Engineering machinery intelligent anti-drag shovel loading control system and method based on material identification

Abstract

The invention provides an engineering machinery intelligent anti-drag shovel loading control system and method based on material identification. The control system specifically comprises a signal collector, a hydraulic system controller and a shovel loading anti-drag actuator. The signal collector collects an engineering machinery operation signal and then transmits the signal to the hydraulic controller, and after the data processing module recognizes the collected signal, the judgment module judges and recognizes the shovel loading operation working condition based on the processed data. If it is judged that the current working condition is the shoveling working condition and has the resistance reduction requirement, shoveling materials are sampled and uploaded to a hydraulic system controller, then comparison and recognition are conducted on the shoveling materials, and finally the shoveling resistance reduction actuator is controlled to conduct different resistance reduction actions on different materials based on the recognition result. The engineering machinery shovel loading system has the advantages of being rich in function, high in automation degree, low in power consumption and easy to operate, the operation intensity of a driver can be reduced to a certain degree, and the purpose of reducing energy consumption of engineering machinery shovel loading operation can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of loader energy-saving control, and in particular to an intelligent drag-reducing shoveling control system and method for engineering machinery based on material identification. Background Art

[0002] Engineering machinery is a type of construction vehicle machinery widely used in construction sites, including bulldozers, loaders, and excavators. It is mainly used for shoveling and unloading bulk materials such as slag and gravel, and can also be used for shoveling operations of materials such as boulders and hard soil. However, during the material shoveling operation, as the bucket and other working devices are inserted, the material insertion resistance gradually increases, eventually causing the wheels to slip, causing tire wear, increased engine fuel consumption, and other phenomena; At present, in order to reduce the insertion resistance during shoveling operations, the operator needs to manually adjust the boom posture (angle), but this method requires the operator to have rich operating experience, is difficult to control, and has low efficiency. In addition, the physical properties of the material during shoveling operations, such as particle size, shape, and friction, vary, which brings challenges to the drag reduction shoveling of the loader. The variable shoveling operation objects are disadvantageous to the universality of the drag reduction shoveling strategy, and aggravate the difficulty of realizing intelligent drag reduction shoveling. Summary of the invention

[0003] In order to solve the problems raised in the above-mentioned background technology, the intelligent shoveling and drag reduction control system and method adopted by the present invention is based on the acquisition of controller and sensor signals, judges the shoveling conditions and drag reduction requirements, and compares and identifies the shoveling materials with the model library, and controls the actuators such as the boom to perform different drag reduction actions.

[0004] An intelligent drag reduction shovel control system for construction machinery based on material identification, comprising a signal collector, a hydraulic system controller and a shovel drag reduction actuator; The signal collector collects data from the vehicle controller and sensors and transmits them to the hydraulic controller. The vehicle controller includes a gearbox controller and an engine controller. The sensors include an accelerator pedal sensor and a boom angle sensor. The signal collector collects vehicle speed and gear position data from the gearbox controller, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, collects boom angle data from the boom angle sensor, and transmits them to the data processing module of the hydraulic controller for processing. The hydraulic system controller analyzes, processes, judges and compares the current shoveling conditions and drag reduction requirements based on the collected data, and samples and identifies the materials; The shovel-mounted drag reduction actuator controls the boom lifting to different angles to produce different drag reduction actions based on the working conditions and material identification results.

[0005] The hydraulic system controller includes a data processing module, a judgment module and a visual imaging processing module; Data processing module: collects vehicle speed and gear position data from the transmission controller by the signal collector, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, and collects boom angle data from the boom angle sensor for processing, and finally outputs high and low level signals; Specifically, the boom operating angle, vehicle speed and operating gear data are compared and analyzed with the preset values. When the vehicle speed is not greater than 6km / h, the vehicle speed data is output as a high-level signal, otherwise a low-level signal is output; when the operating gear is in the first forward gear, the operating gear data is output as a high level, otherwise it is a low level; when the boom angle is less than 15 degrees, the boom operating angle data is output as a high level, otherwise it is a low level, and the accelerator pedal opening is used as the engine demand torque, and it is compared with the actual engine output torque data, the boom operating angle, vehicle speed and operating gear level signal to the judgment module, and the judgment content includes the judgment of the shoveling working condition and the judgment of the drag reduction demand; Judgment module: Based on the processing result of the data processing module, the level signal is logically operated to determine whether the current working condition is a shoveling condition, the real-time output torque of the engine is subtracted from the required torque, the difference result is compared with the preset value, and it is determined whether there is a demand for drag reduction in the current shoveling condition; Specifically, the specific method for judging the shoveling working condition is: if the boom operating angle, vehicle speed and working gear level signals are all high levels, the current working condition is judged to be the shoveling working condition; the specific method for judging the drag reduction demand is: the judgment module performs a difference operation on the collected actual engine output torque and the engine demand torque and compares them with the preset value. If the difference result is greater than the preset value, there is a drag reduction demand for the current shoveling operation, otherwise there is no drag reduction demand; Visual imaging processing module: After clarifying the shoveling conditions and drag reduction requirements, the camera at the front end of the vehicle is called to take photos and samples of the shoveled materials, and the sampling results are uploaded to the visual imaging processing module. The samples are compared and analyzed with the model library through the image recognition model based on the BP algorithm to identify the current shoveled materials and realize automatic identification of materials. The image recognition model of the BP algorithm has self-learning ability. The collected material samples are stored in the model material library as a comparison model after analysis and processing.

[0006] Specifically, if there is a need for drag reduction in the current shoveling working condition, the visual imaging processing module calls the camera to take pictures of the shoveled materials and upload them to the hydraulic controller. The shoveled materials are compared and analyzed with the built-in material model library and identified through the image recognition model based on the BP algorithm; based on the recognition result, the control circuit outputs currents of different sizes to the shoveling actuator, so that the electromagnetic proportional pressure reducing valve opens to different degrees. After the oil flows through the electromagnetic proportional pressure reducing valve and the reversing valve in turn, it enters the boom cylinder to control the lifting of the boom, thereby reducing the resistance of the shoveling operation.

[0007] Based on the identification result, the control circuit of the shovel loading drag reduction actuator outputs currents of different sizes to the shovel loading actuator, so that the electromagnetic proportional pressure reducing valve opens to different degrees. After the oil flows through the electromagnetic proportional pressure reducing valve and the reversing valve in turn, it enters the boom cylinder to control the boom lifting to different angles, thereby changing the mode of shovel loading operation and achieving the purpose of reducing operating resistance and improving operating efficiency.

[0008] An intelligent drag reduction shoveling method for construction machinery based on material identification comprises the following steps: Step 1: The data collector collects vehicle controller and sensor data and transmits it to the hydraulic controller. Step 2: The data processing module in the hydraulic controller analyzes, processes, judges and compares the current shoveling working condition and the drag reduction demand based on the collected data, and samples and identifies the materials; Step 3: The shovel-loading drag reduction actuator controls the boom lifting to different angles to produce different drag reduction actions based on the working conditions and material identification results.

[0009] The step 1 comprises: a signal collector collects data from a vehicle controller and sensors and transmits the data to a hydraulic controller, the vehicle controller comprises a gearbox controller and an engine controller, the sensors comprise an accelerator pedal sensor and a boom angle sensor, the signal collector collects vehicle speed and gear position data from the gearbox controller, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, collects boom angle data from the boom angle sensor, and transmits the data to a data processing module of the hydraulic controller for processing; The step 2 comprises: collecting vehicle speed and gear position data from the transmission controller by the signal collector, collecting real-time engine output torque from the engine controller, collecting accelerator pedal data from the accelerator pedal sensor, and collecting boom angle data from the boom angle sensor for processing, and finally outputting high and low level signals; Based on the processing result of the data processing module, a logic operation is performed on the level signal to determine whether the current working condition is a shoveling condition, the real-time output torque of the engine is subtracted from the required torque, and the difference result is compared with the preset value to determine whether there is a drag reduction demand in the current shoveling condition; After clarifying the shoveling conditions and drag reduction requirements, the camera located at the front end of the vehicle is called to take photos and samples of the shoveled materials, and the sampling results are uploaded to the visual imaging processing module. The samples are compared and analyzed with the model library through the image recognition model based on the BP algorithm to identify the current shoveled materials and realize automatic identification of materials. The image recognition model of the BP algorithm has self-learning ability. The collected material samples are stored in the model material library as a comparison model after analysis and processing.

[0010] The step three includes controlling the circuit to output currents of different sizes to the shoveling actuator based on the identification results, so that the electromagnetic proportional pressure reducing valve opens to different degrees. After the oil flows through the electromagnetic proportional pressure reducing valve and the reversing valve in sequence, it enters the boom cylinder to control the boom to lift different angles, thereby changing the shoveling operation mode and achieving the purpose of reducing operation resistance and improving operation efficiency.

[0011] The beneficial effects of the present invention are: 1. The present invention provides a method for identifying shoveling conditions, which can intelligently identify shoveling conditions by real-time monitoring of vehicle operation data.

[0012] 2. The present invention provides a method for identifying shoveled materials. After clarifying the shoveled working conditions and shoveled requirements, the visual imaging processing module samples the shoveled materials by photographing them, and identifies and analyzes the materials through an image recognition model based on the BP algorithm. In addition, the recognition model also has self-learning capabilities. After analysis and processing, the collected material samples will be uploaded to the model material library as a comparison model.

[0013] 3. Based on the identified material type, the present invention can output currents of different sizes and control the electromagnetic proportional reversing valve to open at different degrees, and finally control the boom lifting angles to achieve a one-to-one correspondence between the material and the boom lifting angle, thereby achieving an intelligent drag reduction function. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a composition diagram of the intelligent drag reduction control system for engineering machinery of the present invention; Figure 2 It is a step diagram of the intelligent shovel-mounted drag reduction control system of the present invention; Figure 3 It is a signal collector acquisition flow chart of the present invention; Figure 4 It is a data processing module workflow diagram of the present invention; Figure 5 It is a workflow diagram of the judgment module of the present invention; Figure 6 Visual imaging processing module workflow; Figure 7 It is a working flow chart of the shovel-mounted drag reduction actuator of the present invention. DETAILED DESCRIPTION

[0015] See also Figures 1 to 7As shown, the present invention provides an intelligent shoveling drag reduction control system and method for engineering machinery, and the control system specifically includes a signal collector, a hydraulic system controller and a shoveling drag reduction actuator. The signal collector collects the operation signal of the engineering machinery and transmits it to the hydraulic controller. After the data processing module identifies and processes the collected signal, the judgment module judges and identifies the shoveling operation condition based on the processed data. If the current operation condition is determined to be a shoveling condition, the visual imaging processing module identifies and judges the shoveling material and controls the action of the shoveling drag reduction actuator.

[0016] Based on the above intelligent shovel loading control system, the control method will be described in detail below: Step 1: If Figure 3 As shown, the signal collector collects operation data of engineering machinery vehicles from various controllers and sensor devices through the CAN bus. The specific steps are as follows: The various controllers and sensors specifically include reading real-time vehicle speed and driving gear information from the transmission controller, reading actual engine output torque data from the engine controller, reading accelerator pedal opening data from the accelerator pedal sensor, and reading boom angle data from the boom angle sensor. The above controller and sensor data are read and transmitted to the hydraulic controller in real time during the operation process; Step 2: The data collected in step 1 is transmitted to the hydraulic controller. After identification processing, the data is transmitted to the judgment module to judge the working condition. If the current working condition is determined to be a shoveling condition, the visual processing module learns, compares, and identifies the current shoveling material, and outputs different control current signals based on the learning results. The specific steps are as follows: The data processing module first obtains the data of each controller and sensor collected by the information collector, and performs preliminary processing on the data. The specific steps are as follows: The obtained vehicle speed data and gear information are the current vehicle speed and gear status; The accelerator pedal opening data obtained is the real-time required torque data N of the engine under the current working condition of the vehicle. i . Transmitting the engine demand torque data to the judgment module; The obtained actual output torque of the engine is the actual output torque data No of the engine under the current vehicle driving working condition, and the actual output torque of the engine is directly transmitted from the data processing module to the judgment module; The obtained boom operating angle data is the current vehicle boom and frame angle A g The boom angle data is directly transmitted from the data processing module to the judgment module.

[0017] like Figure 4As shown, the data processing module performs preliminary calculations on the vehicle speed data, gear data and boom angle data; when the vehicle speed is not greater than 6 km / h, the data processing module outputs the vehicle speed mark as a high level; when the vehicle speed is greater than 6 km / h, the data processing module outputs the vehicle speed mark as a low level; when the gear is in forward gear 1, the data processing module outputs the gear data mark as a high level; when the gear is in any gear except forward gear 1, the data processing module outputs the gear data mark as a low level; when the boom angle is 0-15 degrees, the data processing module outputs the boom angle data mark as a high level; when the boom angle is greater than 15 degrees, the data processing module outputs the boom angle data mark as a low level.

[0018] like Figure 5 After obtaining and processing various data, the judgment module analyzes and compares the data, and judges whether the current working condition is a shovel loading condition and determines the drag reduction demand according to the analysis results. The specific steps are as follows: The data processing module converts the vehicle speed data, gear position data and boom angle data into high and low level signals, and determines whether the current working condition is a shoveling condition through logic and operation; When the vehicle speed is greater than 6km / h, the gear is in a gear other than forward 1st gear, or the boom angle is greater than 15 degrees, the calculation result of the data processing module is false, and the output shoveling condition determination signal is a low-level signal, and it is determined that the current working condition is not a shoveling condition, so the drag reduction demand is not determined for the current working condition; when the vehicle speed is not greater than 6km / h, the gear is in forward 1st gear, and the boom angle is 0-15 degrees, the calculation result of the data processing module is true, and the output shoveling condition determination signal is a high-level signal, and the current working condition is determined to be a shoveling condition, and it is determined whether there is a drag reduction demand under the current shoveling condition; During the shoveling operation, the bucket of the construction machinery vehicle gradually inserts into the shoveled material. When the insertion resistance is greater than the vehicle driving force, the wheels slip and the actual output torque of the engine decreases. The judgment module calculates the difference between the engine demand torque data and the engine actual output torque data to obtain the torque difference data N d , the expression is as follows: N d =N i -N o； Default value N o Based on the current working environment and the tire slip degree during driving operation, the judgment module determines the difference data N d Compare and judge, if the difference data N d Less than the preset value N d0, the judgment module determines that there is no wheel slippage during the current vehicle operation, and there is no drag reduction requirement for shoveling and loading operations, and the judgment module outputs an electrical signal as a low-level signal; if the difference data N d Greater than the preset value N d0 , the judgment module determines that the wheels of the current vehicle are slipping during operation, and there is a need for drag reduction in the shoveling operation. The judgment module outputs an electrical signal that is a high-level signal, and the preset value N d0 The critical value of the engine torque when the vehicle is slipping is obtained through experiments. Specifically, when the vehicle is performing shoveling operations, the data processing module can actually collect the feedback torque at the wheel. When there is no wheel slippage, the wheel torque gradually increases as the shoveling operation proceeds; if the wheel slips, the wheel torque gradually increases as the shoveling operation proceeds, but when slippage occurs, the wheel torque decreases instantly. In this process, the maximum wheel torque is determined as the critical value N of the engine torque value when the vehicle slips during operation. d0。

[0019] like Figure 6 After receiving the control signal of the judgment result of the judgment module, the visual imaging processing module collects the shoveled material information and compares and identifies it, and finally outputs the control current signal. The specific steps are as follows: First, in the visual imaging processing module, there is a library containing 5 material categories such as sand, gravel, and boulders, and 30 images are preset for each material category. Then, after the visual imaging processing module receives the control signal that the shoveling condition is determined to be completed, the camera located at the front end of the vehicle is called to shoot the shoveled materials and upload them to the visual imaging processing module. Then, the image recognition model based on the BP algorithm in the visual imaging processing module compares and matches the collected material sample images with the model image data, and finally completes the recognition of the shoveled materials, and outputs the recognition results to the shoveled drag reduction actuator in the form of current signals through the control circuit. For different materials such as sand, gravel, and boulders, the visual imaging processing module amplifies the output current at different magnifications and outputs it to the drag reduction actuator, realizing a one-to-one correspondence between 5 types of materials and 5 types of output currents. With the accumulation of the operation process, the sample images of shoveled materials collected by the visual imaging processing module are uploaded to the material library, expanding the material matching model, and enabling the control system to have a self-learning function.

[0020] Step 3: If Figure 7 The shovel-mounted drag reduction actuator performs drag reduction actions to different degrees after receiving the control current signal. The specific steps are as follows: After the current signal is transmitted to the shoveling drag reduction actuator, the electro-hydraulic proportional pressure reducing valve converts the current signal into a pilot pressure signal and opens the valve core. The hydraulic oil flow rate changes, and 5 pilot pressure openings are preset. Different current signals correspond to different pilot pressure openings, so that the shoveling material corresponds to the valve core opening and the hydraulic oil flow rate. After the electro-hydraulic proportional pressure reducing valve core is opened, the hydraulic oil flows through the reversing valve into the boom cylinder to control the lifting of the boom. When shoveling different materials, the hydraulic cylinder has different extension strokes and the boom lifting angles are different, realizing intelligent drag reduction operations under shoveling conditions.

Claims

1. An intelligent drag reduction and shoveling control system for construction machinery based on material identification, characterized in that: It includes a signal collector, a hydraulic system controller and a shovel-mounted drag reduction actuator; The signal collector collects data from the vehicle controller and sensors and transmits them to the hydraulic controller. The vehicle controller includes a gearbox controller and an engine controller. The sensors include an accelerator pedal sensor and a boom angle sensor. The signal collector collects vehicle speed and gear position data from the gearbox controller, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, collects boom angle data from the boom angle sensor, and transmits them to the data processing module of the hydraulic controller for processing. The hydraulic system controller analyzes, processes, judges and compares the current shoveling conditions and drag reduction requirements based on the collected data, and samples and identifies the materials; The shovel-mounted drag reduction actuator controls the boom lifting to different angles to produce different drag reduction actions based on the working conditions and material identification results.

2. According to the material identification-based intelligent drag reduction and shoveling control system for construction machinery according to claim 1, it is characterized by: The hydraulic system controller includes a data processing module, a judgment module and a visual imaging processing module; Data processing module: collects vehicle speed and gear position data from the transmission controller by the signal collector, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, and collects boom angle data from the boom angle sensor for processing, and finally outputs high and low level signals; Judgment module: Based on the processing result of the data processing module, the level signal is logically operated to determine whether the current working condition is a shoveling condition, the real-time output torque of the engine is subtracted from the required torque, the difference result is compared with the preset value, and it is determined whether there is a demand for drag reduction in the current shoveling condition; Visual imaging processing module: After clarifying the shoveling conditions and drag reduction requirements, the camera at the front end of the vehicle is called to take photos and samples of the shoveled materials, and the sampling results are uploaded to the visual imaging processing module. The samples are compared and analyzed with the model library through the image recognition model based on the BP algorithm to identify the current shoveled materials and realize automatic identification of materials. The image recognition model of the BP algorithm has self-learning ability. The collected material samples are stored in the model material library as a comparison model after analysis and processing.

3. According to the material identification-based intelligent drag reduction and shoveling control system for construction machinery according to claim 1, it is characterized by: Based on the identification result, the control circuit of the shovel loading drag reduction actuator outputs currents of different sizes to the shovel loading actuator, so that the electromagnetic proportional pressure reducing valve opens to different degrees. After the oil flows through the electromagnetic proportional pressure reducing valve and the reversing valve in turn, it enters the boom cylinder to control the boom lifting to different angles, thereby changing the mode of shovel loading operation and achieving the purpose of reducing operating resistance and improving operating efficiency.

4. A method for intelligent drag reduction and shoveling of construction machinery based on material identification, based on the intelligent drag reduction and shoveling control system for construction machinery based on material identification according to claims 1 to 3, comprising the following steps: Step 1: The data collector collects vehicle controller and sensor data and transmits it to the hydraulic controller. Step 2: The data processing module in the hydraulic controller analyzes, processes, judges and compares the current shoveling working condition and the drag reduction demand based on the collected data, and samples and identifies the materials; Step 3: The shovel-loading drag reduction actuator controls the boom lifting to different angles to produce different drag reduction actions based on the working conditions and material identification results.

5. The intelligent drag reduction and shoveling method for construction machinery based on material identification according to claim 4 is characterized in that: The step 1 comprises: a signal collector collects data from a vehicle controller and sensors and transmits the data to a hydraulic controller, the vehicle controller comprises a gearbox controller and an engine controller, the sensors comprise an accelerator pedal sensor and a boom angle sensor, the signal collector collects vehicle speed and gear position data from the gearbox controller, collects real-time engine output torque from the engine controller, collects accelerator pedal data from the accelerator pedal sensor, collects boom angle data from the boom angle sensor, and transmits the data to a data processing module of the hydraulic controller for processing; The step 2 comprises: collecting vehicle speed and gear position data from the transmission controller by the signal collector, collecting real-time engine output torque from the engine controller, collecting accelerator pedal data from the accelerator pedal sensor, and collecting boom angle data from the boom angle sensor for processing, and finally outputting high and low level signals; Based on the processing result of the data processing module, a logic operation is performed on the level signal to determine whether the current working condition is a shoveling condition, the real-time output torque of the engine is subtracted from the required torque, and the difference result is compared with the preset value to determine whether there is a drag reduction demand in the current shoveling condition; After clarifying the shoveling conditions and drag reduction requirements, the camera at the front of the vehicle is called to take photos and samples of the shoveled materials, and the sampling results are uploaded to the visual imaging processing module. The sample is compared and analyzed with the model library through the image recognition model based on the BP algorithm to identify the current shoveled materials and realize automatic identification of materials. The image recognition model of the BP algorithm has self-learning ability. The collected material samples are stored in the model material library as a comparison model after analysis and processing; The step three includes controlling the circuit to output currents of different sizes to the shoveling actuator based on the identification results, so that the electromagnetic proportional pressure reducing valve opens to different degrees. After the oil flows through the electromagnetic proportional pressure reducing valve and the reversing valve in sequence, it enters the boom cylinder to control the boom to lift different angles, thereby changing the shoveling operation mode and achieving the purpose of reducing operation resistance and improving operation efficiency.

Images