Embedded-based robot power intelligent adjustment system and method

Abstract

This invention discloses an embedded-based intelligent robot power regulation system and method, belonging to the field of intelligent power regulation technology. It includes: a battery data acquisition and power monitoring module, a task requirement analysis and power calculation module, a power output strategy formulation module, a real-time power output monitoring and anomaly judgment module, a power strategy update module, a power consumption prediction model construction module, and a task allocation module. The system collects robot battery data, calculates the power consumption of robot tasks, formulates power output strategies for tasks, monitors the real-time power output of the robot during tasks, determines whether the power components are working properly, updates the power output strategy upon completion of any robot task, constructs a power consumption prediction model, calculates the task completion rate, and reallocates robot tasks. This invention, through the power consumption prediction model, accurately predicts the robot's power consumption in different task scenarios, optimizing the logistics scheduling process.

Description

Technical Field

[0001] This invention relates to the field of intelligent power regulation technology, specifically to an embedded robot intelligent power regulation system and method. Background Technology

[0002] With the deep penetration of robots into various fields, energy management has become one of the core factors restricting their development. Traditional robot power monitoring methods generally suffer from low accuracy and poor real-time performance, only able to make rough estimates and unable to accurately calculate real-time power. This often causes robots to stop midway through tasks due to incorrect power estimation, seriously affecting work efficiency. Taking robots in the warehousing and logistics field as an example, they need to continuously operate in warehouses to complete goods handling tasks. If power monitoring is inaccurate, it may cause them to stop suddenly during transportation, not only causing goods backlog but also disrupting the entire logistics scheduling plan and increasing operating costs. In addition, unreasonable power output will cause energy waste and shorten the robot's endurance. Today, robot application scenarios are becoming increasingly complex and diverse. Once the power components malfunction, it will not only cause task execution interruption but may also lead to safety accidents. Traditional monitoring and adjustment methods are slow to react and cannot capture and correct power deviations in real time, seriously threatening the stability and reliability of equipment. If the robot's robotic arm has a power deviation and is not adjusted in time, it will reduce the assembly precision of parts, leading to product quality problems, and even causing collisions between equipment, resulting in equipment damage and production stoppage. Summary of the Invention

[0003] The purpose of this invention is to provide an embedded intelligent robot power adjustment system and method to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: an embedded-based intelligent robot power adjustment method, comprising the following steps:

[0005] S1. Collect robot battery data to complete real-time power monitoring;

[0006] S2. Analyze the task requirements and calculate the power consumption of the robot task;

[0007] S3. After finding the estimated power consumption required for the task, formulate a power output strategy for the task.

[0008] S4. Monitor the robot's real-time power output during the task to determine whether the power components are working properly;

[0009] S5. When the robot completes any robot task, update the robot's power output strategy;

[0010] S6. Combine historical environmental data to construct a power consumption prediction model;

[0011] S7. Calculate the task completion rate. When the completion rate is lower than the threshold, the robot task is reassigned.

[0012] Furthermore, in step S1, according to the system-specified acquisition time interval, the battery voltage data V, current data I, and temperature data T are acquired through the power monitoring module in the embedded system. The battery current at time point γ is acquired through a current sensor, and the open-circuit voltage is acquired through a voltage sensor. Based on the battery's rated capacity, initial capacity, and set standard temperature, the real-time battery power S is calculated.

[0013] ;

[0014] Where S0 is the initial battery charge, obtained through calibration; C is the battery rated capacity (Ah), provided by the manufacturer; I(γ) is the battery current at time γ, acquired by a current sensor; k is a coefficient obtained by fitting charge-discharge experimental curves at different temperatures; V is the real-time open-circuit voltage, measured by a high-precision voltage sensor; V ref This is the reference open-circuit voltage value at a standard temperature (e.g., 25℃). The collected and calculated data are accumulated to form a basic data pool, providing data support for subsequent predictive models and enabling real-time power monitoring. Through the collaborative work of multiple sensors, including current and voltage sensors, accurate capture of various key data is achieved, ensuring the accuracy of the data required for power calculation. When calculating the real-time battery power, multiple factors such as the battery's rated capacity, initial capacity, and temperature are comprehensively considered, making the calculated power results more realistic. The accumulated data forms a basic data pool, laying a solid foundation for subsequent predictive models, helping to detect battery anomalies early and ensuring the stable operation of the robot.

[0015] Furthermore, in step S2, after completing real-time power monitoring, the task requirements are analyzed. For any robot task, the specific parameters of the robot task include: the total mass of the robot and its load, and the robot's frontal area. Based on the coefficient of friction between the robot and the ground, the robot's movement speed, and air density, the method for obtaining the robot's frontal area is, for example, to use 3D modeling software to accurately model the robot based on its actual external dimensions. The projected area in the direction of movement, i.e., the frontal area, can be directly measured in the model.

[0016] The power consumption P for any robot task is calculated as follows:

[0017] ;

[0018] Where μ is the coefficient of friction between the robot and the ground, measured through experiments on different ground materials; m is the total mass of the robot and its load, determined by design parameters and actual load; g is taken as 9.8; v is the robot's speed, measured by a speed sensor; ρ is the air density, calculated based on local meteorological data or sensor measurements; k is the air resistance coefficient, obtained through wind tunnel experiments or by referring to historical data of similar robots; and A is the robot's frontal area in the direction of movement, calculated based on its dimensions. When any robot task is assigned, the estimated power consumption required for the task is retrieved from the database based on the specific parameters of the task. Before task execution, by comprehensively considering multiple dimensions such as the total mass of the robot and its load, the coefficient of friction with the ground, the speed, air density, and frontal area, the power consumption P can be accurately calculated. This provides a scientific basis for rationally planning energy supply and estimating battery life, avoiding task interruptions due to insufficient power. Simultaneously, when a task is assigned, the estimated power consumption can be quickly retrieved from the database, facilitating advance resource allocation, optimizing task processes, improving robot efficiency, and ensuring that the robot can complete its work stably and efficiently under different working conditions and task requirements.

[0019] Furthermore, in step S3, after querying the estimated power consumption required for the task, a power output strategy is formulated. For the real-time power level S, a high power threshold E1 and a low power threshold E2 are set. When S > E1, the task is allocated power normally; when E2 ≤ S ≤ E1, the power output of critical and non-critical tasks is reduced proportionally. The power reduction ratio for critical tasks is set as α, and the power reduction ratio for non-critical tasks is set as β. Then, the actual power P of the critical task is... (k_n) =(1-α)*P (K_n) Non-critical task actual power P (n_m) =(1-β)*P (N_m) , where P (k_n) P is the projected power consumption for any critical mission. (N_m) The system calculates the estimated power consumption for any non-critical task. When S < E2, non-critical tasks are paused, thus establishing a power output strategy. This strategy significantly improves the robot's operational stability and energy efficiency. By setting high and low battery thresholds, power allocation can be flexibly adjusted based on real-time battery power. When the battery is sufficient, power is allocated normally to ensure smooth task progress. When the battery is at a moderate level, power is reduced for critical and non-critical tasks at different ratios, balancing task execution and energy conservation. When the battery is too low, non-critical tasks are paused, prioritizing critical tasks to prevent task failure due to insufficient power. Overall, this system achieves rational energy allocation, improves the robot's reliability under complex battery conditions, and extends the effective battery life.

[0020] Furthermore, in step S4, after the power output strategy is formulated, the real-time power output P of the robot during the completion of any robot task is monitored. out Calculate the dynamic deviation ΔP=P out -P tar If the dynamic deviation exceeds the preset deviation threshold ΔP th When a malfunction is detected in the power component, the system immediately adjusts the control parameters to reduce the power deviation. The control parameters include voltage, current, and frequency.

[0021] Furthermore, in step S5, when the robot completes any robot task, the power output strategy update mechanism is triggered. The update mechanism includes reclassifying critical tasks and non-critical tasks, and also includes reallocating power to critical tasks and non-critical tasks.

[0022] Furthermore, in step S6, the ratio of initial battery power to maximum battery power (x1) and the ratio of robot usage time to maximum usage time (x2) are used, combined with historical environmental data, to construct a power consumption prediction model and calculate the predicted power consumption ΔS.

[0023] ;

[0024] The environmental data includes: the ratio of ambient temperature to standard temperature x3, the ratio of ambient humidity to standard humidity x4, and the ratio of the robot's friction coefficient on the task area ground to the average friction coefficient of the robot on the factory ground x5. Then, by collecting historical data, the power consumption prediction model is optimized using the least squares method to establish the objective function:

[0025] ;

[0026] Where n is the number of historical data samples, ΔS i x is the actual power consumption value of the i-th sample. i1 x is the ratio of the initial battery level to the maximum battery level in the i-th sample. i2 x is the ratio of the robot's usage time to its maximum usage time in the i-th sample. i3 X is the ratio of the ambient temperature to the standard temperature in the i-th sample. i4 X is the ratio of the ambient humidity to the standard humidity in the i-th sample. i5This is the ratio of the robot's friction coefficient against the task area to the average friction coefficient against the factory floor in the i-th sample. By comprehensively considering multiple factors such as the ratio of initial to maximum power consumption, robot usage time, ambient temperature, humidity, and ground friction coefficient, key factors affecting power consumption can be accurately analyzed. Utilizing historical data combined with the least squares method to optimize the model continuously improves prediction accuracy. Accurate power consumption prediction helps in advance planning of energy replenishment, avoiding disruptions to robot task execution due to insufficient power. In complex and changing working environments, this ensures stable robot operation, reduces maintenance costs, and improves overall work efficiency and resource utilization.

[0027] Furthermore, in step S7, based on the predicted total power consumption s and the predicted remaining power consumption Δs of any robot, combined with the total number of tasks Q and the number of remaining tasks q, the task completion rate C is calculated:

[0028] ;

[0029] The predicted total power consumption value 's' is obtained by statistically analyzing the expected power consumption required for the task, and the predicted remaining power consumption value 'Δs' is obtained by statistically analyzing the expected power consumption required for the robot's remaining tasks. When the task completion rate 'C' is less than 1, it is determined that the robot cannot complete the task, and any remaining task is assigned to another robot. The task completion rate is then recalculated until it is greater than or equal to 1. By combining the predicted total power consumption value, the predicted remaining power consumption value, the total number of tasks, and the number of remaining tasks, the likelihood of the robot completing the task can be assessed intuitively and accurately. Once the task completion rate is determined to be less than 1, the remaining tasks are promptly assigned to other robots. This greatly improves the flexibility and success rate of task execution, avoids task interruption due to insufficient power, and ensures the smooth progress of the entire task process. This dynamic allocation mechanism can fully integrate resources, improve work efficiency, reduce task costs, and ensure the efficient and orderly completion of tasks.

[0030] An embedded intelligent power regulation system for robots includes: a battery data acquisition and power monitoring module, a task requirement analysis and power calculation module, a power output strategy formulation module, a power output real-time monitoring and anomaly judgment module, a power strategy update module, a power consumption prediction model construction module, and a task allocation module.

[0031] The battery data acquisition and power monitoring module collects robot battery data and completes real-time power monitoring.

[0032] The task requirement analysis and power calculation module analyzes the task requirements and calculates the power consumption of the robot task.

[0033] After the power output strategy formulation module queries the estimated power consumption required by the task, it formulates a power output strategy for the task.

[0034] The real-time power output monitoring and anomaly detection module monitors the robot's real-time power output during the task and determines whether the power components are working properly.

[0035] The power strategy update module updates the robot's power output strategy when the robot completes any robot task;

[0036] The power consumption prediction model building module combines historical environmental data to construct a power consumption prediction model;

[0037] The task allocation module calculates the task completion rate, and when the completion rate is lower than the threshold, the robot task is reallocated.

[0038] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: On the one hand, by accurately collecting battery voltage, current, and temperature data through the power monitoring module in the embedded system, the real-time battery power can be accurately calculated, avoiding robot downtime due to power estimation errors and reducing unnecessary energy waste. Simultaneously, an intelligent power output strategy is formulated based on the power required for the task and the real-time battery power. Power is allocated normally when the battery is sufficient, and when the battery is insufficient, power output for critical and non-critical tasks is reasonably reduced, or even non-critical tasks are suspended, effectively improving energy utilization efficiency, extending robot endurance, and reducing operating costs.

[0039] On the one hand, by using power consumption prediction models, combined with the ratio of the robot's initial power to its maximum power, the ratio of usage time to maximum usage time, and historical environmental data such as ambient temperature, humidity, and ground friction coefficient, the power consumption of robots in different task scenarios can be accurately predicted. This allows task planners to know in advance the power required for robots to complete tasks, and thus rationally arrange the task sequence and allocate tasks to different robots. For example, in a logistics warehouse where multiple robots work collaboratively, based on the prediction results, tasks with high power consumption can be assigned to robots with sufficient power, avoiding task interruptions or delays due to insufficient power, greatly improving the continuity and overall efficiency of task execution, optimizing logistics scheduling processes, and reducing operating costs.

[0040] On the other hand, real-time monitoring of the robot's power output during task completion calculates power deviation. Once the deviation exceeds a threshold, the system immediately adjusts control parameters such as voltage, current, and frequency to reduce power deviation, promptly detects and resolves abnormal power component operation issues, effectively ensuring stable robot operation and preventing task interruptions and safety accidents caused by power anomalies. For example, in an automobile manufacturing plant, this can prevent problems such as decreased assembly accuracy of parts, equipment collisions, and production stoppages caused by power deviations in robotic arms. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a structural diagram of an embedded intelligent robot power adjustment system according to the present invention;

[0043] Figure 2 This is a flowchart of an embedded-based intelligent adjustment method for robot power according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 and Figure 2 The present invention provides a technical solution: an embedded-based intelligent adjustment method for robot power, comprising the following steps:

[0046] S1. Collect robot battery data to complete real-time power monitoring;

[0047] S2. Analyze the task requirements and calculate the power consumption of the robot task;

[0048] S3. After finding the estimated power consumption required for the task, formulate a power output strategy for the task.

[0049] S4. Monitor the robot's real-time power output during the task to determine whether the power components are working properly;

[0050] S5. When the robot completes any robot task, update the robot's power output strategy;

[0051] S6. Combine historical environmental data to construct a power consumption prediction model;

[0052] S7. Calculate the task completion rate. When the completion rate is lower than the threshold, the robot task is reassigned.

[0053] In step S1, according to the system's specified acquisition time interval, the battery voltage data V, current data I, and temperature data T are acquired by the power monitoring module in the embedded system. The battery current at time point γ is acquired by the current sensor, and the open-circuit voltage is acquired by the voltage sensor. Based on the battery's rated capacity, initial capacity, and set standard temperature, the real-time battery power S is calculated.

[0054] ;

[0055] Where S0 is the initial battery charge, obtained through calibration; C is the battery rated capacity (Ah), provided by the manufacturer; I(γ) is the battery current at time γ, acquired by a current sensor; k is a coefficient obtained by fitting charge-discharge experimental curves at different temperatures; V is the real-time open-circuit voltage, measured by a high-precision voltage sensor; V ref This is the reference open-circuit voltage value at a standard temperature (e.g., 25℃). The collected and calculated data are accumulated to form a basic data pool, providing data support for subsequent predictive models and enabling real-time power monitoring. Through the collaborative work of multiple sensors, including current and voltage sensors, accurate capture of various key data is achieved, ensuring the accuracy of the data required for power calculation. When calculating the real-time battery power, multiple factors such as the battery's rated capacity, initial capacity, and temperature are comprehensively considered, making the calculated power results more realistic. The accumulated data forms a basic data pool, laying a solid foundation for subsequent predictive models, helping to detect battery anomalies early and ensuring the stable operation of the robot.

[0056] In step S2, after real-time power monitoring is completed, the task requirements are analyzed. For any robot task, the specific parameters of the robot task include: the total mass of the robot and its load, and the robot's frontal area. Based on the coefficient of friction between the robot and the ground, the robot's speed, and the air density, the power consumption P of any robot task is calculated.

[0057] ;

[0058] Where μ is the coefficient of friction between the robot and the ground, measured through experiments on different ground materials; m is the total mass of the robot and its load, determined by design parameters and actual load; g is taken as 9.8; v is the robot's speed, measured by a speed sensor; ρ is the air density, calculated based on local meteorological data or sensor measurements; k is the air resistance coefficient, obtained through wind tunnel experiments or by referring to historical data of similar robots; and A is the robot's frontal area in the direction of movement, calculated based on its dimensions. When any robot task is assigned, the estimated power consumption required for the task is retrieved from the database based on the specific parameters of the task. Before task execution, by comprehensively considering multiple dimensions such as the total mass of the robot and its load, the coefficient of friction with the ground, the speed, air density, and frontal area, the power consumption P can be accurately calculated. This provides a scientific basis for rationally planning energy supply and estimating battery life, avoiding task interruptions due to insufficient power. Simultaneously, when a task is assigned, the estimated power consumption can be quickly retrieved from the database, facilitating advance resource allocation, optimizing task processes, improving robot efficiency, and ensuring that the robot can complete its work stably and efficiently under different working conditions and task requirements.

[0059] In step S3, after querying the estimated power consumption required for the task, a power output strategy is formulated. For the real-time power level S, a high power threshold E1 and a low power threshold E2 are set. When S > E1, the task is allocated power normally; when E2 ≤ S ≤ E1, the power output of critical and non-critical tasks is reduced proportionally. The power reduction ratio for critical tasks is set as α, and the power reduction ratio for non-critical tasks is set as β. Then, the actual power P of the critical task is... (k_n) =(1-α)*P (K_n) Non-critical task actual power P (n_m) =(1-β)*P (N_m) , where P (k_n) P is the projected power consumption for any critical mission. (N_m) The system calculates the estimated power consumption for any non-critical task. When S < E2, non-critical tasks are paused, thus establishing a power output strategy. This strategy significantly improves the robot's operational stability and energy efficiency. By setting high and low battery thresholds, power allocation can be flexibly adjusted based on real-time battery power. When the battery is sufficient, power is allocated normally to ensure smooth task progress. When the battery is at a moderate level, power is reduced for critical and non-critical tasks at different ratios, balancing task execution and energy conservation. When the battery is too low, non-critical tasks are paused, prioritizing critical tasks to prevent task failure due to insufficient power. Overall, this system achieves rational energy allocation, improves the robot's reliability under complex battery conditions, and extends the effective battery life.

[0060] In step S4, after the power output strategy is formulated, the real-time power output P of the robot is monitored in real time during the process of completing any robot task. out Calculate the dynamic deviation ΔP=P out -P tar If the dynamic deviation exceeds the preset deviation threshold ΔP th When a malfunction is detected in the power component, the system immediately adjusts the control parameters to reduce the power deviation. The control parameters include voltage, current, and frequency.

[0061] In step S5, when the robot completes any robot task, the power output strategy update mechanism is triggered. The update mechanism includes reclassifying critical tasks and non-critical tasks, and also includes reallocating power to critical tasks and non-critical tasks.

[0062] In step S6, the ratio of initial battery power to maximum battery power (x1) and the ratio of robot usage time to maximum usage time (x2) are used. Combined with historical environmental data, a power consumption prediction model is constructed to calculate the predicted power consumption ΔS.

[0063] ;

[0064] The environmental data includes: the ratio of ambient temperature to standard temperature x3, the ratio of ambient humidity to standard humidity x4, and the ratio of the robot's friction coefficient on the task area ground to the average friction coefficient of the robot on the factory ground x5. Then, by collecting historical data, the least squares method is used to optimize the power consumption prediction model, and an objective function is established:

[0065] ;

[0066] Where n is the number of historical data samples, ΔS i x is the actual power consumption value of the i-th sample. i1 x is the ratio of the initial battery level to the maximum battery level in the i-th sample. i2 x is the ratio of the robot's usage time to its maximum usage time in the i-th sample. i3 X is the ratio of the ambient temperature to the standard temperature in the i-th sample. i4 X is the ratio of the ambient humidity to the standard humidity in the i-th sample. i5This is the ratio of the robot's friction coefficient against the ground in the task area to the average friction coefficient of the robot against the ground in the factory area for the i-th sample. By continuously optimizing this coefficient, the model can accurately predict power consumption within the next Δt time period based on the current input factors. By comprehensively considering multiple dimensions such as the ratio of initial power to maximum power, robot usage time, ambient temperature, humidity, and ground friction coefficient, the model can accurately analyze the key factors affecting power consumption. Utilizing historical data combined with the least squares method to optimize the model continuously improves the accuracy of predictions. Accurate power consumption prediction helps in advance planning of energy replenishment, avoiding disruptions to robot task execution due to insufficient power. In complex and changing working environments, this ensures stable robot operation, reduces maintenance costs, and improves overall work efficiency and resource utilization.

[0067] In step S7, based on the predicted total power consumption s and the predicted remaining power consumption Δs of any robot, combined with the total number of tasks Q and the number of remaining tasks q, the task completion rate C is calculated:

[0068] ;

[0069] The predicted total power consumption value 's' is obtained by statistically analyzing the expected power consumption required for the task, and the predicted remaining power consumption value 'Δs' is obtained by statistically analyzing the expected power consumption required for the robot's remaining tasks. When the task completion rate 'C' is less than 1, it is determined that the robot cannot complete the task, and any remaining task is assigned to another robot. The task completion rate is then recalculated until it is greater than or equal to 1. By combining the predicted total power consumption value, the predicted remaining power consumption value, the total number of tasks, and the number of remaining tasks, the likelihood of the robot completing the task can be assessed intuitively and accurately. Once the task completion rate is determined to be less than 1, the remaining tasks are promptly assigned to other robots. This greatly improves the flexibility and success rate of task execution, avoids task interruption due to insufficient power, and ensures the smooth progress of the entire task process. This dynamic allocation mechanism can fully integrate resources, improve work efficiency, reduce task costs, and ensure the efficient and orderly completion of tasks.

[0070] An embedded intelligent power regulation system for robots includes: a battery data acquisition and power monitoring module, a task requirement analysis and power calculation module, a power output strategy formulation module, a power output real-time monitoring and anomaly judgment module, a power strategy update module, a power consumption prediction model construction module, and a task allocation module.

[0071] The battery data acquisition and power monitoring module collects robot battery data and completes real-time power monitoring.

[0072] The task requirement analysis and power calculation module analyzes the task requirements and calculates the power consumption of the robot task.

[0073] After the power output strategy formulation module queries the estimated power consumption required by the task, it formulates a power output strategy for the task.

[0074] The real-time power output monitoring and anomaly detection module monitors the robot's real-time power output during the task and determines whether the power components are working properly.

[0075] The power strategy update module updates the robot's power output strategy when the robot completes any robot task;

[0076] The power consumption prediction model building module combines historical environmental data to construct a power consumption prediction model;

[0077] The task allocation module calculates the task completion rate, and when the completion rate is lower than the threshold, the robot task is reallocated.

[0078] Example 1: In a large logistics warehouse, a large number of handling robots are working busily, their task being to accurately move goods from the storage area to the sorting area.

[0079] In the real-time power monitoring phase, the embedded system inside the robot plays a crucial role. The power monitoring module collects battery data every 10 minutes as programmed. At a specific moment, it successfully collected battery voltage data of 36 volts, current data of 5 amps, and temperature data of 28 degrees Celsius. Simultaneously, the current sensor accurately captured the battery current at time point γ, also at 5 amps, and the voltage sensor collected an open-circuit voltage of 36.5 volts. Based on the known battery rated capacity of 20 amp-hours, initial charge of 18 amp-hours, and standard temperature of 25 degrees Celsius, the current real-time battery charge can be calculated. If the data collection interval is 10 minutes (1 / 6 hour), and assuming the current remains constant at 5 amps during this period, the consumed power is 17.17 amp-hours.

[0080] When analyzing the requirements of material handling tasks, it's discovered that different tasks have different parameters. For example, in one handling task, the total mass of the robot and the goods it carries reaches 500 kg, and its frontal area is 0.8 square meters. Preliminary experiments on different ground materials have determined the coefficient of friction between the robot and the ground to be 0.3. Using a speed sensor, the robot's real-time speed is measured to be 1 meter per second, and referencing local meteorological data, the current air density is obtained as 1.2 kg per cubic meter. Furthermore, wind tunnel experiments have determined the air drag coefficient to be 0.5. Using this data, the power consumption for this task can be calculated. Then, based on these detailed task parameters, the estimated power consumption required to complete the task can be quickly retrieved from the database.

[0081] To allocate power efficiently, a high battery threshold of 80% and a low battery threshold of 30% are pre-set. When the robot's battery level is above 80%, it will handle goods according to normal power allocation to ensure efficient task completion. Once the battery level is between 30% and 80%, the power output for both critical and non-critical tasks will be reduced according to a pre-defined ratio. For example, the power reduction ratio for critical tasks is set to 0.1, and the power reduction ratio for non-critical tasks is set to 0.2, and then the power is adjusted according to a formula. When the battery level is below 30%, non-critical tasks such as tidying up surrounding debris will be suspended to ensure the smooth completion of core goods handling tasks.

[0082] During the robot's cargo handling process, its power output is continuously monitored in real time. Once a power deviation exceeds a preset threshold, the system reacts quickly by adjusting control parameters such as voltage, current, and frequency to ensure stable robot operation and prevent disruption to the cargo handling task.

[0083] Once the robot successfully completes a transport task, a power output strategy update mechanism is triggered. Under this mechanism, the priority of tasks is reassessed, critical and non-critical tasks are reclassified, and power is redistributed to different tasks according to the new classification to adapt to the new work requirements.

[0084] To more accurately predict power consumption, multiple factors are considered. A power consumption prediction model is constructed by combining historical data such as the ratio of the robot's initial power to its maximum power, the ratio of the robot's usage time to its maximum usage time, the ratio of ambient temperature to standard temperature, the ratio of ambient humidity to standard humidity, and the ratio of the robot's coefficient of friction with the task area's ground to the average coefficient of friction with the factory ground. Subsequently, by collecting a large amount of historical data, the model is continuously optimized using the least squares method to make its predictions more accurate.

[0085] Finally, by statistically analyzing the predicted total and remaining power consumption of the robot, and combining this with the total number of tasks and the number of remaining tasks, the task completion rate can be calculated. If the calculated task completion rate is less than 1, it means that the current robot cannot successfully complete all tasks. In this case, the remaining tasks will be reasonably allocated to other robots, and the task completion rate will be recalculated. This process is repeated until the task completion rate is greater than or equal to 1, thus ensuring that the entire logistics handling task can be completed efficiently and smoothly.

[0086] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for embedded-based robot power intelligent conditioning, characterized in that: The method includes the following steps: S1. Collect robot battery data to complete real-time power monitoring; S2. Analyze the task requirements and calculate the power consumption of the robot task; S3. After finding the estimated power consumption required for the task, formulate a power output strategy for the task. S4. Monitor the robot's real-time power output during the task to determine whether the power components are working properly; S5. When the robot completes any robot task, update the robot's power output strategy; S6. Combine historical environmental data to construct a power consumption prediction model; S7. Calculate the task completion rate. When the completion rate is lower than the threshold, the robot task is reallocated. In step S3, after querying the estimated power consumption required for the task, a power output strategy is formulated. For the real-time power level S, a high power threshold E1 and a low power threshold E2 are set. When S > E1, the task is allocated power normally; when E2 ≤ S ≤ E1, the power output of critical and non-critical tasks is reduced proportionally. The power reduction ratio for critical tasks is set as α, and the power reduction ratio for non-critical tasks is set as β. Then, the actual power P of the critical task is... (k_n) =(1-α)*P (K_n) Non-critical task actual power P (n_m) =(1-β)*P (N_m) , where P (k_n) P is the projected power consumption for any critical mission. (N_m) The estimated power consumption for any non-critical task; when S < E2, the non-critical task is paused, thus completing the power output strategy. In step S4, after the power output strategy is formulated, the real-time power output P of the robot is monitored in real time during the process of completing any robot task. out Calculate the dynamic deviation ΔP=P out -P tar If the dynamic deviation exceeds the preset deviation threshold ΔP th When a malfunction is detected in the power component, the system immediately adjusts the control parameters to reduce the power deviation. The control parameters include voltage, current, and frequency. In step S6, the ratio of initial power to maximum power (x1) and the ratio of robot usage time to maximum usage time (x2) are called. Combined with historical environmental data, a power consumption prediction model is constructed. The environmental data includes: the ratio of ambient temperature to standard temperature (x3), the ratio of ambient humidity to standard humidity (x4), and the ratio of the robot's friction coefficient on the task area ground to the average friction coefficient of the robot on the factory ground (x5). Then, by collecting historical data, the power consumption prediction model is optimized using the least squares method. In step S7, based on the predicted total power consumption s and the predicted remaining power consumption Δs of any robot, combined with the total number of tasks Q and the number of remaining tasks q, the task completion rate C is calculated. The total power consumption prediction value s is obtained by statistically analyzing the expected power consumption required for the task, and the remaining power consumption prediction value Δs is obtained by statistically analyzing the expected power consumption required for the remaining tasks of the robot. When the task completion degree C is less than 1, it is determined that the robot cannot complete the task, and any remaining task is assigned to other robots. The task completion degree is recalculated until the task completion degree is greater than or equal to 1.

2. The method of claim 1, wherein: In step S1, according to the acquisition time interval specified by the system, the battery voltage data V, current data I, and temperature data T of the robot battery are acquired by the power monitoring module in the embedded system. The battery current at time point γ is acquired by the current sensor, and the open circuit voltage is acquired by the voltage sensor. Based on the battery rated capacity, the battery initial capacity, and the set standard temperature, the real-time battery power S is calculated, thus completing the real-time power monitoring.

3. The method of claim 2, wherein: In step S2, after real-time power monitoring is completed, the task requirements are analyzed. For any robot task, the specific parameters of the robot task include: the total mass of the robot and its load and the windward area of ​​the robot. Based on the friction coefficient between the robot and the ground, the robot's movement speed and air density, the power consumption P of any robot task is calculated. According to the specific parameters of the robot task, the expected power consumption required for the task is queried from the database.

4. The method of claim 3, wherein: In step S5, when the robot completes any robot task, the power output strategy update mechanism is triggered. The update mechanism includes reclassifying critical tasks and non-critical tasks, and also includes reallocating power to critical tasks and non-critical tasks.

5. An embedded-based intelligent robot power adjustment system, wherein the system is applied to the embedded-based intelligent robot power adjustment method according to any one of claims 1-4, characterized in that: The system includes: a battery data acquisition and power monitoring module, a task requirement analysis and power calculation module, a power output strategy formulation module, a power output real-time monitoring and anomaly judgment module, a power strategy update module, a power consumption prediction model construction module, and a task allocation module. The battery data acquisition and power monitoring module collects robot battery data and completes real-time power monitoring. The task requirement analysis and power calculation module analyzes the task requirements and calculates the power consumption of the robot task. After the power output strategy formulation module queries the estimated power consumption required for the task, it formulates a power output strategy for the task. Specifically, after querying the estimated power consumption required for the task, it formulates a power output strategy. For the real-time power level S, it sets a high power threshold E1 and a low power threshold E2. When S > E1, the task is allocated power normally. When E2 ≤ S ≤ E1, the power output of critical and non-critical tasks is reduced proportionally. The power reduction ratio for critical tasks is set as α, and the power reduction ratio for non-critical tasks is set as β. Then, the actual power P of the critical task is... (k_n) =(1-α)*P (K_n) Non-critical task actual power P (n_m) =(1-β)*P (N_m) , where P (k_n) P is the projected power consumption for any critical mission. (N_m) The estimated power consumption for any non-critical task; when S < E2, the non-critical task is paused, thus completing the power output strategy. The real-time power output monitoring and anomaly detection module monitors the robot's real-time power output during the task process and determines whether the power components are working properly. Specifically, after the power output strategy is formulated, it monitors the robot's real-time power output P during the completion of any robot task. out Calculate the dynamic deviation ΔP=P out -P tar If the dynamic deviation exceeds the preset deviation threshold ΔP th When a malfunction is detected in the power component, the system immediately adjusts the control parameters to reduce the power deviation. The control parameters include voltage, current, and frequency. The power strategy update module updates the robot's power output strategy when the robot completes any robot task; The power consumption prediction model construction module combines historical environmental data to construct a power consumption prediction model. Specifically, it calls the ratio of initial power to maximum power (x1), the ratio of robot usage time to maximum usage time (x2), and combines historical environmental data to construct a power consumption prediction model. The environmental data includes: the ratio of ambient temperature to standard temperature (x3), the ratio of ambient humidity to standard humidity (x4), and the ratio of the robot's friction coefficient on the task area ground to the average friction coefficient of the robot on the factory ground (x5). Then, by collecting historical data, the power consumption prediction model is optimized using the least squares method. The task allocation module calculates the task completion rate. When the completion rate is lower than the threshold, the robot tasks are reallocated. Specifically, the task completion rate C is calculated based on the predicted total power consumption s and the predicted remaining power consumption Δs of any robot, combined with the total number of tasks Q and the number of remaining tasks q. The total power consumption prediction value s is obtained by statistically analyzing the expected power consumption required for the task, and the remaining power consumption prediction value Δs is obtained by statistically analyzing the expected power consumption required for the remaining tasks of the robot. When the task completion degree C is less than 1, it is determined that the robot cannot complete the task, and any remaining task is assigned to other robots. The task completion degree is recalculated until the task completion degree is greater than or equal to 1.

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