Remote control method and system for self-walking generator device

Abstract

The invention discloses a remote control method and system for a self-walking generator device, and the method comprises the steps: synchronously generating a virtual mirror image system when a main control unit is started, loading a preset parameter model through a 5G-MEC edge calculation node, executing a three-stage self-inspection program by an electric control unit of each generator device, uploading a self-inspection data package, and transmitting the self-inspection data package to a server; the main control unit compares the mirror image system prediction value to generate an equipment health degree index; in the environment sensing stage, a three-dimensional environment model is constructed, a temperature field distribution diagram is established, an optimal moving path is calculated by adopting an improved path planning algorithm, and in the equipment deployment process, a force feedback control strategy is adopted to adjust actions of a lifting mechanism, mechanical parameters of a contact surface are monitored in real time, and a supporting mode is automatically switched in combination with a geological feature recognition result; in the operation control stage, a hierarchical communication network architecture is established, a time-sensitive network is adopted to transmit a control instruction, and power generation tasks are distributed based on a dynamic load balancing algorithm; and an efficient, safe and intelligent generator set control scheme is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of generators, and in particular to a remote control method and system for a self-propelled generator device. Background Art

[0002] With the growing global demand for renewable energy and temporary power supply, generator sets, especially vehicle-mounted generator sets, are widely used in many fields such as field engineering, construction, and disaster emergency response. In these scenarios, generator sets not only need to provide stable power efficiently, but also must have the ability to adapt to complex environments. In addition, with the continuous advancement of technology, automation, remote monitoring and intelligent control have become the key directions for the development of generator set systems. The efficient operation, intelligent control and safety assurance of generator sets play a vital role in improving equipment availability, reducing manual intervention and reducing the risk of failure.

[0003] In the existing technology, although a variety of remote control technologies have been applied to the monitoring and management of generator sets, most existing control systems still have many problems. Traditional remote control technologies for generator sets usually rely on a single monitoring method, such as data transmission and status monitoring of the unit through traditional communication protocols, but these technologies cannot effectively cope with the rapid dispatch and safety management of equipment in complex environments or harsh conditions. For example, in the event of strong environmental interference (such as high temperature, excessive wind speed, etc.) and equipment failure, the traditional system has a slow response speed and cannot adjust the equipment operation strategy in real time, resulting in the equipment being unable to operate within a safe range, increasing the risk of failure.

[0004] In view of this, it is necessary to improve the remote control technology of the generator device in the prior art to solve the technical problem of slow response of its safety management. Summary of the invention

[0005] The purpose of the present invention is to provide a remote control method and system for a self-propelled generator device to solve the above technical problems.

[0006] To achieve this object, the present invention adopts the following technical solutions: A remote control method for a self-propelled generator device comprises the following steps: S1, when the main control unit is started, a virtual mirror system is synchronously generated, and the preset parameter model is loaded through the 5G-MEC edge computing node. The electronic control unit of each generator device executes the three-level self-test program and uploads the self-test data packet through the OPC UA protocol. The main control unit compares the predicted value of the mirror system and generates the equipment health index; S2, the environmental perception stage, builds a three-dimensional environmental model through a multi-spectral detection device, establishes a temperature field distribution map by combining infrared thermal imaging technology, calculates the optimal movement path using an improved path planning algorithm, and dynamically adjusts the energy consumption model based on environmental parameters; S3, during equipment deployment, a force feedback control strategy is used to adjust the lifting mechanism action, and the mechanical parameters of the contact surface are monitored in real time through multi-axis sensors, and the support mode is automatically switched based on the geological feature recognition results; S4, the operation and control phase, establishes a hierarchical communication network architecture, uses a time-sensitive network to transmit control instructions between the main control unit and the generator set, builds an ad hoc network communication link between each generator set, and allocates power generation tasks based on a dynamic load balancing algorithm.

[0007] Optionally, the step S1 specifically includes the following steps: S11, when the main control unit is started, the preset device digital twin model library is called, and a virtual mirror system is built locally through the distributed computing resources of the 5G-MEC edge computing node, where the virtual mirror system includes a mechanical structure dynamics model, an electrical system simulation model, and a communication network topology model; S12, establishes a dedicated transmission channel through 5G network slicing technology to download the latest parameter configuration file from the cloud server. The configuration file includes equipment operation history data, environmental adaptation parameters and fault feature library; S13, the electronic control unit of each generator set starts the parallel self-test program and performs three-level tests: the first-level test of the mechanical system includes collecting hydraulic pressure values, vibration acceleration and displacement sensor data; the second-level test of the electrical system includes measuring insulation resistance, winding temperature and output voltage fluctuation rate; the third-level test of the communication system includes testing network delay, packet loss rate and signal strength; S14: The electronic control unit encapsulates the self-test data in a structured manner according to the OPC UA information model. The data packet contains timestamp, device ID, test item code and measurement value. The data is encrypted using the AES-256 encryption algorithm and uploaded to the main control unit via the 5G network. The amount of data transmitted at a single time is controlled within 1KB.

[0008] Optionally, after step S14, the following steps may be further performed: S15, after receiving the self-test data packet, the main control unit calls the virtual mirror system to perform real-time simulation calculation, compares and analyzes the measured value with the simulated predicted value, and calculates the deviation rate of each parameter. The deviation rate calculation formula is: Δ= (|measured value-predicted value| / range)×100%; S16. Based on the fuzzy comprehensive evaluation algorithm, the detection results of the mechanical, electrical, and communication dimensions of the three-level detection are weighted and calculated, with weight coefficients of 0.4, 0.35, and 0.25 respectively, to generate the equipment health index HHI. The value range of HHI is 0 to 1. When HHI > 0.8, it is determined to be in a healthy state; when 0.5 < HHI ≤ 0.8, it indicates an attention state; when HHI ≤ 0.5, an early warning is triggered. S17. Write the health assessment results into the blockchain distributed ledger, and use smart contract technology to achieve the tamper-proof storage of the assessment data. At the same time, synchronize the assessment results to the cloud digital twin platform through the 5G network.

[0009] Optionally, the step S2 specifically includes the following steps: S21. Scan the working environment through a multi-spectral LiDAR sensor, collect terrain elevation data, obstacle distribution information, and surface features, with a scanning frequency of 10 Hz, and construct a three-dimensional point cloud map. S22. The infrared thermal imaging module continuously collects environmental temperature data, with a temperature measurement range of -20°C to 300°C, combines with the thermal radiation model to generate a temperature gradient field distribution map, and identifies the high-temperature heat source area and marks the danger level. S23. Adopt an improved A* path planning algorithm, combine the three-dimensional point cloud map and the temperature gradient field data, and calculate the optimal movement path. The path planning objective function is: E = α·distance + β·slope + γ·thermal radiation attenuation coefficient, where α, β, and γ are weight coefficients, with values of 0.6, 0.3, and 0.1 respectively; S24. Dynamically adjust the energy consumption model based on environmental parameters, and calculate the real-time movement energy consumption. The energy consumption calculation formula is: P = k1·v + k2·m·g·sinθ + k3·T, where v is the movement speed, m is the device mass, θ is the slope angle, T is the environmental temperature, and k1, k2, and k3 are correction coefficients.

[0010] Optionally, after the step S24, it further includes: S25. Upload the environmental perception data to the main control unit through the 5G network. The data packet includes three-dimensional map data, temperature field distribution map, and path planning results. S26. The main control unit generates a movement instruction according to the environmental perception result, and issues it to the self-propelled mechanism through the time-sensitive network, controlling the device to move along the planned path. The dynamic adjustment range of the movement speed is 0.1 m / s - 1.5 m / s.

[0011] Optionally, the step S3 specifically includes the following steps: S31, when the lifting mechanism is started, the six-axis force sensor monitors the pressure distribution of the contact surface in real time to obtain mechanical parameters; S32, uses fuzzy PID controller to dynamically adjust the hydraulic output and control the movement speed of the lifting mechanism; S33, scan the geological features of the landing area and measure the surface hardness parameters.

[0012] Optionally, after step S33, the following steps are further included: S34, automatically switches the support mode according to the ground hardness parameters: rigid support pads are used in hard ground mode, extended support plates are used in soft ground mode, and adaptive leveling mechanism is used in slope terrain; S35, upload the mechanical parameters and surface hardness parameters to the main control unit through the 5G network. The main control unit monitors the separation process in real time and immediately triggers an emergency stop command when abnormal mechanical parameters are detected; S36, after the separation is completed, the main control unit generates a deployment completion signal and uploads the deployment status data to the cloud digital twin platform through the OPC UA protocol.

[0013] Optionally, step S4 specifically includes the following steps: S41, establish a two-layer communication network architecture: the first layer is the communication link between the main control unit and the generator set, using a time-sensitive network to transmit control instructions; the second layer is the communication link between the generator sets, using LoRa self-organizing network technology; S42, the main control unit allocates power generation tasks through a dynamic load balancing algorithm, and the task allocation formula is: P_alloc = (C_i·HHI_i) / (Σ(C_j·HHI_j)) × P_total, Where C_i is the real-time capacity factor of the i-th generator set, HHI_i is the equipment health index of the i-th generator set, C_j is the real-time capacity factor of the j-th generator set, HHI_j is the equipment health index of the j-th generator set, and P_total is the total load demand; S43, each generator set collects operating data in real time, the operating data including output voltage, output current and frequency, and uploads the data to the main control unit through the OPC UA protocol.

[0014] Optionally, after step S43, the following steps are further included: S44, the main control unit performs real-time analysis of the operating data. When an abnormal parameter is detected, a three-level response strategy is triggered: local power reduction operation in case of yellow warning, isolation of the faulty unit and activation of the standby unit in case of orange warning, and execution of the emergency shutdown protocol in case of red warning; S45, records operation data and fault events through blockchain technology, and uses smart contracts to achieve tamper-proof storage of data; S46, the main control unit regularly generates an operation report, which includes power generation efficiency, equipment health status and fault statistics, and is uploaded to the cloud platform via the 5G network.

[0015] The present invention also provides a remote control system for a self-propelled generator device, which is controlled by the remote control method for a self-propelled generator device as described above, wherein the remote control system is communicatively connected with a plurality of groups of generator devices, each group of the generator devices being provided with a self-propelled mechanism and a lifting mechanism; The remote control system comprises: The main control unit is equipped with a high-performance processor and edge computing nodes, supporting 5G-MEC network connection; The virtual mirror system module is built based on digital twin technology and synchronized with the cloud server through the 5G network to load preset parameter models and historical data; Environmental perception module, including multispectral LiDAR sensor, infrared thermal imager and millimeter wave radar, to support three-dimensional environmental modeling and temperature gradient field analysis; The self-check monitoring module is integrated into the electronic control unit of each generator set and is used to perform a three-level self-check procedure; The communication module adopts a two-layer communication architecture and supports the OPC UA protocol for data transmission; The safety protection module is integrated into each generator set and is used to implement the three-level safety response strategy.

[0016] Compared with the prior art, the present invention has the following beneficial effects: first, the main control unit synchronously generates a virtual mirror system at startup, loads a preset parameter model in combination with the 5G-MEC edge computing node, and performs self-inspection on each generator device to ensure that the equipment is in a good state; then, in the environmental perception stage, an environmental model is constructed through multi-spectral detection and infrared thermal imaging technology, and an improved path planning algorithm is used to dynamically adjust the energy consumption model in combination with environmental parameters to optimize the driving path and energy efficiency; then, during the equipment deployment process, the lifting mechanism is adjusted through force feedback control to ensure that the equipment is smoothly detached, and the support mode is automatically switched according to the geological characteristics. In the operation control stage, a hierarchical communication network architecture and a dynamic load balancing algorithm are used to ensure the reasonable allocation of power generation tasks and the effective collaboration between various devices, thereby improving the overall operating efficiency; an efficient, safe and intelligent generator set control solution is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0019] Figure 1 This is one of the flow charts of the remote control method of the self-propelled generator device of the first embodiment of the present invention; Figure 2 This is a second flow chart of the remote control method of the self-propelled generator device of the first embodiment; Figure 3 This is a structural schematic diagram of a self-propelled generator device according to the second embodiment of the present invention; Figure 4 This is a schematic diagram of the main structure of the self-propelled generator device of the second embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a centrally arranged component at the same time.

[0022] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0023] Embodiment 1: Combination Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a remote control method for a self-propelled generator device, comprising the following steps: S1, when the main control unit starts, it will synchronously generate a virtual mirror system, load the preset parameter model through the 5G-MEC edge computing node, and the electric control unit of each generator device will execute the three-level self-test program and upload the self-test data packet through the OPC UA protocol. The main control unit will compare the predicted value of the mirror system and generate the equipment health index. Among them, 5G-MEC edge (5G MobileEdge Computing, 5G mobile edge computing): edge computing technology in 5G networks, deploys computing resources at the edge of the network close to users to reduce latency and improve data processing efficiency.

[0024] S2, the environmental perception stage, builds a three-dimensional environmental model through a multi-spectral detection device, establishes a temperature field distribution map by combining infrared thermal imaging technology, calculates the optimal movement path using an improved path planning algorithm, and dynamically adjusts the energy consumption model based on environmental parameters.

[0025] S3, during the equipment deployment process, a force feedback control strategy is used to adjust the movement of the lifting mechanism 40, and the mechanical parameters of the contact surface are monitored in real time through a multi-axis sensor, and the support mode is automatically switched in combination with the geological feature recognition results.

[0026] S4, the operation and control phase, establishes a hierarchical communication network architecture, uses a time-sensitive network to transmit control instructions between the main control unit and the generator set, builds an ad hoc network communication link between each generator set, and allocates power generation tasks based on a dynamic load balancing algorithm.

[0027] The working principle of the present invention is as follows: first, the main control unit synchronously generates a virtual mirror system at startup, loads a preset parameter model in combination with the 5G-MEC edge computing node, and performs self-inspection on each generator device to ensure that the equipment is in a good state; then, in the environmental perception stage, an environmental model is constructed through multi-spectral detection and infrared thermal imaging technology, and an improved path planning algorithm is used to dynamically adjust the energy consumption model in combination with environmental parameters to optimize the driving path and energy efficiency; then, during the equipment deployment process, the lifting mechanism is adjusted through force feedback control to ensure that the equipment is smoothly detached, and the support mode is automatically switched according to the geological characteristics. In the operation control stage, a hierarchical communication network architecture and a dynamic load balancing algorithm are used to ensure the reasonable allocation of power generation tasks and the effective collaboration between various devices, thereby improving the overall operating efficiency; an efficient, safe and intelligent generator set control solution is provided.

[0028] In this embodiment, specifically, step S1 specifically includes the following steps: S11. When the main control unit starts, it calls the preset device digital twin model library and constructs a virtual mirror system locally through the distributed computing resources of the 5G-MEC edge computing node. The virtual mirror system includes a mechanical structure dynamics model, an electrical system simulation model, and a communication network topology model; the model accuracy reaches over 95%.

[0029] S12. Establish a dedicated transmission channel through 5G network slicing technology and download the latest parameter configuration file from the cloud server. The configuration file includes device operation historical data, environment adaptation parameters, and a fault feature library; the size of the configuration file is controlled within 10MB, and the transmission delay is less than 50ms.

[0030] S13. Each generator set electronic control unit starts a parallel self-check program and performs three-level detection: The first-level detection of the mechanical system includes collecting hydraulic pressure values (range 0 - 30MPa), vibration acceleration (threshold < 0.5g), and displacement sensor data; the second-level detection of the electrical system includes measuring insulation resistance (standard value > 10MΩ), winding temperature (threshold < 120°C), and output voltage volatility (< ±2%); the third-level detection of the communication system includes testing network delay (< 100ms), packet loss rate (< 0.1%), and signal strength (> -85dBm).

[0031] S14. The electronic control unit structurally encapsulates the self-check data according to the OPC UA information model. The data packet contains a timestamp, device ID, detection item code, and measured value, and uses the AES-256 encryption algorithm for data encryption, and uploads it to the main control unit through the 5G network. The single-transmission data volume is controlled within 1KB. OPC UA is an industrial communication protocol used to achieve secure and reliable data exchange between devices.

[0032] S15. After receiving the self-check data packet, the main control unit calls the virtual mirror system for real-time simulation calculation, compares and analyzes the measured value with the simulation prediction value, and calculates the deviation rate of each parameter. The deviation rate calculation formula is: Δ = (|measured value - predicted value| / range) × 100%.

[0033] S16. Based on the fuzzy comprehensive evaluation algorithm, perform weighted calculation on the detection results of the mechanical, electrical, and communication dimensions of the three-level detection. The weight coefficients are 0.4, 0.35, and 0.25 respectively to generate the device health index HHI. The value range of HHI is 0 - 1. When HHI > 0.8, it is determined to be in a healthy state; when 0.5 < HHI ≤ 0.8, it prompts an attention state; when HHI ≤ 0.5, it triggers an alarm.

[0034] S17, writes the health assessment results into the blockchain distributed ledger, uses smart contract technology to achieve tamper-proof storage of assessment data, and synchronizes the assessment results to the cloud digital twin platform through the 5G network for subsequent equipment performance optimization analysis.

[0035] In this embodiment, it is specifically explained that step S2 specifically includes the following steps: S21 uses a multispectral LiDAR sensor to scan the working environment, collect terrain elevation data, obstacle distribution information and surface features, with a scanning frequency of 10Hz, and build a three-dimensional point cloud map. Among them, the multispectral LiDAR sensor (Light Detection and Ranging) is a remote sensing technology that measures distance through laser pulses and is used to build a three-dimensional environmental model.

[0036] S22, the infrared thermal imaging module collects ambient temperature data in real time. The temperature measurement range is -20℃ to 300℃. It generates a temperature gradient field distribution map in combination with the thermal radiation model to identify high-temperature heat source areas and mark the danger level.

[0037] S23, using the improved A* path planning algorithm, combined with the three-dimensional point cloud map and temperature gradient field data, calculates the optimal moving path. The path planning objective function is: E = α·distance + β·slope + γ·thermal radiation attenuation coefficient, Among them, α, β, and γ are weight coefficients, which are 0.6, 0.3, and 0.1 respectively.

[0038] S24, dynamically adjust the energy consumption model based on environmental parameters, and calculate the mobile energy consumption in real time. The energy consumption calculation formula is: P = k1·v + k2·m·g·sinθ + k3·T, Where v is the moving speed, m is the mass of the device, θ is the slope angle, T is the ambient temperature, and k1, k2, and k3 are correction coefficients.

[0039] S25 uploads environmental perception data to the main control unit through the 5G network. The data packet contains three-dimensional map data, temperature field distribution map and path planning results. The compression algorithm is used to control the data packet size within 5MB, and the transmission delay is less than 100ms.

[0040] S26, the main control unit generates a movement instruction based on the environmental perception result, and sends it to the self-propelled mechanism 30 through the time-sensitive network. The control device moves along the planned path, and the dynamic adjustment range of the movement speed is 0.1m / s-1.5m / s to ensure a balance between safety and efficiency.

[0041] In this embodiment, it is specifically explained that step S3 specifically includes the following steps: S31, when the lifting mechanism 40 is started, the six-axis force sensor monitors the pressure distribution of the contact surface in real time to obtain mechanical parameters; the pressure measurement range is 0-50kN, and the acquisition frequency is 100Hz, to ensure the mechanical stability of the separation process; S32, a fuzzy PID controller is used to dynamically adjust the hydraulic output to control the movement speed of the lifting mechanism 40; the speed adjustment range is 0.05-0.2m / s, ensuring that the vibration acceleration during the separation process is ≤0.3g; wherein, the fuzzy PID controller (Proportional-Integral-Derivative) is a commonly used control algorithm used to adjust the output of the system to achieve the desired target value.

[0042] S33, scans the geological features of the landing area and measures the surface hardness parameters. The hardness measurement range is 0-100MPa, with an accuracy of ±1MPa, and can identify soft soil, hard ground or slope terrain.

[0043] S34, automatically switches the support mode according to the surface hardness parameters: rigid support pads are used in hard ground mode, extended support plates are used in soft soil mode, and adaptive leveling mechanism is enabled in slope terrain to ensure the stability of the device.

[0044] S35, uploads the mechanical parameters and surface hardness parameters to the main control unit through the 5G network. The main control unit monitors the separation process in real time. When abnormal mechanical parameters are detected, the emergency stop command is immediately triggered to ensure safe separation.

[0045] S36, after the separation is completed, the main control unit generates a deployment completion signal and uploads the deployment status data to the cloud digital twin platform through the OPC UA protocol for subsequent operation monitoring and optimization.

[0046] In this embodiment, it is specifically explained that step S4 specifically includes the following steps: S41, establish a two-layer communication network architecture: the first layer is the communication link between the main control unit and the generator set, using a time-sensitive network to transmit control instructions, with a transmission delay of less than 10ms and a packet loss rate of <0.01%; the second layer is the communication link between the generator sets, using LoRa self-organizing network technology, with a transmission distance of up to 5km and power consumption less than 100mW. LoRa (Long Range): a low-power wide area network communication technology.

[0047] S42, the main control unit allocates power generation tasks through a dynamic load balancing algorithm, and the task allocation formula is: P_alloc = (C_i·HHI_i) / (Σ(C_j·HHI_j))×P_total, Where C_i is the real-time capacity factor of the i-th generator set, HHI_i is the equipment health index of the i-th generator set, C_j is the real-time capacity factor of the j-th generator set, HHI_j is the equipment health index of the j-th generator set, and P_total is the total load demand.

[0048] For example: the rated capacity of a unit is 100kW. If the current maximum output power is 80kW due to ambient temperature or equipment aging, then its C_j = 0.8 (ratio form) or C_j = 80kW (absolute value form).

[0049] The numerator (C_i·HHI_i) represents the comprehensive weight of the real-time capacity (C_i) and health status (HHI_i) of the i-th unit. The larger the value, the higher the priority of the assigned task.

[0050] The denominator Σ(C_j·HHI_j) is the weighted sum of the real-time capacity and health status of all units (j=1,2,...,n). The function of the denominator is normalization to ensure that the total allocation ratio of all units is 100%.

[0051] S43, each generator set collects operating data in real time, including output voltage (range 0-400V, accuracy ±0.5V), output current (range 0-100A, accuracy ±0.1A) and frequency (range 50±0.2Hz), and uploads it to the main control unit through the OPC UA protocol.

[0052] S44, the main control unit performs real-time analysis of the operating data. When a parameter abnormality is detected, a three-level response strategy is triggered: local power reduction operation in case of yellow warning, isolation of the faulty unit and activation of the backup unit in case of orange warning, and execution of the emergency shutdown protocol in case of red warning.

[0053] S45 records operating data and fault events through blockchain technology, and uses smart contracts to achieve tamper-proof storage of data, ensuring the traceability of the operating process.

[0054] S46, the main control unit regularly generates operation reports, which include power generation efficiency, equipment health status and fault statistics, and are uploaded to the cloud platform through the 5G network for optimizing operation strategies and maintenance plans.

[0055] Embodiment 2: Combination Figure 3 and Figure 4As shown, the present invention also provides a remote control system for a self-propelled generator device, which is controlled by using the remote control method for a self-propelled generator device as in the first embodiment, and the remote control system is connected to multiple groups of generator devices in communication, and each group of generator devices 100 is provided with a power generation mechanism 10, a power storage module 20, a self-propelled mechanism 30 and a lifting mechanism 40; The remote control system includes: The main control unit is equipped with high-performance processors and edge computing nodes, supporting 5G-MEC network connection.

[0056] The virtual mirror system module is built based on digital twin technology, synchronized with the cloud server through the 5G network, and loaded with preset parameter models and historical data.

[0057] Environmental perception module, including multispectral LiDAR sensor, infrared thermal imager and millimeter wave radar, is used to support three-dimensional environmental modeling and temperature gradient field analysis.

[0058] The self-check monitoring module is integrated into the electronic control unit of each generator set and is used to execute the three-level self-check procedure.

[0059] The communication module adopts a two-layer communication architecture and supports the OPC UA protocol to realize data transmission.

[0060] The safety protection module is integrated into each generator set and is used to implement the three-level safety response strategy.

[0061] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote control method for a self-propelled generator device, characterized in that: It includes the following steps: S1. When the main control unit starts, a virtual image system is synchronously generated, and a preset parameter model is loaded through the 5G-MEC edge computing node. The electronic control units of each generator device execute a three-level self-check program, and upload the self-check data packet through the OPC UA protocol. The main control unit compares the predicted values of the image system to generate an equipment health index; S2. In the environmental perception stage, a three-dimensional environmental model is constructed through a multispectral detection device, a temperature field distribution map is established by combining infrared thermal imaging technology, an improved path planning algorithm is used to calculate the optimal moving path, and the energy consumption model is dynamically adjusted based on environmental parameters; S3. During the equipment deployment process, a force feedback control strategy is used to adjust the actions of the lifting mechanism, the mechanical parameters of the contact surface are monitored in real time through a multi-axis sensor, and the support mode is automatically switched in combination with the geological feature recognition result; S4. In the operation control stage, a hierarchical communication network architecture is established. The time-sensitive network is used to transmit control instructions between the main control unit and the generator sets. A self-organizing network communication link is constructed between the generator sets, and the power generation tasks are allocated based on the dynamic load balancing algorithm.

2. The remote control method of the self-propelled generator device according to claim 1, characterized in that: The specific steps of step S1 include the following steps: S11. When the main control unit starts, a preset device digital twin model library is called, and a virtual image system is locally constructed through the distributed computing resources of the 5G-MEC edge computing node. The virtual image system includes a mechanical structure dynamics model, an electrical system simulation model, and a communication network topology model; S12. A dedicated transmission channel is established through 5G network slicing technology, and the latest parameter configuration file is downloaded from the cloud server. The configuration file includes equipment operation historical data, environment adaptation parameters, and a fault feature library; S13. The electronic control units of each generator set start a parallel self-check program and perform three-level detection: the first-level detection of the mechanical system includes collecting hydraulic pressure values, vibration acceleration, and displacement sensor data; the second-level detection of the electrical system includes measuring insulation resistance, winding temperature, and output voltage volatility; the third-level detection of the communication system includes testing network delay, packet loss rate, and signal strength; S14. The electronic control unit structurally encapsulates the self-check data according to the OPC UA information model. The data packet includes a timestamp, equipment ID, detection item code, and measurement value. The AES-256 encryption algorithm is used for data encryption and uploaded to the main control unit through the 5G network. The single transmission data volume is controlled within 1KB.

3. The remote control method of the self-propelled generator device according to claim 2, characterized in that: After step S14, it also includes: S15. After receiving the self-check data packet, the main control unit calls the virtual image system for real-time simulation calculation, compares and analyzes the measured value with the simulation predicted value, and calculates the deviation rate of each parameter. The deviation rate calculation formula is: Δ = (|measured value - predicted value| / range) × 100%; S16. Based on the fuzzy comprehensive evaluation algorithm, the detection results of the three dimensions of mechanical, electrical, and communication in the three-level detection are weighted and calculated. The weight coefficients are 0.4, 0.35, and 0.25 respectively to generate an equipment health index HHI. The value range of HHI is 0~1. When HHI>0.8, it is determined to be in a healthy state. When 0.5<HHI≤0.8, it prompts an attention state. When HHI≤0.5, an early warning is triggered; S17, writes the health assessment results into the blockchain distributed ledger, uses smart contract technology to achieve tamper-proof storage of assessment data, and synchronizes the assessment results to the cloud digital twin platform through the 5G network.

4. The remote control method of the self-propelled generator device according to claim 1, characterized in that: The step S2 specifically includes the following steps: S21, uses a multispectral LiDAR sensor to scan the working environment, collect terrain elevation data, obstacle distribution information and surface features, with a scanning frequency of 10Hz, and construct a three-dimensional point cloud map; S22, the infrared thermal imaging module collects ambient temperature data in real time, with a temperature measurement range of -20°C to 300°C. It generates a temperature gradient field distribution map in combination with the thermal radiation model, identifies high-temperature heat source areas and marks the danger level; S23, using the improved A* path planning algorithm, combined with the three-dimensional point cloud map and temperature gradient field data, calculates the optimal moving path. The path planning objective function is: E = α·distance + β·slope + γ·thermal radiation attenuation coefficient, Among them, α, β, and γ are weight coefficients, which are 0.6, 0.3, and 0.1 respectively; S24, dynamically adjust the energy consumption model based on environmental parameters, and calculate the mobile energy consumption in real time. The energy consumption calculation formula is: P = k1·v + k2·m·g·sinθ + k3·T, Where v is the moving speed, m is the mass of the device, θ is the slope angle, T is the ambient temperature, and k1, k2, and k3 are correction coefficients.

5. The remote control method of the self-propelled generator device according to claim 4, characterized in that: After step S24, the following steps are also included: S25, uploads environmental perception data to the main control unit through the 5G network. The data packet contains three-dimensional map data, temperature field distribution map and path planning results; S26, the main control unit generates a movement instruction based on the environmental perception result, and sends it to the self-propelled mechanism through the time-sensitive network. The control device moves along the planned path, and the dynamic adjustment range of the movement speed is 0.1m / s-1.5m / s.

6. The remote control method of the self-propelled generator device according to claim 1, characterized in that: The step S3 specifically includes the following steps: S31, when the lifting mechanism is started, the six-axis force sensor monitors the pressure distribution of the contact surface in real time to obtain mechanical parameters; S32, uses fuzzy PID controller to dynamically adjust the hydraulic output and control the movement speed of the lifting mechanism; S33, scan the geological features of the landing area and measure the surface hardness parameters.

7. The remote control method of the self-propelled generator device according to claim 6, characterized in that: After step S33, the following steps are also included: S34, automatically switches the support mode according to the ground hardness parameters: rigid support pads are used in hard ground mode, extended support plates are used in soft ground mode, and adaptive leveling mechanism is used in slope terrain; S35, upload the mechanical parameters and surface hardness parameters to the main control unit through the 5G network. The main control unit monitors the separation process in real time and immediately triggers an emergency stop command when abnormal mechanical parameters are detected; S36, after the separation is completed, the main control unit generates a deployment completion signal and uploads the deployment status data to the cloud digital twin platform through the OPC UA protocol.

8. The remote control method of the self-propelled generator device according to claim 1, characterized in that: The step S4 specifically includes the following steps: S41, establish a two-layer communication network architecture: the first layer is the communication link between the main control unit and the generator set, using a time-sensitive network to transmit control instructions; the second layer is the communication link between the generator sets, using LoRa self-organizing network technology; S42, the main control unit allocates power generation tasks through a dynamic load balancing algorithm, and the task allocation formula is: P_alloc = (C_i·HHI_i) / (Σ(C_j·HHI_j)×P_total, Where C_i is the real-time capacity factor of the i-th generator set, HHI_i is the equipment health index of the i-th generator set, C_j is the real-time capacity factor of the j-th generator set, HHI_j is the equipment health index of the j-th generator set, and P_total is the total load demand; S43, each generator set collects operating data in real time, the operating data including output voltage, output current and frequency, and uploads the data to the main control unit through the OPC UA protocol.

9. The remote control method of the self-propelled generator device according to claim 8, characterized in that: After step S43, the following steps are further included: S44, the main control unit performs real-time analysis of the operating data. When an abnormal parameter is detected, a three-level response strategy is triggered: local power reduction operation in case of yellow warning, isolation of the faulty unit and activation of the standby unit in case of orange warning, and execution of the emergency shutdown protocol in case of red warning; S45, records operation data and fault events through blockchain technology, and uses smart contracts to achieve tamper-proof storage of data; S46, the main control unit regularly generates an operation report, which includes power generation efficiency, equipment health status and fault statistics, and is uploaded to the cloud platform via the 5G network.

10. A remote control system for a self-propelled generator device, characterized in that: The control is realized by using the remote control method of the self-propelled generator device according to any one of claims 1 to 9, wherein the remote control system is in communication connection with a plurality of groups of generator devices, each group of the generator devices being provided with a self-propelled mechanism and a lifting mechanism; The remote control system comprises: The main control unit is equipped with a high-performance processor and edge computing nodes, supporting 5G-MEC network connection; The virtual mirror system module is built based on digital twin technology and synchronized with the cloud server through the 5G network to load preset parameter models and historical data; Environmental perception module, including multispectral LiDAR sensor, infrared thermal imager and millimeter wave radar, to support three-dimensional environmental modeling and temperature gradient field analysis; The self-check monitoring module is integrated into the electronic control unit of each generator set and is used to perform a three-level self-check procedure; The communication module adopts a two-layer communication architecture and supports the OPC UA protocol for data transmission; The safety protection module is integrated into each generator set and is used to implement the three-level safety response strategy.

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