Industrial robot wireless charging system and dynamic power regulation method thereof

Abstract

The present application relates to the technical field of industrial robot wireless charging system, and discloses an industrial robot wireless charging system and a dynamic power regulation method thereof.The system comprises an energy transmitting device, an energy receiving device and a state monitoring module.The method is as follows: when the robot enters the charging area, the state monitoring module collects charging environment parameters, robot load state and battery remaining capacity; the collected data is preprocessed, the effective interval value of the environment parameters is screened, the load state change node is marked, and the battery charging stage is labeled; the environment adaptation, load demand and battery capacity characteristics are extracted; the input power regulation model is analyzed to generate a regulation characteristic set; the target power value, energy transmission efficiency threshold and safety boundary condition are determined to form a power regulation instruction set; the execution logic is arranged to drive the energy transmitting device to dynamically adjust the power and update the instruction with the robot working state.The method realizes dynamic power regulation, improves charging efficiency and safety.

Description

Technical Field

[0001] This invention relates to the field of wireless charging systems for industrial robots, specifically to a wireless charging system for industrial robots and its dynamic power adjustment method. Background Technology

[0002] In the field of industrial automation, industrial robots are being used more and more widely, and their charging methods directly affect production efficiency and equipment operational stability. Traditional wired charging methods have many limitations. For example, charging interfaces are prone to poor contact due to frequent plugging and unplugging, which affects charging efficiency and may even lead to equipment failure; cables will gradually age over long-term use, posing safety hazards and potentially causing short circuits and other dangerous situations; moreover, wired charging severely restricts the range of motion of industrial robots. In some complex industrial scenarios, robots need to operate in different locations, and wired charging cannot meet their flexible movement needs, which greatly restricts the expansion of industrial robot application scenarios.

[0003] With the development of wireless charging technology, its application in the field of industrial robots has gradually attracted attention. However, existing wireless charging systems for industrial robots still face many challenges in practical applications. Industrial production environments are often complex, with constantly changing environmental parameters such as temperature and humidity. These changes can significantly affect the efficiency and stability of wireless charging. For example, high-temperature environments may cause the charging equipment to overheat, reducing charging efficiency and even damaging the equipment; high-humidity environments may cause internal short circuits, affecting charging safety. Simultaneously, industrial robots experience significant differences in load conditions and power requirements at different operating stages, such as startup, stable operation, and shutdown. The startup stage requires significant power to drive the robot to quickly reach its operating speed, the power demand is relatively stable during stable operation, and drops sharply during shutdown. If the wireless charging system cannot dynamically adjust its power according to changes in load conditions, it will lead to energy waste, reduced charging efficiency, and may even damage the robot's battery due to power mismatch.

[0004] Batteries exhibit significantly different charging characteristics at different stages of charging, such as pre-charge, constant current, constant voltage, and float charging. The pre-charge stage requires a small current for initial charging, the constant current stage needs a constant current for rapid charging, the constant voltage stage requires a stable voltage to ensure the battery reaches full charge, and the float charging stage is for maintaining the battery's charge level. Existing wireless charging systems often fail to accurately identify the battery's charging stage, thus failing to provide matching charging power and parameters. This not only prolongs charging time but may also affect battery lifespan and performance.

[0005] Most existing power regulation methods employ fixed adjustment strategies, which cannot adapt to the dynamic changes in the working state and environment of industrial robots in real time. This fixed adjustment method makes it difficult to achieve efficient and safe charging in complex industrial environments, and cannot meet the high requirements of industrial production for robot charging. Therefore, there is an urgent need for an industrial robot wireless charging system and method that can dynamically adjust power based on multi-dimensional information such as environmental parameters, load status, and remaining battery power, in order to improve charging efficiency, ensure charging safety, extend battery life, and expand the application scenarios of industrial robots. Summary of the Invention

[0006] The purpose of this invention is to provide a wireless charging system for industrial robots and a dynamic power adjustment method thereof, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a wireless charging system for industrial robots and a dynamic power adjustment method thereof. The system includes an energy transmitting device, an energy receiving device, and a status monitoring module. The method includes the following processes:

[0008] When the industrial robot enters the charging area, the status monitoring module collects charging environment parameters, robot load status and remaining battery power in real time as the basis for adjustment.

[0009] The collected environmental parameters, load status and remaining battery power are preprocessed, including filtering valid range values ​​for environmental parameters, marking real-time change nodes for load status, and indicating the current charging stage for remaining battery power.

[0010] Based on the preprocessed environmental parameters, load status and remaining battery power, feature extraction is performed to form environmental adaptation features, load demand features and battery capacity features.

[0011] By inputting environmental adaptability characteristics, load demand characteristics, and battery capacity characteristics into the power regulation model, a multi-dimensional correlation analysis is performed to generate a set of regulation features containing parameter correspondences.

[0012] Based on the set of adjustment features, the target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage are determined, and a set of power adjustment commands is formed.

[0013] The execution logic is arranged according to the time sequence and priority of the power adjustment command set, driving the energy emission device to dynamically adjust the output power, and updating the adjustment commands as the robot's working state changes.

[0014] Preferably, the collected environmental parameters, load status, and remaining battery power are preprocessed, and the specific process is as follows:

[0015] Environmental parameters are filtered using a threshold filtering algorithm to remove extreme values ​​that exceed the equipment's tolerance range, while retaining the effective range values ​​for temperature, humidity, and electromagnetic interference intensity.

[0016] The load status is divided into three stages: startup, stability, and shutdown, using the time slicing method, and the start and end time nodes of each stage are marked.

[0017] The remaining battery power is read in real time by the power metering chip, and the charging curve indicates whether the battery is currently in the pre-charging, constant current, constant voltage, or float charging stage.

[0018] Preferably, feature extraction is performed based on the preprocessed environmental parameters, load status, and remaining battery power. The specific process is as follows:

[0019] Environmental parameters are analyzed using historical data comparison models to determine the deviation between current values ​​and standard values, and to extract environmental adaptation features.

[0020] The load condition is calculated using a power demand prediction model to determine the average and peak energy consumption at each stage, and to extract load demand characteristics.

[0021] The remaining battery capacity is determined by using a charging characteristic analysis model to identify the current voltage change rate and current acceptance capability, thereby extracting battery capacity characteristics.

[0022] Preferably, environmental adaptability characteristics, load demand characteristics, and battery capacity characteristics are input into the power regulation model for multi-dimensional correlation analysis. The specific process is as follows:

[0023] Establish a time synchronization mechanism to align environmental adaptation characteristics, load demand characteristics, and battery capacity characteristics to the same time axis according to time nodes;

[0024] The influence coefficients between different features are calculated by weighting algorithm, focusing on the correlation of environmental parameters with load demand and battery capacity within the same time period.

[0025] The associated features are cross-validated and fused to generate a set of adjustable features that includes information on environmental, load, and battery dimensions.

[0026] Preferably, the target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage are determined based on the adjustment feature set. The specific process is as follows:

[0027] By clustering the adjustment feature set using a power matching model, the base power value and dynamic adjustment range for the current charging stage can be determined.

[0028] Within the basic power range, the minimum energy loss value that meets the load requirements is calculated using an efficiency optimization model and used as the transmission efficiency threshold.

[0029] By analyzing environmental parameters and equipment tolerance through a safety constraint model, upper limits of voltage, current, and temperature are extracted as safety boundary conditions.

[0030] Preferably, the execution logic is arranged according to the time sequence and priority of the power adjustment command set, and the specific process is as follows:

[0031] The adjustment instructions are arranged in the time sequence of the charging stages, and the corresponding target power value and efficiency threshold are listed under each stage.

[0032] Within each stage, voltage and current limiting instructions are executed first, based on the constraint strength of the safety boundary conditions.

[0033] Dynamic adjustment instructions are listed separately, with corresponding triggering conditions and response delays noted, forming a clearly hierarchical execution logic list.

[0034] Preferably, the drive energy emission device dynamically adjusts its output power and updates the adjustment command, and the specific process is as follows:

[0035] The execution logic list is converted into control signals, and the "stage-power-constraint" control logic is used to drive the transmitter to adjust.

[0036] When the robot enters a new working phase, it repeats the process of state data acquisition, preprocessing, feature extraction, correlation analysis, and instruction generation to obtain new adjustment instructions;

[0037] The new adjustment instructions are inserted into the corresponding stage or dynamic adjustment position to correct the parameters and sequence of the original execution logic and maintain the synchronization between the adjustment and the robot's state.

[0038] Preferably, when the status monitoring module collects environmental parameters, load status, and remaining battery power in real time, the specific collection process is as follows:

[0039] Environmental parameters are collected by temperature and humidity sensors and electromagnetic intensity detectors distributed in the charging area, and filtering algorithms are used to reduce noise interference.

[0040] The load status is read in real time through the robot controller interface, including motor current, motion speed and working mode data, including stage identifier, timestamp and parameter values.

[0041] The remaining battery power is obtained through a power detection circuit integrated inside the robot, which includes battery voltage, charging current and temperature information, forming a multi-source heterogeneous state data set.

[0042] Preferably, when calculating the influence coefficient between different features using the weighting algorithm, the specific calculation process is as follows:

[0043] Based on time nodes, environmental adaptation characteristics, load demand characteristics, and battery capacity characteristics within the same time period are grouped into feature groups.

[0044] For each set of features, calculate the degree of matching between environmental parameters and load requirements;

[0045] Calculate the degree of fit between battery capacity and target power;

[0046] Influence coefficients are assigned based on the matching degree and the degree of adaptation; the higher the coefficient, the stronger the correlation between features.

[0047] Preferably, when the remaining battery power is read in real time by the power metering chip, the specific reading process is as follows:

[0048] The battery voltage signal is divided into sampling segments of fixed duration, and noise is filtered in each segment to identify the start and end positions of the effective voltage.

[0049] Feature extraction is performed on the effective voltage segment, and the voltage features are converted into candidate energy values ​​through a pre-calibrated energy metering chip;

[0050] Historical data is used to correct the candidate power values, and the most reasonable value is selected as the final reading result by combining charging current and time information.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] Regarding charging environment adaptability, the system collects environmental parameters such as temperature, humidity, and electromagnetic interference intensity in real time through a status monitoring module. These parameters are preprocessed to remove extreme values ​​exceeding the equipment's tolerance range, retaining only valid values ​​within the range. Then, a historical data comparison model analyzes the deviation between the current value and the standard value to extract environmental adaptation characteristics. Based on these characteristics, the power regulation model can perform multi-dimensional correlation analysis to generate a set of regulation features, thereby determining safety boundary conditions. This enables the system to dynamically adjust power according to environmental changes, effectively coping with complex industrial environments and improving charging stability and equipment safety.

[0053] In terms of load condition matching, the system uses time slicing to divide the load condition into three stages: startup, stable operation, and shutdown. Each stage's time node is marked, and the average and peak energy consumption for each stage are calculated using a power demand prediction model to extract load demand characteristics. Combining these characteristics, the system can accurately match the power demand of different load stages, providing sufficient power during startup and maintaining appropriate power during stable operation. This avoids energy waste and battery damage caused by power mismatch, improving charging efficiency and equipment operational stability.

[0054] In terms of battery charging management, the system reads the real-time value of the remaining battery capacity through a power metering chip, marks the charging stages using a charging curve, and identifies the voltage change rate and current acceptance capacity through a charging characteristic analysis model to extract battery capacity characteristics. Based on these characteristics, the system can determine the target power value for each charging stage, achieving precise power adjustment for battery pre-charging, constant current, constant voltage, and float charging stages, shortening charging time, extending battery life, and improving battery performance.

[0055] Regarding dynamic adjustment capabilities, the system arranges the execution logic of power adjustment commands according to time sequence and priority. When the robot enters a new working phase, it repeats the data acquisition and preprocessing process to obtain new adjustment commands and insert them into the corresponding positions, correcting the execution logic to synchronize the adjustment with the robot's state. This dynamic adjustment method can adapt to changes in the robot's working state and environment in real time, ensuring efficient and safe charging.

[0056] In terms of data processing and analysis, the system preprocesses, extracts features, and performs multi-dimensional correlation analysis on the collected multi-source heterogeneous state data. It calculates the influence coefficient between features through a weight allocation algorithm, cross-validates and fuses features, and generates a comprehensive set of regulation features. This provides strong data support for precise power regulation and improves the accuracy and reliability of system decision-making.

[0057] In terms of execution logic and control, the system converts the execution logic list into control signals and uses "stage-power-constraint" control logic to drive the transmitter to adjust. It prioritizes the execution of voltage and current limit commands with safety boundary conditions, dynamically adjusts the command label trigger conditions and response delays, and forms a hierarchical execution logic list to ensure that the charging process is orderly and safe, thereby improving the system's controllability and stability. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the working principle of the industrial robot wireless charging system and its dynamic power adjustment method according to the present invention.

[0059] Figure 2 A flowchart for data preprocessing;

[0060] Figure 3 This is a flowchart of feature extraction;

[0061] Figure 4 A flowchart for multi-dimensional correlation analysis;

[0062] Figure 5 A flowchart for determining power and safety conditions. Detailed Implementation

[0063] 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.

[0064] Please see Figures 1-5 This invention provides a wireless charging system for industrial robots and its dynamic power adjustment method. The system includes an energy transmitting device, an energy receiving device, and a status monitoring module. The specific process of the method is as follows:

[0065] When the industrial robot enters the charging area, the status monitoring module collects charging environment parameters, robot load status, and remaining battery power in real time as the basis for adjustment. Environmental parameters are collected by temperature and humidity sensors and electromagnetic intensity detectors distributed throughout the charging area, and filtering algorithms are used to reduce noise interference. Load status data, including motor current, speed, and operating mode, is read in real time through the robot controller interface, including stage identifiers, timestamps, and parameter values. Remaining battery power is obtained through a power detection circuit integrated within the robot, including battery voltage, charging current, and temperature information, forming a multi-source heterogeneous status data set.

[0066] The collected environmental parameters, load status, and remaining battery power are preprocessed. Environmental parameters are filtered using a threshold filtering algorithm to remove extreme values ​​exceeding the device's tolerance range, retaining only the effective range values ​​for temperature, humidity, and electromagnetic interference intensity. The load status is segmented into three stages—start-up, stabilization, and shutdown—using a time-slicing method, with the start and end times of each stage marked. The remaining battery power is read in real-time by a power metering chip, and the charging curve indicates whether the current stage is pre-charging, constant current, constant voltage, or float charging. Specifically, the battery voltage signal is segmented into fixed-duration sampling segments. Noise filtering is performed on each segment, the start and end positions of the effective voltage are identified, features are extracted from the effective voltage segments, and the voltage features are converted into candidate power values ​​using a pre-calibrated power metering chip. These candidate values ​​are then corrected using historical data, and the most reasonable value is selected as the final reading result by combining charging current and time information.

[0067] Based on the preprocessed environmental parameters, load status, and remaining battery power, feature extraction is performed to form environmental adaptation features, load demand features, and battery capacity features. Environmental parameters are analyzed using a historical data comparison model to determine the deviation between current values ​​and standard values, extracting environmental adaptation features. Load status is calculated using a power demand prediction model to determine the average and peak energy consumption at each stage, extracting load demand features. Remaining battery power is analyzed using a charging characteristic analysis model to identify the current voltage change rate and current acceptance capability, extracting battery capacity features.

[0068] Environmental adaptation features, load demand features, and battery capacity features are input into the power regulation model for multi-dimensional correlation analysis, generating a set of regulation features containing parameter correspondences. A time synchronization mechanism is established to align environmental adaptation features, load demand features, and battery capacity features to the same timeline according to time nodes. The influence coefficients between different features are calculated through a weight allocation algorithm, focusing on associating the feature information of environmental parameters with load demand and battery capacity within the same time period. Specifically, based on time nodes, environmental adaptation features, load demand features, and battery capacity features within the same time period are grouped into feature groups. The matching degree between environmental parameters and load demand and the adaptability of battery capacity to target power are calculated for each feature group. Influence coefficients are assigned based on the matching degree and adaptability results, with higher coefficients indicating a stronger correlation between features. The correlated features are then cross-validated and fused to generate a set of regulation features containing information from environmental, load, and battery dimensions.

[0069] Based on the set of adjustment features, the target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage are determined, forming a set of power adjustment commands. The adjustment feature set is then clustered using a power matching model to determine the base power value and dynamic adjustment range for the current charging stage. Within the base power value range, the minimum energy loss value required to meet load demands is calculated using an efficiency optimization model as the transfer efficiency threshold. Finally, environmental parameters and equipment tolerance are analyzed using a safety constraint model to extract upper limits for voltage, current, and temperature as safety boundary conditions.

[0070] The execution logic is arranged according to the time sequence and priority of the power adjustment command set, driving the energy transmitter to dynamically adjust its output power and updating the adjustment commands as the robot's working state changes. The adjustment commands are arranged according to the time sequence of the charging stages, with corresponding target power values ​​and efficiency thresholds listed under each stage. Within each stage, commands are prioritized based on the constraint strength of safety boundary conditions, with voltage and current limiting commands executed first. Dynamic adjustment commands are listed separately, with corresponding trigger conditions and response delays noted, forming a clearly hierarchical execution logic list. The execution logic list is converted into control signals, using a "stage-power-constraint" control logic to drive the transmitter's adjustment. When the robot enters a new working stage, the process of state data acquisition, preprocessing, feature extraction, correlation analysis, and command generation is repeated to obtain new adjustment commands. These new adjustment commands are inserted into the corresponding stage or dynamic adjustment position, correcting the parameters and order of the original execution logic to maintain synchronization between the adjustment and the robot's state.

[0071] Example 1:

[0072] This embodiment describes the preprocessing of collected environmental parameters, load status, and remaining battery power. Regarding environmental parameter preprocessing, after the status monitoring module collects parameters such as temperature, humidity, and electromagnetic interference intensity in the charging environment, the system uses a threshold filtering algorithm to process these parameters. This algorithm pre-sets a threshold range that the device can tolerate. For each environmental parameter, the system compares the collected value with the threshold. If the value of a temperature parameter exceeds the device's maximum tolerable temperature threshold (e.g., the device's maximum tolerable temperature is 40 degrees Celsius, but the collected temperature is 45 degrees Celsius), this extreme value of 45 degrees Celsius will be discarded. Similarly, if the humidity value exceeds the device's minimum or maximum tolerable humidity threshold, or the electromagnetic interference intensity value exceeds the corresponding tolerable threshold, these extreme values ​​exceeding the threshold will be discarded by the system. Ultimately, only environmental parameter values ​​such as temperature, humidity, and electromagnetic interference intensity within the valid range are retained.

[0073] In the load state preprocessing stage, the system uses a time-slicing method to segment the load state. Specifically, the system monitors the robot's working state changes in real time. When the robot starts, the system records the start time of the startup phase. As the robot runs, when parameters such as motor current and speed reach a stable state, the system marks the end time of the startup phase and records the start time of the stabilization phase. When the robot is about to stop, the system records the end time of the stabilization phase and the start time of the shutdown phase, until the robot completely stops and the end time of the shutdown phase is recorded. In this way, the load state is clearly divided into three phases: startup, stabilization, and shutdown, and the start and end times of each phase are accurately marked. This time information provides important time reference for subsequent power adjustment.

[0074] The preprocessing of remaining battery power is relatively complex. First, the system reads the real-time battery voltage signal using a power metering chip integrated within the robot. During voltage signal reading, the system divides the signal into multiple fixed-duration sampling segments. The duration of each segment can be set according to actual conditions, such as 10 milliseconds or other suitable durations. For each sampling segment, the system performs noise filtering, removing noise interference from the signal using a specific filtering algorithm to identify the start and end positions of the effective voltage. Next, the system extracts features from the effective voltage segments, extracting characteristics such as voltage amplitude and trends. Then, using a pre-calibrated power metering chip, the extracted voltage features are converted into candidate battery power values. Since these candidate values ​​may contain some error, the system corrects them using historical data, while also considering charging current and time information. Multiple candidate values ​​are analyzed and compared, and the most reasonable value is ultimately selected as the final reading of the remaining battery power. After obtaining the accurate remaining battery power value, the system uses the charging curve to determine the current charging stage of the battery. The charging curve reflects the change in battery capacity at different charging stages. By comparing the current remaining battery capacity with the charging curve, the system can determine whether the battery is in the pre-charging stage, constant current stage, constant voltage stage, or float charging stage, and mark the current charging stage.

[0075] Throughout the preprocessing process, the effective range values ​​of environmental parameters, the time nodes of each stage of the load state, and the labeling of the current charging stage of the remaining battery power provide an accurate and reliable data foundation for subsequent feature extraction and power adjustment. The system strictly follows predetermined algorithms and procedures in each step, ensuring that the preprocessed data accurately reflects the actual working state and environmental conditions of the industrial robot's wireless charging system. For example, when processing environmental parameters, the threshold setting is based on the actual tolerance capacity of the equipment, effectively ensuring that the equipment operates within a safe environmental parameter range; when segmenting the load state stages, the time nodes are determined by real-time monitoring of changes in the robot's working parameters, making the division of load states more consistent with the robot's actual working conditions; when processing the remaining battery power, multiple steps of processing and correction ensure the accuracy of the power value, thereby accurately determining the battery's charging stage. This preprocessed information is interrelated and together constitutes an important basis for subsequent power adjustment. Through further processing and analysis of this information, the system can achieve precise dynamic power adjustment of the industrial robot's wireless charging system to meet the charging needs of different working states and environments, ensuring efficient, safe, and stable charging.

[0076] Example 2:

[0077] This embodiment details the process of feature extraction based on preprocessed environmental parameters, load status, and remaining battery power. During environmental parameter feature extraction, the system inputs the preprocessed effective range values ​​for temperature, humidity, and electromagnetic interference intensity into a historical data comparison model. This model stores a large number of standard values ​​for historical environmental parameters, determined through long-term data collection and analysis under normal device operating conditions. The system compares the current preprocessed environmental parameter values ​​with the standard values ​​one by one, calculating the deviation between the current value and the standard value for each parameter. For example, if the standard temperature is 25 degrees Celsius and the current preprocessed temperature value is 28 degrees Celsius, the temperature deviation is 3 degrees Celsius; if the standard humidity is 50%RH and the current preprocessed humidity value is 55%RH, the humidity deviation is 5%RH. In this way, the system extracts environmental adaptation features from the environmental parameters that reflect the difference between the current environment and the standard environment. These features help the system determine whether the current environment is suitable for the device to charge and the degree of influence of environmental factors on charging power adjustment.

[0078] Load state feature extraction primarily relies on a power demand prediction model. After the load state is divided into three stages—startup, stabilization, and shutdown—using a time-slicing method and each stage's time node is marked, the system processes each stage separately. For the startup stage, the system reads data such as motor current, movement speed, and operating mode output from the robot controller interface in real time, collecting all power consumption data points for this stage. Then, the power demand prediction model performs statistical analysis on these data points to calculate the average energy consumption of the startup stage. For example, if 100 power data points are collected during the 10-second startup stage, summing these data points and dividing by 100 yields an average energy consumption of 1000 watts. Simultaneously, the model scans these data points to find the maximum value, i.e., the peak energy consumption. Assuming the maximum power reaches 1500 watts during the startup stage, then 1500 watts is the peak energy consumption of the startup stage. Similarly, for the stabilization and shutdown stages, the system collects power data in the same way, calculating the average and peak energy consumption. By using this energy consumption data, the system can extract the power demand characteristics of the load at different stages. These characteristics can reflect the different power demands of the load at different operating stages, providing an important basis for subsequent power regulation.

[0079] Battery remaining capacity feature extraction is achieved through a charging characteristic analysis model. After the battery remaining capacity is preprocessed to obtain an accurate value and the current charging stage is determined, the system performs an in-depth analysis of the battery characteristics at this charging stage. The charging characteristic analysis model stores the voltage and current change patterns of the battery at different charging stages. Taking the constant current charging stage as an example, the system monitors the battery voltage changes in real time and calculates the voltage change rate per unit time, such as a voltage increase of 0.5 volts per hour. Simultaneously, the model analyzes the battery's current acceptance capability at the current stage based on the current remaining capacity and charging current; for example, in the constant current stage, the battery can accept a charging current of 5 amps. Through the analysis of the voltage change rate and current acceptance capability, the system extracts battery capacity features from the remaining capacity data. These features reflect the battery's charging state and acceptable power input at the current charging stage, helping the system determine the appropriate charging power to ensure safe and efficient charging of the battery.

[0080] Throughout the feature extraction process, the system adheres to strict procedures and methods for extracting features related to environmental parameters, load status, and remaining battery power. For environmental parameters, the use of historical data comparison models ensures that the extracted environmental adaptability features accurately reflect the differences between the current environment and the standard environment. The power demand prediction model's statistical analysis of energy consumption data at each stage of the load ensures that the extracted load demand features accurately reflect the power requirements of the load at different stages. The charging characteristic analysis model's analysis of battery voltage change rate and current acceptance capability guarantees that the extracted battery capacity features accurately reflect the battery's charging status. These feature extraction results are interconnected and mutually influential, collectively forming the input data for the subsequent power adjustment model. For example, environmental adaptability features affect the performance of load demand features; the power demand at load startup may differ under different ambient temperatures. Battery capacity features, in turn, combine with load demand features to jointly determine the charging power output by the system. Through accurate extraction and comprehensive analysis of these features, the system provides a solid foundation for subsequent multi-dimensional correlation analysis and power adjustment command generation. This enables more precise and efficient dynamic power adjustment of the entire industrial robot wireless charging system, adapting to different working environments and load statuses, and ensuring the stability and reliability of the charging process.

[0081] Example 3:

[0082] This embodiment describes the process of inputting environmental adaptability features, load demand features, and battery capacity features into a power regulation model for multi-dimensional correlation analysis, generating a set of regulation features containing parameter correspondences. During multi-dimensional correlation analysis, the system first establishes a time synchronization mechanism. This mechanism aligns the three types of features—environmental adaptability features, load demand features, and battery capacity features—to the same timeline. Specifically, the system adds a precise timestamp to each feature data point; the precision of the timestamp can be set according to actual needs, such as to the millisecond level. Then, using the timeline as a reference, different types of feature data are arranged in chronological order, ensuring that each environmental adaptability feature, load demand feature, and battery capacity feature corresponds to the same accurate time point, making the three types of features consistent and comparable in the time dimension.

[0083] After time synchronization is complete, the system calculates the influence coefficients between different features using a weighted allocation algorithm. This process uses a time node as a baseline, grouping environmental adaptation features, load demand features, and battery capacity features within the same time period into a feature group. The time period can be set according to actual conditions, such as 1 second or a shorter interval. For each feature group, the system calculates the degree of matching between environmental parameters and load demand, as well as the degree of adaptation between battery capacity and target power.

[0084] When calculating the match between environmental parameters and load demand, the system analyzes the impact of changes in environmental parameters on load power demand over the same time period. For example, if the ambient temperature is 30 degrees Celsius and the load is in the startup phase during a certain period, the system will analyze the impact of the temperature increase on the load's startup power demand based on historical data and preset models, thereby deriving a match value between environmental parameters and load demand. This match value can be represented by a specific numerical value; the larger the value, the higher the match between environmental parameters and load demand.

[0085] When calculating the compatibility between battery capacity and target power, the system analyzes the battery's ability to adapt to the target power in its current state, based on battery capacity characteristics such as remaining charge, voltage change rate, and current acceptance, as well as the preset target power value. For example, if the battery currently has 50% remaining charge and the target power is 800 watts, the system will assess whether the battery can accept 800 watts of power input with 50% remaining charge, and whether the state of accepting this power input is safe and efficient. This results in a value representing the degree of compatibility between battery capacity and target power, which is also expressed numerically.

[0086] Based on the calculated matching and fitness results, the system assigns influence coefficients to different features. The principle for allocating influence coefficients is: the higher the matching and fitness levels, the larger the corresponding influence coefficient, indicating a stronger correlation between features. For example, if in a certain feature group, the environmental parameters have a high degree of matching with the load requirements, and the battery capacity also has a high degree of fitness with the target power, then the influence coefficients among these three types of features will be assigned higher, and vice versa.

[0087] After allocating the influence coefficients, the system performs cross-validation fusion on the associated features. This process involves multi-faceted and multi-dimensional analysis and validation of feature data from different feature groups. The system checks the logical relationships between different features for rationality, data consistency, and the presence of contradictions or anomalies. For contradictory or anomaly-prone data, the system conducts further analysis and processing, potentially re-examining the data collection, preprocessing, and feature extraction processes to ensure data accuracy and reliability. Upon successful validation, the system fuses these feature data to generate a set of adjustment features containing information on environmental, load, and battery dimensions.

[0088] This set of adjustment features contains a wealth of information. The environmental dimension reflects the current charging environment and its impact on the charging process; the load dimension reflects the power demand of the load at different stages; and the battery dimension shows the current state and charging characteristics of the battery. These pieces of information are interconnected and interact with each other, forming a comprehensive feature set that provides sufficient basis for subsequently determining the target power value, energy transfer efficiency threshold, and safety boundary conditions.

[0089] Throughout the multi-dimensional correlation analysis process, the accurate establishment of the time synchronization mechanism ensured the consistency of feature data over time, the reasonable application of the weight allocation algorithm accurately reflected the influence relationships between features, and the cross-validation fusion process guaranteed the accuracy and reliability of the adjustment feature set. Through rigorous processing procedures and methods, the system organically linked the originally independent environmental adaptation features, load demand features, and battery capacity features, forming an adjustment feature set that comprehensively reflects the working status of the industrial robot wireless charging system. Each feature in this set contains rich information, and their combined effect enables the system to more accurately grasp various influencing factors during the charging process, providing a solid foundation for subsequent power adjustment decisions. This allows for precise dynamic power adjustment of the industrial robot wireless charging system to meet the charging needs of different working scenarios, ensuring efficient, safe, and stable operation of the charging process.

[0090] Example 4:

[0091] This embodiment describes the process of determining the target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage based on a set of adjustment features, thus forming a set of power adjustment commands. When determining the target power value, the system clusters the parameters in the adjustment feature set using a power matching model. For example, suppose the adjustment feature set contains multiple power-related parameters, such as the power demand during load startup at different ambient temperatures and the acceptable power of the battery at different remaining charge levels. The system inputs these parameters into the power matching model, which then categorizes them into different classes based on parameter similarity.

[0092] Taking a specific scenario as an example, when an industrial robot is in a charging area with an ambient temperature of 30 degrees Celsius, the load is in the startup phase, and the battery has 30% remaining power, the adjustment feature set includes environmental adaptation features at this temperature, load demand features during the startup phase, and battery capacity features with 30% remaining power. The power matching model clusters the power-related parameters in these features and finds that in historical data similar to this scenario, power parameters under similar ambient temperatures, load phases, and battery levels are mainly concentrated in the range of 500 watts to 700 watts. Through cluster analysis, the base power value for the current charging phase is determined to be 600 watts, with a dynamic adjustment range of ±100 watts. That is, the target power value can be dynamically adjusted between 500 watts and 700 watts according to the actual situation.

[0093] When determining the energy transfer efficiency threshold, the system calculates the minimum energy loss value required to meet load demands within a base power range using an efficiency optimization model. Continuing with the above scenario, the base power is 600 watts, with a dynamic adjustment range of 500 to 700 watts. The efficiency optimization model analyzes different power values ​​within this range, calculating the energy loss at each power value. For example, when the power is set to 500 watts, the calculated energy loss is 15%; when the power is set to 600 watts, the energy loss is 12%; and when the power is set to 700 watts, the energy loss is 10%. Simultaneously, the model considers the load's power demand during startup to determine which power value can meet the load demand while minimizing energy loss. For instance, at 700 watts, although the minimum energy loss is 10%, the peak power demand during load startup is 650 watts, which 700 watts can meet. Therefore, the system will use 10% as the transfer efficiency threshold, requiring that the energy transfer efficiency not be lower than 10% during this charging phase.

[0094] When determining safety boundary conditions, the system analyzes environmental parameters and equipment tolerance through a safety constraint model, extracting upper limits for voltage, current, and temperature. Using the previous scenario as an example, with an ambient temperature of 30 degrees Celsius, the system analyzes the equipment's tolerance at this temperature, combining other environmental parameters such as humidity and electromagnetic interference intensity, as well as the equipment's rated parameters, to determine the upper limits for voltage, current, and temperature. Assuming the equipment operates at 30 degrees Celsius with a rated voltage of 250 volts, a rated current of 10 amps, and a maximum tolerable temperature of 45 degrees Celsius, the safety constraint model will set the upper limit for voltage at 250 volts, the upper limit for current at 10 amps, and the upper limit for temperature at 45 degrees Celsius. During charging, if the voltage, current, or temperature exceeds these upper limits, the system will take corresponding protective measures to ensure the safe operation of the equipment.

[0095] When forming a set of power regulation commands, the system integrates the determined target power value, energy transfer efficiency threshold, and safety boundary conditions. For the scenario described above, the target power value is 600 watts, with a dynamic adjustment range of 500 to 700 watts; the energy transfer efficiency threshold is 10%; and the safety boundary conditions are a voltage limit of 250 volts, a current limit of 10 amps, and a temperature limit of 45 degrees Celsius. These parameters are organized into explicit commands, such as "The target power value for the current charging phase is 600 watts, which can be dynamically adjusted within the range of 500 to 700 watts. The energy transfer efficiency must not be lower than 10%, the voltage must not exceed 250 volts, the current must not exceed 10 amps, and the temperature must not exceed 45 degrees Celsius."

[0096] The entire process of determining the target power value, energy transfer efficiency threshold, and safety boundary conditions relies heavily on multi-dimensional information from the adjustment feature set. The power matching model's cluster analysis of parameters ensures that the target power value is set based on the similarity between historical data and the current scenario, thus possessing a certain degree of rationality and reference value. The efficiency optimization model, while meeting load requirements, aims to find the point of minimum energy loss as the efficiency threshold to improve the efficiency of the charging system. The safety constraint model, from the perspective of safe equipment operation, sets safety boundary conditions based on environmental parameters and equipment tolerance to ensure the safety of the charging process.

[0097] The determination process for these three parameters varies depending on the charging stage and the working scenario. For example, when the robot load is stable and the battery has 80% remaining charge, the parameters in the adjustment feature set will change. The base power value obtained by the power matching model clustering may decrease, the minimum energy loss value calculated by the efficiency optimization model may also differ, and the safety constraint model will adjust the safety boundary conditions set according to the environmental parameters at that time. In this way, the system dynamically determines the target power value, energy transmission efficiency threshold, and safety boundary conditions suitable for the current situation based on the real-time adjustment feature set, forming an accurate power adjustment command set. This provides specific parameter basis for the subsequent power adjustment execution logic arrangement, enabling the industrial robot wireless charging system to safely and efficiently adjust the charging power in different working states and environments.

[0098] Example 5:

[0099] This embodiment describes the process of dynamically adjusting the output power of an energy-emitting device by arranging the execution logic according to the time sequence and priority of a set of power adjustment instructions, and updating the adjustment instructions as the robot's working state changes. During the execution logic arrangement, the system first arranges the adjustment instructions according to the time sequence of the charging stages. Taking the industrial robot battery in the pre-charging, constant current, constant voltage, and float charging stages as an example, each stage lists the corresponding target power value and efficiency threshold. For instance, in the pre-charging stage, the target power value is set to 300 watts, and the efficiency threshold is 85%; in the constant current stage, the target power value is 500 watts, and the efficiency threshold is 90%; in the constant voltage stage, the target power value is 400 watts, and the efficiency threshold is 88%; and in the float charging stage, the target power value is 100 watts, and the efficiency threshold is 80%. The system arranges these instructions sequentially according to the time sequence of pre-charging, constant current, constant voltage, and float charging, forming a preliminary execution sequence.

[0100] Within each stage, the system prioritizes voltage and current limit commands based on the strength of the safety boundary conditions. For example, in the constant current stage, safety boundary conditions include a maximum voltage of 240 volts, a maximum current of 8 amps, and a maximum temperature of 45 degrees Celsius. Since exceeding these limits can directly impact device safety and charging efficiency, their constraints are more stringent. Therefore, the system prioritizes commands limiting voltage to 240 volts and current to 8 amps in this stage. Temperature limits, on the other hand, have a less direct impact in the short term than voltage and current limits, and are therefore placed after them. This ensures that voltage and current remain within safe limits before considering other constraints such as temperature.

[0101] The system lists dynamic adjustment commands separately, noting their corresponding trigger conditions and response delays, forming a clearly hierarchical execution logic list. For example, a dynamic adjustment command might be triggered when the battery temperature exceeds 40 degrees Celsius, with a response delay set to 2 seconds; or it might be triggered when the load switches from a stable phase to a shutdown phase, with a response delay of 1 second. These dynamic adjustment commands are categorized separately, distinguishing them from regular commands arranged by phase, facilitating real-time monitoring of trigger conditions during system operation.

[0102] When the power output of the energy transmitter is dynamically adjusted, the system converts the execution logic list into control signals, using a "stage-power-constraint" control logic to drive the transmitter's adjustment. Taking the constant current stage as an example, the target power value in the execution logic list for this stage is 500 watts, the efficiency threshold is 90%, and the priority voltage and current limit commands are voltage not exceeding 240 volts and current not exceeding 8 amperes. The system converts these commands into corresponding control signals, sends them to the energy transmitter, controls its output power to 500 watts, and monitors the voltage and current in real time to ensure they do not exceed the limits.

[0103] When the robot enters a new working phase, the system repeatedly executes the processes of state data acquisition, preprocessing, feature extraction, correlation analysis, and instruction generation to obtain new adjustment instructions. For example, if the robot was originally in a stable working phase and charging in a constant current charging phase, when the robot begins to stop and enters the shutdown working phase, the system will collect load state data during the shutdown phase through the state monitoring module, such as a gradual decrease in motor current and a reduction in movement speed, while also collecting environmental parameters and the remaining battery power. This data is then preprocessed, removing extreme values, marking time nodes, and determining the battery charging stage. Next, the feature extraction model extracts environmental adaptation features, load demand features, and battery capacity features. These features are then input into the power adjustment model for correlation analysis to generate adjustment instructions for the shutdown working phase. For example, the target power value may be reduced, and efficiency thresholds and safety boundary conditions may be adjusted accordingly.

[0104] The system will insert new adjustment commands into the corresponding stage or dynamic adjustment position, correcting the parameters and order of the original execution logic to maintain synchronization between the adjustment and the robot's state. Continuing with the above scenario, the newly added adjustment command for the shutdown working stage will be inserted after the constant current stage in the execution logic list, or, if dynamic adjustment is triggered, into the dynamic adjustment command section. Simultaneously, the system will correct the parameters in the original execution logic. For example, if the battery temperature changes during the shutdown stage, the upper temperature limit in the safety boundary conditions may need adjustment, or the target power value may be modified according to new load requirements, ensuring that the execution logic always matches the robot's current working state and charging environment.

[0105] Throughout the process, the execution logic is strictly arranged according to time sequence and priority, ensuring that the system can execute the corresponding power adjustment commands in an orderly manner at different charging stages and operating states. For example, in the pre-charging stage, the low-power target command and corresponding efficiency requirements for this stage are executed first, while prioritizing the satisfaction of voltage and current safety limits. When the robot's operating state changes, new commands are generated and the execution logic is updated in a timely manner through real-time data acquisition and processing, enabling the output power of the energy transmitter to be dynamically adjusted according to the robot's state. This dynamic adjustment mechanism allows the charging system to adapt to the changing power demands of the robot at different operating stages, while ensuring the safety and efficiency of the charging process. For example, when the robot switches from running to stopped mode, the load power demand decreases, and the system updates the command to reduce the target power value to avoid energy waste; when the battery temperature is detected to be close to the safety limit, the dynamic adjustment command triggers a power reduction to ensure equipment safety. In this way, the system achieves precise dynamic adjustment of the power of the industrial robot wireless charging system, ensuring that the entire charging process is efficient, safe, and stable.

[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0107] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic power adjustment in wireless charging of an industrial robot, characterized in that, The method includes the following procedures: When the industrial robot enters the charging area, the status monitoring module collects charging environment parameters, robot load status and remaining battery power in real time as the basis for adjustment. The collected environmental parameters, load status and remaining battery power are preprocessed, including filtering valid range values ​​for environmental parameters, marking real-time change nodes for load status, and indicating the current charging stage for remaining battery power. Based on the preprocessed environmental parameters, load status and remaining battery power, feature extraction is performed to form environmental adaptation features, load demand features and battery capacity features. By inputting environmental adaptability characteristics, load demand characteristics, and battery capacity characteristics into the power regulation model, a multi-dimensional correlation analysis is performed to generate a set of regulation features containing parameter correspondences. Based on the set of adjustment features, the target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage are determined, and a set of power adjustment commands is formed. The execution logic is arranged according to the time sequence and priority of the power adjustment command set, driving the energy emission device to dynamically adjust the output power, and updating the adjustment commands as the robot's working state changes.

2. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 1, characterized in that, The collected environmental parameters, load status, and remaining battery power are preprocessed as follows: Environmental parameters are filtered using a threshold filtering algorithm to remove extreme values ​​that exceed the equipment's tolerance range, while retaining the effective range values ​​for temperature, humidity, and electromagnetic interference intensity. The load status is divided into three stages: startup, stability, and shutdown, using the time slicing method, and the start and end time nodes of each stage are marked. The remaining battery power is read in real time by the power metering chip, and the charging curve indicates whether the battery is currently in the pre-charging, constant current, constant voltage or float charging stage. When the remaining battery power is read in real time via the power metering chip, the specific reading process is as follows: The battery voltage signal is divided into sampling segments of fixed duration, and noise is filtered in each segment to identify the start and end positions of the effective voltage. Feature extraction is performed on the effective voltage segment, and the voltage features are converted into candidate energy values ​​through a pre-calibrated energy metering chip; Historical data is used to correct the candidate power values, and the most reasonable value is selected as the final reading result by combining charging current and time information.

3. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 1, characterized in that, Feature extraction is performed based on the preprocessed environmental parameters, load status, and remaining battery power. The specific process is as follows: Environmental parameters are analyzed using historical data comparison models to determine the deviation between current values ​​and standard values, and to extract environmental adaptation features. The load condition is calculated using a power demand prediction model to determine the average and peak energy consumption at each stage, and to extract load demand characteristics. The remaining battery capacity is determined by using a charging characteristic analysis model to identify the current voltage change rate and current acceptance capability, thereby extracting battery capacity characteristics.

4. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 3, characterized in that, The environmental adaptability characteristics, load demand characteristics, and battery capacity characteristics are input into the power regulation model for multi-dimensional correlation analysis. The specific process is as follows: Establish a time synchronization mechanism to align environmental adaptation characteristics, load demand characteristics, and battery capacity characteristics to the same time axis according to time nodes; The influence coefficients between different features are calculated by weighting algorithm, focusing on the correlation of environmental parameters with load demand and battery capacity within the same time period. The associated features are cross-validated and fused to generate a set of adjustable features that includes information on environmental, load, and battery dimensions.

5. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 4, characterized in that, The target power value, energy transfer efficiency threshold, and safety boundary conditions for the current charging stage are determined based on the set of adjustment features. The specific process is as follows: By clustering the adjustment feature set using a power matching model, the base power value and dynamic adjustment range for the current charging stage can be determined. Within the basic power range, the minimum energy loss value that meets the load requirements is calculated using an efficiency optimization model and used as the transmission efficiency threshold. By analyzing environmental parameters and equipment tolerance through a safety constraint model, upper limits of voltage, current, and temperature are extracted as safety boundary conditions.

6. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 5, characterized in that, The execution logic is arranged according to the time sequence and priority of the power regulation command set. The specific process is as follows: The adjustment instructions are arranged in the time sequence of the charging stages, and the corresponding target power value and efficiency threshold are listed under each stage. Within each stage, voltage and current limiting instructions are executed first, based on the constraint strength of the safety boundary conditions. Dynamic adjustment instructions are listed separately, with corresponding triggering conditions and response delays noted, forming a clearly hierarchical execution logic list.

7. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 6, characterized in that, The drive energy emission device dynamically adjusts its output power and updates the adjustment command. The specific process is as follows: The execution logic list is converted into control signals, and the "stage-power-constraint" control logic is used to drive the transmitter to adjust. When the robot enters a new working phase, it repeats the process of state data acquisition, preprocessing, feature extraction, correlation analysis, and instruction generation to obtain new adjustment instructions; The new adjustment instructions are inserted into the corresponding stage or dynamic adjustment position to correct the parameters and sequence of the original execution logic and maintain the synchronization between the adjustment and the robot's state.

8. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 1, characterized in that, When the status monitoring module collects environmental parameters, load status, and remaining battery power in real time, the specific collection process is as follows: Environmental parameters are collected by temperature and humidity sensors and electromagnetic intensity detectors distributed in the charging area, and filtering algorithms are used to reduce noise interference. The load status is read in real time through the robot controller interface, including motor current, motion speed and working mode data, including stage identifier, timestamp and parameter values. The remaining battery power is obtained through a power detection circuit integrated inside the robot, which includes battery voltage, charging current and temperature information, forming a multi-source heterogeneous state data set.

9. The method for dynamic power adjustment of wireless charging for industrial robots according to claim 4, characterized in that, The specific calculation process for calculating the influence coefficient between different features using the weighting algorithm is as follows: Based on time nodes, environmental adaptation characteristics, load demand characteristics, and battery capacity characteristics within the same time period are grouped into feature groups. For each set of features, calculate the degree of matching between environmental parameters and load requirements; Calculate the degree of fit between battery capacity and target power; Influence coefficients are assigned based on the matching degree and the degree of adaptation; the higher the coefficient, the stronger the correlation between features.

10. A wireless charging system for industrial robots, characterized in that, The system includes an energy transmitting device, an energy receiving device, and a status monitoring module. The system is used to implement the steps of the industrial robot wireless charging dynamic power adjustment method as described in any one of claims 1 to 9.

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