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

Abstract

The application discloses a fork mobile robot power distribution unit dynamic distribution control method and system, and relates to the technical field of intelligent robots. The method is characterized in that sensors are arranged at the input end of the robot PDU and each output channel, real-time operation data sets are collected, and dynamic characteristic data groups are formed after preprocessing. A main control microcontroller performs power response analysis and evaluates the channel response state based on the dynamic characteristic data groups, generates a preliminary power distribution instruction, calculates the target current MI of the preliminary power distribution, and carries out thermal stress analysis to construct a thermal stress identification index Htt for thermal accumulation risk assessment. When there is a thermal accumulation risk, the channel target current MII of the secondary power distribution is calculated, and the MOSFET switch duty cycle is adjusted for thermal suppression regulation. After the distribution is completed, the energy utilization rate Nsys is calculated, and the energy efficiency state is evaluated. The method realizes adaptive distribution and safe operation management of the multi-channel power supply of the robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent robots, in particular to a dynamic distribution control method and system for a power distribution unit of a fork mobile robot. BACKGROUND

[0002] As a key equipment in intelligent logistics systems, the fork mobile robot integrates driving motors, lifting servo, navigation sensors, visual recognition units, and communication modules, and other high-energy-consumption subsystems, and its running stability depends on the power supply coordination capability of the power distribution unit PDU. The traditional power distribution unit PDU adopts a fixed power distribution scheme, and when the load state of the whole machine changes or the running condition switches, only the static power path distribution is relied on to maintain power supply, lacking real-time sensing and dynamic scheduling capability. With the increase of the complexity of the robot task and the increase of the parallel running of multiple modules, a single power distribution mode is difficult to meet the dual needs of system energy efficiency and thermal stability. Therefore, it is necessary to construct a power distribution unit dynamic distribution control method that can combine multi-source sensing data and realize adaptive adjustment according to channel response and thermal state change, which is a necessary direction to improve the energy utilization rate and running safety of the robot.

[0003] The existing fork mobile robot generally has problems such as power response lag, thermal load imbalance, and single distribution strategy in the process of energy dynamic distribution. Most control methods only make static adjustments according to the average of the channel current or output power, lack of identification of real-time response characteristics of different load channels, resulting in overload of some high-dynamic-load channels at the moment of instantaneous peak power input, while the low-load channels still maintain fixed power supply, which reduces the overall energy distribution efficiency. In addition, the existing methods often lack real-time identification mechanism for thermal stress accumulation, and the thermal coupling relationship between the battery pack shell and the power device cannot be dynamically sensed, causing high-temperature channels to not be able to be reduced or diverted in time, which easily leads to uneven power supply, energy waste and thermal degradation under long-term operation, affecting the running reliability of the robot. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a dynamic distribution control method and system for a power distribution unit of a fork mobile robot, which solves the problems in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a dynamic distribution control method for a power distribution unit of a fork mobile robot, comprising the following steps:

[0006] S1, real-time acquisition of the running data set of the robot according to the sensor group arranged at the input end of the robot PDU and each output channel, and obtaining the dynamic characteristic data set after preprocessing;

[0007] S2, power response analysis is performed on each channel of the robot according to the dynamic characteristic data set, a channel response state evaluation is generated according to the analysis result, and a preliminary power distribution instruction is triggered;

[0008] S3, the robot master microcontroller performs preliminary power distribution on each channel when receiving the preliminary power distribution instruction, calculates the target current MI of each channel during preliminary power distribution, performs thermal stress analysis on each channel after the distribution is completed, generates a thermal accumulation risk evaluation according to the analysis result, and triggers a secondary power distribution instruction;

[0009] S4, the robot master microcontroller performs secondary power distribution on all channels with thermal accumulation risk when receiving the secondary power distribution instruction, calculates the target current MII of each channel during secondary power distribution, records the output power Pe i , input voltage Ue i and total output current Ic, calculates the energy utilization rate Nsys of the robot, and generates an energy efficiency state evaluation.

[0010] Preferably, the S1 comprises S11;

[0011] S11, a sensor group is arranged at the input end of the robot PDU and each output channel, respectively, to collect the running data set of the robot in real time;

[0012] The sensor group comprises a voltage sensor, a current sensor, a power sensor and a temperature sensor;

[0013] The voltage sensor is used to be installed between the positive electrode of the battery pack output and the PDU input busbar, and to collect the input voltage U in real time;

[0014] The current sensor is used to be respectively connected in series in the positive electrode wire of each output channel and in the input busbar loop, to collect the channel current I and the total input current Iz of each channel of the robot, respectively;

[0015] The power sensor is used to be arranged at the output port of each channel and the load connection, to directly measure the channel output power P;

[0016] The temperature sensor is used to be fixed on the thermal center area of the battery shell surface and the center point of each channel heat sink, respectively, to collect the battery pack shell temperature Tb and the channel heat state T, respectively;

[0017] The channel refers to the load power supply branch composed of an independent power switch unit and a corresponding output port inside the fork mobile robot PDU.

[0018] Preferably, the S1 further comprises S12;

[0019] S12, the master microcontroller of the robot establishes a real-time communication channel with the battery management system BMS and the sensor group through the CAN bus, and transmits the running data set to the master microcontroller in real time through the CAN bus for preprocessing to obtain a dynamic characteristic data set;

[0020] The preprocessing includes denoising, time series alignment, filtering processing and dimensionless processing.

[0021] The denoising retains the transient structure characteristics of the running data set and removes the high-frequency noise in the running data set through wavelet denoising technology; the time series alignment is online aligned by the streaming processing framework according to the time stamp collected by the sensor; the filtering processing is performed by the master microcontroller on the synchronously collected running data set to smooth the instantaneous fluctuation, and then the amplitude limiting filtering is performed to remove the abnormal peak value to obtain a continuous data sequence; and the dimensionless processing removes the dimension influence of the running data set by the Max-Min maximum and minimum method.

[0022] The dynamic characteristic data set includes input voltage U, channel current I, output power P, total input current Iz, battery pack shell temperature Tb and thermal state T.

[0023] Preferably, the S2 includes S21.

[0024] S21, a power response analysis module is preset in the master microcontroller, and the dynamic characteristic data set is input into the power response analysis module to analyze the power response of each channel of the robot and construct an electrical load dynamic distribution index Edz, analyze the response degree of different channels to input power change, and reflect the sensitivity of power change under current channel unit input energy, specifically: , wherein Edz i represents the electrical load dynamic distribution index of the i-th channel, U i and I i represents the input voltage and channel current of the i-th channel, ΔP i represents the change amount of output power of the i-th channel, ΔP i / Δt represents the output power change rate of the i-th channel, and Δt represents the sampling time interval.

[0025] Preferably, the S2 further includes S22.

[0026] S22, the mean and standard deviation of the historical electrical load dynamic distribution index Edz are calculated according to the statistical method, the difference between the mean and the standard deviation is set as the channel stable response threshold Ty, the sum of the mean and the standard deviation is set as the channel load response threshold Tf, and the electrical load dynamic distribution index Edz obtained in real time is evaluated for channel response state, and the specific evaluation scheme is as follows.

[0027] When the electrical load dynamic distribution index Edz is less than the channel steady response threshold Ty, it indicates that the current channel power load response is steady, the channel operation is stable and the upper limit is not reached, and the preliminary power distribution instruction is triggered at this time;

[0028] When the channel steady response threshold Ty is less than or equal to the electrical load dynamic distribution index Edz and less than or equal to the channel load response threshold Tf, it indicates that the current channel power load response is in a reasonable dynamic response interval, and the current power supply strategy is maintained.

[0029] When the electrical load dynamic distribution index Edz is greater than the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, and the preliminary power distribution instruction is triggered at this time.

[0030] Preferably, the S3 comprises S31;

[0031] S31, the host microcontroller performs preliminary power distribution on each channel according to the electrical load dynamic distribution index Edz and the total input current Iz of each channel of the robot when receiving the preliminary power distribution instruction, and calculates the target current MI of each channel after preliminary power distribution, specifically: , wherein MI i represents the target current of the i-th channel after preliminary power distribution, ΣEdz i represents the sum of the electrical load dynamic distribution indexes of all channels.

[0032] Preferably, the S3 comprises S32 and S33;

[0033] S32, the host microcontroller calls a thermal management task function and inputs a dynamic characteristic data set into the thermal management task function to perform thermal stress analysis on each channel and construct a thermal stress identification index Htt after the preliminary power distribution of each channel is completed, which represents the thermal flow coupling strength of the battery and the channel and reflects the thermal accumulation state of the current channel under the heat dissipation capacity, specifically: , wherein Htt i represents the thermal stress identification index Htt of the i-th channel, ΔT represents the temperature rise rate of the battery pack shell, ΔT i represents the thermal state of the i-th channel, ΔT i / Δt represents the thermal state change rate of the i-th channel, P i 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;

[0034] S33, the thermal stress identification index Htt mean value when there is no thermal accumulation risk in history is calculated by a statistical method, and the mean value is set as a thermal accumulation risk threshold Fy, and the thermal accumulation risk is evaluated by comparing the real-time obtained thermal stress identification index Htt with the thermal accumulation risk threshold Fy, and the specific evaluation scheme is as follows:

[0035] When the thermal stress identification index Htt is less than the thermal accumulation risk threshold Fy, it indicates that there is no thermal accumulation risk in the current channel, and the preliminary power distribution is qualified. At this time, the current power input is maintained;

[0036] 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 preliminary power distribution is unqualified. At this time, the secondary power distribution instruction is triggered.

[0037] Preferably, the S4 comprises S41;

[0038] S41, when the secondary power distribution instruction is received, the master microcontroller performs secondary power distribution on all channels with thermal accumulation risk, and real-time correction is performed on the target current MI of the preliminary power distribution according to the thermal stress identification index Htt. The channel target current MII in the secondary power distribution is generated by calculation, and the secondary power distribution is performed by controlling the duty cycle of the MOSFET switch of each channel through the PWM modulator. Specifically: , wherein MII i represents the target current of the i-th channel after secondary power distribution, and ΣHtt i represents the sum of the thermal stress identification indexes of all channels, reflecting the current overall thermal load level.

[0039] Preferably, the S4 further comprises S42 and S43;

[0040] S42, after the secondary power distribution is completed, the master microcontroller records the output power Pe i , the input voltage Ue i and the total output current Ic of each channel after secondary correction, and performs dimensionless processing to calculate the energy utilization rate Nsys of the robot according to the energy efficiency function, which is used to analyze the overall performance of the robot after the thermal load and power dual-domain regulation. Specifically: , wherein ΔTb / Δt represents the temperature rise change rate of the robot battery pack, and kth represents the dimensionless constant of the allowable temperature rise of the robot under standard conditions;

[0041] S43, record the energy utilization rate Nsys of one thousand groups of robots under the condition of rated voltage and rated load, and calculate the mean value according to the statistical method. The mean value is preset as the comprehensive energy efficiency utilization threshold Ny, and then the energy efficiency state is evaluated by comparing the real-time obtained energy utilization rate Nsys. The specific evaluation scheme is as follows:

[0042] When the energy utilization rate Nsys is less than the comprehensive energy efficiency utilization threshold Ny, it indicates that there is energy waste and thermal accumulation risk in the operation of the robot. At this time, the iterative distribution is performed through S2. If the distribution is qualified for three consecutive distribution periods, the electronic switch is immediately controlled to cut off the power supply to the corresponding channel, and a distribution efficiency decay report is generated to notify the maintenance personnel;

[0043] When the energy utilization rate Nsys is greater than or equal to the comprehensive energy utilization threshold Ny, it indicates that the current distribution scheme is stable, and the next control cycle is entered.

[0044] The fork mobile robot power distribution unit dynamic distribution control system comprises a data acquisition module, a load analysis module, a thermal accumulation analysis module and an energy efficiency analysis module.

[0045] The data acquisition module is used for collecting the running data set of the robot in real time according to the sensor group arranged at the input end of the robot PDU and each output channel, and obtaining the dynamic characteristic data set after preprocessing.

[0046] The load analysis module is used for performing power response analysis on each channel of the robot according to the dynamic characteristic data set, generating a channel response state evaluation and triggering a preliminary power distribution instruction according to the analysis result.

[0047] The thermal accumulation analysis module is used for receiving the preliminary power distribution instruction by the robot master microcontroller, performing preliminary power distribution on each channel, calculating the target current MI of each channel at the time of preliminary power distribution, and performing thermal stress analysis on each channel after the distribution is completed, and then generating a thermal accumulation risk evaluation and triggering a secondary power distribution instruction according to the analysis result.

[0048] The energy efficiency analysis module is used for receiving the secondary power distribution instruction by the robot master microcontroller, performing secondary power distribution on all channels with thermal accumulation risks, calculating the target current MII of each channel at the time of secondary power distribution, recording the output power Pe i , input voltage Ue i and total output current Ic of each channel after the distribution is completed, calculating the energy utilization rate Nsys of the robot, and generating an energy efficiency state evaluation.

[0049] The present application provides a fork mobile robot power distribution unit dynamic distribution control method and system.

[0050] (1) The method realizes real-time monitoring of the running data set of the robot by arranging multiple types of sensor groups at the input end of the fork mobile robot PDU and each output channel, and forms a dynamic characteristic data set by combining the preprocessing and data fusion of the master microcontroller. The data set provides high-precision basic information for subsequent power distribution, can obtain the load change trend and energy distribution state between channels, and has the condition of dynamically adjusting the power distribution process. Through this mechanism, the robot can identify the power response difference of each channel in the early stage of operation, generate a preliminary power distribution instruction according to the channel response characteristics, and realize immediate shunt and load balance control of input energy.

[0051] (2) The method uses the power response analysis module built in the master microcontroller to calculate the dynamic characteristic data set, constructs the dynamic load distribution index Edz, and uses it to quantitatively describe the power response sensitivity of each channel under the change of unit input energy, reflects the immediate response characteristics of the channel to the change of energy input, and combines the smooth response threshold Ty and the load response threshold Tf calculated by the statistical method to divide the channel running state into the smooth area, the reasonable area and the overload area. After analyzing the response state of the channel, the preliminary power distribution instruction is triggered according to different intervals to realize the dynamic balance of the load between channels. When the channel is in an overload state, the current output is automatically redistributed, so that the overload channel obtains current limiting adjustment, and the low load channel obtains compensation input. This intelligent distribution mechanism based on response characteristics optimizes the distribution of energy between channels according to the real-time working condition, reduces the energy redundancy and instantaneous load impact. Then, the thermal stress identification index Htt is used to analyze the thermal stress of the preliminary power distribution result, and the thermal cumulative risk threshold Fy is used for thermal cumulative risk evaluation, to automatically identify the thermal cumulative risk of the channel and trigger the secondary distribution instruction, to establish a dynamic feedback relationship between the electric load regulation and the thermal balance.

[0052] (3) After the master microcontroller completes the secondary power distribution, the output power Pe i , the input voltage Ue i and the total output current Ic of each channel after correction are recorded, and the overall energy utilization rate Nsys is calculated by combining the battery temperature rise rate ΔTb / Δt to measure the overall energy efficiency performance of the robot under the thermal load and power regulation. By comparing with the comprehensive energy efficiency utilization threshold Ny, it is determined whether the current operation is in the high efficiency balance interval. When the energy utilization rate Nsys is lower than the comprehensive energy efficiency utilization threshold Ny, the iteration distribution cycle is automatically entered, and the power response module and the thermal management module are called again to perform dynamic adjustment; if the qualified state is reached for continuous multiple periods, the corresponding channel power is automatically turned off to avoid invalid energy consumption. This process constitutes a complete closed-loop regulation mechanism, which has self-diagnosis, self-adjustment and self-protection capabilities in operation. Through four levels of dynamic acquisition, response analysis, thermal stress evaluation and energy efficiency closed-loop feedback, the power adaptive distribution and thermal risk control of the fork mobile robot under complex operating conditions are realized, and the overall energy utilization level and operation stability of the fork mobile robot are improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 It is a dynamic distribution control method step schematic diagram of the power distribution unit of the fork mobile robot of the present application.

[0054] Figure 2 It is a dynamic distribution control system flow schematic diagram of the power distribution unit of the fork mobile robot of the present application. DETAILED DESCRIPTION

[0055] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application. Embodiment 1

[0056] Please refer to Figure 1 The present application provides a dynamic allocation control method for a power distribution unit of a fork mobile robot. To achieve the above object, the present application is implemented by the following technical solutions: comprising the following steps:

[0057] S1, real-time collection of the running data set of the robot according to the sensor group arranged at the input end of the robot PDU and each output channel, and acquisition of the dynamic characteristic data set after preprocessing;

[0058] S2, power response analysis of each channel of the robot according to the dynamic characteristic data set, generation of the channel response state evaluation and triggering of the preliminary power distribution instruction according to the analysis result;

[0059] S3, the robot main control microcontroller performs preliminary power distribution on each channel when receiving the preliminary power distribution instruction, calculates the target current MI of each channel at the time of preliminary power distribution, and performs thermal stress analysis on each channel after the distribution is completed, and then generates the thermal accumulation risk evaluation and triggers the secondary power distribution instruction according to the analysis result;

[0060] S4, the robot main control microcontroller performs secondary power distribution on all channels with thermal accumulation risk when receiving the secondary power distribution instruction, calculates the target current MII of each channel at the time of secondary power distribution, and records the output power Pe i , input voltage Ue i and total output current Ic to calculate the energy utilization rate Nsys of the robot, and generates the energy efficiency state evaluation.

[0061] In this embodiment, in S1, by arranging voltage sensors, current sensors, power sensors and temperature sensors at the input end of the robot PDU and at each output channel, real-time collection of operating data sets during robot operation is realized. After denoising, time alignment, filtering and dimensionless processing by the master microcontroller, dynamic characteristic data sets are obtained, providing high-precision input parameters for subsequent allocation calculation. This step realizes multi-point synchronous collection on the basis of existing technology which only relies on single-point detection or fixed measurement, enabling the power distribution to reflect the change in operating load in a real-time and continuous manner, laying a foundation for subsequent dynamic allocation. In S2 and S3, the method forms a continuous double-layer regulation mechanism through power response analysis and thermal stress analysis. Power response analysis calculates the dynamic allocation index Edz of the electrical load according to the dynamic characteristic data sets, to quantify the power change sensitivity of different channels, and judges the channel operating state through statistical threshold. When the power load response of a channel is detected to be stable and overloaded, a preliminary power distribution instruction is triggered. After the master microcontroller executes the preliminary power distribution, it further calculates the target current MI and calls the thermal stress identification algorithm to construct the thermal stress identification index Htt, to analyze the heat dissipation capacity and heat accumulation degree of the channel. When the thermal stress identification index Htt exceeds the heat accumulation risk threshold Fy, a secondary power distribution instruction is triggered; by establishing a dynamic feedback relationship between electrical load regulation and thermal balance, the method avoids the overheating problem of channels caused by insufficient heat dissipation in traditional fixed allocation strategies, improving the safety and response rationality of the energy distribution process. In S4, when the master microcontroller receives the secondary power distribution instruction, it calculates the target current MII of the channel at the time of secondary power distribution, and controls the MOSFET switch duty cycle of each channel through the PWM modulator to perform secondary power distribution. After completing the secondary power distribution, the output power Pe i , input voltage Ue i and total output current Ic of each channel are recorded, and the energy utilization rate Nsys is calculated. The energy utilization rate Nsys reflects the overall energy efficiency state after thermal stress correction. By comparing with the preset comprehensive energy utilization threshold Ny, the energy efficiency state evaluation is completed. When the energy utilization rate Nsys is lower than the comprehensive energy utilization threshold Ny, the redistribution cycle is automatically executed for correction; when the energy utilization rate Nsys reaches the comprehensive energy utilization threshold Ny, the current distribution strategy is maintained and the next control cycle is entered. This closed-loop calculation process realizes dynamic adjustment of the whole process from power distribution to energy efficiency evaluation. Compared with the traditional method which only uses current threshold for static judgment, the present application realizes adaptive regulation of power distribution through real-time calculation and multi-stage correction. In summary, the present method completes the dynamic monitoring, distribution optimization and thermal safety correction of the power distribution of the fork mobile robot, and improves the energy utilization rate and operation stability. Embodiment 2

[0062] Please refer to Figure 1 , Specifically: the S1 includes S11;

[0063] S11, respectively, at the robot PDU input and each output channel sensor group is arranged, real-time acquisition of the running data set of the robot;

[0064] The sensor group includes voltage sensor, current sensor, power sensor and temperature sensor;

[0065] The voltage sensor is used to be installed between the positive electrode of the battery pack output and the PDU input bus, and the input voltage U is collected in real time;

[0066] The current sensor is used to be respectively connected in series in the positive electrode wire of each output channel and in the input bus loop, and the channel current I and the total input current Iz of each channel of the robot are collected respectively;

[0067] The power sensor is used to be set at the output port of each channel and the load connection, and the channel output power P is directly measured;

[0068] The temperature sensor is used to be fixed respectively on the surface thermal center area of the battery shell and the center point of each channel heat sink, and the battery pack shell temperature Tb and the channel heat state T are collected respectively;

[0069] The channel refers to the load power supply branch composed of independent power switch unit and corresponding output port inside the fork type mobile robot PDU.

[0070] The S1 also includes S12;

[0071] S12, the main control microcontroller of the robot establishes real-time communication channel with the battery management system BMS and the sensor group through CAN bus, and transmits the running data set to the main control microcontroller for pretreatment through CAN bus, and obtains dynamic characteristic data set;

[0072] The pretreatment includes denoising, time series alignment, filtering and dimensionless processing;

[0073] The denoising retains the transient structure characteristics of the running data set and removes the high frequency noise in the running data set through wavelet denoising technology; Time series alignment is performed through streaming processing framework according to the time stamp collected by the sensor to align the running data set with the same time stamp online; The filtering processing is performed by the main control microcontroller on the synchronously collected running data set in turn to smooth the instantaneous fluctuation, and then performs amplitude limiting filtering to remove abnormal peak value to obtain continuous data sequence; The dimensionless processing removes the dimension influence of the running data set by Max-Min maximum and minimum method;

[0074] The dynamic characteristic data set includes input voltage U, channel current I, output power P, total input current Iz, battery pack shell temperature Tb and thermal state T.

[0075] In this embodiment, voltage sensors, current sensors, power sensors and temperature sensors are arranged at the input end of the fork mobile robot PDU and at each output channel to construct a complete robot operation data set acquisition network. The voltage sensor is installed between the battery pack output positive electrode and the PDU input busbar for real-time acquisition of input voltage U; the current sensor is connected in series in the positive electrode lead of each output channel and the input busbar loop for synchronous acquisition of channel current I and total input current Iz; the power sensor is arranged at the output port of each channel for direct measurement of channel output power P; the temperature sensor is fixed to the thermal center of the battery shell surface and the center point of the channel heat sink for monitoring the battery shell temperature Tb and the channel thermal state T. The main control microcontroller establishes real-time communication channels with the battery management system BMS and the sensor group through the CAN bus, receives the operation data set and performs denoising, time sequence alignment, filtering and dimensionless processing, and finally forms the dynamic characteristic data set to provide a unified data basis for subsequent power distribution and thermal risk analysis. Through the implementation of this step, the fork mobile robot realizes synchronous acquisition and high-precision preprocessing of multiple channels and multiple physical quantities, makes the operation data have continuity and comparability, avoids measurement error problems caused by sampling delay, noise interference or dimension difference in traditional methods, and improves the real-time performance and stability of power distribution control. Embodiment 3

[0076] Please refer to Figure 1 , specifically: the S2 includes S21;

[0077] S21, a power response analysis module is preset in the main control microcontroller, and the dynamic characteristic data set is input into the power response analysis module to analyze the power response of each channel of the robot and construct an electrical load dynamic distribution index Edz, analyze the response degree of different channels to input power change, reflect the sensitivity of power change under current channel unit input energy, specifically: , in the formula, Edz i represents the electrical load dynamic distribution index of the i-th channel, U i and I i represent the input voltage and channel current of the i-th channel, ΔP i represents the change amount of output power of the i-th channel, ΔP i / Δt represents the output power change rate of the i-th channel, and Δt represents the sampling time interval.

[0078] The S2 further includes S22;

[0079] S22, calculate the mean and standard deviation of the historical electric load dynamic allocation index Edz according to the statistical method, and set the difference between the mean and the standard deviation as the channel stable 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 state with the real-time obtained electric load dynamic allocation index Edz, and the specific evaluation scheme is as follows:

[0080] When the electric load dynamic allocation index Edz is less than the channel stable response threshold Ty, it indicates that the current channel power load response is stable, the channel operation is stable and has not reached the upper limit, at this time, the preliminary power distribution instruction is triggered;

[0081] When the channel stable response threshold Ty is less than or equal to the electric load dynamic allocation index Edz and the electric load dynamic allocation index Edz is less than or equal to the channel load response threshold Tf, it indicates that the current channel power load response is in a reasonable dynamic response interval, and the current power supply strategy is maintained;

[0082] When the electric load dynamic allocation index Edz is greater than the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, at this time, the preliminary power distribution instruction is triggered.

[0083] In this embodiment, S21 constructs the electric load dynamic allocation index Edz by presetting a power response analysis module in the main control microcontroller to analyze the dynamic characteristic data set in real time, and analyzes the power change sensitivity of each channel under unit input energy. By calculating ΔP i / Δt reflects the channel power response rate, and combining the input voltage U i and the channel current I i , the dynamic load characteristics of the channel can be accurately identified. The theoretical basis of the electric load dynamic allocation index Edz formula is derived from the relative response relationship between the power change rate and the input energy, and its derivation logic belongs to an improved structure combining power physical formula and normalized rate analysis, which contains both the power definition in classical electrical engineering and the change rate idea in 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 power of the robot changes in a period of time, the change amount is ΔP i =P i (t2)-P i (t1), and the power change rate is ΔP i / Δt, which is a direct form derived from the definition of change rate in physics (i.e. the concept of derivative), reflecting the first-order response characteristics of power to time. In order to describe the sensitivity of power change to input energy, the input energy must be introduced as a reference quantity, and the average value of input power can be represented as P in,i =U i ×I i, which is the classic electric energy input formula, representing the energy input of the i-th channel from the power supply per unit time. In order to obtain the power change sensitivity under unit input energy, the power change rate ΔP i / Δt is divided by the input power U i ×I i , and finally the electric load dynamic distribution index Edz is obtained. S22 calculates the mean and standard deviation of the historical electric load dynamic distribution index Edz data according to statistical methods, and establishes the channel stable response threshold Ty and the channel load response threshold Tf, respectively, to realize the classification judgment of the current running state. When the electric load dynamic distribution index Edz is lower than the channel stable response threshold Ty, it indicates that the channel is running stably, when the electric load dynamic distribution index Edz is between the channel stable response threshold Ty and the channel load response threshold Tf, it indicates that the channel response is in a reasonable range, and when the electric load dynamic distribution index Edz exceeds the channel load response threshold Tf, it is identified as power overload and triggers the preliminary power distribution instruction. Through the implementation of this step, the host microcontroller can realize dynamic adaptive judgment according to historical operation data without relying on fixed thresholds, accurately identify power response abnormalities and timely adjust the distribution, avoiding energy imbalance or voltage drop problems caused by channel overload. Compared with the traditional static control method using current threshold or fixed value logic, dynamic quantization and real-time feedback of the electric load state are realized, and the energy utilization stability and power regulation response efficiency during robot operation are improved. Embodiment 4

[0084] Please refer to Figure 1 , specifically: the S3 includes S31;

[0085] S31, the host microcontroller performs preliminary power distribution on each channel according to the electric load dynamic distribution index Edz and the total input current Iz of each channel of the robot when receiving the preliminary power distribution instruction, and calculates the target current MI of each channel after preliminary power distribution, specifically: , wherein MI i represents the target current of the i-th channel after preliminary power distribution, ΣEdz i represents the sum of the electric load dynamic distribution indexes of all channels.

[0086] The S3 includes S32 and S33;

[0087] S32, the host 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 after the preliminary power distribution of each channel is completed, and constructs a thermal stress identification index Htt, which represents the thermal flow coupling strength of the battery and the channel and reflects the thermal accumulation state of the current channel under the heat dissipation capacity, specifically: , wherein Htti Htt represents the thermal stress identification index of the i-th channel, ΔT represents the temperature rise rate of the battery pack shell, i ΔT represents the thermal state of the i-th channel, i / Δt represents the thermal state change rate of the i-th channel, P i P 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;

[0088] S33, the average of the thermal stress identification index Htt when there is no thermal accumulation risk in history is calculated according to the statistical method, and the average is set as the thermal accumulation risk threshold Fy, and then the thermal accumulation risk is evaluated by comparing the real-time obtained thermal stress identification index Htt, and the specific evaluation scheme is as follows:

[0089] When the thermal stress identification index Htt is less than the thermal accumulation risk threshold Fy, it indicates that there is no thermal accumulation risk in the current channel, and the preliminary power distribution is qualified, at this time the current power input is maintained;

[0090] 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 preliminary power distribution is unqualified, at this time the secondary power distribution instruction is triggered.

[0091] In this embodiment, in S31, the main control microcontroller distributes the target current MI of each channel according to the real-time calculated dynamic load distribution index Edz and the total input current Iz after receiving the preliminary power distribution instruction, so as to realize the balanced distribution of electrical energy among the branches. The derivation basis of the target current MI formula is derived from the principle of electrical energy distribution and the normalized proportional distribution algorithm. In essence, the formula realizes the dynamic allocation of multi-channel current through the weighted balancing method under the constraints of the law of conservation of energy and the principle of proportional distribution, which is an engineering extension of the classical distribution model in the field of electrical control. In the distribution constraints of the law of conservation of energy and Ohm's law, the total input current Iz satisfies the energy conservation condition Iz = ΣI i , that is, the input current is equal to the sum of all output channel currents; in mathematics, if a set of characteristic quantities {X i} represents the response intensity or weight of each channel, and the total amount is ΣX i , then the normalized distribution ratio is Wi = X i / ΣX i, which is derived from linear proportional distribution algorithm, is widely used in resource allocation, signal normalization and power scheduling. In S32, after the allocation is completed, the master calls the thermal management task function to analyze the thermal stress of the dynamic characteristic data set, calculates the thermal stress identification index Htt, which is used to represent the thermal flow coupling strength and heat dissipation capacity between the battery and each channel. Through the comprehensive analysis of the battery temperature rise rate, channel thermal state change rate and output power and other parameters, it can be identified whether there is a risk of thermal accumulation in the channel during operation. The formula of thermal stress identification index Htt belongs to the compound derivation form in the field of thermodynamics and heat transfer based on the law of conservation of energy and heat transfer equation, which is a kind of thermal stress quantitative discrimination model combining thermal conduction rate, power input characteristics and heat dissipation capacity. According to the basic relationship between heat power and temperature change based on the law of conservation of energy and Fourier heat conduction law Q=m×C×ΔT, where Q is heat, m is mass, C is specific heat capacity, and ΔT is temperature change, the heat power expression can be obtained by taking the derivative with respect to time , where P in is the channel power input, P out is the heat dissipation loss power, that is, the power input of the system is equal to its heat rate, when the heat dissipation and input power reach steady state, the heat flux density q satisfies the Fourier law , which represents the linear relationship between temperature gradient and conduction heat flow, if considering the thermal resistance Rth of heat dissipation path, then , the non-steady state heat balance differential equation is obtained as , the PDU channel of the cross mobile robot belongs to a discrete system, and the master microcontroller performs operation based on sampling data, so , and the battery pack overall temperature rise rate and the channel local temperature rise rate ΔT i / Δt are introduced, the geometric and material factors of thermal resistance are summarized as the equivalent heat dissipation area Mj which can be measured, and 1 / Rth is replaced by Mj / Pi, after discretization and substitution into the original equation, the following equation is obtained , after arrangement, the proportional relationship of thermal accumulation strength 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 for thermal accumulation risk evaluation. 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 instruction is triggered to readjust the current distribution ratio. This step combines electrical load distribution calculation and thermal risk evaluation, so that the power distribution is not only based on electrical parameters, but also considers thermal safety factors synchronously, forming an adaptive regulation mechanism in the electrical-thermal dual domain. Compared with the traditional distribution method relying on fixed current ratio or time slice switching, this method can automatically correct the distribution result under the condition of fluctuating channel load and uneven heat dissipation, prevent overload failure caused by thermal imbalance, and thus improve the accuracy, thermal safety and overall energy utilization efficiency of power distribution. Embodiment 5

[0092] Please refer to Figure 1 Specifically, the S4 includes S41.

[0093] S41, the master microcontroller performs secondary power distribution on all channels with thermal accumulation risk when receiving the secondary power distribution instruction, and real-time corrects the target current MI of the preliminary power distribution according to the thermal stress identification index Htt, generates the channel target current MII in the secondary power distribution by calculation, and controls the MOSFET switch duty cycle of each channel through the PWM modulator to perform secondary power distribution, specifically: , wherein MII i represents the target current of the i-th channel after secondary power distribution, and ΣHtt i represents the sum of the thermal stress identification indexes of all channels, reflecting the current overall thermal load level.

[0094] The S4 further includes S42 and S43.

[0095] S42, the master microcontroller records the output power Pe i , the input voltage Ue i and the total output current Ic of each channel after secondary correction, and calculates the energy utilization rate Nsys of the robot according to the energy efficiency function after dimensionless processing, for analyzing the overall performance of the robot after thermal load and power dual domain regulation, specifically: , wherein ΔTb / Δt represents the temperature rise change rate of the robot battery pack, and kth represents the dimensionless constant of the allowable temperature rise of the robot under standard conditions.

[0096] S43, record the energy utilization rate Nsys of the thousand robots under the rated voltage and rated load conditions, and calculate the mean value according to the statistical method, and preset the mean value as the comprehensive energy utilization threshold Ny, and then perform energy efficiency state evaluation with the real-time obtained energy utilization rate Nsys, and the specific evaluation scheme is as follows:

[0097] When the energy utilization rate Nsys is less than the comprehensive energy utilization threshold Ny, it indicates that there is energy waste and heat accumulation risk in the operation of the robot, at this time, iterative distribution is performed through S2, if the distribution is qualified for 3 consecutive distribution periods, the corresponding channel power is immediately controlled to be turned off by the electronic switch, and a distribution efficiency decay report is generated to notify the maintenance personnel;

[0098] When the energy utilization rate Nsys is greater than or equal to the comprehensive energy utilization threshold Ny, it indicates that the current distribution scheme is stable, and enters the next control period.

[0099] In the formula, S41, after the main control microcontroller receives the secondary power distribution instruction, the primary power distribution target current MI is corrected in real time according to the thermal stress identification index Htt, the secondary target current MII is calculated and generated, and the duty cycle of each channel MOSFET switch is adjusted through the PWM modulator to realize fine distribution control of the thermal risk channel. The target current MII formula is derived according to the proportional correction model, and the proportional correction principle is derived from the error feedback equation in the control theory as Δx i =k×(E i / ΣE i ), wherein E i is the error of the i-th object, ΣE i is the total error sum, and the thermal stress identification index Htt is substituted into the proportional correction model to obtain the target current MII. S42, the main control microcontroller records the output power Pe i , the input voltage Ue i and the total output current Ic after secondary distribution, and calculates the energy utilization rate Nsys according to the energy efficiency function, so as to quantitatively evaluate the coordination state of the thermal and electrical domains. In the electrical energy system, the total energy utilization rate η can be written as η=P out / P in=∑Pe / ∑(Ue×Ic), which is the basic formula for measuring energy output and input efficiency in electrical engineering, reflecting the power transmission efficiency of the system in the electrical domain. In a multi-channel PDU with thermal influence, 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 is needed to reflect the influence of the thermal domain on the electrical domain efficiency. In thermodynamics, the influence of temperature rise rate on system efficiency can be represented by the correction coefficient model as ηT=1-(ΔT / Δt) / k, where k is the allowed temperature rise coefficient of the system, and ΔT / Δt is the temperature rise rate. Based on the above results, the thermal and electrical dual-domain comprehensive utilization rate expression η total =η electric ×η thermal =∑(Pe i / Ue i )×(1-(ΔTb / Δt) / kth), and finally the energy utilization rate Nsys is obtained. By comparing the energy utilization rate Nsys with the comprehensive energy efficiency utilization threshold Ny, the method can automatically judge the stability of the current distribution scheme. When the energy efficiency is insufficient, the redistribution iteration is executed and the efficiency decay report is generated. When the energy efficiency meets the standard, it enters the stable period. Through this process, the method completes the whole process control from thermal risk correction to energy efficiency closed-loop evaluation, realizes the timely intervention of channel overheating, establishes the self-adaptive adjustment mechanism of energy utilization rate, and improves the defects of traditional fixed distribution method that cannot dynamically respond to thermal load changes. Embodiment 6

[0100] Please refer to Figure 2 , the fork mobile robot power distribution unit dynamic distribution control system includes a data acquisition module, a load analysis module, a thermal accumulation analysis module and an energy efficiency analysis module.

[0101] The data acquisition module is used to collect the running data set of the robot in real time according to the sensor group arranged at the input end and each output channel of the robot PDU, and to obtain the dynamic characteristic data set after preprocessing;

[0102] The load analysis module is used to perform power response analysis on each channel of the robot according to the dynamic characteristic data set, generate a channel response state evaluation according to the analysis result, and trigger a preliminary power distribution instruction;

[0103] The thermal accumulation analysis module is used to perform preliminary power distribution on each channel according to the preliminary power distribution instruction received by the robot master microcontroller, calculate the target current MI of each channel in the preliminary power distribution, and perform thermal stress analysis on each channel after the distribution is completed. According to the analysis result, a thermal accumulation risk evaluation is generated and a secondary power distribution instruction is triggered;

[0104] The energy efficiency analysis module is configured to receive secondary power distribution instructions from the robot master microcontroller, distribute secondary power to all channels with thermal accumulation risks, calculate target current MII of each channel during secondary power distribution, record output power Pe of each channel after distribution is completed, and calculate energy utilization rate Nsys of the robot based on input voltage Ue, total output current Ic, and output power Pe, and generate an energy efficiency state evaluation. i i i

[0105] While the embodiments of the application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.​

Claims

1. A method of dynamic allocation control of a power distribution unit for a fork mobile robot, characterized by: The method comprises the following steps: S1, collecting the running data set of the robot in real time according to the sensor groups arranged at the input end of the robot PDU and each output channel, and obtaining the dynamic characteristic data set after preprocessing; S2, performing power response analysis on each channel of the robot according to the dynamic characteristic data set, generating a channel response state evaluation and triggering a preliminary power distribution instruction according to the analysis result; S3, when the robot main control microcontroller receives the preliminary power distribution instruction, performing preliminary power distribution on each channel, calculating the target current MI of each channel during preliminary power distribution, and performing thermal stress analysis on each channel after distribution is completed, and then generating a thermal accumulation risk evaluation and triggering a secondary power distribution instruction according to the analysis result; S4, when the robot main control microcontroller receives the secondary power distribution instruction, performing secondary power distribution on all channels with thermal accumulation risk, and calculating the target current MII of each channel during secondary power distribution; The main control microcontroller records the output power Pe of each channel after the secondary power distribution is completed, and performs dimensionless processing, and then calculates the energy utilization rate Nsys of the robot according to the energy efficiency function, which is used to analyze the overall performance of the robot after the thermal load and power dual-domain regulation. i , input voltage Ue i and total output current Ic, and performs dimensionless processing, and then calculates the energy utilization rate Nsys of the robot according to the energy efficiency function, which is used to analyze the overall performance of the robot after the thermal load and power dual-domain regulation. , wherein ΔTb / Δt represents the temperature rise change rate of the robot battery pack, and kth represents the dimensionless constant of the allowable temperature rise of the robot under standard conditions. Record 1000 sets of energy utilization rate Nsys of the robot under the condition of rated voltage and rated load, and calculate the mean value according to the statistical method, and preset the mean value as the comprehensive energy efficiency utilization threshold Ny, and then perform energy efficiency state evaluation with the real-time obtained energy utilization rate Nsys, and the specific evaluation scheme is as follows: When the energy utilization rate Nsys is less than the comprehensive energy efficiency utilization threshold Ny, it indicates that there is energy waste and thermal accumulation risk in the operation of the robot, at this time, iterative distribution is performed through S2, if the distribution is qualified for 3 consecutive distribution periods, the electronic switch is immediately controlled to cut off the power supply of the corresponding channel, and a distribution 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 distribution scheme is stable, and enters the next control period.

2. The forked mobile robot power distribution unit dynamic allocation control method of claim 1, wherein: The S1 comprises S11; S11, arranging sensor groups at the input end of the robot PDU and each output channel respectively to collect the running data set of the robot in real time; The sensor group comprises a voltage sensor, a current sensor, a power sensor and a temperature sensor; The voltage sensor is used to be installed between the positive electrode of the battery pack output and the PDU input busbar to collect the input voltage U in real time; The current sensor is used to be respectively connected in series in the positive electrode wire of each output channel and the input busbar loop 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 arranged at the output port of each channel and the load connection to directly measure the channel output power P; The temperature sensor is used to be fixed on the thermal center area of the battery shell surface and the center point of each channel heat sink respectively to collect the battery pack shell temperature Tb and the channel thermal state T respectively; The channel refers to the load power supply branch composed of an independent power switch unit and a corresponding output port inside the fork mobile robot PDU.

3. The forked mobile robot power distribution unit dynamic assignment control method of claim 2, wherein: The S1 further comprises S12; S12, the main control microcontroller of the robot establishes real-time communication channels with the battery management system BMS and the sensor group through the CAN bus, and transmits the running data set to the main control microcontroller in real time through the CAN bus for preprocessing to obtain the dynamic characteristic data set; The preprocessing comprises denoising, time sequence alignment, filtering processing and dimensionless processing; The denoising preserves the transient structure characteristics of the operation data set and removes the high-frequency noise in the operation data set through a wavelet denoising technology; The time sequence alignment aligns the operation data sets with the same time stamp in an online manner through a streaming processing framework according to the time stamp collected by the sensor; The filtering processing sequentially performs a moving average filtering on the synchronously collected operation data sets through the master microcontroller to smooth transient fluctuations, and then performs a limiting amplitude filtering to remove abnormal sharp peaks to obtain a continuous data sequence; and the dimensionless processing removes the dimension influence of the operation data set through a Max-Min maximum-minimization method; The dynamic characteristic data set includes an input voltage U, a channel current I, an output power P, a total input current Iz, a battery pack shell temperature Tb and a thermal state T.

4. The forked mobile robot power distribution unit dynamic allocation control method of claim 3, wherein: The S2 includes S21; S21, presetting a power response analysis module in the main control microcontroller, inputting a dynamic characteristic data set into the power response analysis module to perform power response analysis on each channel of the robot, and constructing an electrical load dynamic distribution index Edz, analyzing the response degree of different channels to input power change, reflecting the sensitivity of power change under current channel unit input energy, specifically: , wherein Edz i represents the electrical load dynamic distribution index of the i th channel, U i and I i represent the input voltage and channel current of the i th channel, ΔP i represents the change amount of the output power of the i th channel, ΔP i / Δt represents the output power change rate of the i th channel, and Δt represents the sampling time interval.

5. The forked mobile robot power distribution unit dynamic assignment control method of claim 4, wherein: The S2 further includes S22; S22, according to the statistical method, calculates the mean and standard deviation of the historical electrical load dynamic allocation index Edz, sets the difference between the mean and the standard deviation as a channel stable response threshold Ty, sets the sum of the mean and the standard deviation as a channel load response threshold Tf, and then performs a channel response state evaluation on the real-time acquired electrical load dynamic allocation index Edz, and the specific evaluation scheme is as follows: When the electrical load dynamic allocation index Edz is less than the channel stable response threshold Ty, it indicates that the current channel power load response is stable, the channel operation is stable and has not reached the upper limit, and a preliminary power distribution instruction is triggered at this time; When the channel stable response threshold Ty is less than or equal to the electrical load dynamic allocation index Edz and is less than or equal to the channel load response threshold Tf, it indicates that the current channel power load response is in a dynamic response reasonable interval, and the current power supply strategy is maintained; When the electrical load dynamic allocation index Edz is greater than the channel load response threshold Tf, it indicates that the current channel power load response is overloaded, and a preliminary power distribution instruction is triggered at this time.

6. The forked mobile robot power distribution unit dynamic assignment control method of claim 5, wherein: The S3 includes S31; S31, the main control microcontroller, upon receiving the preliminary power distribution instruction, preliminarily distributes power to each channel according to the electrical load dynamic distribution index Edz and the total input current Iz of each channel of the robot, and calculates the target current MI of each channel during preliminary power distribution, specifically: , wherein MI i represents the target current of the i-th channel after preliminary power distribution, ΣEdz i represents the sum of the electrical load dynamic distribution indexes of all channels.

7. The forked mobile robot power distribution unit dynamic assignment control method of claim 6, wherein: The S3 includes S32 and S33; S32, after the preliminary power distribution of each channel is completed, the master microcontroller calls a thermal management task function, inputs a dynamic characteristic data set into the thermal management task function to perform thermal stress analysis on each channel, constructs a thermal stress identification index Htt, which represents the thermal flow coupling strength of the battery and the channel and reflects the thermal accumulation state of the current channel under the heat dissipation capacity, and specifically: , in the formula, Htt i represents the thermal stress identification index Htt of the i th channel, represents the temperature rise rate of the battery pack shell, ΔT i represents the thermal state of the i th channel, ΔT i / Δt represents the thermal state change rate of the i th channel, P i 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, according to the statistical method, calculates the mean of the thermal stress identification index Htt when there is no thermal accumulation risk, and sets the mean as a thermal accumulation risk threshold Fy, and then performs a thermal accumulation risk evaluation on the real-time acquired thermal stress identification index Htt, and the specific evaluation scheme is as follows: When the thermal stress identification index Htt is less than the thermal accumulation risk threshold Fy, it indicates that there is no thermal accumulation risk in the current channel, and the preliminary power distribution is qualified, and the current power input is maintained at this time; 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 preliminary power distribution is unqualified, and a secondary power distribution instruction is triggered at this time.

8. The forked mobile robot power distribution unit dynamic assignment control method of claim 7, wherein: The S4 includes S41; S41, the main control microcontroller, upon receiving the secondary power distribution instruction, distributes secondary power to all channels with thermal accumulation risks, and modifies the target current MI of the preliminary power distribution in real time according to the thermal stress identification index Htt, generates the channel target current MII during secondary power distribution through calculation, and controls the MOSFET switch duty cycle of each channel through the PWM modulator to perform secondary power distribution, specifically: , wherein MII i represents the target current of the i-th channel after secondary power distribution, and ΣHtt i represents the sum of the thermal stress identification indexes of all channels, reflecting the current overall thermal load level.

9. A dynamic allocation control system for a forklift truck power distribution unit, comprising the dynamic allocation control method for a forklift truck power distribution unit according to any one of claims 1 to 8, characterized in that: The data acquisition module, the load analysis module, the thermal accumulation analysis module and the energy efficiency analysis module are included; The data acquisition module is used to collect the operation data set of the robot in real time according to the sensor group arranged at the input end of the robot PDU and each output channel, and to obtain the dynamic characteristic data set after preprocessing; The load analysis module is used to perform power response analysis on each channel of the robot according to the dynamic characteristic data set, to generate a channel response state evaluation according to the analysis result, and to trigger a preliminary power distribution instruction; The thermal accumulation analysis module is configured to receive a preliminary power distribution instruction from the robot master microcontroller, to perform preliminary power distribution for each channel, to calculate a target current MI of each channel during the preliminary power distribution, to perform thermal stress analysis for each channel after the distribution is completed, and to generate a thermal accumulation risk assessment and trigger a secondary power distribution instruction according to the analysis result. 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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