Scraper electrical control system and method based on intelligent control

Abstract

The invention relates to the technical field of engineering machinery control, and discloses a carry-scraper electrical control system and method based on intelligent control, and the system comprises a sensor module, a man-machine interaction module, a control module, an execution module, and a communication module. The method comprises the following steps: collecting real-time operation data and an operator instruction, generating a predictive deterministic map by a control module, and outputting a final control instruction for energy consumption optimization according to the predictive deterministic map to drive the carry-scraper. And meanwhile, the system continuously monitors the state of the equipment to realize fault early warning, calculates an energy consumption optimization index, and finally graphically displays the operation data, the fault early warning and the energy consumption index through the man-machine interaction module. According to the invention, an intelligent algorithm is integrated, so that accurate control and early warning of faults can be realized, energy consumption is optimized, and safety and economy are improved; and through simplified man-machine interaction, the defects of poor adaptability, complex operation and high energy consumption of a traditional system are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery control, and in particular to an electrical control system and method for a scraper based on intelligent control. Background Art

[0002] As core equipment in mining, tunnel construction and other fields, the performance of the control system of the scraper directly determines the operating efficiency and safety. Traditional scrapers mostly rely on pure hydraulic or mechanical control systems, which have slow response speed, limited control accuracy, and poor performance in energy utilization efficiency.

[0003] With the development of electrical and automation technologies, electronic control systems have gradually been applied to scrapers, improving the performance of the equipment to a certain extent. However, the existing electronic control systems are still not adaptable enough when faced with complex and changing operating conditions. Their control logic is often relatively rigid and difficult to dynamically adjust according to real-time conditions such as rock hardness, material viscosity, and slope changes, thus affecting the accuracy and smoothness of operations.

[0004] In addition, these systems generally lack the ability to conduct in-depth real-time monitoring of critical equipment conditions and proactive fault warnings, and can usually only respond after a fault occurs. This poses a hidden danger to the safety and reliability of equipment operation. At the same time, in terms of energy consumption management, its power output strategy is also difficult to accurately match real-time load requirements, resulting in unnecessary energy loss and increased operating costs.

[0005] Therefore, the present invention proposes an electrical control system and method for a scraper based on intelligent control to solve the deficiencies of the prior art. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides an electrical control system and method for a scraper based on intelligent control. By integrating sensors, controllers and actuators, it can achieve precise control, status monitoring and energy consumption optimization of the scraper, thereby improving the operating efficiency and safety of the equipment.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electrical control system for a scraper based on intelligent control, comprising the following steps:

[0008] A sensor module, used for collecting real-time operating status data of the scraper;

[0009] A human-computer interaction module, configured to receive operator instructions and display the real-time operating status data;

[0010] a control module configured to generate a predictive deterministic map representing uncertainty of future operating conditions based on the real-time operating status data and operator instructions, and to generate a final control instruction based on the predictive deterministic map;

[0011] An execution module, configured to receive the final control instruction and drive the scraper to move;

[0012] The communication module is used to transmit the real-time operation status data, operator instructions and final control instructions.

[0013] Preferably, the sensor module includes a motion state sensor, a posture and load sensor, and a power system sensor;

[0014] The motion state sensor is used to collect speed and angular velocity data of the scraper;

[0015] The attitude and load sensor is used to collect attitude angle and hydraulic system pressure data of the scraper;

[0016] The power system sensor is used to collect the motor speed and motor load data of the scraper;

[0017] The speed and angular speed data, the attitude angle and hydraulic system pressure data, and the motor speed and motor load data together constitute the real-time operating status data.

[0018] Preferably, the human-computer interaction module includes an input device and a display screen;

[0019] The input device is used to receive the operator instruction including the desired speed and desired angle information;

[0020] The display screen is used to graphically display the real-time operating status data.

[0021] Preferably, the control module includes:

[0022] Constructing a historical state action sequence based on the real-time operating state data and the historical final control instructions;

[0023] The control module generates the prediction deterministic map by taking the historical state-action sequence as input and evaluating the uncertainty of future operating conditions of executing multiple preset candidate strategies through a prediction model.

[0024] Preferably, the step of the control module evaluating the uncertainty of future operating conditions for executing multiple candidate strategies through a prediction model includes:

[0025] For each candidate strategy, multiple random forward propagations are performed through the prediction model to obtain multiple future state prediction results;

[0026] Calculating the variance of the multiple future state prediction results;

[0027] Based on the variance, a prediction certainty score is generated for each candidate strategy, and the prediction certainty scores of all candidate strategies together constitute the prediction certainty map.

[0028] Preferably, the step of the control module generating the final control instruction according to the predicted deterministic map includes:

[0029] Running an efficiency priority strategy network and a detection priority strategy network in parallel, wherein the efficiency priority strategy network outputs an efficiency collaboration intention vector, and the detection priority strategy network outputs a detection collaboration intention vector;

[0030] The control module calculates the prediction deterministic map as input to determine the fusion weight of the efficiency priority strategy network and the detection priority strategy network;

[0031] Based on the fusion weight, the efficiency cooperation intention vector and the detection cooperation intention vector are weightedly fused to generate a final cooperation intention vector. The fusion formula of the final cooperation intention vector is:

[0032] I final =w eff I eff +w probe I probe ;

[0033] Where: I final is the final collaborative intention vector; eff is the fusion weight of the efficiency priority strategy network; I eff is the efficiency coordination intention vector; probe is the fusion weight of the detection priority strategy network; I probe is the detection collaborative intention vector.

[0034] Preferably, the final collaborative intention vector is a power allocation vector; the control module further includes a bottom-level controller, which is configured to receive the power allocation vector and, in combination with the operator instruction received from the human-computer interaction module, generate a physical control instruction for driving a corresponding subsystem in the execution module, and use the physical control instruction as the final control instruction;

[0035] The bottom controller adopts a control strategy combining a fuzzy control algorithm and a PID control algorithm to generate the physical control instructions;

[0036] The control module is further configured to run a machine learning algorithm, which optimizes control parameters of the fuzzy control algorithm and the PID control algorithm based on the historical real-time operating status data and the final control instructions.

[0037] Preferably, the execution module includes a motor driver, a hydraulic proportional valve group and a brake.

[0038] Preferably, the communication module includes a wired communication unit and a wireless communication unit;

[0039] The wired communication unit uses a controller area network bus or industrial Ethernet to establish a data transmission link between the sensor module, the control module, the human-computer interaction module and the execution module;

[0040] The wireless communication unit is used to exchange data with the sensor module, control module, human-computer interaction module, execution module and remote monitoring terminal.

[0041] The present invention also provides an electrical control method for a scraper based on intelligent control, the method comprising the following steps:

[0042] S1. The sensor module collects real-time operating status data of the scraper, and the human-computer interaction module receives operator instructions and transmits the real-time operating status data and the operator instructions to the control module through the communication module;

[0043] S2. The control module generates a predictive deterministic map representing uncertainty of future operating conditions by dynamically adjusting operating parameters of the motor and hydraulic system based on the real-time operating status data and the operator instructions, and generates final control instructions for energy consumption optimization based on the predictive deterministic map;

[0044] S3, the control module transmits the final control instruction to the execution module through the communication module, and the execution module receives the final control instruction and drives the scraper to move;

[0045] S4, the control module determines whether there is an abnormality in the real-time operating status data, and generates a fault warning if an abnormality is present, and the human-computer interaction module displays the real-time operating status data and the fault warning;

[0046] S5. Based on the final control instruction, calculate and obtain an energy consumption optimization index, use the human-computer interaction module to receive the energy consumption optimization index, and display the energy consumption optimization index.

[0047] The present invention provides an electrical control system and method for a scraper based on intelligent control.

[0048] It has the following beneficial effects:

[0049] 1. This invention integrates multi-source sensors and intelligent control algorithms to achieve precise control and adaptive adjustment of scrapers. Unlike the fixed control logic used in existing technologies, this solution optimizes strategies online based on real-time operating conditions, resolving the technical issues of traditional control methods, such as poor adaptability and insufficient control accuracy under variable operating conditions.

[0050] 2. This invention features real-time status monitoring and fault warning capabilities, improving the safety and reliability of equipment operation. Compared to existing technologies that rely on a passive, post-event maintenance model, this solution proactively identifies and warns of potential risks, resolving the technical shortcoming of untimely fault detection leading to sudden equipment damage and threats to operational safety.

[0051] 3. This invention utilizes an energy consumption optimization algorithm to dynamically adjust system operating parameters based on real-time load, improving operational efficiency. Conventional scrapers typically operate in a fixed, high-power mode. This invention distributes power on demand, resolving the existing technical issues of excessive energy consumption and poor economic efficiency under non-heavy load conditions.

[0052] 4. The human-computer interaction module of this invention simplifies the operational process and reduces the technical requirements for operators. While existing technologies are complex and highly dependent on operator experience, this solution, through a graphical interface and intelligent assistance, delegates complex judgment and decision-making to the system, resolving the technical challenges of long operator training cycles and inconsistent work quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a system architecture diagram of the present invention;

[0054] Figure 2 This is a schematic diagram of the sensor module architecture of the present invention;

[0055] Figure 3 This is a schematic diagram of the human-computer interaction module architecture of the present invention;

[0056] Figure 4 This is a schematic diagram of the control module flow of the present invention;

[0057] Figure 5 This is a schematic diagram of the execution module architecture of the present invention;

[0058] Figure 6 This is a schematic diagram of the communication module architecture of the present invention;

[0059] Figure 7 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] See also Figures 1-6 The embodiment of the present invention provides an electrical control system for a scraper based on intelligent control, comprising the following steps:

[0062] A sensor module, used for collecting real-time operating status data of the scraper;

[0063] In this embodiment, the sensor module serves as the perception unit of the system. Its core function is to comprehensively and real-time collect the operating status data of the scraper under various operating conditions, and provide accurate and reliable data input for the intelligent decision-making of the subsequent control module.

[0064] In one possible implementation, the sensor module is not a single device, but rather a distributed sensor network system deployed at key locations throughout the scraper. The system works collaboratively to provide a multi-dimensional state data stream. The sensor module specifically includes motion state sensors, attitude and load sensors, and powertrain sensors.

[0065] Specifically, the motion state sensor is used to capture the macroscopic kinematic characteristics of the scraper. The motion state sensor may include a speed sensor installed at the wheel of the vehicle transmission system to measure the linear speed of the scraper traveling on the ground. At the same time, it may include one or more inertial measurement units. Preferably, the inertial measurement unit is arranged near the geometric center of the vehicle body to collect the pitch angular velocity, roll angular velocity and yaw angular velocity generated by the scraper when turning, driving on a slope or traveling on uneven roads. These angular velocity data together constitute the angular velocity data of the scraper. The speed and angular velocity data are the basic parameters for the control module to perform motion planning, trajectory tracking and attitude stabilization control.

[0066] Specifically, the attitude and load sensor is used to sense the body attitude of the scraper and the key loads it bears during operation. The attitude and load sensor may include a high-precision inclination sensor arranged on the frame to measure the pitch angle and roll angle that reflect the stability of the vehicle in real time. These two angles together constitute the attitude angle data. The attitude and load sensor also includes a pressure sensor installed in the key oil circuit of the hydraulic system. For example, it is installed in the rodless cavity and rod cavity oil circuit of the lifting cylinder and the bucket cylinder respectively. The hydraulic system pressure data collected by it can directly and accurately quantify the load size borne by the working device when performing different actions such as digging, shoveling, and lifting materials. The attitude angle data is the key basis for the control module to conduct safety assessment and judgment to prevent the vehicle from overturning in complex terrain or heavy load conditions. The hydraulic system pressure data provides direct data support for the control module to evaluate operating resistance, optimize excavation strategies, and manage energy consumption.

[0067] Specifically, the power system sensor is used to monitor the working status of the power source and transmission system of the scraper. The power system sensor may include a rotary encoder or resolver installed coaxially with the drive motor to collect high-precision motor speed data. The motor load data is not directly measured by a single sensor, but is indirectly calculated through a measurement combination. In one possible implementation, the measurement combination includes a voltage sensor and a current sensor arranged at the input end of the motor driver. These two sensors collect the working voltage and working current of the motor in real time. After receiving the voltage and current signals, the control module can obtain the real-time output power that characterizes the motor load state through the motor power calculation formula based on the preset motor model parameters.

[0068] In order to convert the collected electrical parameters into load indicators with clearer physical meanings, the control module can use the following motor power calculation formula:

[0069] P motor =U·I·η·cosφ;

[0070] Where: P motor is the real-time output power of the motor, which is the main component of the motor load data; U is the instantaneous operating voltage of the motor collected by the voltage sensor; I is the instantaneous operating current of the motor collected by the current sensor; η is the operating efficiency of the motor at this operating point, which can be pre-calibrated and stored in the control module in the form of a lookup table; cosφ is the power factor of the motor at this operating point, which can also be pre-calibrated.

[0071] In some embodiments, the power system sensors also include a temperature sensor. Alternatively, the temperature sensor can be a thermistor or thermocouple, with its measuring end tightly attached to the motor housing, bearing housing, or embedded within the motor windings, to monitor the operating temperature of key power system components. This temperature data serves as an important basis for the control module to implement overheat protection, fault warnings, and equipment health management.

[0072] In an embodiment of the present invention, the speed and angular velocity data, attitude angle and hydraulic system pressure data, and motor speed and motor load data collected by the various sensors, after signal conditioning, filtering, and time-stamp synchronization within the sensor module or within the control module's pre-processing unit, collectively constitute a multi-dimensional state vector. This state vector is the real-time operating state data. This real-time operating state data is periodically transmitted to the control module via the communication module, serving as the sole source of data for all subsequent state analysis, uncertainty assessment, and decision-making control algorithms.

[0073] A human-computer interaction module, configured to receive operator instructions and display the real-time operating status data;

[0074] In this embodiment, the human-machine interaction module establishes a two-way channel for information exchange and command transmission between the operator and the scraper's electrical control system. This module is designed to present complex system states to the operator in an intuitive manner and receive the operator's operational intent in a standardized manner, thereby achieving efficient and safe human-machine collaborative operation.

[0075] In one possible implementation, the human-machine interaction module is physically integrated into the cab of the scraper, and specifically includes an input device and one or more output devices. The output device preferably includes a display screen and a voice prompt device.

[0076] Specifically, the input device is used to receive the operator instructions issued by the operator. In some embodiments, the input device can be an integrated industrial operating handle, a physical button group, or a touch-sensitive layer directly integrated on the display screen. The operator converts his operating intention into an electrical signal recognizable by the system by operating the input device. These signals are parsed into the operator instructions containing expected speed and expected angle information. The expected speed information here can represent the operator's expected vehicle travel speed, while the expected angle information can represent the operator's expected vehicle steering angle or the working angle of the working device (such as a boom or bucket). These instructions do not act directly on the execution module, but are transmitted to the control module as a high-level intention input, and the control module combines the real-time operating status data to make subsequent decisions and instruction decomposition.

[0077] Specifically, the display screen is preferably an industrial-grade, high-brightness, and vibration-resistant LCD touch screen. Its core function is to graphically display the real-time operating status data. The control module converts the massive, abstract data received and processed from the sensor module into a series of intuitive graphical interface elements.

[0078] As an option, the graphical display may include:

[0079] Presenting the speed data in the form of a virtual dashboard;

[0080] Presenting the attitude angle data in the form of an aircraft attitude meter or a level meter so that the operator can clearly perceive the tilt state of the vehicle body;

[0081] The hydraulic system pressure data and motor load data are presented in the form of dynamically changing color bar graphs or numbers.

[0082] The display screen is also used to display other key information generated by the control module. When the control module determines that the real-time operating status data is abnormal and generates a fault warning, the display screen will display the fault warning in the form of a high-brightness icon, a color-changing parameter reading, or a pop-up warning window.

[0083] In addition, the display screen is also used to display the energy consumption optimization index calculated by the control module. In one possible implementation, the energy consumption optimization index can be displayed as an instantaneous energy consumption value, average energy consumption per unit operating cycle, or a comprehensive energy efficiency score, thereby providing clear data reference for operators to optimize their driving habits or for managers to evaluate equipment performance.

[0084] In some embodiments, the voice prompt device serves as a supplemental output to the display screen. Upon receiving specific instructions from the control module, such as when a high-level fault warning is triggered or the vehicle's posture approaches a critical threshold, the device will issue a preset voice warning or buzzer prompt. This combined audio and visual alarm ensures that critical information can be effectively conveyed even in noisy operating environments or when the operator's attention is highly focused.

[0085] Typically, the human-computer interaction module also provides an interactive interface for operators to view and adjust parameters. By touching the display screen, operators can fine-tune some non-core control parameters of the system within their preset permissions, or query alarm history and equipment maintenance information, thereby improving system usability and maintainability.

[0086] a control module configured to generate a predictive deterministic map representing uncertainty of future operating conditions based on the real-time operating status data and operator instructions, and to generate a final control instruction based on the predictive deterministic map;

[0087] In this embodiment, the control module serves as the core of the electrical control system, responsible for central processing and intelligent decision-making. The control module receives real-time operating status data from the sensor module and operator instructions from the human-computer interaction module, and based on this, generates final control instructions to drive the execution module to complete the specified task.

[0088] In one possible implementation, the control module logically implements a hierarchical, forward-looking decision-making control framework. Its operation is designed to dynamically assess the uncertainty of future operating conditions and, based on this, balance operational efficiency and safety.

[0089] Specifically, to achieve forward-looking decision-making, the control module first needs to predict and evaluate the evolution of future operating conditions. This process begins by constructing a historical state-action sequence. The control module continuously records and maintains a time series in its internal memory. This sequence consists of historical real-time operating state data (defined as state S) paired with the historical final control instructions (defined as action A) issued by the control module in past time steps.

[0090] Based on this historical state-action sequence, the control module uses a built-in, pre-trained prediction model to generate a predictive deterministic map that characterizes the uncertainty of future operating conditions. Optionally, the prediction model can be a dynamic system model based on a recurrent neural network or a long short-term memory network, which can predict future state evolution based on the historical sequence.

[0091] The computational process for generating the predicted certainty map is as follows:

[0092] The control module presets multiple logically feasible candidate strategies for the current state. Each candidate strategy is a candidate action sequence within a finite number of future time steps. For each candidate strategy, the control module performs multiple random forward propagation calculations using the prediction model. The randomness here can be achieved by introducing random perturbations during the model propagation process, such as the Monte Carlo dropout method. Each random forward propagation produces a corresponding future state prediction result. Therefore, after performing multiple forward propagations for the same candidate strategy, a set of prediction results for the future state is obtained.

[0093] The control module then calculates the variance of the multiple future state predictions. The magnitude of this variance directly quantifies the degree of divergence in future operating conditions when executing the candidate strategy, i.e., the uncertainty of future operating conditions. A large variance value indicates that executing the candidate strategy is likely to result in a variety of widely varying outcomes, indicating high uncertainty; conversely, a small variance value indicates a high degree of convergence and low uncertainty.

[0094] Based on the calculated variance, the control module generates a corresponding prediction certainty score for each candidate strategy. This score can be designed to be inversely proportional to the variance. The prediction certainty scores of all candidate strategies together constitute the prediction certainty map. This map provides a quantitative reference for the risk level of different selected paths in the subsequent decision-making process.

[0095] After obtaining the predicted deterministic map, the control module generates the final control instructions based on the map. This generation process utilizes a dual-strategy dynamic fusion mechanism. Specifically, the control module runs two policy networks in parallel: an efficiency-first policy network and a detection-first policy network.

[0096] The efficiency-first strategy network has an optimization goal of maximizing the immediate performance indicators of the current task, such as the shortest operation cycle time or the maximum material excavation volume, and outputs an efficiency coordination intention vector based on the current state.

[0097] The detection priority strategy network has an optimization goal of executing actions that can minimize the uncertainty of future working conditions, and tends to execute some detection actions with higher information gain. It outputs a detection cooperation intention vector based on the current state.

[0098] The control module uses the generated predictive deterministic map as input to dynamically determine the fusion weights of the efficiency-first strategy network and the detection-first strategy network. Generally, when the predictive deterministic map indicates a high degree of certainty in the current operating conditions, the control module assigns a higher fusion weight to the efficiency-first strategy network to achieve higher operating efficiency. Conversely, when the predictive deterministic map indicates a high degree of uncertainty in the operating conditions, the control module assigns a higher fusion weight to the detection-first strategy network to prioritize safety and proactively acquire environmental information.

[0099] Based on the fusion weight, the control module performs weighted fusion on the efficiency cooperation intention vector and the detection cooperation intention vector to generate a final cooperation intention vector. This process can be represented by the following final cooperation intention vector fusion formula:

[0100] I final =w eff I eff+w probe I probe ;

[0101] Where: I final is the final collaborative intention vector; eff is the fusion weight of the efficiency priority strategy network; I eff is the efficiency coordination intention vector; probe is the fusion weight of the detection priority strategy network; I probe is the detection collaborative intention vector.

[0102] In an embodiment of the present invention, the final coordination intention vector is physically represented by a power allocation vector. This vector does not directly specify the specific action parameters of the actuators, but rather issues instructions at the macro level of energy distribution, for example, specifying the power ratio allocated to the vehicle's traction system and the power ratio allocated to the hydraulic working device system.

[0103] To convert these macro instructions into executable physical instructions, the control module also includes multiple low-level controllers. These low-level controllers can optionally be hardware-independent auxiliary controllers or independent software tasks running in the main controller. These low-level controllers are configured to receive the power allocation vector and, in conjunction with the operator instructions received from the human-computer interaction module, generate physical control instructions that drive the corresponding subsystems in the execution module, using these physical control instructions as the final control instructions.

[0104] To improve control accuracy and adaptability to complex operating conditions, the underlying controller employs a control strategy that combines fuzzy and PID control algorithms. The PID control algorithm provides precise error tracking, while the fuzzy control algorithm utilizes its fuzzy rule base to address nonlinearities and uncertainties in the system, resulting in smoother control outputs and improved overall machine operation.

[0105] Furthermore, to enable the system to adapt and self-optimize, the control module also runs a machine learning algorithm. This machine learning algorithm, as a background process, continuously learns and analyzes stored historical state-action sequence data. Based on this historical data, the machine learning algorithm performs online, automated optimization of the control parameters of the fuzzy and PID control algorithms in the underlying controller. For example, it automatically adjusts the gain parameters of the PID controller and optimizes the membership function and fuzzy rules of the fuzzy controller. This mechanism enables the system to continuously adapt to model parameter drift caused by factors such as mechanical wear and environmental changes, maintaining efficient and stable control performance over the long term.

[0106] An execution module, configured to receive the final control instruction and drive the scraper to move;

[0107] In this embodiment, the execution module is the physical execution terminal of the electrical control system. Its responsibility is to convert the final control instructions from the control module into specific physical actions of the scraper, thereby driving the scraper to complete a series of tasks such as moving, turning, and working device operation.

[0108] In a possible implementation, the execution module includes one or more motor drivers, a hydraulic proportional valve group, and a brake. These components work together to form the physical execution layer of the final control instruction.

[0109] Specifically, the execution module includes one or more motor drivers. The motor driver receives physical control instructions generated by the underlying controller in the control module, such as a target speed instruction or a target torque instruction. As an option, the system may include a traction motor driver for driving the entire vehicle to move, and a working motor driver for driving the hydraulic system oil pump. The traction motor driver accurately adjusts the output torque and speed of the traction motor according to the instructions, thereby controlling the forward and reverse speeds and acceleration of the scraper. The working motor driver controls the operation of the pump motor to provide a hydraulic source with the required pressure and flow for the hydraulic proportional valve group.

[0110] Specifically, the execution module also includes a hydraulic proportional valve group. The hydraulic proportional valve group receives physical control instructions from the underlying controller in the control module, which are usually analog electrical signals or digital communication messages. The core of the hydraulic proportional valve group is that it can proportionally control the opening of the valve port according to the size of the instruction signal, thereby achieving continuous and precise adjustment of the flow, pressure and flow direction of the hydraulic oil flowing to the hydraulic actuators such as the lifting cylinder and bucket cylinder. Through this adjustment, it is possible to achieve refined control of the lifting speed, lowering speed and flip angle of the scraper working device, such as the boom and bucket.

[0111] In one possible implementation, the execution module further includes a brake. The brake is used to execute braking commands issued by the control module, thereby decelerating, stopping, or emergency braking the scraper. Generally, the brake can be a service brake or a parking brake. Upon receiving an electrical signal, it generates braking force through electronically controlled hydraulics or pneumatics, acting on the wheels to ensure safety during operation.

[0112] In this embodiment, the motor driver, the hydraulic proportional valve assembly, and the brake function as an integrated whole, translating the intelligent decisions generated by the control module based on complex algorithms into precise, coordinated mechanical motions for the scraper. They receive and faithfully execute the final control instructions, and the resulting motion effects are captured by the sensor module, forming the indispensable execution link in the closed-loop control loop of the entire intelligent control system.

[0113] A communication module, used for transmitting the real-time operation status data, operator instructions and final control instructions;

[0114] In this embodiment, the communication module serves as the data transmission hub inside and outside the electrical control system. It is responsible for establishing a stable and efficient data transmission link between the various functional modules of the system and between the system and the outside world for key information flows such as the real-time operating status data, the operator instructions, and the final control instructions.

[0115] In a possible implementation, in order to take into account both the high reliability requirement of the real-time control within the system and the flexibility requirement of the data interaction outside the system, the communication module structurally includes a wired communication unit and a wireless communication unit.

[0116] Specifically, the wired communication unit is used to establish an internal data transmission network within the scraper, between the sensor module, the control module, the human-machine interface module, and the execution module. This network aims to achieve low-latency, high-reliability data exchange to ensure the real-time performance of the control closed loop.

[0117] Alternatively, the wired communication unit can utilize a Controller Area Network (CAN) bus. The CAN bus is a differential signal, message-based serial communication protocol. Its error detection, priority arbitration, and high tolerance to electromagnetic interference make it ideally suited for the complex electromagnetic environments within construction machinery. Data such as real-time operating status data, operator commands, and final control instructions can be encapsulated in CAN message frames with different identifiers and broadcast over the bus.

[0118] Alternatively, when the system requires higher data transmission bandwidth, such as when transmitting large amounts of diagnostic data or performing complex data synchronization between controllers, the wired communication unit can utilize Industrial Ethernet technology. Preferably, an Industrial Ethernet protocol with real-time deterministic transmission capabilities, such as Ether-CAT or PROFINET, can be used to ensure that control command transmission latency is maintained at the microsecond or millisecond level.

[0119] Specifically, the wireless communication unit is used to implement data exchange between the entire electrical control system of the scraper and one or more remote monitoring terminals. This unit provides a technical channel for remote management, diagnosis and data analysis.

[0120] In one possible implementation, the wireless communication unit can be a wireless module supporting cellular network communication protocols such as 4G or 5G. Through this module, the scraper can upload its real-time operating status data, accumulated operating data, historical fault codes, and other information to a remote server or monitoring center. Furthermore, managers or technical experts at the remote monitoring terminal can use this wireless link to issue high-level operating tasks to the scraper, query system status, or perform remote software updates and parameter calibration.

[0121] In this embodiment, the wired communication unit constitutes the system's "nervous system," ensuring real-time and safe vehicle control. The wireless communication unit serves as the system's "window" to the broader information network, enabling the integration of standalone intelligence into fleet- and mine-level management and optimization systems. Together, these two components complete the functionality of the communication module described in this invention.

[0122] See also Figure 7 The present invention also provides an electrical control method for a scraper based on intelligent control, the method comprising the following steps:

[0123] S1. The sensor module collects real-time operating status data of the scraper, and the human-computer interaction module receives operator instructions and transmits the real-time operating status data and the operator instructions to the control module through the communication module;

[0124] The sensor module is activated to continuously and in real time collect multi-dimensional operating status data of the scraper; these data may specifically include: driving speed and steering angle for characterizing vehicle dynamics; attitude angles of the boom and bucket, pressure and temperature at various points of the hydraulic system for characterizing the state of the working device; and speed, current and load rate of the traction motor and working motor for characterizing the working condition of the power system. At the same time, the input device of the human-computer interaction module, such as an operating handle, a touch screen or a physical button, receives clear operating instructions from the operator, such as intentions to move forward, backward, lift or turn the bucket. The real-time operating status data collected and the operator instructions received are packaged into standardized data frames through the wired communication unit inside the communication module and reliably transmitted to the control module as the basis for subsequent decision analysis.

[0125] S2. The control module generates a predictive deterministic map representing uncertainty of future operating conditions by dynamically adjusting operating parameters of the motor and hydraulic system based on the real-time operating status data and the operator instructions, and generates final control instructions for energy consumption optimization based on the predictive deterministic map;

[0126] After receiving the data, the control module first analyzes the real-time operating status data and historical control data, and uses its built-in predictive model to assess the degree of uncertainty of the upcoming operating conditions and generate a digital predictive deterministic map that characterizes the risks and predictability of future operating conditions. Subsequently, the control module dynamically and intelligently allocates weights between the "efficiency priority" and "detection priority" control strategies based on the level of uncertainty revealed by the map, and fuses them to generate a final, optimized collaborative intention vector. This vector is essentially the optimal allocation plan for the power of the entire machine. Finally, the underlying controller in the control module combines this collaborative intention vector with the real-time operator instructions, and through its built-in fuzzy PID control algorithm, it parses and converts it into a specific and executable final control instruction. This instruction accurately defines the target operating parameters of each motor driver and hydraulic proportional valve group at the next moment, thereby naturally achieving energy consumption optimization.

[0127] S3, the control module transmits the final control instruction to the execution module through the communication module, and the execution module receives the final control instruction and drives the scraper to move;

[0128] The control module re-issues the generated final control command via the communication module. Upon receiving the command, the execution module immediately parses and physically converts it. Specifically, the motor driver adjusts the voltage and frequency output to the traction motor and working motor according to the command to achieve the target speed or torque. The hydraulic proportional valve assembly precisely adjusts the valve opening according to the command, controlling the flow and pressure of hydraulic oil to each hydraulic cylinder.

[0129] S4, the control module determines whether there is an abnormality in the real-time operating status data, and generates a fault warning if an abnormality is present, and the human-computer interaction module displays the real-time operating status data and the fault warning;

[0130] Throughout the entire operation process, the control module continuously monitors and analyzes the real-time operating status data collected in step S1, comparing this real-time data with preset safety thresholds or a normal operating model established through machine learning. Upon detecting any parameter anomalies, such as excessive oil temperature, sudden pressure drop, or abnormal motor current fluctuations, the system immediately identifies a potential or actual fault and generates a corresponding fault warning. This warning is immediately transmitted to the human-computer interaction module via the communication module and presented to the operator on the display screen as highlighted text, a pop-up window, or an audible alarm. The screen also continuously refreshes to display normal real-time operating status data.

[0131] S5. Calculate an energy consumption optimization index based on the final control instruction, receive the energy consumption optimization index using the human-computer interaction module, and display the energy consumption optimization index;

[0132] To provide intuitive energy efficiency feedback to operators, the control module calculates a series of energy optimization indicators in real time based on the final control instructions it generates and the actual power consumption data obtained from the sensor module. These indicators can include instantaneous energy consumption, energy consumption per unit of material transport, or an overall energy efficiency score. Once calculated, these energy optimization indicator data are transmitted to the human-computer interaction module and clearly displayed in the form of numbers, charts, or dashboards in a specific area of ​​the display screen, allowing operators to intuitively understand the economic efficiency of the current operating method.

[0133] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An electrical control system for a scraper based on intelligent control, characterized in that: The system comprises: A sensor module, used for collecting real-time operating status data of the scraper; A human-computer interaction module, configured to receive operator instructions and display the real-time operating status data; a control module configured to generate a predictive deterministic map representing uncertainty of future operating conditions based on the real-time operating status data and operator instructions, and to generate a final control instruction based on the predictive deterministic map; An execution module, configured to receive the final control instruction and drive the scraper to move; The communication module is used to transmit the real-time operation status data, operator instructions and final control instructions.

2. The electrical control system for scraper based on intelligent control according to claim 1, characterized in that: The sensor module includes a motion state sensor, a posture and load sensor, and a power system sensor; The motion state sensor is used to collect speed and angular velocity data of the scraper; The attitude and load sensor is used to collect attitude angle and hydraulic system pressure data of the scraper; The power system sensor is used to collect the motor speed and motor load data of the scraper; The speed and angular speed data, the attitude angle and hydraulic system pressure data, and the motor speed and motor load data together constitute the real-time operating status data.

3. The electrical control system for scraper based on intelligent control according to claim 1, characterized in that: The human-computer interaction module includes an input device and a display screen; The input device is used to receive the operator instruction including the desired speed and desired angle information; The display screen is used to graphically display the real-time operating status data.

4. The electrical control system for scraper based on intelligent control according to claim 1, characterized in that: The control module includes: Constructing a historical state action sequence based on the real-time operating state data and the historical final control instructions; The control module generates the prediction deterministic map by taking the historical state-action sequence as input and evaluating the uncertainty of future operating conditions of executing multiple preset candidate strategies through a prediction model.

5. The electrical control system for scraper based on intelligent control according to claim 4, characterized in that: The step of the control module evaluating the uncertainty of future operating conditions for executing multiple candidate strategies through a prediction model includes: For each candidate strategy, multiple random forward propagations are performed through the prediction model to obtain multiple future state prediction results; Calculating the variance of the multiple future state prediction results; Based on the variance, a prediction certainty score is generated for each candidate strategy, and the prediction certainty scores of all candidate strategies together constitute the prediction certainty map.

6. The electrical control system for scraper based on intelligent control according to claim 4, characterized in that: The step of the control module generating a final control instruction according to the predicted deterministic map includes: Running an efficiency priority strategy network and a detection priority strategy network in parallel, wherein the efficiency priority strategy network outputs an efficiency collaboration intention vector, and the detection priority strategy network outputs a detection collaboration intention vector; The control module calculates the prediction deterministic map as input to determine the fusion weight of the efficiency-first strategy network and the detection-first strategy network; Based on the fusion weight, the efficiency cooperation intention vector and the detection cooperation intention vector are weightedly fused to generate a final cooperation intention vector. The fusion formula of the final cooperation intention vector is: AND final =in eff ·AND eff +in probe ·AND probe ; Where: I final is the final collaborative intention vector; eff is the fusion weight of the efficiency priority strategy network; I eff is the efficiency coordination intention vector; probe is the fusion weight of the detection priority strategy network; I probe is the detection collaborative intention vector.

7. The electrical control system for scraper based on intelligent control according to claim 6, characterized in that: The final collaborative intention vector is a power allocation vector; the control module further includes a bottom-level controller, which is configured to receive the power allocation vector and, in combination with the operator instruction received from the human-computer interaction module, generate a physical control instruction to drive a corresponding subsystem in the execution module, and use the physical control instruction as the final control instruction; The bottom controller adopts a control strategy combining a fuzzy control algorithm and a PID control algorithm to generate the physical control instructions; The control module is further configured to run a machine learning algorithm, which optimizes control parameters of the fuzzy control algorithm and the PID control algorithm based on the historical real-time operating status data and the final control instructions.

8. The electrical control system for scraper based on intelligent control according to claim 1, characterized in that: The execution module includes a motor driver, a hydraulic proportional valve group and a brake.

9. The electrical control system for scraper based on intelligent control according to claim 1, characterized in that: The communication module includes a wired communication unit and a wireless communication unit; The wired communication unit uses a controller area network bus or industrial Ethernet to establish a data transmission link between the sensor module, the control module, the human-computer interaction module and the execution module; The wireless communication unit is used to exchange data with the sensor module, control module, human-computer interaction module, execution module and remote monitoring terminal.

10. An electrical control method for a scraper based on intelligent control, applied to the system according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: S1. The sensor module collects real-time operating status data of the scraper, and the human-computer interaction module receives operator instructions and transmits the real-time operating status data and the operator instructions to the control module through the communication module; S2. The control module generates a predictive deterministic map representing uncertainty of future operating conditions by dynamically adjusting operating parameters of the motor and hydraulic system based on the real-time operating status data and the operator instructions, and generates final control instructions for energy consumption optimization based on the predictive deterministic map; S3, the control module transmits the final control instruction to the execution module through the communication module, and the execution module receives the final control instruction and drives the scraper to move; S4. The control module determines whether there is an abnormality in the real-time operating status data. If there is an abnormality, a fault warning is generated, and the human-computer interaction module displays the real-time operating status data and the fault warning. S5. Based on the final control instruction, calculate and obtain an energy consumption optimization index, use the human-computer interaction module to receive the energy consumption optimization index, and display the energy consumption optimization index.

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