Dynamic distribution control method and system for power distribution unit of fork type mobile robot

Abstract

The invention discloses a fork type mobile robot power distribution unit dynamic distribution control method and system, and relates to the technical field of intelligent robots, and the method comprises the steps: arranging sensors at a robot PDU input end and each output channel, collecting an operation data set in real time, and carrying out the preprocessing to form a dynamic feature data set; the main control microcontroller carries out power response analysis and evaluates a channel response state based on the dynamic characteristic data set, generates a preliminary power distribution instruction, calculates a target current MI during preliminary power distribution, carries out thermal stress analysis, and constructs a thermal stress identification index Htt to carry out thermal accumulation risk evaluation; when a heat accumulation risk exists, calculating a channel target current MII when the secondary power supply is distributed, and adjusting the duty ratio of an MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) switch to carry out heat suppression regulation and control; and after the distribution is completed, calculating an energy utilization rate Nsys and executing energy efficiency state evaluation. According to the method, adaptive distribution and safe operation management of the multi-channel power supply of the robot are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent robot technology, specifically to a dynamic allocation control method and system for the power distribution unit of a forklift mobile robot. Background Technology

[0002] Forklift mobile robots, as key equipment in intelligent logistics systems, integrate multiple high-energy-consuming subsystems such as drive motors, lifting servos, navigation sensors, vision recognition units, and communication modules. Their operational stability depends on the power distribution unit (PDU)'s power supply coordination capabilities. Traditional PDUs mostly adopt fixed power distribution schemes, relying solely on static power path allocation to maintain power supply when the overall load changes or operating conditions switch, lacking real-time sensing and dynamic scheduling capabilities. With the increasing complexity of robot tasks and the growing parallel operation of multiple modules, a single power distribution mode can hardly meet the system's dual requirements for energy efficiency and thermal stability. Therefore, constructing a dynamic power distribution control method that can combine multi-source sensor data and achieve adaptive adjustment based on channel response and thermal state changes has become a necessary direction for improving robot energy utilization and operational safety.

[0003] Existing forklift mobile robots commonly suffer from problems such as lag in power response, thermal load imbalance, and simplistic allocation strategies during dynamic energy distribution. Most control methods rely solely on static adjustments based on the average channel current or output power, lacking the ability to identify the real-time response characteristics of different load channels. This leads to overload in some high-dynamic-load channels during peak power inputs, while low-load channels maintain a fixed power supply, resulting in decreased overall energy distribution efficiency. Furthermore, existing methods often lack real-time identification mechanisms for accumulated thermal stress, and the thermal coupling between the battery pack casing and power devices cannot be dynamically sensed. This results in high-temperature channels not receiving timely power reduction or diversion, which can easily lead to uneven power supply, energy waste, and thermal degradation over long-term operation, affecting the robot's operational reliability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic power distribution control method and system for a forklift mobile robot, solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic power distribution control method for a forklift mobile robot, comprising the following steps: S1. Based on the sensor group deployed at the input end and each output channel of the robot PDU, the robot's running data dataset is collected in real time, and the dynamic feature data group is obtained after preprocessing. S2. Based on the dynamic characteristic data set, perform power response analysis on each channel of the robot, generate channel response status assessment based on the analysis results, and trigger preliminary power distribution command. S3. When the robot's main control microcontroller receives the initial power allocation command, it performs initial power allocation for each channel, calculates the target current MI for each channel during the initial power allocation, and performs thermal stress analysis for each channel after the allocation is completed. Based on the analysis results, it generates a thermal accumulation risk assessment and triggers a secondary power allocation command. S4. When the robot's main control microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, calculates the target current MII for each channel during secondary power distribution, and records the output power Pe of each channel after the distribution is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

[0006] Preferably, S1 includes S11; S11. Sensor groups are installed at the input end of the robot PDU and each output channel to collect the robot's operation data in real time. The sensor group includes a voltage sensor, a current sensor, a power sensor, and a temperature sensor; The voltage sensor is installed between the positive output terminal of the battery pack and the input busbar of the PDU to collect the input voltage U in real time. The current sensor is used to be connected in series in the positive wire of each output channel and in the input bus circuit to collect the channel current I and the total input current Iz of each channel of the robot, respectively. The power sensor is used to be installed at the connection point between the output port of each channel and the load to directly measure the channel output power P; The temperature sensors are fixed to the thermal center area on the surface of the battery casing and the center point of each channel heat sink plate, respectively, to collect the battery casing temperature Tb and the channel thermal state T. The channel refers to the load power supply branch inside the PDU of the forklift mobile robot, which consists of an independent power switch unit and a corresponding output port.

[0007] Preferably, S1 further includes S12; S12. The robot’s main control microcontroller establishes a real-time communication channel with the battery management system (BMS) and sensor group through the CAN bus. The running dataset is transmitted to the main control microcontroller in real time through the CAN bus for preprocessing and to obtain dynamic feature data. The preprocessing includes denoising, timing alignment, filtering, and dimensionless processing. The denoising process uses wavelet denoising technology to preserve the transient structural features of the running dataset and remove high-frequency noise. Timing alignment is performed online using a streaming processing framework to align running datasets with the same timestamp based on the timestamps collected by the sensors. Filtering is performed by the main microcontroller sequentially applying moving average filtering to the synchronously acquired running datasets to smooth transient fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence. Dimensionless processing removes the dimensional influence of the running dataset using the Max-Min method. The dynamic characteristic data set includes input voltage U, channel current I, output power P, total input current Iz, battery pack casing temperature Tb, and thermal state T.

[0008] Preferably, S2 includes S21; S21. A power response analysis module is pre-installed inside the main control microcontroller, and the dynamic characteristic data set is input into the power response analysis module to perform power response analysis on each channel of the robot. An electric load dynamic allocation index Edz is constructed to analyze the response degree of different channels to changes in input power, reflecting the sensitivity of the current channel to power changes per unit input energy. Specifically: In the formula, Edz i U represents the dynamic load distribution index of the i-th channel. i and I i ΔP represents the input voltage and channel current of the i-th channel. i ΔP represents the change in output power of the i-th channel. i / Δt represents the rate of change of the output power of the i-th channel, and Δt represents the sampling time interval.

[0009] Preferably, S2 further includes S22; S22. Calculate the mean and standard deviation of the historical dynamic load distribution index Edz according to the statistical method, and set the difference between the mean and the standard deviation as the channel smooth response threshold Ty, and set the sum of the mean and the standard deviation as the channel load response threshold Tf. Then, evaluate the channel response status with the real-time obtained dynamic load distribution index Edz. The specific evaluation scheme is as follows. When the dynamic load allocation index Edz < channel smooth response threshold Ty, it means that the current channel power load response is smooth, the channel is running stably and has not reached the upper limit, and at this time the initial power allocation command is triggered. When the channel stable response threshold Ty ≤ the dynamic load distribution index Edz ≤ the channel load response threshold Tf, it means that the current channel power load response is within the reasonable range of dynamic response, and the current power supply strategy is maintained. When the dynamic load allocation index Edz > the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, and a preliminary power allocation command is triggered.

[0010] Preferably, S3 includes S31; S31. When the main control microcontroller receives the preliminary power allocation command, it performs preliminary power allocation for each channel based on the dynamic load allocation index Edz and the total input current Iz of each channel of the robot, and calculates the target current MI for the preliminary power allocation of each channel, specifically: In the formula, MI i ΣEdz represents the target current after the initial power allocation of the i-th channel. i This represents the sum of the dynamic load distribution indices for all channels.

[0011] Preferably, S3 includes S32 and S33; S32. After the initial power allocation to each channel is completed, the main control microcontroller calls the thermal management task function and inputs the dynamic characteristic data set into the thermal management task function to perform thermal stress analysis on each channel, constructing a thermal stress identification index Htt, which represents the thermal flow coupling strength between the battery and the channel, reflecting the current thermal accumulation state of the channel under the heat dissipation capacity. Specifically: In the formula, Htt i Htt represents the thermal stress identification index of the i-th channel. ΔT represents the rate of temperature rise of the battery pack casing. i ΔT represents the thermal state of the i-th channel. i / Δt represents the rate of change of thermal state of the i-th channel, P i Mj represents the output power of the i-th channel, and Mj represents the equivalent heat dissipation area of ​​the power device corresponding to the channel. S33. Calculate the mean value of the thermal stress identification index Htt when there is no thermal accumulation risk in the past using statistical methods, and set the mean value as the thermal accumulation risk threshold Fy. Then, conduct a thermal accumulation risk assessment with the real-time thermal stress identification index Htt. The specific assessment scheme is as follows. When the thermal stress identification index Htt < the thermal accumulation risk threshold Fy, it means that there is no thermal accumulation risk in the current channel and the initial power allocation meets the standard. At this time, the current power input is maintained. When the thermal stress identification index Htt is greater than or equal to the thermal accumulation risk threshold Fy, it indicates that there is a thermal accumulation risk in the current channel and the initial power allocation is not up to standard. At this time, a secondary power allocation command is triggered.

[0012] Preferably, S4 includes S41; S41. When the main microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, and corrects the target current MI of the initial power distribution in real time according to the thermal stress identification index Htt. It calculates and generates the target current MII of the channel during secondary power distribution, and controls the duty cycle of the MOSFET switching of each channel through the PWM modulator to perform secondary power distribution. Specifically: In the formula, MII i ΣEdz represents the target current after the secondary power supply allocation for the i-th channel. i This represents the sum of the thermal stress identification indices for all channels, reflecting the current overall thermal load level.

[0013] Preferably, S4 further includes S42 and S43; S42. After the secondary power supply is distributed, the main microcontroller adjusts the output power Pe of each channel after secondary correction. i Input voltage Ue i The total output current Ic is recorded, and after dimensionless processing, the robot's energy utilization rate Nsys is calculated based on the energy efficiency function. This is used to analyze the robot's overall performance after dual-domain regulation of thermal load and power supply. Specifically: In the formula, ΣEdz i Pe represents the output power Pe after secondary correction for all channels. i The sum of ΔTb / Δt represents the rate of temperature change of the robot's battery pack, and kth represents the dimensionless constant of temperature rise allowed for robot operation under standard conditions; S43. Record the energy utilization rate Nsys of 1,000 robots under rated voltage and rated load conditions, and calculate the mean value according to the statistical method. Set the mean value as the comprehensive energy efficiency utilization threshold Ny, and then evaluate the energy efficiency status with the real-time energy utilization rate Nsys. The specific evaluation scheme is as follows. When the energy utilization rate Nsys < the comprehensive energy efficiency utilization threshold Ny, it indicates that there is energy waste and heat accumulation risk in the operation of the robot. At this time, iterative allocation is performed through S2. If the allocation cycle is qualified for 3 consecutive cycles, the electronic switch is immediately controlled to cut off the power supply of the corresponding channel and an allocation efficiency decay report is generated to notify the maintenance personnel. When the energy utilization rate Nsys is greater than or equal to the comprehensive energy efficiency utilization threshold Ny, it indicates that the current allocation scheme is stable and the next control cycle begins.

[0014] The dynamic power distribution control system for the power distribution unit of the forklift mobile robot includes a data acquisition module, a load analysis module, a heat accumulation analysis module, and an energy efficiency analysis module. The data acquisition module is used to collect the robot's running data set in real time based on the sensor group deployed at the input end and each output channel of the robot PDU, and obtain the dynamic feature data group after preprocessing. The load analysis module is used to perform power response analysis on each channel of the robot based on the dynamic characteristic data set, generate channel response status assessment based on the analysis results, and trigger preliminary power allocation instructions. The thermal accumulation analysis module is used to perform preliminary power allocation on each channel according to the preliminary power allocation command received by the robot's main control microcontroller, calculate the target current MI of each channel during the preliminary power allocation, and perform thermal stress analysis on each channel after the allocation is completed. Then, based on the analysis results, a thermal accumulation risk assessment is generated and a secondary power allocation command is triggered. The energy efficiency analysis module is used to perform secondary power allocation on all channels with heat accumulation risk based on the secondary power allocation command received by the robot's main control microcontroller, calculate the target current MII for each channel during secondary power allocation, and record the output power Pe of each channel after allocation is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

[0015] This invention provides a dynamic power distribution control method and system for a forklift mobile robot's power distribution unit. It offers the following advantages: (1) This method achieves real-time monitoring of the robot's operational dataset by deploying multiple sensor groups at the PDU input end and each output channel of the forklift mobile robot. Combined with preprocessing and data fusion by the main control microcontroller, a dynamic feature data group is formed. This data group provides high-precision basic information for subsequent power distribution, enabling the acquisition of load change trends and energy distribution status between channels, and providing the conditions for dynamic adjustment of the power distribution process. Through this mechanism, the robot can identify the power response differences of each channel in the early stage of operation, generate preliminary power distribution commands based on the channel response characteristics, and realize instantaneous diversion and load balancing control of input energy.

[0016] (2) This method utilizes the built-in power response analysis module of the main control microcontroller to calculate the dynamic characteristic data set and construct the electrical load dynamic allocation index Edz. This index is used to quantitatively describe the power response sensitivity of each channel under unit input energy change, reflecting the channel's instantaneous response characteristics to energy input changes. Combined with the steady-state response threshold Ty and load response threshold Tf calculated by statistical methods, the channel operating state is divided into a steady zone, a reasonable zone, and an overload zone. After analyzing the channel response state, a preliminary power allocation command is triggered according to different zones to achieve dynamic load balance between channels. When a channel is in an overload state, the current output is automatically redistributed, so that the overloaded channel receives current limiting adjustment, while the low-load channel receives compensation input. This intelligent allocation mechanism based on response characteristics enables energy to be optimally allocated between channels according to real-time operating conditions, reducing power redundancy and instantaneous load impact. Then, the thermal stress identification index Htt is used to perform thermal stress analysis on the preliminary power allocation results, and thermal accumulation risk assessment is performed with the thermal accumulation risk threshold Fy. The channel is automatically identified as having thermal accumulation risk and a secondary allocation command is triggered, establishing a dynamic feedback relationship between electrical load regulation and thermal balance.

[0017] (3) After completing the secondary power distribution, the main control microcontroller records the corrected output power Pe of each channel. i Input voltage Ue i The total output current Ic is calculated, and the overall energy utilization rate Nsys is calculated in conjunction with the battery temperature rise rate ΔTb / Δt to measure the robot's overall energy efficiency performance under thermal load and power control. By comparing it with the comprehensive energy efficiency utilization threshold Ny, it is determined whether the current operation is in a high-efficiency balance range. When the energy utilization rate Nsys is lower than the comprehensive energy efficiency utilization threshold Ny, it automatically enters an iterative allocation cycle, and the power response module and thermal management module are called back to perform dynamic adjustment; if multiple consecutive cycles reach the qualified state, the power supply of the corresponding channel is automatically shut off to avoid ineffective energy consumption. This process constitutes a complete closed-loop control mechanism with self-diagnosis, self-adjustment, and self-protection capabilities during operation. This invention realizes adaptive power allocation and thermal risk control of the forklift mobile robot under complex operating conditions through four levels: dynamic acquisition, response analysis, thermal stress assessment, and energy efficiency closed-loop feedback, thereby improving the overall energy utilization level and operational stability of the forklift mobile robot. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the dynamic allocation control method for the power distribution unit of the fork-type mobile robot of the present invention. Figure 2 This is a schematic diagram of the dynamic power distribution control system for the fork-type mobile robot of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 This invention provides a dynamic power distribution control method for a forklift mobile robot. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps: S1. Based on the sensor group deployed at the input end and each output channel of the robot PDU, the robot's running data dataset is collected in real time, and the dynamic feature data group is obtained after preprocessing. S2. Based on the dynamic characteristic data set, perform power response analysis on each channel of the robot, generate channel response status assessment based on the analysis results, and trigger preliminary power distribution command. S3. When the robot's main control microcontroller receives the initial power allocation command, it performs initial power allocation for each channel, calculates the target current MI for each channel during the initial power allocation, and performs thermal stress analysis for each channel after the allocation is completed. Based on the analysis results, it generates a thermal accumulation risk assessment and triggers a secondary power allocation command. S4. When the robot's main control microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, calculates the target current MII for each channel during secondary power distribution, and records the output power Pe of each channel after the distribution is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

[0021] In this embodiment, in S1, voltage sensors, current sensors, power sensors, and temperature sensors are deployed at the robot's PDU input and each output channel to collect real-time data during robot operation. After denoising, timing alignment, filtering, and dimensionless processing by the main control microcontroller, a dynamic feature data set is obtained, providing high-precision input parameters for subsequent power allocation calculations. This step achieves multi-point synchronous acquisition, building upon existing technologies that rely solely on single-point detection or fixed measurements. This allows power allocation to reflect changes in operating load in a real-time and continuous manner, laying the foundation for subsequent dynamic allocation. In S2 and S3, the method forms a continuous two-layer control mechanism through power response analysis and thermal stress analysis. Power response analysis calculates the dynamic load allocation index Edz based on the dynamic feature data set to quantify the power change sensitivity of different channels and judges the channel's operating status using statistical thresholds. When a channel's power load response is detected to be stable or overloaded, a preliminary power allocation command is triggered. After performing initial power allocation, the main microcontroller further calculates the target current MI and calls the thermal stress identification algorithm to construct a thermal stress identification index Htt, analyzing the channel's heat dissipation capacity and heat accumulation level. When the thermal stress identification index Htt exceeds the heat accumulation risk threshold Fy, a secondary power allocation command is triggered. By establishing a dynamic feedback relationship between electrical load regulation and thermal balance, this method avoids the channel overheating problem caused by insufficient heat dissipation in traditional fixed allocation strategies, improving the safety and response rationality of the energy allocation process. In S4, when the main microcontroller receives the secondary power allocation command, it calculates and generates the channel target current MII for secondary power allocation and controls the MOSFET switching duty cycle of each channel through a PWM modulator to perform secondary power allocation. After completing the secondary power allocation, it adjusts the output power Pe of each channel. i Input voltage Ue i The total output current Ic is recorded, and the energy utilization rate Nsys is calculated. Nsys reflects the overall energy efficiency status after thermal stress correction. Energy efficiency status is assessed by comparing it with a preset comprehensive energy efficiency threshold Ny. When Nsys is lower than the comprehensive energy efficiency threshold Ny, a redistribution cycle is automatically executed for correction; when Nsys reaches the comprehensive energy efficiency threshold Ny, the current allocation strategy is maintained and the next control cycle begins. This closed-loop calculation process achieves dynamic adjustment throughout the entire process from power allocation to energy efficiency assessment. Compared to traditional methods that rely solely on static judgment based on current thresholds, this invention achieves adaptive control of power allocation through real-time calculation and multi-stage correction. In summary, this method completes the tasks of dynamic monitoring, allocation optimization, and thermal safety correction for the power distribution of a forklift mobile robot, achieving improvements in energy utilization and operational stability.

[0022] Example 2 Please refer to Figure 1 Specifically: S1 includes S11; S11. Sensor groups are installed at the input end of the robot PDU and each output channel to collect the robot's operation data in real time. The sensor group includes a voltage sensor, a current sensor, a power sensor, and a temperature sensor; The voltage sensor is installed between the positive output terminal of the battery pack and the input busbar of the PDU to collect the input voltage U in real time. The current sensor is used to be connected in series in the positive wire of each output channel and in the input bus circuit to collect the channel current I and the total input current Iz of each channel of the robot, respectively. The power sensor is used to be installed at the connection point between the output port of each channel and the load to directly measure the channel output power P; The temperature sensors are fixed to the thermal center area on the surface of the battery casing and the center point of each channel heat sink plate, respectively, to collect the battery casing temperature Tb and the channel thermal state T. The channel refers to the load power supply branch inside the PDU of the forklift mobile robot, which consists of an independent power switch unit and a corresponding output port.

[0023] S1 further includes S12; S12. The robot’s main control microcontroller establishes a real-time communication channel with the battery management system (BMS) and sensor group through the CAN bus. The running dataset is transmitted to the main control microcontroller in real time through the CAN bus for preprocessing and to obtain dynamic feature data. The preprocessing includes denoising, timing alignment, filtering, and dimensionless processing. The denoising process uses wavelet denoising technology to preserve the transient structural features of the running dataset and remove high-frequency noise. Timing alignment is performed online using a streaming processing framework to align running datasets with the same timestamp based on the timestamps collected by the sensors. Filtering is performed by the main microcontroller sequentially applying moving average filtering to the synchronously acquired running datasets to smooth transient fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence. Dimensionless processing removes the dimensional influence of the running dataset using the Max-Min method. The dynamic characteristic data set includes input voltage U, channel current I, output power P, total input current Iz, battery pack casing temperature Tb, and thermal state T.

[0024] In this embodiment, voltage sensors, current sensors, power sensors, and temperature sensors are deployed at the PDU input terminal and each output channel of the forklift mobile robot to construct a complete robot operation data acquisition network. The voltage sensor is installed between the positive output terminal of the battery pack and the PDU input busbar to acquire the input voltage U in real time. The current sensor is connected in series in the positive wire of each output channel and the input busbar circuit to synchronously acquire the channel current I and the total input current Iz. The power sensor is set at the output port of each channel to directly measure the channel output power P. The temperature sensor is fixed at the thermal center of the battery casing surface and the center point of the channel heat sink to monitor the battery casing temperature Tb and the channel thermal state T. The main control microcontroller establishes a real-time communication channel with the battery management system (BMS) and the sensor group via a CAN bus, receives the operation data set, and performs noise reduction, timing alignment, filtering, and dimensionless processing to ultimately form a dynamic feature data set, providing a unified data foundation for subsequent power allocation and thermal risk analysis. By implementing this step, the forklift mobile robot achieves simultaneous acquisition and high-precision preprocessing of multiple channels and multiple physical quantities, making the operating data continuous and comparable, avoiding measurement error problems caused by sampling delay, noise interference or dimensional differences in traditional methods, and improving the real-time performance and stability of power distribution control.

[0025] Example 3 Please refer to Figure 1 Specifically: S2 includes S21; S21. A power response analysis module is pre-installed inside the main control microcontroller, and the dynamic characteristic data set is input into the power response analysis module to perform power response analysis on each channel of the robot. An electric load dynamic allocation index Edz is constructed to analyze the response degree of different channels to changes in input power, reflecting the sensitivity of the current channel to power changes per unit input energy. Specifically: In the formula, Edz i U represents the dynamic load distribution index of the i-th channel. i and I i ΔP represents the input voltage and channel current of the i-th channel. i ΔP represents the change in output power of the i-th channel. i / Δt represents the rate of change of the output power of the i-th channel, and Δt represents the sampling time interval.

[0026] S2 further includes S22; S22. Calculate the mean and standard deviation of the historical dynamic load distribution index Edz according to the statistical method, and set the difference between the mean and the standard deviation as the channel smooth response threshold Ty, and set the sum of the mean and the standard deviation as the channel load response threshold Tf. Then, evaluate the channel response status with the real-time obtained dynamic load distribution index Edz. The specific evaluation scheme is as follows. When the dynamic load allocation index Edz < channel smooth response threshold Ty, it means that the current channel power load response is smooth, the channel is running stably and has not reached the upper limit, and at this time the initial power allocation command is triggered. When the channel stable response threshold Ty ≤ the dynamic load distribution index Edz ≤ the channel load response threshold Tf, it means that the current channel power load response is within the reasonable range of dynamic response, and the current power supply strategy is maintained. When the dynamic load allocation index Edz > the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, and a preliminary power allocation command is triggered.

[0027] In this embodiment, S21 performs real-time analysis of dynamic characteristic data sets by pre-installing a power response analysis module inside the main control microcontroller, constructs the electrical load dynamic allocation index Edz, and analyzes the power change sensitivity of each channel under unit input energy. This is achieved by calculating ΔP. i / Δt reflects the channel power response rate, and is combined with the input voltage U of each channel. i With channel current I i This allows for precise identification of the dynamic load characteristics of the channel. The Edz formula for the dynamic load distribution index is theoretically based on the relative response relationship between the rate of power change and the input energy. Its derivation logic is an improved structure combining the physical formula of electrical power with normalized rate analysis. It incorporates both the power definition from classical electrical engineering and the concept of rate of change from digital signal processing. In electrical engineering, instantaneous power is defined as P(t) = U(t) × I(t), where U(t) is voltage and I(t) is current. If the robot's power changes over a period of time, the change is ΔP. i =P i (t2)-P i (t1), while the rate of change of power is ΔP i / Δt, this is a direct form derived from the definition of rate of change (i.e., the concept of derivative) in physics, reflecting the first-order response characteristics of power to time. In order to describe the sensitivity of power changes to input energy, the input energy must be introduced as a reference quantity. The average value of the input power can be expressed as P in,i =U i ×I i This is the classic electrical energy input formula, representing the energy input obtained by the i-th channel from the power source per unit time. To obtain the power change sensitivity under unit input energy, the power change rate ΔP can be used. i / Δt divided by input power U i ×I iThe final result is the dynamic load distribution index Edz. S22 uses statistical methods to calculate the mean and standard deviation of the historical dynamic load distribution index Edz data, establishing a channel stability response threshold Ty and a channel load response threshold Tf to achieve a graded judgment of the current operating state. When the dynamic load distribution index Edz is lower than the channel stability response threshold Ty, it indicates that the channel is operating stably. When the dynamic load distribution index Edz is between the channel stability response threshold Ty and the channel load response threshold Tf, it indicates that the channel response is within a reasonable range. When the dynamic load distribution index Edz exceeds the channel load response threshold Tf, it is considered a power overload and triggers a preliminary power distribution command. Through this step, the main control microcontroller can dynamically and adaptively judge based on historical operating data without relying on fixed thresholds, accurately identify abnormal power response, and make timely distribution adjustments to avoid energy imbalance or voltage drop problems caused by channel overload. Compared with the traditional static control method that only uses current thresholds or setpoint logic, this achieves dynamic quantification and real-time feedback of the electrical load state, improving the energy utilization stability and power regulation response efficiency during robot operation.

[0028] Example 4 Please refer to Figure 1 Specifically: S3 includes S31; S31. When the main control microcontroller receives the preliminary power allocation command, it performs preliminary power allocation for each channel based on the dynamic load allocation index Edz and the total input current Iz of each channel of the robot, and calculates the target current MI for the preliminary power allocation of each channel, specifically: In the formula, MI i ΣEdz represents the target current after the initial power allocation of the i-th channel. i This represents the sum of the dynamic load distribution indices for all channels.

[0029] S3 includes S32 and S33; S32. After the initial power allocation to each channel is completed, the main control microcontroller calls the thermal management task function and inputs the dynamic characteristic data set into the thermal management task function to perform thermal stress analysis on each channel, constructing a thermal stress identification index Htt, which represents the thermal flow coupling strength between the battery and the channel, reflecting the current thermal accumulation state of the channel under the heat dissipation capacity. Specifically: In the formula, Htt i Htt represents the thermal stress identification index of the i-th channel. ΔT represents the rate of temperature rise of the battery pack casing. i ΔT represents the thermal state of the i-th channel. i / Δt represents the rate of change of thermal state of the i-th channel, P iMj represents the output power of the i-th channel, and Mj represents the equivalent heat dissipation area of ​​the power device corresponding to the channel. S33. Calculate the mean value of the thermal stress identification index Htt when there is no thermal accumulation risk in the past using statistical methods, and set the mean value as the thermal accumulation risk threshold Fy. Then, conduct a thermal accumulation risk assessment with the real-time thermal stress identification index Htt. The specific assessment scheme is as follows. When the thermal stress identification index Htt < the thermal accumulation risk threshold Fy, it means that there is no thermal accumulation risk in the current channel and the initial power allocation meets the standard. At this time, the current power input is maintained. When the thermal stress identification index Htt is greater than or equal to the thermal accumulation risk threshold Fy, it indicates that there is a thermal accumulation risk in the current channel and the initial power allocation is not up to standard. At this time, a secondary power allocation command is triggered.

[0030] In this embodiment, in step S31, after receiving the initial power allocation command, the main control microcontroller allocates the target current MI to each channel based on the real-time calculated dynamic load allocation index Edz and the total input current Iz, thereby achieving balanced power distribution among the branches. The derivation of the target current MI formula is based on the power allocation principle and the normalized proportional allocation algorithm. Essentially, the formula dynamically adjusts the current across multiple channels using a weighted equalization method under the constraints of the law of energy conservation and the proportional allocation principle, representing an engineering extension of the classic allocation model in the field of electrical control. In the allocation constraints of energy conservation and Ohm's law, the total input current Iz satisfies the energy conservation condition Iz = ΣI i That is, the input current equals the sum of the currents of all output channels; mathematically, if a set of characteristic quantities {X} i} represents the response strength or weight of each channel, and its total is ΣX i Then the normalized allocation ratio is Wi=X i / ΣX i Originating from the linear proportional allocation algorithm, it is widely used in resource allocation, signal normalization, and power scheduling. In S32, after allocation, the main controller calls the thermal management task function to perform thermal stress analysis on the dynamic characteristic data set and calculate the thermal stress identification index Htt, which is used to characterize the thermal flow coupling strength and heat dissipation capacity between the battery and each channel. Through comprehensive analysis of parameters such as battery temperature rise rate, channel thermal state change rate, and output power, it is possible to identify whether there is a risk of heat accumulation in the channel during operation. The formula for the thermal stress identification index Htt belongs to the composite derivation form based on the law of conservation of energy and the heat transfer balance equation in the field of thermodynamics and heat transfer. It is a quantitative thermal stress discrimination model that combines heat conduction rate, power input characteristics, and heat dissipation capacity. According to the law of conservation of energy and Fourier's law of heat conduction, the basic relationship between heat power and temperature change is Q=m×C×ΔT, where Q is heat, m is mass, C is specific heat capacity, and ΔT is temperature change. Differentiating with respect to time yields the expression for heat power. , where P in It is the channel power input, P out This refers to the power loss due to heat dissipation, meaning the system's power input equals its rate of change of heat. When the heat dissipation and input power reach a steady state, the heat flux density q satisfies Fourier's law. This represents the linear relationship between the temperature gradient and the conductive heat flow. If the thermal resistance Rth of the heat dissipation path is considered, then... The unsteady thermal equilibrium differential equation is derived as follows: The PDU channel of a forklift mobile robot is a discrete system; the main control microcontroller performs calculations based on sampled data. And introduce the overall temperature rise rate of the battery pack. and the local temperature rise rate ΔT of the channel i / Δt, reducing the geometric and material factors of thermal resistance to a measurable equivalent heat dissipation area Mj, and replacing 1 / Rth with Mj / Pi, discretizing and substituting the substitution into the original equation, yields After sorting, the proportional relationship of heat accumulation intensity is obtained. Finally, the thermal stress identification index Htt is obtained. In S33, the thermal stress identification index Htt is compared with the thermal accumulation risk threshold Fy to assess the thermal accumulation risk. When the thermal stress identification index Htt is lower than the thermal accumulation risk threshold Fy, the current power supply strategy is maintained; when the thermal stress identification index Htt is greater than or equal to the thermal accumulation risk threshold Fy, a secondary power distribution command is immediately triggered to readjust the current distribution ratio. This step combines electrical load distribution calculation with thermal risk assessment, making power distribution not only based on electrical parameters but also simultaneously considering thermal safety factors, forming an adaptive control mechanism in both electrical and thermal domains. Compared with traditional distribution methods that rely on fixed current ratios or time-slice switching, this method can automatically correct the distribution results under conditions of channel load fluctuations and uneven heat dissipation, preventing overload faults caused by thermal imbalance, thereby improving the accuracy of power distribution, thermal safety, and overall energy utilization efficiency.

[0031] Example 5 Please refer to Figure 1 Specifically: S4 includes S41; S41. When the main microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, and corrects the target current MI of the initial power distribution in real time according to the thermal stress identification index Htt. It calculates and generates the target current MII of the channel during secondary power distribution, and controls the duty cycle of the MOSFET switching of each channel through the PWM modulator to perform secondary power distribution. Specifically: In the formula, MII i ΣEdz represents the target current after the secondary power supply allocation for the i-th channel. iThis represents the sum of the thermal stress identification indices for all channels, reflecting the current overall thermal load level.

[0032] S4 further includes S42 and S43; S42. After the secondary power supply is distributed, the main microcontroller adjusts the output power Pe of each channel after secondary correction. i Input voltage Ue i The total output current Ic is recorded, and after dimensionless processing, the robot's energy utilization rate Nsys is calculated based on the energy efficiency function. This is used to analyze the robot's overall performance after dual-domain regulation of thermal load and power supply. Specifically: In the formula, ΣEdz i Pe represents the output power Pe after secondary correction for all channels. i The sum of ΔTb / Δt represents the rate of temperature change of the robot's battery pack, and kth represents the dimensionless constant of temperature rise allowed for robot operation under standard conditions; S43. Record the energy utilization rate Nsys of 1,000 robots under rated voltage and rated load conditions, and calculate the mean value according to the statistical method. Set the mean value as the comprehensive energy efficiency utilization threshold Ny, and then evaluate the energy efficiency status with the real-time energy utilization rate Nsys. The specific evaluation scheme is as follows. When the energy utilization rate Nsys < the comprehensive energy efficiency utilization threshold Ny, it indicates that there is energy waste and heat accumulation risk in the operation of the robot. At this time, iterative allocation is performed through S2. If the allocation cycle is qualified for 3 consecutive cycles, the electronic switch is immediately controlled to cut off the power supply of the corresponding channel and an allocation efficiency decay report is generated to notify the maintenance personnel. When the energy utilization rate Nsys is greater than or equal to the comprehensive energy efficiency utilization threshold Ny, it indicates that the current allocation scheme is stable and the next control cycle begins.

[0033] In this formula, after the main microcontroller receives the secondary power allocation command, S41 corrects the initial target current MI of the power allocation in real time based on the thermal stress identification index Htt, calculates and generates the secondary target current MII, and adjusts the duty cycle of each channel MOSFET switch through the PWM modulator to achieve fine-grained allocation control of the thermal risk channel. The target current MII formula is derived based on the proportional correction model, and the proportional correction principle originates from the error feedback equation Δx in control theory. i =k×(E i / ΣE i ), where E i Let ΣE be the error of the i-th object. i To sum the overall error, the thermal stress identification index Htt is substituted into the proportional correction model to obtain the target current MII for the initial power allocation. S42, the main microcontroller calculates the output power Pe after the secondary allocation. i Input voltage Uei The total output current Ic is recorded, and the energy utilization rate Nsys is calculated based on the energy efficiency function to quantitatively assess the synergistic state of the thermal and electrical domains. In an electrical system, the total energy utilization rate η can be written as η = P out / P in =ΣPe / Σ(Ue×Ic), this is the fundamental formula for measuring energy output and input efficiency in electrical engineering, reflecting the power transfer efficiency of the system at the electrical domain level. In multi-channel PDUs with thermal effects, the actual energy utilization efficiency is not only determined by electrical conversion but also constrained by the temperature rise rate (ΔTb / Δt). Therefore, a thermal correction term needs to be introduced to reflect the influence of the thermal domain on the electrical domain efficiency. In thermodynamics, the influence of the temperature rise rate on system efficiency can be expressed by a correction coefficient model as ηT=1-(ΔT / Δt) / k, where k is the system's allowable temperature rise coefficient, and ΔT / Δt is the temperature rise rate, derived from the principle of thermal efficiency correction. Applying the above results to the field of power distribution, we can obtain the comprehensive utilization rate expression η for both thermal and electrical domains. total =η electric ×η thermal =Σ(Pe i / Ue i The energy utilization rate Nsys is finally obtained by calculating (1-(ΔTb / Δt) / kth) × (1-(ΔTb / Δt) / kth). S43 compares the energy utilization rate Nsys with the comprehensive energy efficiency threshold Ny. The method can automatically determine the stability of the current allocation scheme. When energy efficiency is insufficient, it performs a reallocation iteration and generates an efficiency decay report. When energy efficiency meets the standard, it enters a stable cycle. Through this process, the method completes the entire process control from thermal risk correction to energy efficiency closed-loop assessment, realizes timely intervention in channel overheating, establishes an adaptive adjustment mechanism for energy utilization rate, and improves the shortcomings of traditional fixed allocation methods that cannot dynamically respond to changes in heat load.

[0034] Example 6 Please refer to Figure 2 The dynamic power distribution control system for the power distribution unit of the forklift mobile robot includes a data acquisition module, a load analysis module, a heat accumulation analysis module, and an energy efficiency analysis module. The data acquisition module is used to collect the robot's running data set in real time based on the sensor group deployed at the input end and each output channel of the robot PDU, and obtain the dynamic feature data group after preprocessing. The load analysis module is used to perform power response analysis on each channel of the robot based on the dynamic characteristic data set, generate channel response status assessment based on the analysis results, and trigger preliminary power allocation instructions. The thermal accumulation analysis module is used to perform preliminary power allocation on each channel according to the preliminary power allocation command received by the robot's main control microcontroller, calculate the target current MI of each channel during the preliminary power allocation, and perform thermal stress analysis on each channel after the allocation is completed. Then, based on the analysis results, a thermal accumulation risk assessment is generated and a secondary power allocation command is triggered. The energy efficiency analysis module is used to perform secondary power allocation on all channels with heat accumulation risk based on the secondary power allocation command received by the robot's main control microcontroller, calculate the target current MII for each channel during secondary power allocation, and record the output power Pe of each channel after allocation is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic power distribution control method for a forklift mobile robot, characterized in that: Includes the following steps: S1. Based on the sensor group deployed at the input end and each output channel of the robot PDU, the robot's running data dataset is collected in real time, and the dynamic feature data group is obtained after preprocessing. S2. Based on the dynamic characteristic data set, perform power response analysis on each channel of the robot, generate channel response status assessment based on the analysis results, and trigger preliminary power distribution command. S3. When the robot's main control microcontroller receives the initial power allocation command, it performs initial power allocation for each channel, calculates the target current MI for each channel during the initial power allocation, and performs thermal stress analysis for each channel after the allocation is completed. Based on the analysis results, it generates a thermal accumulation risk assessment and triggers a secondary power allocation command. S4. When the robot's main control microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, calculates the target current MII for each channel during secondary power distribution, and records the output power Pe of each channel after the distribution is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

2. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 1, characterized in that: S1 includes S11; S11. Sensor groups are installed at the input end of the robot PDU and each output channel to collect the robot's operation data in real time. The sensor group includes a voltage sensor, a current sensor, a power sensor, and a temperature sensor; The voltage sensor is installed between the positive output terminal of the battery pack and the input busbar of the PDU to collect the input voltage U in real time. The current sensor is used to be connected in series in the positive wire of each output channel and in the input bus circuit to collect the channel current I and the total input current Iz of each channel of the robot, respectively. The power sensor is used to be installed at the connection point between the output port of each channel and the load to directly measure the channel output power P; The temperature sensors are fixed to the thermal center area on the surface of the battery casing and the center point of each channel heat sink plate, respectively, to collect the battery casing temperature Tb and the channel thermal state T. The channel refers to the load power supply branch inside the PDU of the forklift mobile robot, which consists of an independent power switch unit and a corresponding output port.

3. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 2, characterized in that: S1 further includes S12; S12. The robot’s main control microcontroller establishes a real-time communication channel with the battery management system (BMS) and sensor group through the CAN bus. The running dataset is transmitted to the main control microcontroller in real time through the CAN bus for preprocessing and to obtain dynamic feature data. The preprocessing includes denoising, timing alignment, filtering, and dimensionless processing. The denoising process uses wavelet denoising technology to preserve the transient structural features of the running dataset and remove high-frequency noise from the running dataset. Timing alignment is performed online using a streaming processing framework to align runtime datasets with the same timestamps based on the timestamps collected by the sensors. The filtering process involves sequentially performing moving average filtering on the synchronously acquired running dataset by the main control microcontroller to smooth instantaneous fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence; the dimensionless processing removes the dimensional influence of the running dataset through the Max-Min method. The dynamic characteristic data set includes input voltage U, channel current I, output power P, total input current Iz, battery pack casing temperature Tb, and thermal state T.

4. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 3, characterized in that: S2 includes S21; S21. A power response analysis module is pre-installed inside the main control microcontroller, and the dynamic characteristic data set is input into the power response analysis module to perform power response analysis on each channel of the robot. An electric load dynamic allocation index Edz is constructed to analyze the response degree of different channels to changes in input power, reflecting the sensitivity of the current channel to power changes per unit input energy. Specifically: In the formula, Edz i U represents the dynamic load distribution index of the i-th channel. i and I i ΔP represents the input voltage and channel current of the i-th channel. i ΔP represents the change in output power of the i-th channel. i / Δt represents the rate of change of the output power of the i-th channel, and Δt represents the sampling time interval.

5. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 4, characterized in that: S2 further includes S22; S22. Calculate the mean and standard deviation of the historical dynamic load distribution index Edz according to the statistical method, and set the difference between the mean and the standard deviation as the channel smooth response threshold Ty, and set the sum of the mean and the standard deviation as the channel load response threshold Tf. Then, evaluate the channel response status with the real-time obtained dynamic load distribution index Edz. The specific evaluation scheme is as follows. When the dynamic load allocation index Edz < channel smooth response threshold Ty, it means that the current channel power load response is smooth, the channel is running stably and has not reached the upper limit, and at this time the initial power allocation command is triggered. When the channel stable response threshold Ty ≤ the dynamic load distribution index Edz ≤ the channel load response threshold Tf, it means that the current channel power load response is within the reasonable range of dynamic response, and the current power supply strategy is maintained. When the dynamic load allocation index Edz > the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, and a preliminary power allocation command is triggered.

6. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 5, characterized in that: S3 includes S31; S31. When the main control microcontroller receives the preliminary power allocation command, it performs preliminary power allocation for each channel based on the dynamic load allocation index Edz and the total input current Iz of each channel of the robot, and calculates the target current MI for the preliminary power allocation of each channel, specifically: In the formula, MI i ΣEdz represents the target current after the initial power allocation of the i-th channel. i This represents the sum of the dynamic load distribution indices for all channels.

7. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 6, characterized in that: S3 includes S32 and S33; S32. After the initial power allocation to each channel is completed, the main control microcontroller calls the thermal management task function and inputs the dynamic characteristic data set into the thermal management task function to perform thermal stress analysis on each channel, constructing a thermal stress identification index Htt, which represents the thermal flow coupling strength between the battery and the channel, reflecting the current thermal accumulation state of the channel under the heat dissipation capacity. Specifically: In the formula, Htt i Htt represents the thermal stress identification index of the i-th channel. ΔT represents the rate of temperature rise of the battery pack casing. i ΔT represents the thermal state of the i-th channel. i / Δt represents the rate of change of thermal state of the i-th channel, P i Mj represents the output power of the i-th channel, and Mj represents the equivalent heat dissipation area of ​​the power device corresponding to the channel. S33. Calculate the mean value of the thermal stress identification index Htt when there is no thermal accumulation risk in the past using statistical methods, and set the mean value as the thermal accumulation risk threshold Fy. Then, conduct a thermal accumulation risk assessment with the real-time thermal stress identification index Htt. The specific assessment scheme is as follows. When the thermal stress identification index Htt < the thermal accumulation risk threshold Fy, it means that there is no thermal accumulation risk in the current channel and the initial power allocation meets the standard. At this time, the current power input is maintained. When the thermal stress identification index Htt is greater than or equal to the thermal accumulation risk threshold Fy, it indicates that there is a thermal accumulation risk in the current channel and the initial power allocation is not up to standard. At this time, a secondary power allocation command is triggered.

8. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 7, characterized in that: S4 includes S41; S41. When the main microcontroller receives the secondary power distribution command, it performs secondary power distribution on all channels with heat accumulation risk, and corrects the target current MI of the initial power distribution in real time according to the thermal stress identification index Htt. It calculates and generates the target current MII of the channel during secondary power distribution, and controls the duty cycle of the MOSFET switching of each channel through the PWM modulator to perform secondary power distribution. Specifically: In the formula, MII i ΣEdz represents the target current after the secondary power supply allocation for the i-th channel. i This represents the sum of the thermal stress identification indices for all channels, reflecting the current overall thermal load level.

9. The dynamic allocation control method for the power distribution unit of the forklift mobile robot according to claim 8, characterized in that: S4 further includes S42 and S43; S42. After the secondary power supply is distributed, the main control microcontroller adjusts the output power Pe of each channel after secondary correction. i Input voltage Ue i The total output current Ic is recorded, and after dimensionless processing, the robot's energy utilization rate Nsys is calculated based on the energy efficiency function. This is used to analyze the robot's overall performance after dual-domain regulation of thermal load and power supply. Specifically: In the formula, ΣEdz i Pe represents the output power Pe after secondary correction for all channels. i The sum of ΔTb / Δt represents the rate of temperature change of the robot's battery pack, and kth represents the dimensionless constant of temperature rise allowed for robot operation under standard conditions. S43. Record the energy utilization rate Nsys of 1,000 robots under rated voltage and rated load conditions, and calculate the mean value according to the statistical method. Set the mean value as the comprehensive energy efficiency utilization threshold Ny, and then evaluate the energy efficiency status with the real-time energy utilization rate Nsys. The specific evaluation scheme is as follows. When the energy utilization rate Nsys < the comprehensive energy efficiency utilization threshold Ny, it indicates that there is energy waste and heat accumulation risk in the operation of the robot. At this time, iterative allocation is performed through S2. If the allocation cycle is qualified for 3 consecutive cycles, the electronic switch is immediately controlled to cut off the power supply of the corresponding channel and an allocation efficiency decay report is generated to notify the maintenance personnel. When the energy utilization rate Nsys is greater than or equal to the comprehensive energy efficiency utilization threshold Ny, it indicates that the current allocation scheme is stable and the next control cycle begins.

10. A dynamic power distribution control system for a forklift mobile robot, comprising the dynamic power distribution control method for a forklift mobile robot as described in any one of claims 1-9, characterized in that: It includes a data acquisition module, a load analysis module, a heat accumulation analysis module, and an energy efficiency analysis module; The data acquisition module is used to collect the robot's running data set in real time based on the sensor group deployed at the input end and each output channel of the robot PDU, and obtain the dynamic feature data group after preprocessing. The load analysis module is used to perform power response analysis on each channel of the robot based on the dynamic characteristic data set, generate channel response status assessment based on the analysis results, and trigger preliminary power allocation instructions. The thermal accumulation analysis module is used to perform preliminary power allocation on each channel according to the preliminary power allocation command received by the robot's main control microcontroller, calculate the target current MI of each channel during the preliminary power allocation, and perform thermal stress analysis on each channel after the allocation is completed. Then, based on the analysis results, a thermal accumulation risk assessment is generated and a secondary power allocation command is triggered. The energy efficiency analysis module is used to perform secondary power allocation on all channels with heat accumulation risk based on the secondary power allocation command received by the robot's main control microcontroller, calculate the target current MII for each channel during secondary power allocation, and record the output power Pe of each channel after allocation is completed. i Input voltage Ue i The total output current Ic is used to calculate the robot's energy utilization rate Nsys and generate an energy efficiency status assessment.

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