Environment self-adaptive energy-saving method adaptive to energy-saving robot

Abstract

The invention relates to an environment self-adaptive energy-saving method adaptive to an energy-saving robot. The method comprises the following steps: carrying out data acquisition through pressure sensing control, integrating acquired temperature signals of corresponding parts, and obtaining multi-dimensional environment-load data; noise reduction is carried out on the multi-dimensional environment-load data, and meanwhile, a load state judgment result is obtained by combining temperature signal correction based on a preset load threshold grading standard; the adaptive driving mode is matched to generate a driving wheel retracting and releasing and rotating speed instruction, the temperature sensing heat dissipation preparation instruction is integrated, and a cooperative control execution instruction set is formed according to priority ranking; electric push rod stretching parameters are analyzed and executed according to the instruction set, and a driving mode is obtained through cooperation of driving and auxiliary supporting mechanisms; distributing power in combination with a working condition and terrain adaptation principle to obtain an adaptive activity mechanism; real-time temperature is collected and fed back through the signal processing circuit, the DSP chip calculates the rotating speed correction amount, the rotating speed of the fan is dynamically adjusted, and self-adaptive energy-saving control over the robot is achieved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent robot and energy-saving control technology, and specifically relates to an environmental adaptive energy-saving method adapted to energy-saving robots. Background Technology

[0002] Currently, energy-saving robots still have the following areas for improvement: With the rapid development of artificial intelligence and automation technologies, robots have widely penetrated all aspects of production and daily life, becoming core equipment for improving work efficiency and reducing labor costs. However, existing energy-saving robots still face many technical bottlenecks in practical applications, hindering further improvements in their energy-saving benefits and scenario adaptability. Existing wheeled robots mostly employ a fixed number of drive wheels (such as four-wheel drive or six-wheel drive), and the number of drive wheels and power output mode cannot be dynamically adjusted according to load changes. Under no-load or light-load conditions, the excess drive wheels generate additional friction, leading to wasted energy; while under heavy loads or complex terrain, the fixed drive mode struggles to provide sufficient support and power, easily resulting in insufficient power and unstable movement. Furthermore, the power distribution of traditional robots lacks specificity and is not optimized based on the force characteristics of the drive wheels and terrain adaptation requirements, further exacerbating the contradiction between energy redundancy and operational efficiency.

[0003] Current robot cooling systems generally operate in a fixed mode, starting automatically upon power-on, without being adapted to the device's heat output or ambient temperature. In low-temperature environments (such as outdoor winter environments or cold storage operations) or under low load and low power consumption, the cooling system continues to operate at full capacity, resulting in unnecessary energy waste. Conversely, in high-temperature environments or under high load conditions, the fixed-speed cooling fan struggles to dissipate heat quickly, potentially causing overheating of core components such as the control board and drivers, reducing their operating efficiency, or even leading to malfunctions.

[0004] Most energy-saving robots can only collect environmental or load parameters from a single dimension, lacking the ability to fuse and analyze multi-dimensional data. For example, some robots only monitor load data but ignore the impact of ambient temperature on power output, or only detect temperature but fail to adjust heat dissipation strategies in conjunction with load changes. Furthermore, the collected data is susceptible to interference and lacks effective noise reduction mechanisms, leading to significant deviations in state judgments and a mismatch between energy-saving strategies and actual working conditions, making it difficult to achieve a dynamic balance between energy consumption and operational performance in complex and changing environments.

[0005] Currently, most robot functional modules, such as drive control, heat dissipation control, and power distribution, operate independently, lacking a unified command integration and priority ranking mechanism. When multiple modules need to work collaboratively, problems such as command conflicts and response delays easily occur, leading to untimely or inadequate execution of energy-saving strategies. In addition, the control algorithms of some robots are relatively simple, unable to quickly process multi-dimensional perception data and generate optimal control commands, further reducing the robot's adaptability and energy-saving efficiency.

[0006] In summary, existing energy-saving robots suffer from deficiencies in drive modes, heat dissipation control, environmental perception, and collaborative decision-making, resulting in low energy efficiency, short battery life, and poor scenario adaptability. These shortcomings fail to meet current technological trends towards low-carbon and environmentally friendly practices and diverse application demands. Therefore, developing an environmentally adaptive energy-saving robot with multi-dimensional perception, intelligent collaborative decision-making, and dynamic mode adjustment capabilities is of significant practical importance and has broad application prospects. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides an environmentally adaptive energy-saving method for energy-saving robots; The objective of this invention can be achieved through the following technical solutions: S1: Acquire multi-dimensional environmental-load data; S2: Based on the multi-dimensional environment-load acquisition data, perform digital signal filtering processing to obtain noise-reduced data; combine with a preset load threshold to obtain the load status judgment result; based on the load status judgment result, generate drive wheel instructions through drive mode matching; and obtain a collaborative control execution instruction set through the instruction integration mechanism of the control motherboard. S3: Based on the aforementioned collaborative control execution instruction set, obtain the extension and retraction signal of the electric push rod and execute the corresponding operation; obtain the drive mode through the coordinated cooperation of the drive support structure and the auxiliary support structure; and use the control motherboard to distribute the machine power to obtain the motion mechanism adapted to the working conditions. S4: Based on the motion mechanism of the aforementioned working conditions, the operating state of the cooling fan is adjusted through the temperature sensing and heat dissipation mechanism of the driver; a temperature feedback signal is acquired, and based on the temperature feedback signal, it is transmitted through the thermocouple signal processing circuit to dynamically correct the cooling fan speed command and obtain an energy-saving heat dissipation scheme that continuously adapts to temperature changes.

[0008] As a preferred technical solution of the present invention, the specific process of acquiring multi-dimensional environmental-load data includes: acquiring pressure data transmitted by the placement plate based on the gravity of the load-bearing object; obtaining the compression of the spring and the displacement data of the pressure-sensitive slider based on the pressure data; obtaining a start signal for the detection circuit by triggering the push switch at the bottom of the damper; obtaining the object load electrical signal by converting the start signal detection circuit; and acquiring multi-dimensional environmental-load data by integrating the standardized temperature detection signal through the control motherboard.

[0009] Specifically, the process of performing digital signal filtering includes: using the multi-dimensional environment-load acquisition data to obtain the target frequency band for digital signal filtering; and processing the multi-dimensional environment-load acquisition data using the Kalman filtering algorithm based on the target frequency band to obtain filtered data.

[0010] Specifically, the process of obtaining the load status judgment result includes: extracting the actual load value using the filtered data; constructing a grading standard based on a preset load threshold, comparing the actual load value with the grading standard to obtain a preliminary load status judgment result; and correcting the deviation based on the preliminary judgment result and the ambient temperature signal to obtain a standard load status judgment result.

[0011] Specifically, generating drive wheel commands includes: retrieving a preset drive mode database based on the load state standard judgment result; matching the corresponding drive mode using the drive mode database and generating basic commands for drive wheel retraction and speed adjustment; and optimizing parameters based on terrain adaptability requirements to obtain drive wheel commands.

[0012] Specifically, the process of obtaining the collaborative control execution instruction set includes: obtaining instruction information for drive wheel retraction and speed control based on the drive wheel instructions; using the instruction information, integrating the corresponding preparatory instructions for temperature sensing and heat dissipation, and establishing an instruction priority sorting rule; and using the instruction priority sorting rule to fuse and adapt the corresponding types of instructions to obtain the collaborative control execution instruction set.

[0013] Specifically, the process of acquiring the extension and retraction signal of the electric linear actuator and performing the corresponding operation includes: based on the collaborative control execution instruction set, parsing the extension and retraction direction and stroke parameters of the electric linear actuator and generating the corresponding electric linear actuator extension and retraction electrical signal; using the electric linear actuator extension and retraction electrical signal, controlling the linear actuator to perform the corresponding action and providing position feedback.

[0014] Specifically, the process of obtaining the drive mode includes: obtaining the working state of the drive support mechanism and the auxiliary support mechanism based on the extension and retraction state of the electric push rod; obtaining the appropriate drive mode type and calibrating the power output distribution ratio by collecting the number of activated drive wheels and combining the load state judgment results, thereby obtaining the steady-state drive mode.

[0015] Specifically, the process of allocating machine power output using the control motherboard includes: obtaining the total power output demand required by the machine under the corresponding working conditions through power demand quantification calculation based on the steady-state drive mode; constructing a power distribution constraint model by combining the force characteristics of the drive wheels and the terrain adaptation principle; calculating the power distribution quota of the drive wheels through an improved weighted allocation algorithm; and sending power control signals to the drive wheel drivers through a power signal generation algorithm based on the power distribution quota to obtain the motion mechanism adapted to the working conditions.

[0016] Specifically, the process of adjusting the operating state of the cooling fan includes: based on the motion mechanism of the adapted working conditions, obtaining the heat generation power of the control motherboard and driver, and retrieving the airflow adjustment reference parameters of the temperature-sensing heat dissipation; combined with the ambient temperature, generating the initial operating command of the cooling fan; based on the initial operating command, controlling the cooling fan to enter the corresponding operating state, and adjusting the operating state.

[0017] Specifically, the process of acquiring the temperature feedback signal includes: collecting real-time temperature data via thermocouples based on the operating status of the cooling fan; using the real-time temperature data, a thermocouple signal processing circuit (connecting the thermocouple and the DSP chip, responsible for converting the temperature physical quantity collected by the thermocouple into an electrical signal. During the day, the circuit converts the temperature signal into a corresponding electrical signal, which is then filtered and transmitted to the DSP chip, instructing the cooling fan to run at low speed; at night, the converted electrical signal triggers the DSP chip to shut down the cooling fan. Its high-precision conversion design ensures accurate temperature signal accuracy, providing a reliable basis for heat dissipation control) performs preliminary conversion; and using the converted electrical signal to process transient fluctuations, obtaining a stable temperature detection signal and generating a temperature feedback signal that is fed back to the control motherboard.

[0018] Specifically, the process of dynamically correcting the cooling fan speed command includes: obtaining the deviation between the actual temperature and the target temperature based on the temperature feedback signal; calculating the speed correction amount through the signal processing algorithm of the DSP chip, and generating a correction command in combination with the performance parameters of the cooling fan; dynamically adjusting the speed of the cooling fan based on the correction command to obtain an energy-saving cooling solution that continuously adapts to temperature changes.

[0019] The beneficial effects of this invention are as follows: This invention reduces friction loss under no-load / light-load conditions by dynamically adjusting the number of drive wheels (from six-wheel drive to four-wheel drive), avoids ineffective heat dissipation energy consumption by combining temperature-sensing stepped heat dissipation (low-temperature shutdown, high-temperature speed regulation), and further reduces overall energy consumption compared to traditional robots by combining precise power distribution; with the same battery capacity, the battery life is improved, effectively solving the core pain point of short battery life in traditional robots.

[0020] Multi-scenario adaptive and highly adaptable: By sensing load changes through the pressure-sensitive control module and sensing the environment and component temperature through the temperature detection module, combined with terrain-adaptive drive mode matching, the robot can flexibly adapt to various working conditions such as no load / light load / heavy load, flat road surface / complex terrain, high temperature / low temperature, etc. It can meet the needs of multiple scenarios such as industrial inspection, warehousing and transportation, and home service without customized modification, and has strong versatility.

[0021] Efficient collaborative decision-making and stable and reliable operation: The core control module establishes an instruction priority sorting mechanism to enable multiple modules such as drive, power, and heat dissipation to work together and avoid instruction conflicts and response delays; the Kalman filter algorithm ensures the accuracy of perception data, the improved weighted allocation algorithm achieves optimal power allocation, and the DSP chip quickly processes signals and generates instructions to ensure that the robot operates stably in complex environments and that its work efficiency is not affected.

[0022] Adopting a modular design concept, each functional module is independently packaged with standardized interfaces, facilitating future maintenance and component replacement. The core algorithm is built into the control motherboard, enabling autonomous adaptive adjustment without manual intervention, simplifying operation and reducing usage and maintenance costs. A temperature-sensing heat dissipation module monitors the temperature of core components in real time to prevent overheating damage; a power management module monitors battery level in real time, triggering energy-saving protection and return-to-charge commands when the battery is low; and the drive support module features multiple support mechanisms and a dynamic retraction design, improving the robot's stability and obstacle-crossing ability, effectively extending the equipment's lifespan. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating an environmental adaptive energy-saving method for an energy-saving robot according to the present invention. Figure 2 This is a flowchart illustrating the collaborative control and instruction verification of the machine in this invention. Figure 3 This is a schematic diagram of the structure of the energy-saving robot of the present invention, which can adjust its working mode based on the environment. Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0026] Please see Figure 1-3 An environmentally adaptive energy-saving method adapted to energy-saving robots; S1: Acquire multi-dimensional environmental-load data; S2: Based on the multi-dimensional environment-load acquisition data, perform digital signal filtering processing to obtain noise-reduced data; combine with a preset load threshold to obtain the load status judgment result; based on the load status judgment result, generate drive wheel instructions through drive mode matching; and obtain a collaborative control execution instruction set through the instruction integration mechanism of the control motherboard. S3: Based on the aforementioned collaborative control execution instruction set, obtain the extension and retraction signal of the electric push rod and execute the corresponding operation; obtain the drive mode through the coordinated cooperation of the drive support structure and the auxiliary support structure; and use the control motherboard to distribute the machine power to obtain the motion mechanism adapted to the working conditions. S4: Based on the motion mechanism of the aforementioned working conditions, the operating state of the cooling fan is adjusted through the temperature sensing and heat dissipation mechanism of the driver; a temperature feedback signal is acquired, and based on the temperature feedback signal, it is transmitted through the thermocouple signal processing circuit to dynamically correct the cooling fan speed command and obtain an energy-saving heat dissipation scheme that continuously adapts to temperature changes.

[0027] As a preferred technical solution of the present invention, the specific process of acquiring multi-dimensional environmental-load data includes: acquiring pressure data transmitted by the placement plate based on the gravity of the load-bearing object; obtaining the compression of the spring and the displacement data of the pressure-sensitive slider based on the pressure data; obtaining a start signal for the detection circuit by triggering the push switch at the bottom of the damper; obtaining the object load electrical signal by converting the start signal detection circuit; and acquiring multi-dimensional environmental-load data by integrating the standardized temperature detection signal through the control motherboard.

[0028] In this embodiment, taking an industrial inspection scenario as an example, the robot performs equipment inspection and light material transportation tasks. For example... Figure 3The robot shown has the following structure: 1. Main body; 2. Control motherboard module; 3. Pressure-sensitive control module; 4. Placement plate; 5. Drive support mechanism; 6. Base plate; 7. Auxiliary support mechanism; 8. Temperature sensing and heat dissipation module. The main body is the core framework integrating all functional modules. Internally, the base plate serves as the support structure. Drive support mechanisms are mounted around the perimeter, and auxiliary support mechanisms are installed at both ends. The top integrates the control motherboard and the pressure-sensitive control module (gravity is transmitted to the damper of the pressure-sensitive control module through the placement plate. When the package weight is light, the spring compression is small, the pressure-sensitive slider displacement does not trigger the bottom push switch, the detection circuit is not activated, and only a light-load signal is transmitted to the control motherboard, keeping the robot in four-wheel drive mode. When the package weight is heavy, the spring is significantly compressed, the pressure-sensitive slider presses down, triggering the push switch. The detection circuit converts the mechanical displacement signal into a heavy-load electrical signal, which is fed back to the control motherboard, triggering the auxiliary support mechanism to release the drive wheels, switching to six-wheel drive mode and optimizing power distribution). The robot features a built-in temperature-sensing heat dissipation module at the bottom, which includes a heat sink, a cooling fan, a thermocouple signal processing circuit, thermocouple temperature signals, a driver, a control switch, and a DSP chip. Thermocouple temperature signals are collected by thermocouple sensors, covering both ambient temperature and the temperatures of core components (control motherboard and driver). Near high-temperature equipment, the thermocouples simultaneously collect both ambient and control motherboard operating temperatures, generating two temperature signals. These signals are transmitted to the thermocouple signal processing circuit, converted, and then fed back to the DSP chip. The chip combines these two signals to determine heat dissipation requirements, preventing overheating of core components due to excessively high ambient temperatures. The DSP chip is the core of digital signal processing, simultaneously receiving heavy-load signals from the pressure-sensitive control module, high-temperature signals from the thermocouples, and signals from the terrain sensor indicating rough road surfaces. After noise reduction using a Kalman filter algorithm, the robot quickly matches the six-wheel drive mode command, generates a high-speed cooling fan command, and then optimizes the power distribution to each drive wheel using an improved weighted allocation algorithm, ensuring the robot can adapt to complex working conditions promptly. In cold storage, thermocouples collect low-temperature signals, which are transmitted to the DSP chip via signal processing circuitry. The chip determines that no heat dissipation is needed and controls the switch to turn off the cooling fan, saving energy. Upon entering a high-temperature workshop, thermocouples collect temperature signals in real time, and the DSP chip calculates the speed correction. Through driver commands, it controls the switch to start the cooling fan, initially setting the speed to medium-high. As the temperature of core components rises, the DSP chip dynamically corrects the commands, gradually increasing the fan speed to achieve tiered heat dissipation and prevent overheating. When the robot performs unloaded patrol tasks, the main body retracts the drive wheels of the auxiliary support mechanism via control motherboard commands, switching to four-wheel drive mode to reduce energy consumption. During heavy-load transport, the main body senses the load through the pressure-sensitive control module, triggering the auxiliary support mechanism to extend the drive wheels, switching to six-wheel drive mode to improve stability. The overall structure ensures that all modules respond collaboratively to environmental and load changes.When transporting inspection tools or small spare parts, the materials are placed on the placement plate of the pressure-sensitive control module. The placement plate is the component that directly supports the object. Its bottom is connected to the damper of the pressure-sensitive control module (the damper has a built-in spring (a high-elasticity return spring, built into the damper, encasing the pressure-sensitive slide rod). When irregular goods are placed, the spring buffers localized concentrated pressure through its elasticity, ensuring even force distribution on the placement plate and preventing false triggering of the pressure-sensitive slide rod due to excessive localized force. After the goods are removed, the spring quickly returns to its original position, causing the pressure-sensitive slide rod to rise, the press switch to disconnect, and the detection circuit to stop transmitting the load signal, ensuring the robot switches back to energy-saving mode promptly in an unloaded state). The pressure-sensitive slide rod (slidably inserted into the spring, with its top contacting the placement plate and its bottom corresponding to the press switch) is also connected to the pressure-sensitive slide rod (the pressure-sensitive slide rod slides into the spring, its top contacting the placement plate and its bottom corresponding to the press switch). As the weight of the goods gradually increases, the spring compression increases synchronously, and the pressure-sensitive slide rod presses down accordingly, with the displacement linearly related to the load. When the weight reaches the heavy load threshold, the bottom of the pressure-sensitive slide rod contacts and presses the switch, triggering the detection circuit to start and transmitting a heavy load signal to the control motherboard. Its high-precision sliding design ensures... To ensure the continuity and accuracy of load sensing, the damper (which incorporates a spring and a pressure-sensitive slider, and is enclosed in a sealed shell) absorbs vibration energy through its internal damping structure during sudden bumps. This prevents the spring from resonating due to severe vibration, ensuring stable displacement of the pressure-sensitive slider and preventing accidental switch presses. Simultaneously, the damper's sealed design prevents dust and moisture from entering, protecting the internal spring and pressure-sensitive slider and extending the lifespan of the pressure-sensitive control module. The weight of the object causes the placement plate to transmit pressure downwards, compressing the spring within the damper. The pressure-sensitive slider, slidably connected within the spring, moves synchronously with the degree of compression (the cooperation between the spring and the pressure-sensitive slider is the core structure for converting gravity into a mechanical displacement signal). When the material weight reaches a certain level, the displacement of the pressure-sensitive slider triggers a push-button switch at the bottom of the damper. At this point, the detection circuit, electrically connected to the push-button switch, is activated, converting the mechanical signals corresponding to the spring compression and slider displacement into a stable electrical signal indicating the object's load (the core function of the detection circuit is to convert mechanical signals to electrical signals for processing by the core control module). Simultaneously, the thermocouple sensor collects real-time data on the ambient temperature of the industrial workshop, as well as the operating temperatures of core components such as the control motherboard and drivers, generating standardized temperature detection signals (the advantage of thermocouple sensors is their high temperature detection accuracy, simultaneously considering both ambient and component temperatures). Finally, the control motherboard module—fixed to the top of the temperature-sensing heat dissipation module with bolts—processes two types of signals in real-time: the light-load electrical signal transmitted by the pressure-sensitive control module and the ambient temperature signal fed back by the thermocouple signal processing circuit.After noise reduction using a Kalman filter algorithm, the DSP chip determines the load condition to be light, matches the four-wheel drive mode command, and simultaneously generates a temperature-sensing cooling preparation command (fan runs at low speed). By prioritizing the commands, the drive wheel retraction / extension command is set as high priority, and the cooling command as the second highest priority, forming a coordinated control execution command set to ensure optimal energy consumption and prevent core components from overheating under light load conditions. The system receives load electrical signals and temperature detection signals, and after initial integration, forms multi-dimensional environmental-load data covering load and temperature.

[0029] Specifically, the process of performing digital signal filtering includes: using the multi-dimensional environment-load acquisition data to obtain the target frequency band for digital signal filtering; and processing the multi-dimensional environment-load acquisition data using the Kalman filtering algorithm based on the target frequency band to obtain filtered data.

[0030] In this embodiment, effective target frequency bands related to load and temperature are extracted from multi-dimensional environmental-load acquisition data, and interference signals from irrelevant frequency bands are removed. Subsequently, the data is processed using a Kalman filter algorithm—a recursive filtering algorithm based on the state equation of a linear system, which can optimally estimate noisy observation data by constructing the system state equation and the observation equation. This algorithm filters and corrects clutter in the load electrical signal and temperature detection signal, outputting accurate noise-reduced data after interference removal, ensuring the accuracy of subsequent decisions such as load status judgment and drive mode matching. The formula for the Kalman filter algorithm is as follows: System state equations: , X k : is the system state vector at time k (including the amplitude of the load electrical signal and the temperature detection signal), A is the state transition matrix, X is... k−1 : is the state vector at time k-1, B is the control input matrix, U k : To control the input vector, W k−1 : This represents process noise (which follows a Gaussian distribution).

[0031] Observation equation: , Z k : is the observation vector at time k (raw acquired data), H is the observation matrix, V k : Observation noise (following a Gaussian distribution).

[0032] Kalman gain: , K k : P is the Kalman gain at time k. k∣k−1：Let H be the prior error covariance matrix at time k. T R is the transpose of the observation matrix, and R is the observation noise covariance matrix.

[0033] Status Update: , k : The optimal state estimate at time k (after noise reduction). : is the prior state estimate at time k.

[0034] Error covariance update: , P k : is the posterior error covariance matrix at time k, and I is the identity matrix.

[0035] Specifically, the process of obtaining the load status judgment result includes: extracting the actual load value using the filtered data; constructing a grading standard based on a preset load threshold, comparing the actual load value with the grading standard to obtain a preliminary load status judgment result; and correcting the deviation based on the preliminary judgment result and the ambient temperature signal to obtain a standard load status judgment result.

[0036] In this embodiment, the core control module accurately extracts the actual load value of the material from the noise-reduced data. Based on the material transportation requirements of industrial inspection scenarios, a preset load threshold classification standard (such as light load, medium load, and heavy load) is established. The actual load value is compared with each classification standard to obtain a preliminary judgment result on the load status. Considering that ambient temperature affects the friction of mechanical components and the efficiency of power transmission (for example, friction of mechanical components increases in low-temperature environments, requiring more power for the same load), judging solely based on the load value would lead to deviations. Therefore, the preliminary judgment result is corrected by incorporating the ambient temperature signal. For example, the threshold for classifying light and medium loads is appropriately increased in low-temperature environments and appropriately decreased in high-temperature environments, resulting in a load status standard judgment result that highly matches the actual working conditions, providing an accurate basis for drive mode matching.

[0037] Specifically, generating drive wheel commands includes: retrieving a preset drive mode database based on the load state standard judgment result; matching the corresponding drive mode using the drive mode database and generating basic commands for drive wheel retraction and speed adjustment; and optimizing parameters based on terrain adaptability requirements to obtain drive wheel commands.

[0038] In this embodiment, the core control module has a preset drive mode database, which stores the drive wheel retraction and extension methods and speed adjustment parameters corresponding to different load states (the core of the drive mode database is to establish the correspondence between load and drive). If the load condition standard judgment result is light load (such as transporting only inspection tools), then the basic command to retract the auxiliary support mechanism drive wheel and reduce the drive wheel speed is matched; if it is medium or heavy load (such as transporting small spare parts boxes), then the basic command to extend the auxiliary support mechanism drive wheel and increase the drive wheel speed is matched. The auxiliary support mechanism and the drive support mechanism have the same structure, both consisting of a connecting plate, a limit block, an electric push rod, and a drive wheel. The extension and retraction of the drive wheel is controlled by the electric push rod. The purpose is to adapt the load by adjusting the number of drive wheels (reducing the number of drive wheels under light load can reduce friction and save energy; the limit block is fixed to the top of the connecting plate and has a limit groove inside, in which the electric push rod is inserted. When the control board commands the electric push rod to extend, the limit block limits the maximum stroke of the push rod to ensure that the drive wheel is perpendicular to the ground after landing and does not tilt; when retracting, the limit block prevents the push rod from retracting excessively, avoiding collision damage between the drive wheel and the connecting plate, and extending the service life of the electric push rod and the drive wheel. Increasing the number of drive wheels under heavy load can improve support force and power). At the same time, the command parameters are optimized based on the terrain of the industrial workshop (such as whether the ground is flat, whether there is a slight slope, and whether there are obstacles). For example, in areas with a slight slope, the drive wheel speed parameter is appropriately increased to ensure climbing ability, ultimately balancing load and terrain for precise drive wheel commands.

[0039] Specifically, the process of obtaining the collaborative control execution instruction set includes: obtaining instruction information for drive wheel retraction and speed control based on the drive wheel instructions; using the instruction information, integrating the corresponding preparatory instructions for temperature sensing and heat dissipation, and establishing an instruction priority sorting rule; and using the instruction priority sorting rule to fuse and adapt the corresponding types of instructions to obtain the collaborative control execution instruction set.

[0040] In this embodiment, the core control module first extracts key information regarding drive wheel retraction and speed control from the drive wheel commands, and then retrieves preparatory commands related to heat dissipation (such as cooling fan start threshold and initial speed). Considering that drive mode switching directly affects power output and driving stability during robot operation, while heat dissipation control is an auxiliary and supportive function, a command priority ranking rule is established: drive wheel retraction and speed adjustment commands are set as high priority, and heat dissipation preparatory commands are set as the second highest priority to avoid conflicts between commands from different modules. According to this rule, the two types of commands are integrated and adapted. For example, when the drive wheels switch from four-wheel drive to six-wheel drive (heavy-load condition), the start threshold and initial speed of the cooling fan are simultaneously increased to ensure that drive mode switching and heat dissipation adjustment are carried out in tandem, ultimately forming a conflict-free and highly efficient collaborative control execution command set.

[0041] Specifically, the process of acquiring the extension and retraction signal of the electric linear actuator and performing the corresponding operation includes: based on the collaborative control execution instruction set, parsing the extension and retraction direction and stroke parameters of the electric linear actuator and generating the corresponding electric linear actuator extension and retraction electrical signal; using the electric linear actuator extension and retraction electrical signal, controlling the linear actuator to perform the corresponding action and providing position feedback.

[0042] In this embodiment, the electric actuator is the core execution component of the drive support mechanism and auxiliary support mechanism. Its function is to convert electrical signals into linear reciprocating motion to realize the extension and retraction of the drive wheel. After receiving the coordinated control execution instruction set, the electric actuator execution unit analyzes the electric actuator control information to determine the extension and retraction direction of the electric actuator (extending means releasing the drive wheel, retracting means retracting the drive wheel) and stroke parameters (the length of extension or retraction to ensure that the drive wheel is in contact with or detached from the ground). Based on the analysis results, a corresponding electric actuator extension and retraction electrical signal is generated and transmitted to the electric actuator driver. The driver connects the control motherboard, drive wheel, and cooling fan, and receives power control signals and cooling commands from the control motherboard. When heavily loaded on rugged terrain, the driver converts the power control signal into the speed and torque signals of the drive wheel to increase the output power of the drive wheel; at the same time, it receives cooling commands to control the start, stop, and speed adjustment of the cooling fan. Its stable signal conversion function ensures that the drive wheel and cooling fan accurately respond to the control motherboard commands. The electric actuator moves in a preset direction and stroke, thereby driving the drive wheel to complete the extension and retraction action. During the operation of the electric linear actuator, its built-in position sensor monitors the actual position of the actuator in real time and feeds the position information back to the core control module to ensure the precise retraction and extension of the drive wheel.

[0043] Specifically, the process of obtaining the drive mode includes: obtaining the working state of the drive support mechanism and the auxiliary support mechanism based on the extension and retraction state of the electric push rod; obtaining the appropriate drive mode type and calibrating the power output distribution ratio by collecting the number of activated drive wheels and combining the load state judgment results, thereby obtaining the steady-state drive mode.

[0044] In this embodiment, the core control module determines the working status of the drive support mechanism and the auxiliary support mechanism (i.e., whether the drive wheels of each mechanism are in the active state) based on the extension and retraction feedback signal of the electric push rod. By counting the total number of currently active drive wheels and combining the load condition standard judgment result, the appropriate drive mode type is matched: under light load conditions, the drive wheels of the auxiliary support mechanism retract, and only the drive wheels of the drive support mechanism are activated, forming a four-wheel drive mode; under medium or heavy load conditions, the drive wheels of the auxiliary support mechanism extend, and both the drive support mechanism and the auxiliary support mechanism's drive wheels are activated, forming a six-wheel drive mode. Based on this, the power output distribution ratio of each drive wheel is calibrated according to the load and terrain conditions (for example, increasing the power proportion of the middle drive wheel under heavy load) to ensure that the power distribution is adapted to the load and terrain, forming a stable and reliable steady-state drive mode.

[0045] Specifically, the process of allocating machine power output using the control motherboard includes: obtaining the total power output demand required by the machine under the corresponding working conditions through power demand quantification calculation based on the steady-state drive mode; constructing a power distribution constraint model by combining the force characteristics of the drive wheels and the terrain adaptation principle; calculating the power distribution quota of the drive wheels through an improved weighted allocation algorithm; and sending power control signals to the drive wheel drivers through a power signal generation algorithm based on the power distribution quota to obtain the motion mechanism adapted to the working conditions.

[0046] In this embodiment, based on the steady-state drive mode, the core control module calculates the total power output required by the robot under the current working conditions by quantitatively considering factors such as load size, number of drive wheels, and terrain resistance (e.g., low resistance on flat ground and high resistance on sloping ground). Subsequently, it fully analyzes the force characteristics of each drive wheel (e.g., the load-bearing ratio of drive wheels at different positions and the difference in friction with the ground), and combines this with the terrain adaptation principle (e.g., even power distribution on flat ground and power output emphasizing the downhill drive wheels on sloping ground), constructing a power distribution constraint model. The core of this model is to clearly define the upper and lower limits of power distribution for each drive wheel to ensure reasonable power distribution. An improved weighted allocation algorithm is used to calculate the power allocation quota for each drive wheel according to the requirements of the constraint model (the advantage of the improved weighted allocation algorithm is that it can dynamically adjust the weights according to the actual working conditions, improving allocation accuracy).

[0047] The improved weighted allocation algorithm formula is as follows: , P i : The power allocation quota for the i-th drive wheel, w i : The weighting coefficient of the i-th drive wheel (determined by terrain adaptation principles and force characteristics), F i: represents the force feedback value of the i-th drive wheel, n is the number of drive wheels activated, P total : This refers to the total power output demand.

[0048] Through a power signal generation algorithm, the power distribution quota of each drive wheel is converted into a corresponding power control signal and sent to the driver of each drive wheel to achieve precise power distribution and form a stable motion mechanism that adapts to the current working conditions (such as lightly loaded flat ground and heavily loaded sloping ground).

[0049] Specifically, the process of adjusting the operating state of the cooling fan includes: based on the motion mechanism of the adapted working conditions, obtaining the heat generation power of the control motherboard and driver, and retrieving the airflow adjustment reference parameters of the temperature-sensing heat dissipation; combined with the ambient temperature, generating the initial operating command of the cooling fan; based on the initial operating command, controlling the cooling fan to enter the corresponding operating state, and adjusting the operating state.

[0050] In this embodiment, the temperature-sensing heat dissipation mechanism is crucial for ensuring the normal operation of core components. It consists of a heat sink, a cooling fan, a thermocouple signal processing circuit, a driver, and a control switch. The heat sink is attached to the bottom of the control motherboard to dissipate heat. The cooling fan's start / stop and speed are controlled by the control switch. The thermocouple signal processing circuit is responsible for temperature signal conversion and transmission. Based on the motion mechanism of the adapted operating conditions, the core control module analyzes the current computational load of the control motherboard (e.g., high load results in high heat generation) and the driver's power output intensity (higher power output leads to more heat generation), thereby obtaining the real-time heat generation power of both. It retrieves preset temperature-sensing heat dissipation airflow adjustment benchmark parameters (these parameters are set based on the heat dissipation requirements corresponding to different heat generation powers; the higher the heat generation power, the higher the benchmark airflow), adjusts the benchmark parameters (e.g., appropriately increasing the airflow benchmark in high-temperature environments and decreasing it in low-temperature environments), and generates initial operating commands for the cooling fan (including fan start / stop status and initial speed). The initial operating commands are transmitted to the control switch via the driver—the control switch connects the driver and the cooling fan and is controlled by the DSP chip. In low-temperature standby mode, the DSP chip instruction driver turns off the control switch, and the cooling fan stops running. When the temperature of the core components rises to a threshold, the DSP chip instruction driver turns on the control switch, and the fan starts. The rapid on / off design of the control switch ensures that the cooling fan responds promptly to temperature changes, avoiding unnecessary energy consumption. Controlling the cooling fan to enter the appropriate operating state achieves initial heat dissipation regulation.

[0051] Specifically, the process of acquiring the temperature feedback signal includes: collecting real-time temperature data through thermocouples based on the operating status of the cooling fan; performing preliminary conversion on the real-time temperature data through a thermocouple signal processing circuit; and using the converted electrical signal to process transient fluctuation interference to obtain a stable temperature detection signal and generate a temperature feedback signal that is fed back to the control motherboard.

[0052] In this embodiment, during the operation of the cooling fan, the thermocouple sensor continuously collects real-time temperature data from the control motherboard and drivers—the core advantage of the thermocouple sensor is its fast response speed, enabling it to capture temperature changes in real time. The collected temperature data is transmitted to the thermocouple signal processing circuit, which converts the physical quantity of temperature into a corresponding electrical signal (realizing the digitization of the temperature signal). For any transient fluctuations or interference that may exist in the electrical signal (such as signal fluctuations caused by electromagnetic interference in the workshop), a filtering circuit is used to process and eliminate interference components, obtaining a stable temperature detection signal. Finally, the stable temperature detection signal is encapsulated into a temperature feedback signal and transmitted to the core control module.

[0053] Specifically, the process of dynamically correcting the cooling fan speed command includes: obtaining the deviation between the actual temperature and the target temperature based on the temperature feedback signal; calculating the speed correction amount through the signal processing algorithm of the DSP chip, and generating a correction command in combination with the performance parameters of the cooling fan; dynamically adjusting the speed of the cooling fan based on the correction command to obtain an energy-saving cooling solution that continuously adapts to temperature changes.

[0054] In this embodiment, the DSP chip built into the control motherboard is the core component of digital signal processing, capable of rapidly executing signal processing algorithms. After receiving the temperature feedback signal, the core control module extracts the actual temperature values ​​of the control motherboard and driver, compares them with the preset target temperature value (the normal operating temperature range of the core component), and calculates the deviation. The DSP chip uses signal processing algorithms (such as proportional-integral-derivative control algorithms) to calculate the deviation value, and, combined with the trend of heat dissipation power changes, calculates the speed correction amount for the cooling fan (e.g., calculating the required speed increase when the actual temperature is higher than the target temperature; calculating the required speed decrease when the actual temperature is lower than the target temperature). Referring to the cooling fan's performance parameters (such as maximum speed and speed adjustment accuracy), the speed correction amount is converted into corresponding correction commands. These correction commands are transmitted to the cooling fan via the driver and control switch, dynamically adjusting the fan speed to stabilize the core component temperature within the target range. For example, under heavy load conditions, if the core component temperature continues to rise, the fan speed is gradually increased using correction commands; when the temperature drops to the target range, the speed is decreased using correction commands, forming an energy-saving cooling solution that continuously adapts to temperature changes.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An environmentally adaptive energy-saving method for an energy-saving robot, characterized in that, include: S1: Acquire multi-dimensional environmental-load data; S2: Based on the multi-dimensional environment-load acquisition data, perform digital signal filtering processing to obtain noise-reduced data; Combined with a preset load threshold, a load status judgment result is obtained; based on the load status judgment result, a drive wheel instruction is generated through drive mode matching; and a collaborative control execution instruction set is obtained through the instruction integration mechanism of the control motherboard. S3: Based on the aforementioned collaborative control execution instruction set, obtain the extension and retraction signal of the electric push rod and execute the corresponding operation; obtain the drive mode through the coordinated cooperation of the drive support structure and the auxiliary support structure; and use the control motherboard to distribute the machine power to obtain the motion mechanism adapted to the working conditions. S4: Based on the motion mechanism of the aforementioned working conditions, the operating state of the cooling fan is adjusted through the driver's temperature sensing and heat dissipation mechanism; A temperature feedback signal is acquired, and based on the temperature feedback signal, it is transmitted through a thermocouple signal processing circuit to dynamically correct the cooling fan speed command and obtain an energy-saving heat dissipation scheme that continuously adapts to temperature changes.

2. The method according to claim 1, characterized in that, The specific process of acquiring multi-dimensional environmental-load data includes: acquiring pressure data transmitted by the placement plate based on the gravity of the load-bearing object; obtaining the compression of the spring and the displacement data of the pressure-sensitive slider based on the pressure data; obtaining the start signal of the detection circuit by triggering the push switch at the bottom of the damper; using the start signal detection circuit to convert and obtain the load electrical signal of the object; and combining the standardized temperature detection signal with the control motherboard to integrate and acquire multi-dimensional environmental-load data.

3. The method according to claim 1, characterized in that, The specific process of performing digital signal filtering includes: using the multi-dimensional environment-load acquisition data to obtain the target frequency band for digital signal filtering; and processing the multi-dimensional environment-load acquisition data using the Kalman filtering algorithm based on the target frequency band to obtain filtered data.

4. The method according to claim 1, characterized in that, The specific process for obtaining the load status judgment result includes: extracting the actual load value using the filtered data; constructing a grading standard based on a preset load threshold, comparing the actual load value with the grading standard to obtain a preliminary load status judgment result; and based on the preliminary judgment result, correcting the deviation by combining the ambient temperature signal to obtain a standard load status judgment result.

5. The method according to claim 1, characterized in that, The process of generating drive wheel commands includes: retrieving a preset drive mode database based on the load condition standard judgment result; matching the corresponding drive mode using the drive mode database and generating basic commands for drive wheel retraction and speed adjustment; and optimizing parameters based on terrain adaptability requirements to obtain drive wheel commands.

6. The method according to claim 1, characterized in that, The specific process of obtaining the collaborative control execution instruction set includes: obtaining instruction information for drive wheel retraction and speed control based on the drive wheel instructions; using the instruction information, integrating the corresponding preparatory instructions for temperature sensing and heat dissipation, and establishing an instruction priority sorting rule; and using the instruction priority sorting rule to fuse and adapt the corresponding types of instructions to obtain the collaborative control execution instruction set.

7. The method according to claim 1, characterized in that, The specific process of acquiring the extension and retraction signal of the electric linear actuator and executing the corresponding operation includes: based on the cooperative control execution instruction set, parsing the extension and retraction direction and stroke parameters of the electric linear actuator and generating the corresponding electric linear actuator extension and retraction electrical signal; using the electric linear actuator extension and retraction electrical signal, controlling the linear actuator to perform the corresponding action and providing position feedback.

8. The method according to claim 1, characterized in that, The specific process of obtaining the drive mode includes: obtaining the working state of the drive support mechanism and the auxiliary support mechanism based on the extension and retraction state of the electric push rod; obtaining the appropriate drive mode type and calibrating the power output distribution ratio by collecting the number of activated drive wheels and combining the load state judgment results, thereby obtaining the steady-state drive mode.

9. The method according to claim 1, characterized in that, The specific process of allocating machine power output using the control motherboard includes: obtaining the total power output demand required by the machine under the corresponding working conditions through power demand quantification calculation based on the steady-state drive mode; constructing a power distribution constraint model by combining the force characteristics of the drive wheels and the terrain adaptation principle; calculating the power distribution quota of the drive wheels through an improved weighted allocation algorithm; and sending power control signals to the drive wheel drivers through a power signal generation algorithm based on the power distribution quota to obtain the motion mechanism adapted to the working conditions.

10. The method according to claim 1, characterized in that, The specific process of adjusting the operating state of the cooling fan includes: based on the motion mechanism of the adaptive working conditions, obtaining the heat generation power of the control motherboard and driver, and retrieving the airflow adjustment reference parameters of the temperature-sensing heat dissipation; combined with the ambient temperature, generating the initial operating command of the cooling fan; based on the initial operating command, controlling the cooling fan to enter the corresponding operating state, and adjusting the operating state.

11. The method according to claim 1, characterized in that, The specific process of obtaining the temperature feedback signal includes: collecting real-time temperature data through thermocouples based on the operating status of the cooling fan; performing preliminary conversion based on the real-time temperature data through a thermocouple signal processing circuit; processing transient fluctuation interference using the converted electrical signal to obtain a stable temperature detection signal and generating a temperature feedback signal fed back to the control motherboard.

12. The method according to claim 1, characterized in that, The specific process of dynamically correcting the cooling fan speed command includes: obtaining the deviation between the actual temperature and the target temperature based on the temperature feedback signal; calculating the speed correction amount through the signal processing algorithm of the DSP chip, and generating a correction command in combination with the performance parameters of the cooling fan; dynamically adjusting the speed of the cooling fan based on the correction command to obtain an energy-saving heat dissipation solution that continuously adapts to temperature changes.

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