Digital twinning-based electric shovel electrical control system and intelligent scheduling device and method

Abstract

The invention discloses an electric shovel electrical control system based on digital twinning and an intelligent scheduling device and method, and relates to the technical field of electric shovel electrical control systems. The device comprises a field data acquisition unit, the field data acquisition unit is in bidirectional connection with an edge control system ECM and an intelligent scheduling system, and the output end of the edge control system ECM is connected with the signal input end of a central control and digital twin platform. The signal output end of the intelligent dispatching system is connected with the signal input end of the energy storage system, and the central control and digital twin platform is in bidirectional connection with the energy storage system and the communication and human-computer interface. The device is accurate in scheduling-energy linkage, can realize real-time working condition response, is high in reliability, and can improve the energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of electric shovel electrical control technology, and in particular to an electric shovel electrical control device and method based on digital twins. Background Technology

[0002] As a core piece of equipment in open-pit mines, electric shovels operate in a multi-mode cycle involving hoisting, lowering, loading, slewing, and standby, resulting in significant problems such as drastic power fluctuations and low regenerative energy recovery rates. Traditional electric shovel control systems employ fixed logic control strategies, only capable of basic functions like motor drive and overload protection, and cannot dynamically adjust power distribution schemes according to real-time operating conditions, leading to severe energy waste.

[0003] In existing technologies, some electric shovel systems incorporate energy recovery mechanisms, achieving small-scale energy feedback through resistive braking or simple capacitor storage. However, their control strategies are static and rigid, resulting in limited energy conversion efficiency. Furthermore, they lack closed-loop integration with digital twin technology and the ability to predict operational trends. Intelligent scheduling algorithms primarily focus on task coordination among equipment groups, failing to provide refined control over energy flow. This hinders the efficient recovery and on-demand allocation of regenerated energy, thus failing to meet the demands of intelligent and energy-efficient mining development. Therefore, developing an electric shovel electrical control system that combines real-time response, predictive optimization, and closed-loop energy utilization has become a key direction for technological upgrading in the industry. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to provide a digital twin-based electric shovel electrical control device that can accurately coordinate scheduling and energy and achieve real-time response to working conditions.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an electric shovel electrical control system and intelligent dispatching device based on digital twin, including a field data acquisition unit, which is bidirectionally connected to the edge control system (ECM) and the intelligent dispatching system respectively. The output end of the edge control system (ECM) is connected to the signal input end of the central control and digital twin platform. The signal output end of the intelligent dispatching system is connected to the signal input end of the energy storage system. The central control and digital twin platform is bidirectionally connected to the energy storage system and the communication and human-machine interface.

[0006] Each module constructs a two-layer communication network through industrial Ethernet and CAN bus to achieve high-speed data transmission and precise command issuance, forming a complete control link of "data acquisition - edge control - digital twin decision-making - intelligent scheduling - energy regulation - human-machine interaction".

[0007] This invention also discloses an electric shovel electrical control system and intelligent scheduling method based on digital twins. Using the aforementioned intelligent scheduling device, the method includes the following steps: 1) Data Acquisition and Digital Twin Modeling Stage: After the device is started, the field data acquisition unit first completes sensor self-test and zero-point calibration, and then synchronously acquires the electric shovel's operating parameters. The raw field data is filtered and noise-reduced by the preprocessing module built into the acquisition unit before being transmitted to the edge control system. The edge control system extracts features from the data, calculates the effective value and rate of change of the motor in the electric shovel, and then uploads the processed data to the central control and digital twin platform via a high-speed fiber optic link. After receiving the data, the platform drives the built-in digital twin model of the electric shovel to be updated in real time. The model is based on the formula: ; ; in, This represents the real-time power of the motor. This is the motor terminal voltage. This represents the real-time current of the motor. For the motor output torque, The torque constant of the motor. This is the motor excitation current; Real-time calculation of motor power and torque parameters, constructing a virtual image including mechanical structure, electrical system and energy flow, serving as the core basis for subsequent operating condition identification and energy scheduling; Human-machine interaction synchronous action: The touch screen in the electric shovel operator's cab pushes the sensor self-test status, data acquisition progress and edge control system preprocessing data in real time, and the central control room monitoring host synchronously receives the data acquisition status information. 2) Operating Condition Identification and Energy Prediction Stage: The twin model of the central control and digital twin platform, based on real-time data uploaded from the edge control system and combined with operating condition feature samples from the historical operation database, uses a neural network algorithm to identify operating conditions: when a sudden increase in motor current, speed, and upward displacement of the bucket are detected, it is determined to be a lifting condition; when the current reverses, speed decreases, and the bucket displacement is downward, it is determined to be a lowering condition; when the current fluctuates greatly, the pressure value is high, and the displacement change is small, it is determined to be a loading condition. Simultaneously, the predictive analysis unit, based on historical operation data and real-time operating condition parameters, uses a trend extrapolation algorithm to predict the energy demand for a future set time. The prediction formula is: ; in, For the future Constant energy demand, Historical energy data, For real-time power, These are the characteristic parameters of the operating conditions; The prediction results are transmitted to the intelligent scheduling system via the data bus. Synchronous human-machine interaction: The touch screen automatically switches to display the currently identified working condition type and the energy demand prediction curve for the future set time, while the digital twin interface in the central control room synchronously presents the virtual simulation state of the working condition; 3) Intelligent Scheduling and Strategy Generation Stage: After receiving the operating condition identification results and energy demand prediction data from the digital twin platform, the intelligent scheduling system executes differentiated scheduling logic. When high power demand is predicted, the intelligent scheduling system immediately issues a discharge command. When a lowering or braking condition is detected, it automatically switches to energy recovery mode, and the decision unit issues a charging command. When in standby mode, the prediction and analysis unit issues a heat preservation command to control the energy storage system to maintain the minimum operating voltage and reduce energy loss. While the scheduling command is being executed, the data feedback and learning unit transmits the command information and real-time energy consumption data back to the central control and digital twin platform for model parameter optimization. Synchronized human-machine interaction: The touch screen displays the types of instructions issued by the intelligent scheduling module in real time and marks the execution priority of the instructions. The central control room monitoring host can view the distribution of scheduling strategies for all electric shovels in the mine. 4) ESS Energy Execution and Closed-Loop Control Stage: After receiving scheduling commands, the Energy Storage System (ESS) executes energy conversion actions using its built-in bidirectional DC / DC converter. The energy relationship follows the formula: ; in, To store energy for supercapacitors For capacitance, This is the real-time voltage of the capacitor. The initial voltage of the capacitor; During the discharge phase: The bus voltage detection module detects voltage changes and sends the data to the mode switching logic control unit. After detecting the changes, the intelligent dispatching system controls the bidirectional DC / DC converter to adjust the ESS energy output, the supercapacitor bank releases energy, and the bidirectional DC / DC converter stabilizes the capacitor voltage to the voltage level of the electric shovel's main circuit. The energy is then supplied to the main drive motor power supply circuit through the energy output terminal to supplement the insufficient power supply from the grid. During the recovery phase: The intelligent dispatching system detects changes in the mode switching logic control unit and controls the bidirectional DC / DC converter to adjust the supercapacitor energy storage unit to reduce the current output. The DC / DC converter conducts in reverse, rectifying the regenerative current generated by motor braking into DC power to smoothly charge the supercapacitor module. Throughout the process, the edge control system monitors the energy flow status in real time through current and voltage sensors and executes closed-loop control. When current or voltage fluctuations exceed the threshold, the control signal is adjusted to achieve smooth power regulation. Synchronous human-computer interaction: The touch screen displays the energy storage system parameters in real time. If the current or voltage fluctuation exceeds the threshold, the touch screen will trigger an alarm, and the edge control system will execute emergency strategies. The entire process is visualized on the touch screen. After the electric shovel finishes its shift, the operator issues a sleep command via the touchscreen. The edge control system then controls the on-site data acquisition unit to reduce the acquisition frequency, and the energy storage system enters sleep mode. The central control and digital twin platform automatically saves the day's operating data and twin model logs, generating energy consumption analysis reports to provide data support for the formulation of subsequent work plans.

[0008] The beneficial effects of adopting the above technical solution are as follows: 1) Clear data transmission link, enabling real-time response to operating conditions: The field data acquisition unit (sensor group) directly transmits sensor data to the edge control system (ECU), which then processes the data locally and distributes it synchronously to the intelligent scheduling module and the central platform, avoiding data transmission delays. The local analysis capabilities of the edge control unit ensure that the intelligent scheduling module can quickly respond to changes in operating conditions. Compared with the traditional cloud-based decision-making mode, this significantly improves control timeliness and solves the problem of energy regulation lag when operating conditions change abruptly.

[0009] (2) Precise scheduling and energy linkage to improve energy utilization efficiency: The intelligent scheduling module serves as the core hub. It not only receives real-time data from the edge control unit to achieve rapid adjustment, but also combines the prediction results of the central platform twin model to optimize the strategy. It can precisely control the charging and discharging state of the ESS. When the electric shovel is braked and lowered, it drives the ESS to fully absorb regenerated energy. When it is lifted and loaded, it controls the ESS to release energy in time to replenish energy, realizing an efficient closed loop of energy recovery, storage and utilization, which significantly reduces the electric shovel's dependence on the power grid and energy consumption.

[0010] (3) Edge-Central Cooperative Control, Balancing Reliability and Optimization: The edge control unit (ECU) ensures the independent operation of the local system. Even if the central platform communication is interrupted, the basic ESS control function can still be maintained through the intelligent scheduling module. The central platform provides global optimization suggestions based on the digital twin model. The collaboration between the two enables the system to have both stable local response capability and long-term energy strategy optimization, thereby improving the overall operational stability.

[0011] (4) PLC + touch screen architecture and full life cycle monitoring improve the convenience of operation and maintenance: The combination architecture of PLC and touch screen has the advantages of maturity, stability and easy operation. Each module of the system adopts standardized interface design, which is convenient for on-site installation, debugging and later upgrade and transformation. The touch screen terminal is linked with the remote monitoring platform, supporting local and remote dual monitoring. The operation and maintenance personnel can grasp the equipment status and fault information in real time, realize preventive maintenance, reduce the difficulty of fault diagnosis and improve the reliability of equipment operation. Attached Figure Description

[0012] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0013] Figure 1 This is a schematic block diagram of the intelligent scheduling device described in an embodiment of the present invention; Figure 2 This is a control principle diagram of the intelligent scheduling device described in an embodiment of the present invention; Figure 3 This is a schematic diagram of the intelligent scheduling system in the device described in the embodiment of the present invention; Figure 4 This is a schematic block diagram of the energy storage system (ESS) in the device described in the embodiment of the present invention; Figure 5 This is the main flowchart of the method described in the embodiments of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0015] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0016] Example 1: Overall, such as Figures 1-2 As shown in the figure, an embodiment of the present invention discloses an electric shovel electrical control system and intelligent scheduling device based on digital twin, the device comprising: The field data acquisition unit is bidirectionally connected to both the edge control system (ECM) and the intelligent scheduling system. The output of the ECM is connected to the signal input of the central control and digital twin platform, and the signal output of the intelligent scheduling system is connected to the signal input of the energy storage system. The central control and digital twin platform is bidirectionally connected to the energy storage system and the communication and human-machine interface. Each module constructs a two-layer communication network via industrial Ethernet and CAN bus, enabling high-speed data transmission and precise command issuance, forming a complete control link of "data acquisition - edge control - digital twin decision-making - intelligent scheduling - energy regulation - human-machine interaction".

[0017] The field data acquisition unit (sensor group) is the core of the system's data input, used to collect real-time operating parameters of the main components of the electric shovel. Its signal input end is connected to the force sensor, motor current sensor, speed encoder, displacement sensor, torque sensor, tilt sensor, and temperature sensor of the electric shovel's electrical system via shielded cables. The signal output end communicates bidirectionally with the edge control unit via optical fiber, transmitting the raw acquired data to the edge control unit in real time. The edge control unit (ECU) is the core of local control and data processing, with a built-in embedded processor and real-time operating system, realizing local real-time control, power distribution, and safety protection. After receiving the data transmitted by the field data acquisition unit, it first performs preprocessing such as filtering and noise reduction, and then preliminarily analyzes the working condition based on preset logic. On the one hand, it sends control signals to the intelligent scheduling module in real time, and on the other hand, it uploads the processed and accurate data to the central control and digital twin platform, forming a data interaction closed loop.

[0018] The energy storage system (ESS) is the core of energy regulation. It adopts a composite architecture of supercapacitor bank and bidirectional DC / DC converter, which are used for high-power short-term energy storage and long-term energy balance, respectively. Its control input is connected to the output interface of the intelligent scheduling module to receive charging and discharging regulation commands issued by the intelligent scheduling module. Its energy input is connected to the energy feedback interface of the braking circuit of the electric shovel's electrical control system, and its energy output is connected to the power supply circuit of the electric shovel's drive motor. It can realize the rapid absorption and on-demand release of regenerated energy according to the regulation commands. The central control and digital twin platform serves as the core of the system's decision support, comprising an industrial server and digital twin modeling software. It is used to simulate the operating status of the electric shovel's electrical system in real time, predict future energy demand and recovery potential, and generate optimized power allocation and control strategies. Its data input terminal communicates wirelessly with the edge control unit via industrial Ethernet, receiving preprocessed data transmitted by the edge control unit. It has a built-in digital twin model of the electric shovel under all operating conditions, which is used to realize one-to-one data mapping, real-time simulation, and status prediction between the virtual and physical systems, providing a global decision-making basis for the intelligent scheduling module.

[0019] The intelligent scheduling system is the core of energy regulation and decision-making. Based on the prediction results of the digital twin model and the collaborative status of multiple devices, it optimizes the scheduling of the operating power, task allocation, and energy recovery of the electric shovel group. The intelligent scheduling module is a logically independent energy regulation and decision-making core, physically integrated into the edge control unit, and establishes a dedicated communication link with the central control and digital twin platform, forming a two-layer scheduling mechanism of "local rapid response and cloud-based optimization decision-making". Its data input terminals are connected to the local data interface of the edge control unit and the remote communication interface of the central control and digital twin platform, respectively, receiving real-time operating data from the edge control unit for rapid response, and receiving twin simulation results from the central platform for optimization decision-making. like Figure 3 As shown, the intelligent scheduling module includes a working condition identification unit, a predictive analysis unit, an energy optimization and scheduling decision unit, an execution coordination unit, and a data feedback and learning unit. Each unit works collaboratively through an internal data bus. After analysis and decision-making, it directly issues adjustment commands to the energy storage system (ESS) to achieve real-time control of the ESS's charging and discharging status and energy allocation ratio. At the same time, it transmits the scheduling results back to the edge control unit and the central control and digital twin platform to complete closed-loop verification.

[0020] The communication and human-machine interface includes a touch screen interactive terminal, which establishes communication with the edge control unit through RS232 / RS485 interfaces to realize data interaction and remote monitoring. As the core human-machine interaction carrier, it is used to display the working condition data, intelligent scheduling instructions and ESS operation status transmitted by the edge control unit on site, supports parameter setting and control mode switching, and replaces the traditional complex operation panel. It also includes components such as indicator lights and industrial control computers to assist in monitoring and manual intervention.

[0021] The on-site data acquisition unit (sensor group) adopts a distributed acquisition architecture, with acquisition sub-nodes set up in the electric shovel's lifting mechanism, slewing mechanism, and bucket electrical system. Each sub-node has a built-in data preprocessing module that can filter, amplify, and perform analog-to-digital conversion on the acquired raw signals, effectively eliminating interference data. The acquired parameters cover the electrical system's working pressure, motor operating parameters (current, speed), bucket displacement, key component temperature, tension, torque, tilt angle, and other core indicators. The sampling frequency can be dynamically adapted according to the working conditions to meet the monitoring needs of different operating scenarios.

[0022] Furthermore, the edge control unit (ECU) has a built-in processor and real-time operating system, integrates multiple communication protocols, and supports multi-directional communication with the field data acquisition unit, intelligent scheduling module, and central control and digital twin platform. It internally stores basic working condition identification and emergency control strategies. When communication with the central platform is interrupted, it can independently drive the intelligent scheduling module to execute preset energy regulation schemes to ensure the normal operation of the ESS system and the continuous operation of the electric shovel's basic functions. At the same time, it is equipped with a hardware-level safety protection module. When the acquired data detects motor overcurrent, overvoltage, or ESS abnormality, it can quickly trigger a safety command and control the ESS to stop charging and discharging through the intelligent scheduling module to ensure system safety. It also works in conjunction with components such as PLC, motor driver, power module, and level converter to realize the execution of local control commands and signal adaptation.

[0023] Furthermore, the core control unit of the energy storage system (ESS) receives control commands from the intelligent scheduling module via a CAN bus, and its internal drive unit is directly connected to the PWM signal output interface of the intelligent scheduling module; for example... Figure 4As shown, the Energy Storage System (ESS) includes an AC bus. The signal input terminal of the bus voltage detection module is connected to the AC bus for detecting the AC bus voltage. The output terminal of the bus voltage detection module is connected to the input terminal of the mode switching logic control unit. The output terminal of the mode switching logic control unit is connected to the input terminal of the intelligent scheduling system and the input terminal of the current detection module. The current output terminal of the supercapacitor energy storage power supply is connected to the input terminal of the current detection module, and the supercapacitor energy storage power supply is bidirectionally connected to a bidirectional DC / DC converter. The output terminal of the intelligent scheduling system is connected to the control input terminal of the bidirectional DC / DC converter. The output terminal of the bidirectional DC / DC converter is connected to the input terminal of the DC-AC rectifier bridge, and the output terminal of the DC-AC rectifier bridge is connected to the AC bus.

[0024] The intelligent scheduling module first receives optimization decisions from the central platform via industrial Ethernet, and then sends low-level PWM control signals via CAN bus to achieve seamless linking of upper-level scheduling and lower-level execution. This allows for precise response to the intelligent scheduling module's adjustments. Temperature data is uploaded to the central platform via the edge control unit. The supercapacitor bank adopts a modular series-parallel design, with each capacitor module independently equipped with a voltage monitoring unit. Based on the energy demand predicted by the digital twin platform, the PLC control module dynamically switches module combinations to adapt to power requirements under different operating conditions. The energy distribution controller is the core control component of the ESS, with a built-in energy distribution strategy library. Based on the "operating condition type - energy demand" matching result output by the digital twin model, it executes differentiated distribution logic: when the electric shovel is in the lowering braking state, it controls the bidirectional DC / DC converter to absorb regenerative energy with maximum efficiency, prioritizing charging the supercapacitor bank; when the electric shovel is in high-power conditions such as lifting or loading, it controls the supercapacitor bank to coordinate power supply with the grid, smoothing grid power fluctuations; when the electric shovel is in standby mode, it controls the ESS to enter a low-power insulation mode to maintain the capacitor's base voltage. The bidirectional DC / DC converter has bidirectional energy flow control capability, ensuring the safety and efficiency of energy conversion; the supercapacitor bank is equipped with a dedicated heat dissipation shell and built-in temperature monitoring and active heat dissipation components to ensure that the capacitors operate stably within a suitable temperature range. In conjunction with the PLC's fault diagnosis function, the ESS operating status can be fed back to the digital twin platform in real time. Furthermore, the ESS works in conjunction with the electric shovel's DC-AC rectifier bridge, AC bus, bus voltage detection module, current detection module, and mode switching logic control unit to achieve precise energy distribution and recovery.

[0025] Furthermore, the digital twin model in the central control and digital twin platform adopts a multiphysics coupling modeling method, integrating the mechanical structure, electrical system, and energy flow model of the electric shovel. The model update frequency is synchronized with the data transmission frequency of the edge control unit. Based on the data uploaded by the edge control unit, the virtual image status is updated in real time, achieving accurate mapping with the physical equipment of the electric shovel. After the platform extrapolates the working condition trend through the twin model, it transmits the optimized energy scheduling suggestions to the intelligent scheduling module to assist it in improving the adjustment strategy. The platform has a built-in large-capacity data storage module to store historical data, intelligent scheduling instructions, and ESS operation data uploaded by the edge control unit for a long time, supporting multi-dimensional data query and analysis, providing data support for system optimization, and can also intuitively display the virtual image and operating status through the digital twin interface.

[0026] Furthermore, the intelligent scheduling module's operating condition identification unit quickly identifies real-time operating conditions such as lifting, lowering, and braking based on data transmitted from the edge control unit; the predictive analysis unit combines the edge control unit data with the deduction results of the central platform's twin model to achieve short-term power and recovery potential prediction; the energy optimization and scheduling decision unit integrates the two types of data to output the optimal ESS adjustment command, directly controlling the ESS's charging and discharging power and energy distribution direction, where lowering or braking conditions correspond to the ESS recharge mode, and high-power demand conditions correspond to the ESS discharge mode; the execution coordination unit converts the commands into control signals recognizable by the ESS, and the data feedback and learning unit transmits the actual operating effect of the ESS back to the edge control unit and the central platform, realizing continuous optimization of the scheduling strategy.

[0027] Furthermore, the communication and human-machine interface touch screen interaction terminal adopts an industrial-grade resistive touch screen, which has oil-proof and vibration-resistant characteristics. It establishes a stable connection with the edge control unit through a dedicated communication cable and supports customized Chinese operation interface. The screen interface is divided into a working condition monitoring area, a data transmission area, and an ESS control area. It can display the processing data of the edge control unit, the adjustment instructions of the intelligent scheduling module, and the charging and discharging status of the ESS in real time. Operators can complete the control mode switching, parameter modification, and other operations through touch operation. The operation is convenient and intuitive. It works in conjunction with the communication module and industrial control computer to achieve dual monitoring of local and remote systems. Indicator lights can provide real-time feedback on the system's operating status, helping operators to quickly judge the equipment status.

[0028] Furthermore, each module of the system is integrated with the field data acquisition unit (sensor group), intelligent scheduling module, and ESS in a standardized manner within the edge control unit. Data is displayed on a touchscreen or uploaded to the central platform. All modules employ industrial-grade protection design, capable of withstanding harsh working environments such as mine dust, vibration, and high and low temperatures. A standardized quick-plug interface is provided between the PLC control module and the ESS for easy on-site installation and maintenance. The PLC also links with components such as motor drivers and level converters to achieve motor drive and signal conversion. The system's built-in self-diagnostic module links with the digital twin platform, enabling the location of fault points in each module through a virtual model. It periodically performs comprehensive testing on PLC communication status, ESS energy conversion efficiency, etc., and generates detailed diagnostic reports, reducing maintenance complexity.

[0029] Example 2 like Figure 5 As shown in the figure, this invention also discloses an electric shovel electrical control system and intelligent scheduling method based on digital twins. The core workflow follows the logic of "data acquisition and twin modeling → working condition identification and energy prediction → intelligent scheduling and strategy generation → ESS energy execution and closed-loop control". The specific implementation process is as follows: 1) Data Acquisition and Digital Twin Modeling Phase: After system startup, the field data acquisition unit (sensor group) first completes sensor self-test and zero-point calibration, and then synchronously acquires the electric shovel's operating parameters—the current sensor acquires the three-phase current signal of the motor, the speed encoder (each motor encoder) acquires the real-time motor speed, the pressure sensor acquires the working pressure of the electric shovel's electrical system, and the displacement sensor, tension sensor, torque sensor, and tilt sensor acquire the position, force, torque, and tilt angle information of the bucket boom, respectively. After the field data is filtered and noise-reduced by the preprocessing module built into the acquisition unit, it is transmitted to the edge control unit. The edge control unit extracts features from the data, calculates the effective value and rate of change of the motor operation, and then uploads the processed data to the central control and digital twin platform via a high-speed fiber optic link. After receiving the data, the platform drives the built-in electric shovel digital twin model to update in real time, based on the formula... ; ; in, This represents the real-time power of the motor. This is the motor terminal voltage. This represents the real-time current of the motor. For the motor output torque, The torque constant of the motor. This is the excitation current of the motor.

[0030] The motor power and torque parameters are calculated in real time, and a virtual image including mechanical structure, electrical system and energy flow is constructed as the core basis for subsequent operating condition identification and energy scheduling.

[0031] Synchronous human-machine interaction: The touch screen in the electric shovel operator's cab pushes the sensor self-test status, data acquisition progress, and pre-processed data from the edge control unit in real time. The central control room monitoring host synchronously receives the data acquisition status information. If the sensor self-test fails, the touch screen immediately pops up an alarm, the indicator light in the central control room turns red, and a prompt message is pushed to the maintenance personnel.

[0032] 2) Operating Condition Identification and Energy Prediction Stage: The twin model of the central control and digital twin platform, based on real-time data uploaded by the edge control unit and combined with operating condition feature samples from the historical operation database, uses a neural network algorithm to identify operating conditions. When a sudden increase in motor current, speed, and bucket displacement upwards are detected, it is determined to be a "lifting condition"; when the current reverses, speed decreases, and bucket displacement downwards, it is determined to be a "lowering condition"; when the current fluctuates greatly, the pressure value is high, and the displacement change is small, it is determined to be a "loading condition". Simultaneously, the predictive analysis unit, based on historical operation data and real-time operating condition parameters, uses a trend extrapolation algorithm to predict the energy demand for the next 10 seconds. The prediction formula is as follows: ; in, For the future Constant energy demand, Historical energy data, For real-time power, These are the characteristic parameters of the operating conditions.

[0033] The prediction results are transmitted via data bus to the energy optimization and scheduling decision unit of the intelligent scheduling system.

[0034] Synchronized human-machine interaction: The touch screen automatically switches between displaying the currently identified operating condition type and the energy demand prediction curve for the next 10 seconds. The digital twin interface in the central control room synchronously presents the virtual simulation state of the operating condition. Operators can view the core data on which the operating condition is identified through the touch screen and support manually marking special operating conditions.

[0035] 3) Intelligent Scheduling and Strategy Generation Stage: After receiving the operating condition identification results and energy demand prediction data from the digital twin platform, the intelligent scheduling system executes differentiated scheduling logic by the energy optimization and scheduling decision unit. When high power demand is predicted (such as during hoisting), the unit immediately issues a discharge command; when a lowering or braking condition is detected (the motor is in generator mode), the system automatically switches to energy recovery mode, and the decision unit issues a charging command; when in standby mode, the decision unit issues a heat preservation command to control the energy storage system to maintain the minimum operating voltage and reduce energy loss. Simultaneously with the execution of scheduling commands, the data feedback and learning unit transmits the command information and real-time energy consumption data back to the central control and digital twin platform for model parameter optimization.

[0036] Synchronous human-machine interaction: The touch screen displays the types of instructions (discharge / charge / heat preservation) issued by the intelligent scheduling module in real time, and marks the execution priority of the instructions. The central control room monitoring host can view the distribution of scheduling strategies for all electric shovels in the mine. If the scheduling instructions deviate too much from the preset threshold, the touch screen will pop up a confirmation window, supporting manual intervention by the operator.

[0037] 4) ESS Energy Execution and Closed-Loop Control Stage: After receiving scheduling commands, the energy storage system uses its built-in bidirectional DC / DC converter to perform energy conversion. Its energy relationship follows the formula... ; in, To store energy for supercapacitors For capacitance, This is the real-time voltage of the capacitor. This is the initial voltage of the capacitor.

[0038] During the discharge phase (e.g., during hoisting): The bus voltage detection module detects voltage changes and sends the data to the mode switching logic control unit. The intelligent dispatch system detects these changes and controls the bidirectional DC / DC converter to adjust the ESS energy output. The supercapacitor bank releases energy, and the DC / DC converter stabilizes the capacitor voltage to the main circuit voltage level of the electric shovel, supplying it to the main drive motor power supply circuit through the energy output terminal to supplement insufficient grid power. During the recovery phase (e.g., during descent): The intelligent dispatch system detects changes in the mode switching logic control unit and controls the bidirectional DC / DC converter to adjust the supercapacitor energy storage unit to reduce current output. The DC / DC converter reverses its conduction, rectifying the regenerative current generated by motor braking into DC power to smoothly charge the supercapacitor module. Throughout the process, the edge control unit monitors the energy flow status in real time through current and voltage sensors, executing closed-loop control. When current or voltage fluctuations exceed thresholds, the control signal is immediately adjusted to achieve smooth power regulation, avoiding impact on the grid and motor.

[0039] Synchronous human-machine interaction: The touch screen displays core parameters such as ESS charging and discharging power and capacitor voltage in real time. If the current or voltage fluctuation exceeds the threshold, the touch screen triggers an audible and visual alarm, and the central control room monitoring host pops up a window simultaneously. At the same time, the system automatically sends SMS alarms to maintenance personnel. The process of the edge control unit executing emergency strategies is visualized on the touch screen throughout, making it easy for operators to trace the cause of the abnormality.

[0040] After the electric shovel finishes its shift, the operator issues a system hibernation command via the touchscreen. The edge control unit then controls the on-site data acquisition unit to reduce the acquisition frequency, and the energy storage system enters hibernation mode. The central control and digital twin platform automatically saves the day's operating data and twin model logs, generating energy consumption analysis reports to provide data support for the formulation of subsequent work plans.

[0041] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0042] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A digital twin-based electric shovel electrical control system and intelligent dispatching device, characterized in that: It includes a field data acquisition unit, which is bidirectionally connected to the edge control system (ECM) and the intelligent scheduling system. The output of the edge control system (ECM) is connected to the signal input of the central control and digital twin platform. The signal output of the intelligent scheduling system is connected to the signal input of the energy storage system. The central control and digital twin platform is bidirectionally connected to the energy storage system and the communication and human-machine interface.

2. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The field data acquisition unit is used to collect real-time operating parameters of the main components of the electric shovel. Its signal input end is connected to the force sensor, motor current sensor, speed encoder, displacement sensor, torque sensor, tilt sensor and temperature sensor of the electric shovel electrical system through shielded cables. Its signal output end communicates bidirectionally with the edge control system through optical fiber to transmit the raw collected data to the edge control system in real time.

3. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The edge control system has a built-in embedded processor and real-time operating system to realize local real-time control, power distribution and safety protection. After receiving the data transmitted by the field data acquisition unit, it first performs preprocessing, then performs preliminary analysis of the working condition based on preset logic, sends control signals to the intelligent scheduling system in real time, and uploads the processed data to the central control and digital twin platform to form a data interaction closed loop.

4. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The energy storage system adopts a composite architecture of supercapacitor bank and bidirectional DC / DC converter, which are used for high-power short-term energy storage and long-term energy balance, respectively. Its control input terminal is connected to the output interface of the intelligent scheduling system to receive charging and discharging adjustment commands issued by the intelligent scheduling system. The energy input terminal is connected to the energy feedback interface of the braking circuit of the electric shovel's electrical control system, and the energy output terminal is connected to the power supply circuit of the electric shovel's drive motor. According to the adjustment commands, it realizes the absorption and on-demand release of regenerated energy.

5. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The central control and digital twin platform is a system comprising an industrial server and digital twin modeling software. It is used to simulate the operating status of the electric shovel's electrical system in real time, predict future energy demand and recovery potential, and generate optimized power allocation and control strategies. Its data input terminal communicates wirelessly with the edge control system via industrial Ethernet, receiving pre-processed data transmitted by the edge control system. It has a built-in digital twin model of the electric shovel under all operating conditions, which is used to realize one-to-one data mapping, real-time simulation and status prediction between the virtual and physical systems, providing a global decision-making basis for the intelligent scheduling module.

6. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The intelligent scheduling system optimizes the operation power, task allocation, and energy recovery of the electric shovel group based on the prediction results of the digital twin model and the collaborative status of multiple devices. The intelligent scheduling module is a logically independent energy regulation decision core, physically integrated into the edge control system, and establishes a communication link with the central control and digital twin platform to form a two-layer scheduling mechanism of local rapid response plus cloud-based optimization decision. Its data input end is connected to the local data interface of the edge control system and the remote communication interface of the central control and digital twin platform, respectively, to receive real-time operating data of the edge control system to achieve rapid response, and to receive the twin simulation results of the central platform for optimization decision.

7. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 6, characterized in that: The intelligent scheduling system includes a working condition identification unit, a predictive analysis unit, an energy optimization and scheduling decision unit, an execution coordination unit, and a data feedback and learning unit. The output of the working condition identification unit is connected to the input of the predictive analysis unit and the input of the execution coordination unit, respectively. The output of the predictive analysis unit is connected to the input of the energy optimization and scheduling decision unit, and the output of the execution coordination unit is connected to the data feedback and learning unit. Each unit works collaboratively through an internal data bus. After analysis and decision-making, it directly issues adjustment commands to the energy storage system to achieve real-time control of the ESS charging and discharging status and energy distribution ratio. At the same time, it transmits the scheduling results back to the edge control system and the central control and digital twin platform to complete closed-loop verification.

8. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The communication and human-machine interface includes a touch screen interactive terminal, which establishes communication with the edge control system through RS232 / RS485 interface to realize data interaction and remote monitoring. As the core human-machine interaction carrier, it is used to display the working condition data, intelligent scheduling instructions and ESS operation status transmitted by the edge control system on site, supports parameter setting and control mode switching, and assists in monitoring and manual intervention.

9. The electric shovel electrical control system and intelligent dispatching device based on digital twin as described in claim 1, characterized in that: The energy storage system is bolted to the side of the electric shovel counterweight box. It includes an AC busbar, with the signal input of a busbar voltage detection module connected to the AC busbar for detecting the AC busbar voltage. The output of the busbar voltage detection module is connected to the input of a mode switching logic control unit. The output of the mode switching logic control unit is connected to the input of an intelligent scheduling system and the input of a current detection module. The current output of a supercapacitor energy storage power supply is connected to the input of the current detection module, and the supercapacitor energy storage power supply is bidirectionally connected to a bidirectional DC / DC converter. The output of the intelligent scheduling system is connected to the control input of the bidirectional DC / DC converter. The output of the bidirectional DC / DC converter is connected to the input of a DC-AC rectifier bridge, and the output of the DC-AC rectifier bridge is connected to the AC busbar.

10. A digital twin-based electric shovel electrical control system and intelligent scheduling method, using the intelligent scheduling device as described in any one of claims 1-9, characterized in that... The method includes the following steps: 1) Data Acquisition and Digital Twin Modeling Stage: After the device is started, the field data acquisition unit first completes sensor self-test and zero-point calibration, and then synchronously acquires the electric shovel's operating parameters. The raw field data is filtered and noise-reduced by the preprocessing module built into the acquisition unit before being transmitted to the edge control system. The edge control system extracts features from the data, calculates the effective value and rate of change of the motor in the electric shovel, and then uploads the processed data to the central control and digital twin platform via a high-speed fiber optic link. After receiving the data, the platform drives the built-in digital twin model of the electric shovel to be updated in real time. The model is based on the formula: ； ； in, This represents the real-time power of the motor. This is the motor terminal voltage. This represents the real-time current of the motor. For the motor output torque, The torque constant of the motor. This is the motor excitation current; Real-time calculation of motor power and torque parameters, constructing a virtual image including mechanical structure, electrical system and energy flow, serving as the core basis for subsequent operating condition identification and energy scheduling; Human-machine interaction synchronous action: The touch screen in the electric shovel operator's cab pushes the sensor self-test status, data acquisition progress and edge control system preprocessing data in real time, and the central control room monitoring host synchronously receives the data acquisition status information. 2) Operating Condition Identification and Energy Prediction Stage: The twin model of the central control and digital twin platform, based on real-time data uploaded from the edge control system and combined with operating condition feature samples from the historical operation database, uses a neural network algorithm to identify operating conditions: when a sudden increase in motor current, speed, and upward displacement of the bucket are detected, it is determined to be a lifting condition; when the current reverses, speed decreases, and the bucket displacement is downward, it is determined to be a lowering condition; when the current fluctuates greatly, the pressure value is high, and the displacement change is small, it is determined to be a loading condition. Simultaneously, the predictive analysis unit, based on historical operation data and real-time operating condition parameters, uses a trend extrapolation algorithm to predict the energy demand for a future set time. The prediction formula is: ； in, For the future Constant energy demand, Historical energy data, For real-time power, These are the characteristic parameters of the operating conditions; The prediction results are transmitted to the intelligent scheduling system via the data bus. Synchronous human-machine interaction: The touch screen automatically switches to display the currently identified working condition type and the energy demand prediction curve for the future set time, while the digital twin interface in the central control room synchronously presents the virtual simulation state of the working condition; 3) Intelligent Scheduling and Strategy Generation Stage: After receiving the operating condition identification results and energy demand prediction data from the digital twin platform, the intelligent scheduling system executes differentiated scheduling logic. When high power demand is predicted, the intelligent scheduling system immediately issues a discharge command. When a lowering or braking condition is detected, it automatically switches to energy recovery mode, and the decision unit issues a charging command. When in standby mode, the prediction and analysis unit issues a heat preservation command to control the energy storage system to maintain the minimum operating voltage and reduce energy loss. While the scheduling command is being executed, the data feedback and learning unit transmits the command information and real-time energy consumption data back to the central control and digital twin platform for model parameter optimization. Synchronous human-machine interaction: The touch screen displays the types of instructions issued by the intelligent scheduling module in real time and marks the execution priority of the instructions. The central control room monitoring host can view the distribution of scheduling strategies for all electric shovels in the mine. 4) ESS Energy Execution and Closed-Loop Control Stage: After receiving scheduling commands, the Energy Storage System (ESS) executes energy conversion actions using its built-in bidirectional DC / DC converter. The energy relationship follows the formula: ； in, To store energy for supercapacitors For capacitance, This is the real-time voltage of the capacitor. The initial voltage of the capacitor; During the discharge phase: The bus voltage detection module detects voltage changes and sends the data to the mode switching logic control unit. After detecting the changes, the intelligent dispatching system controls the bidirectional DC / DC converter to adjust the ESS energy output, the supercapacitor bank releases energy, and the bidirectional DC / DC converter stabilizes the capacitor voltage to the voltage level of the electric shovel's main circuit. The energy is then supplied to the main drive motor power supply circuit through the energy output terminal to supplement the insufficient power supply from the grid. During the recovery phase: The intelligent dispatching system detects changes in the mode switching logic control unit and controls the bidirectional DC / DC converter to adjust the supercapacitor energy storage unit to reduce the current output. The DC / DC converter conducts in reverse, rectifying the regenerative current generated by motor braking into DC power to smoothly charge the supercapacitor module. Throughout the process, the edge control system monitors the energy flow status in real time through current and voltage sensors and executes closed-loop control. When current or voltage fluctuations exceed the threshold, the control signal is adjusted to achieve smooth power regulation. Synchronous human-computer interaction: The touch screen displays the energy storage system parameters in real time. If the current or voltage fluctuation exceeds the threshold, the touch screen will trigger an alarm, and the edge control system will execute emergency strategies. The entire process is visualized on the touch screen. After the electric shovel finishes its shift, the operator issues a sleep command via the touchscreen. The edge control system then controls the on-site data acquisition unit to reduce the acquisition frequency, and the energy storage system enters sleep mode. The central control and digital twin platform automatically saves the day's operating data and twin model logs, generating energy consumption analysis reports to provide data support for the formulation of subsequent work plans.

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