, and then obtains the optimal speed command for the boom of the rough-terrain forklift. The execution control module constructs a closed-loop motor speed control algorithm based on the adaptive anti-interference algorithm and according to the optimal speed command, and then obtains the motor drive voltage of the rough-terrain forklift; based on the neural network inverse model and according to the optimal speed command, it further obtains the solenoid valve opening command of the rough-terrain forklift; according to the motor drive voltage and the solenoid valve opening command, it controls the speed of the boom of the rough-terrain forklift.

[0043] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An off-road forklift boom luffing and telescopic speed optimization control system, characterized in that, Including the following steps: Formulate an attachment adaptive recognition strategy, and select the corresponding attachment type according to the attachment adaptive recognition strategy; collect the working posture of the boom of the rough-terrain forklift with the corresponding attachment type and the pressure data of the cylinders of the rough-terrain forklift; Construct a load torque dynamic calculation algorithm based on the working posture, pressure data and attachment type, and obtain the real-time load torque of the boom of the rough-terrain forklift with the corresponding attachment type according to the load torque dynamic calculation algorithm; Construct safety boundary constraint conditions according to the real-time load torque; obtain the safety constraint speed command of the boom of the rough-terrain forklift with the corresponding attachment type according to the safety boundary constraint conditions; Obtain the optimal speed command of the boom of the rough-terrain forklift with the corresponding attachment type according to the safety constraint speed command and the attachment type; Based on the adaptive anti-interference algorithm and according to the optimal speed command, construct a closed-loop control algorithm for the motor speed, and then obtain the motor drive voltage of the rough-terrain forklift with the corresponding attachment type; Based on the neural network inverse model and according to the optimal speed command, further obtain the solenoid valve opening command of the rough-terrain forklift with the corresponding attachment type; control the speed of the boom of the rough-terrain forklift with the corresponding attachment type according to the motor drive voltage and the solenoid valve opening command.

2. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 1, characterized in that, The process of formulating an attachment adaptive recognition strategy and selecting the corresponding attachment type according to the attachment adaptive recognition strategy includes: Establish an attachment feature database through the hard-wired signals of an off-road forklift. The attachment feature database includes: attachments , attachment type names, initial attachment arm lengths, and safety thresholds; Formulate an attachment adaptive recognition strategy according to the attachment feature database, and the attachment adaptive recognition strategy is: ; wherein, is the attachment feature database; is the attachment .

3. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 2, characterized in that, The process of collecting the working posture of the boom of the rough-terrain forklift with the corresponding attachment type and the pressure data of the cylinders of the rough-terrain forklift includes: According to the wire rope sensor, angle sensor and pressure sensor set in the rough-terrain forklift, and set the acquisition period, respectively collect the boom telescopic displacement, boom luffing angle of the boom of the rough-terrain forklift with the corresponding attachment type and the pressure data of the cylinders of the rough-terrain forklift according to the acquisition period; uniformly record the boom telescopic displacement and boom luffing angle as the working posture.

4. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 3, characterized in that, The process of constructing a load torque dynamic calculation algorithm and obtaining the real-time load torque of the boom of the rough-terrain forklift according to the load torque dynamic calculation algorithm includes: The collected boom telescopic displacement, boom luffing angle, and pressure data are respectively denoted as , and ; According to the telescopic displacement of the boom , the luffing angle of the boom , the pressure data and the type of attachment , a dynamic load moment calculation algorithm is constructed; the construction of the dynamic load moment calculation algorithm is as follows: ; wherein, is the real-time load torque; is the real-time cross-sectional area of the off-road forklift cylinder; is the efficiency coefficient of the hydraulic system, obtained by referring to the table by relevant technicians; is the lever arm function of the off-road forklift; the lever arm function is: ; wherein, is the initial lever arm length corresponding to the attachment type .

5. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 4, characterized in that, Construct safety boundary constraint conditions; The process of obtaining the safety constraint speed command of the boom of the rough-terrain forklift with the corresponding attachment type according to the safety boundary constraint conditions includes: Obtain the real-time load torque and the boom luffing angle ; Preset safe load moment threshold and safe boom luffing angle threshold ; According to the real-time load torque , the boom luffing angle , the safety load torque threshold and the safety boom luffing angle threshold , a safety boundary constraint condition is constructed; the safety boundary constraint condition is: ; wherein, is the safety constraint speed command; is the original speed command.

6. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 5, characterized in that, The process of obtaining the optimal speed command of the boom of the rough-terrain forklift with the corresponding attachment type according to the safety constraint speed command and the attachment type includes: Obtain safety constraint speed command ; According to the safety constraint speed command , a multi-objective optimization control algorithm is constructed; the construction of the multi-objective optimization control algorithm is as follows: ; wherein, is the optimal speed command; is the multi-objective particle swarm optimization algorithm; is to minimize the boom operation time; is to minimize the standard deviation of speed fluctuation; is the preset mechanical limit speed of the boom operation.

7. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 6, characterized in that, The process of constructing a closed-loop control algorithm for the motor speed based on the adaptive anti-interference algorithm and according to the optimal speed command, and then obtaining the motor drive voltage of the rough-terrain forklift with the corresponding attachment type includes: Obtain the optimal speed command ; Based on the adaptive anti-interference algorithm, construct a closed-loop control algorithm for the motor speed; the closed-loop control algorithm for the motor speed is: ; where, is the motor drive voltage; is the power amplification factor; is the dynamic correction of the proportional coefficient; is the speed error; is the integral coefficient; is the differential coefficient.

8. An optimized control system for the boom luffing and telescopic speed of an off-road forklift according to claim 7, characterized in that, Based on the neural network inverse model and according to the optimal speed command, further obtain the solenoid valve opening command of the rough-terrain forklift with the corresponding attachment type; The process of controlling the speed of the boom of the rough-terrain forklift with the corresponding attachment type according to the motor drive voltage and the solenoid valve opening command includes: Based on the inverse model of the neural network and according to the optimal speed command , a decoupling control model of the solenoid valve is constructed: The decoupling control model of the solenoid valve is as follows: ; wherein, and are the solenoid valve opening commands; is the luffing speed; is the telescoping speed; is the inverse model obtained by neural network training.

9. An off-road forklift boom luffing and telescopic speed optimization control system, characterized in that, Including: Forklift data acquisition module, attachment identification module, load moment calculation module, moment control module, main control module, and execution control module; Attachment identification module, which is used to formulate an attachment adaptive identification strategy and select the corresponding attachment type according to the attachment adaptive identification strategy; Forklift data acquisition module, which is used to collect the working posture of the off-road forklift boom of the corresponding attachment type and the pressure data of the off-road forklift cylinder; Load moment calculation module, which constructs a load moment dynamic calculation algorithm based on the working posture, pressure data, and attachment type, and obtains the real-time load moment of the off-road forklift boom according to the load moment dynamic calculation algorithm; Moment control module, which constructs a safety boundary constraint condition according to the real-time load moment; and obtains a safety constraint speed command for the off-road forklift boom according to the safety boundary constraint condition; Main control module, which constructs a multi-objective optimization control algorithm based on the safety constraint speed command and the attachment type, and further obtains the optimal speed command for the off-road forklift boom; Execution control module, based on the adaptive anti-interference algorithm, constructs a motor speed closed-loop control algorithm according to the optimal speed command, and further obtains the motor drive voltage of the off-road forklift; based on the neural network inverse model, and according to the optimal speed command, further obtains the solenoid valve opening command of the off-road forklift; controls the speed of the off-road forklift boom according to the motor drive voltage and the solenoid valve opening command.

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