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CN-121979072-A - Environment self-adaptive energy-saving method for adaptive energy-saving robot

CN121979072ACN 121979072 ACN121979072 ACN 121979072ACN-121979072-A

Abstract

The invention relates to an environment self-adaptive energy-saving method for an adaptive energy-saving robot. The method comprises the steps of collecting data through pressure sensing control, integrating temperature signals of corresponding collected components to obtain multi-dimensional environment-load data, conducting noise reduction on the multi-dimensional environment-load data, meanwhile, correcting the multi-dimensional environment-load data based on preset load threshold grading standards to obtain a load state judgment result through combination of temperature signal correction, generating driving wheel retraction and rotation speed instructions through matching of an adaptive driving mode, integrating temperature sensing and heat dissipation preparation instructions, forming a cooperative control execution instruction set according to priority ordering, analyzing electric push rod retraction parameters according to the instruction set and executing, obtaining the driving mode through cooperation of driving and auxiliary supporting mechanisms, distributing power through combining working conditions and a terrain adaptation principle to obtain an adaptive activity mechanism, collecting real-time temperature, feeding back through a signal processing circuit, calculating rotation speed correction quantity through a DSP chip, dynamically adjusting fan rotation speed, and achieving self-adaptive energy-saving control of the robot.

Inventors

  • WANG ENCHENG
  • ZHANG YUMIN

Assignees

  • 上海云见智能科技有限公司

Dates

Publication Date
20260505
Application Date
20260206

Claims (12)

  1. 1. An environment self-adaptive energy-saving method for an adaptive energy-saving robot is characterized by comprising the following steps: S1, acquiring multi-dimensional environment-load acquisition data; S2, carrying out digital signal filtering processing based on the multi-dimensional environment-load acquisition data to obtain noise reduction data, combining a preset load threshold value to obtain a load state judgment result, generating a driving wheel instruction based on the load state judgment result through driving mode matching; S3, based on the cooperative control execution instruction set, acquiring a telescopic signal of the electric push rod and executing corresponding operation, acquiring a driving mode through cooperative cooperation of the driving support structure and the auxiliary support structure, and acquiring a motion mechanism adapting to a working condition by distributing machine power through a control main board; S4, adjusting the running state of the cooling fan through a driver temperature sensing cooling mechanism based on the movement mechanism of the working condition, acquiring a temperature feedback signal, transmitting through a thermocouple signal processing circuit based on the temperature feedback signal, dynamically correcting a cooling fan rotating speed instruction, and acquiring an energy-saving cooling scheme continuously adapting to temperature change.
  2. 2. The method of claim 1, wherein the specific process of multi-dimensional environment-load data acquisition comprises the steps of acquiring pressure data transmitted by a placing plate based on the action of gravity of a bearing object, acquiring compression amount of a spring and displacement data of a pressure-sensitive sliding rod based on the pressure data, acquiring a starting signal of a detection circuit by triggering a pressing switch at the bottom of a damper, acquiring object load electric signals by utilizing conversion of the starting signal detection circuit, and acquiring multi-dimensional environment-load data by controlling main board integration in combination with a standardized temperature detection signal.
  3. 3. The method according to claim 1, wherein the specific process of performing digital signal filtering includes obtaining a target frequency band of digital signal filtering by using the multi-dimensional environment-load acquisition data, and processing the multi-dimensional environment-load acquisition data by a kalman filtering algorithm based on the target frequency band to obtain filtering data.
  4. 4. The method according to claim 1, wherein the specific process of obtaining the load state judgment result includes extracting an actual load value by using the filtering data, constructing a grading standard based on a preset load threshold, comparing the actual load value with the grading standard to obtain a load state preliminary judgment result, and correcting a deviation by combining an ambient temperature signal based on the preliminary judgment result to obtain a load state standard judgment result.
  5. 5. The method of claim 1, wherein generating the driving wheel command comprises retrieving a preset driving pattern database based on the load state standard judgment result, matching corresponding driving patterns by using the driving pattern database, generating a basic command for driving wheel retraction and rotation speed adjustment, and acquiring the driving wheel command by combining with a terrain adaptability requirement optimization parameter.
  6. 6. The method of claim 1, wherein the specific process of obtaining the cooperative control execution instruction set includes obtaining instruction information of driving wheel retraction and rotation speed control based on the driving wheel instruction, integrating preparation instructions corresponding to heat dissipation by using the instruction information, establishing an instruction priority ordering rule, and obtaining the cooperative control execution instruction set by fusing and matching corresponding types of instructions according to the instruction priority ordering rule.
  7. 7. The method according to claim 1, wherein the specific process of obtaining the telescopic signal of the electric push rod and executing the corresponding operation includes analyzing the telescopic direction and the stroke parameter of the electric push rod and generating the corresponding electric push rod telescopic electric signal based on the cooperative control execution instruction set, and controlling the push rod to execute the corresponding action and perform the position feedback by utilizing the electric push rod telescopic electric signal.
  8. 8. The method according to claim 1, wherein the specific process of obtaining the driving mode comprises obtaining working states of the driving support mechanism and the auxiliary support mechanism based on the telescopic state of the electric push rod, obtaining an adaptive driving mode type by collecting the number of enabled driving wheels and combining a load state judgment result, and calibrating a power output distribution proportion to obtain a steady driving mode.
  9. 9. The method of claim 1, wherein the specific process of distributing the power output of the machine by using the control main board comprises the steps of obtaining the output requirement of the machine for the total power required by the corresponding working condition through quantitative calculation of the power requirement based on the steady-state driving mode, constructing a power distribution constraint model by combining the stress characteristic of the driving wheel and the terrain adaptation principle, calculating the power distribution limit of the driving wheel through an improved weighted distribution algorithm, and sending a power control signal to a driver of the driving wheel through a power signal generation algorithm based on the power distribution limit to obtain the movement mechanism of the adaptation working condition.
  10. 10. The method of claim 1, wherein the specific process of adjusting the operation state of the cooling fan comprises the steps of acquiring heating power of a control main board and a driver based on a motion mechanism of an adaptive working condition, adjusting a wind quantity adjusting reference parameter of temperature sensing and heat dissipation, generating an initial operation instruction of the cooling fan by combining the environment temperature, and controlling the cooling fan to enter a corresponding operation state based on the initial operation instruction to adjust the operation state.
  11. 11. The method according to claim 1, wherein the specific process of obtaining the temperature feedback signal includes collecting real-time temperature data through a thermocouple based on an operation state of a cooling fan, performing preliminary conversion through a thermocouple signal processing circuit based on the real-time temperature data, processing instantaneous fluctuation interference by using the converted electric signal to obtain a stable temperature detection signal, and generating the temperature feedback signal fed back to a control main board.
  12. 12. The method according to claim 1, wherein the specific process of dynamically correcting the rotation speed command of the cooling fan comprises the steps of obtaining a deviation value of an actual temperature and a target temperature based on the temperature feedback signal, calculating a rotation speed correction amount through a signal processing algorithm of a DSP chip, combining performance parameters of the cooling fan to generate a correction command, and dynamically adjusting the rotation speed of the cooling fan based on the correction command to obtain an energy-saving cooling scheme continuously adapting to temperature change.

Description

Environment self-adaptive energy-saving method for adaptive energy-saving robot Technical Field The invention belongs to the technical field of intelligent robots and energy-saving control, and particularly relates to an environment self-adaptive energy-saving method for an adaptive energy-saving robot. Background At present, the energy-saving robot still has the following to be improved: With the rapid development of artificial intelligence and automation technology, robots have been widely penetrated into various fields of production and life, and become core equipment for improving working efficiency and reducing labor cost. However, the existing energy-saving robot still has a plurality of technical bottlenecks in practical application, which restrict the further improvement of energy-saving benefit and scene suitability: the existing wheel type robot mostly adopts a design of fixed number of driving wheels (such as four-wheel common driving and six-wheel common driving), and the number of the driving wheels and the power output mode cannot be dynamically adjusted according to load change. Under the working condition of no load or light load, the redundant driving wheels can generate additional friction force to cause energy consumption waste, and under the heavy load or complex terrain, the fixed driving mode is difficult to provide enough supporting force and power, and the problems of insufficient power, unstable running and the like are easy to occur. Meanwhile, the traditional robot is lack of pertinence in power distribution, and the stress characteristics of the driving wheels and the terrain adaptation requirements are not combined for optimization, so that the contradiction between energy consumption redundancy and operation efficiency is further increased. The existing heat dissipation device of the robot generally adopts a fixed operation mode that the robot is started immediately after being started, and is not in linkage adaptation with the heating state and the ambient temperature of equipment. When the cooling device is operated in a low-temperature environment (such as outdoor in winter and in a refrigeration house operation scene) or in low-load and low-power consumption mode, the cooling device still continuously runs in full load to cause unnecessary energy waste, and when the cooling device is operated in a high-temperature environment or in a high-load working condition, the cooling fan with fixed rotation speed is difficult to rapidly dissipate heat, so that the operation efficiency of the control main board, the driver and other core components can be reduced due to overheating, and even faults are caused. Most energy-saving robots can only collect environment or load parameters with a single dimension, and lack fusion analysis capability of multi-dimensional data. For example, some robots only monitor load data but ignore the effect of ambient temperature on power output, or only detect temperature without adjusting the heat dissipation strategy in conjunction with load changes. Meanwhile, the collected data is easy to interfere, and an effective noise reduction processing mechanism is lacked, so that state judgment deviation is large, an energy saving strategy is not matched with an actual working condition, and dynamic balance of energy consumption and operation performance is difficult to realize in a complex and changeable environment. Most of the functional modules of the existing robots, such as driving control, heat dissipation control, power distribution and the like, operate independently, and lack a unified instruction integration and priority ordering mechanism. When the multiple modules need to work cooperatively, the problems of instruction conflict, response delay and the like are easy to occur, so that the energy-saving strategy is not executed timely and in place. In addition, the control algorithm of part of robots is simpler, the multidimensional sensing data cannot be processed rapidly, and an optimal control instruction is generated, so that the self-adaptive capacity and energy-saving benefit of the robots are further reduced. In summary, the existing energy-saving robot has the defects of low energy utilization rate, short endurance time and poor scene suitability in the aspects of driving mode, heat dissipation control, environment perception, collaborative decision-making and the like, and cannot meet the current low-carbon environment-friendly technology development trend and diversified application requirements. Therefore, the environment self-adaptive energy-saving robot with multidimensional sensing, intelligent collaborative decision-making and dynamic mode adjusting capabilities is developed, and the environment self-adaptive energy-saving robot has important practical significance and wide application prospect. Disclosure of Invention In order to solve the problems in the prior art, the invention provides an environment self-adaptive energy-saving m