Robot battery intelligent temperature control method based on environment temperature and user behaviors

Abstract

The invention provides a robot battery intelligent temperature control method based on environment temperature and user behaviors, which comprises the following steps: in combination with a pre-established user behavior habit model, analyzing and classifying a user behavior mode to obtain a behavior classification result; aiming at the behavior classification result and the environment temperature data in the original data set, constructing an association mapping relation between the temperature and the behavior mode to obtain a temperature-behavior mapping model; performing dynamic matching on the current environment temperature data and the user behavior mode through a temperature-behavior mapping model, and adjusting a charging and discharging strategy in combination with the battery state data to obtain a charging and discharging adjustment scheme; joint optimization is carried out on the charging and discharging adjustment scheme, control is carried out on a robot battery system, real-time monitoring is carried out, algorithm parameters are dynamically adjusted according to execution feedback data, a parameter set is obtained, and a final optimization control model is generated; the service life of the battery can be effectively prolonged, and the working efficiency and adaptability of the robot in different environments are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a robot battery intelligent temperature control method based on ambient temperature and user behavior. Background Art

[0002] In modern science and technology, the development of robotics has become a crucial pillar driving the advancement of intelligent systems. Research in energy management and system control, in particular, is directly related to the operational efficiency and lifespan of robots. Optimizing energy use and environmental adaptability through intelligent means is a key issue in this field that urgently needs breakthroughs. While many current solutions attempt to manage robot energy and temperature through fixed rules or simple feedback mechanisms, these approaches often lack the ability to dynamically adapt to complex environments and user behaviors, resulting in low system efficiency in changing scenarios and even potentially causing device overheating and energy waste. More specifically, the challenges facing this field primarily focus on effectively integrating multi-source information and achieving intelligent collaborative control. The primary challenge is that the real-time collection of location information and ambient temperature is often limited by the device's sensor accuracy. Incomplete or delayed data directly impacts the system's accurate understanding of the environment. This lack of sensory input leads to another core issue: the difficulty in dynamically adjusting control strategies based on user behavior. This makes it difficult for the system to flexibly respond to temperature fluctuations and energy demands in different scenarios, ultimately leading to low energy utilization and increased operational risks. These interconnected issues constitute a technical barrier to intelligent management.

[0003] Therefore, how to build a set of self-optimizing temperature control algorithms and dynamic charging and discharging strategies based on location information, real-time collection of ambient temperature, and user behavior habits to achieve intelligent coordinated control of temperature and energy usage has become a key issue in the research of robot energy management systems. Summary of the Invention

[0004] The present invention provides a robot battery intelligent temperature control method based on ambient temperature and user behavior, which mainly includes:

[0005] Acquire position information data and ambient temperature data, perform synchronous calibration and noise suppression processing on the position information data and the ambient temperature data to obtain an original data set; analyze and classify user behavior patterns based on the position information data in the original data set in combination with a pre-established user behavior habit model to obtain a behavior classification result; construct a correlation mapping relationship between temperature and behavior patterns based on the behavior classification result and the ambient temperature data in the original data set to obtain a temperature-behavior mapping model; dynamically match the current ambient temperature data and the user behavior pattern through the temperature-behavior mapping model to generate a first control instruction; adjust the charge and discharge strategy based on the first control instruction in combination with the battery status data to obtain a charge and discharge adjustment plan; jointly optimize the charge and discharge adjustment plan to generate a second control instruction; execute control and real-time monitoring of the robot battery system through the second control instruction to obtain execution feedback data; dynamically adjust algorithm parameters based on the execution feedback data to obtain a parameter set; perform environmental adaptability verification on the parameter set to generate a final optimization control model. Furthermore, the acquisition of position information data and ambient temperature data, synchronous calibration and noise suppression processing for the position information data and the ambient temperature data to obtain the original data set, specifically includes: collecting the original position information data and the original ambient temperature data in the robot's operating environment; segmenting the original position information data and the original ambient temperature data using a preset time interval, and performing preliminary filtering on the data in each time period to obtain an intermediate data set; synchronously calibrating the intermediate data set, aligning the timestamp deviation, and if the timestamp deviation exceeds a preset threshold, interpolating and adjusting the data to obtain a calibrated data set; based on the calibrated data set, using noise suppression technology to smooth the position information data and the ambient temperature data, eliminating abnormal fluctuation points, and obtaining the original data set; using the original data set to provide a data basis for subsequent analysis, obtaining a stability index after data processing, and judging whether the processing result meets the preset standard; if the stability index does not meet the preset standard, adjusting the parameters of the noise suppression technology, reprocessing the data, and obtaining an updated data set.

[0006] Furthermore, the location information data in the original data set is combined with a pre-established user behavior habit model to analyze and classify user behavior patterns to obtain a behavior classification result, specifically including: extracting location coordinate information and corresponding timestamp data from the original data set, segmenting the location coordinate information through a time window partitioning method to obtain a location time series data set; based on the location time series data set, counting the user's stay time and number of visits at each spatial location, if the stay time exceeds a preset threshold, it is marked as a valid usage point, and the usage frequency distribution characteristics are calculated; using the user behavior habit model to perform pattern matching analysis on the usage frequency distribution characteristics to generate a behavior feature vector matrix; classifying the behavior feature vector matrix through a cluster analysis algorithm, calculating the distance between the data point and the cluster center to obtain a preliminary clustering grouping result; for the preliminary clustering grouping result, calculating the behavior pattern similarity index within the group, if the similarity index is lower than the preset threshold, adjusting the clustering parameters for reclassification to obtain the behavior classification result.

[0007] Furthermore, the association mapping relationship between temperature and behavior pattern is constructed for the behavior classification results and the ambient temperature data in the original data set to obtain a temperature-behavior mapping model, which specifically includes: obtaining behavior pattern identifier data from the behavior classification results and sorting them by timestamp; extracting ambient temperature parameter records with corresponding timestamps from the original data set, and if data is missing, using nearest neighbor interpolation to complete it; merging the behavior pattern identifier data and the ambient temperature parameter records to generate an original data correspondence table; calculating the upper and lower threshold ranges of the ambient temperature parameter records, and marking them as abnormal if they exceed the range; fitting the association between the ambient temperature parameter records and the behavior pattern identifier data through a regression analysis method, and if they are marked as abnormal, adjusting the weights and recalculating the regression coefficients to obtain the temperature-behavior mapping model.

[0008] Furthermore, the current ambient temperature data and the user behavior pattern are dynamically matched through the temperature-behavior mapping model to generate a first control instruction, which specifically includes: comparing the current ambient temperature data and the user behavior pattern through the temperature-behavior mapping model to obtain the matching degree and determine the preliminary matching result; based on the preliminary matching result, extracting the corresponding features of the ambient temperature data and the user behavior pattern, and using the decision tree algorithm to infer the control strategy to obtain the strategy calculation result; based on the strategy calculation result, judging whether the current ambient temperature data deviates from the preset threshold range, and if so, generating a temperature adjustment signal set; performing a secondary check on the user behavior pattern through the temperature adjustment signal set to obtain correlation degree data; if the correlation degree data is lower than the preset matching standard, correcting the temperature adjustment signal set to obtain the final first control instruction; based on the first control instruction, continuously monitoring the changing trend of the ambient temperature data to obtain dynamic monitoring results.

[0009] Furthermore, the charging and discharging strategy is adjusted according to the first control instruction in combination with the battery status data to obtain a charging and discharging adjustment plan, which specifically includes: through the first control instruction, collecting the remaining power information in the battery status data, comparing it with the preset threshold, and obtaining a power status evaluation result; based on the power status evaluation result, if the remaining power is lower than the preset threshold, the temperature control demand is verified, and the decision tree algorithm is used to analyze the applicability of the dynamic strategy to determine the priority adjustment direction; through the priority adjustment direction, the charging and discharging adjustment parameters are configured, the adjustment range is obtained, and a preliminary charging and discharging adjustment plan is obtained; according to the preliminary charging and discharging adjustment plan, the battery status data change trend is analyzed to obtain a stability index; if the stability index shows a fluctuation beyond the preset range, the adjustment plan parameters are optimized to obtain the final charging and discharging adjustment plan; through the charging and discharging adjustment plan, the matching degree between temperature control and battery status is continuously monitored to obtain a basis for dynamic control.

[0010] Furthermore, the charge and discharge adjustment scheme is jointly optimized to generate a second control instruction, specifically including: performing parameter matching analysis on the charge and discharge adjustment scheme and the temperature control strategy using a genetic algorithm, calculating a collaborative control index, and obtaining an initial control parameter set; based on the initial control parameter set, if the collaborative control parameter deviation exceeds a preset deviation threshold, performing a preliminary correction to obtain an adjusted parameter combination; through the adjusted parameter combination, monitoring strategy matching changes and obtaining matching degree data; if the matching degree data does not meet the preset standard, performing a secondary iterative process to classify the cause of the deviation and determine a corrected instruction direction; through the corrected instruction direction, obtaining strategy execution status feedback data to determine whether it meets the preset stability range; if the stability does not reach the preset range, fine-tuning the parameter amplitude, filtering the fluctuation data, and obtaining the final second control instruction.

[0011] Furthermore, the robot battery system is controlled and monitored in real time through the second control instruction to obtain execution feedback data, specifically including: sending a control signal to the temperature control execution unit and the charge and discharge execution unit of the battery system through the second control instruction; using a closed-loop feedback mechanism to monitor the execution results and obtain preliminary status data; analyzing the execution result deviation based on the preliminary status data, and if the deviation exceeds the preset target threshold, identifying the source of the deviation and determining the deviation category; adjusting the control signal parameters based on the deviation category, performing stability verification, and obtaining an adjusted signal set; based on the adjusted signal set, continuously tracking state changes and obtaining execution feedback data; if the synergistic effect of the execution feedback data does not reach the preset target, performing a secondary filtering on the fluctuation data to obtain optimized state information and determine the final control signal combination.

[0012] Furthermore, the algorithm parameters are dynamically adjusted according to the execution feedback data to obtain a parameter set, which specifically includes: analyzing the operating performance of the temperature control algorithm through the execution feedback data, filtering outliers, and obtaining preliminary feedback information; identifying parameter adjustment deviations based on the preliminary feedback information, and if the deviation exceeds a preset standard, locating the source of the deviation and determining the deviation category information; using the deviation category information, updating the weight threshold of the temperature control algorithm using an online learning method, performing a stability check, and obtaining an adjusted parameter set; monitoring the state changes of the temperature control system based on the adjusted parameter set, and obtaining updated state data; if the coordinated performance of the updated state data does not meet the preset standard, performing a secondary filtering on the fluctuation data to obtain optimized state information; fine-tuning the operating parameters through the optimized state information, performing simulation verification, and determining the final parameter set.

[0013] Furthermore, the environmental adaptability verification is performed on the parameter set to generate a final optimized control model, specifically including: collecting the parameter set data under different temperature changes and position information through the environmental adaptation module, constructing a simulation environment, and obtaining preliminary environmental response data; simulating fluctuations in extreme environments based on the preliminary environmental response data, performing multi-scenario testing on the algorithm verification module, and determining the operating performance; extracting a stability assessment index from the operating performance, and if it is lower than a preset threshold, dynamically adjusting the parameter set to obtain adjusted parameter response data; re-performing simulation tests based on the adjusted parameter response data to verify adaptability; if the adaptability does not meet the preset standard, analyzing the battery life protection correlation data, using the support vector machine algorithm to adjust the control strategy, and determining the final optimized control model; and continuously monitoring environmental changes through the final optimized control model to obtain real-time feedback data.

[0014] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0015] The present invention discloses a method for intelligent temperature control of robot batteries based on ambient temperature and user behavior. Position and temperature data are collected through a sensor network, cluster analysis is performed in combination with a user behavior habit model, and a correlation mapping relationship between temperature and behavior pattern is constructed. According to the current ambient temperature and user behavior pattern, a decision tree algorithm is used to infer the temperature control strategy, and the charge and discharge strategy is optimized in combination with battery status data. The present invention uses a genetic algorithm to jointly optimize the temperature control and charge and discharge strategies, calculates the collaborative control parameters in real time, and monitors the execution effect through a closed-loop feedback mechanism. An online learning method is used to dynamically adjust the algorithm parameters, and environmental adaptability verification and extreme environment stability evaluation are performed to finally form an optimized control model. This method can effectively extend battery life and improve the robot's work efficiency and adaptability in different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the robot battery intelligent temperature control method based on ambient temperature and user behavior of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 The robot battery intelligent temperature control method based on ambient temperature and user behavior described in this embodiment may specifically include:

[0019] In step S101, the position information data and ambient temperature data of the robot's operating environment are obtained, the position information acquisition module and the ambient temperature monitoring module are synchronously calibrated, and the collected raw position data and temperature data are preliminarily filtered at preset time intervals to obtain a noise-suppressed raw data set.

[0020] Specifically, a sensor network is used to acquire raw data sets of position information and ambient temperature from the robot's operating environment. Based on the raw data sets, the data is segmented using preset time intervals. Preliminary filtering is performed on the position information and ambient temperature data within each time period to produce a preliminarily processed intermediate data set. Synchronous calibration is then performed on the intermediate data sets, aligning the timestamps of the position information and ambient temperature data. If the timestamp deviation exceeds a preset threshold, the data is interpolated and adjusted to produce a time-aligned calibration data set. Based on the calibration data set, noise suppression techniques are used to smooth the position information and ambient temperature data, removing any abnormal fluctuations to produce the noise-suppressed raw data set.

[0021] In one possible implementation, the sensor network obtains raw data sets of position information and ambient temperature from the robot's operating environment, and the real-time and accuracy of data collection must be ensured.

[0022] For example, a robot's GPS module and temperature sensor record its location coordinates and ambient temperature, respectively. Consider an industrial warehouse scenario where a robot collects data once per second, generating a raw data set containing timestamps, longitude and latitude coordinates, and temperature values. For example, the timestamp is 2025-05-26 10:00:00, the location is (114.05, 22.55), and the temperature is 25.3°C. This raw data may contain noise due to sensor accuracy or environmental interference, affecting subsequent analysis. To segment the raw data set, the data can be grouped at preset intervals, such as every 5 minutes.

[0023] For example, the data from 10:00:00 to 10:05:00 is grouped into a segment containing 300 sets of timestamp, location, and temperature data. During initial filtering, obvious outliers can be removed, such as locations outside the warehouse range (114.00 to 114.10, 22.50 to 22.60) or temperatures outside a reasonable range (e.g., -10°C to 50°C). For example, a timestamp recording a temperature of 100°C is clearly abnormal and can be removed to generate an intermediate dataset. This step reduces data redundancy in subsequent processing and improves analysis efficiency.

[0024] It should be noted that the synchronous calibration process requires the alignment of the timestamps of the position and temperature data.

[0025] For example, GPS module signal delays can cause timestamp deviation. For example, if the location data timestamp is 10:00:00.5 and the temperature data is 10:00:00.0, the deviation is 0.5 seconds. If the preset threshold is 0.3 seconds, interpolation adjustment is required.

[0026] In one embodiment, the position of the offset time point can be estimated by linear interpolation, such as calculating the coordinates of 10:00:00.0 based on the previous and next position points. This calibration ensures that the data time is consistent and provides a reliable basis for subsequent analysis.

[0027] In one possible implementation, the noise suppression technique may use a moving average method to smooth the data.

[0028] For example, for position data, the average coordinates are calculated every 5 seconds to reduce fluctuations caused by signal jitter. For temperature data, similar processing can smooth sudden changes. For example, if the temperature at a point suddenly rises to 30°C while being 25°C before and after, this can be considered an outlier and removed. This generates a smoother, less outlier raw data set.

[0029] For example, the smoothed position trajectory is more consistent with the robot's actual path, and the temperature data can better reflect the real changes in the environment.

[0030] Smoothed position data reduces position errors, and stable temperature data helps determine whether the device operating environment is abnormal. For example, high-temperature areas may indicate the risk of device failure.

[0031] These processes ensure that the robot's operation decisions are more reliable, reduce erroneous operations caused by data errors, and improve the overall stability of the system.

[0032] Step S102: Based on the location information data in the original data set and in combination with a pre-established user behavior habit model representing the user's use of the robot, the frequency and duration distribution characteristics of the user's use of the robot in different locations are analyzed, and the user's behavior habits are classified using a cluster analysis method to determine the behavior classification results of the user's behavior pattern.

[0033] Specifically, the location coordinate information in the original dataset is obtained. For each location coordinate, the corresponding timestamp data is extracted. The location data is segmented using a time window partitioning method to generate a location time series data set. Based on this location time series data set, the user's dwell time and number of visits at each spatial location are counted. If the dwell time exceeds a preset threshold, the location is marked as a valid usage point, and the usage frequency distribution characteristics of each location are calculated. Using a pre-established user behavior habit model, pattern matching analysis is performed on the usage frequency distribution characteristics to identify differences in user behavior patterns at different spatial locations and generate a behavior feature vector matrix. The behavior feature vector matrix is automatically classified using the K-means clustering analysis algorithm. The number of cluster centers is set, and the distance between each data point and the cluster center is calculated to obtain preliminary clustering results. Based on the preliminary clustering results, the behavioral pattern similarity index within each group is calculated. If the similarity index falls below a preset threshold, the group is subdivided and the clustering parameters are adjusted for reclassification. Based on the adjusted clustering results, each user behavior pattern is assigned a unique classification identifier, and a mapping table between location coordinates and behavior patterns is established to generate a behavioral classification result for user behavior habits. Based on the behavior pattern identifiers in the behavior classification results, the user quantity distribution and behavior characteristic parameters of each category are counted, and a feature description library of user behavior patterns is constructed to form a complete user behavior classification system.

[0034] In one possible implementation, time window partitioning is the core technology for segmenting location data. For an industrial warehouse robot, for example, the system sets a 10-minute time window and segments continuous location coordinate data into independent segments in chronological order. For example, suppose the robot's position sequence in a certain area is shelf A's coordinates 114.052, 22.552, with timestamps from 10:00:00 to 10:10:00, forming a complete location time series data segment. This partitioning method effectively captures the robot's spatial movement patterns within a specific time period, providing a structured data foundation for subsequent behavioral analysis.

[0035] Specifically, dwell time statistics and visit count calculations are key steps in identifying effective usage points. The system presets a dwell time threshold of 30 seconds. When the robot stays at coordinates 114.052, 22.552 for 35 seconds, the location is marked as a valid usage point. Visit count statistics show that shelf A was visited 15 times in one day, with a total dwell time of 8 minutes and a usage frequency of 0.6 times per hour. In contrast, although the aisle area was visited 50 times, the dwell time was only 2 minutes, indicating that it is a transitional location rather than a key operation area.

[0036] In one embodiment, the user behavior habit model establishes a feature template library based on historical data. The model includes behavior types such as operation mode, inspection mode, and charging mode, and each mode has specific location distribution characteristics and time characteristics.

[0037] For example, a work pattern may include frequent stops in a shelf area, with an average dwell time of 45 seconds and a visit interval of 3 minutes. When the new location data matches the work pattern characteristics at a rate of 85%, the system classifies the behavior as work and generates a corresponding behavior feature vector.

[0038] Specifically, the process of building a user behavior habit model includes the following steps:

[0039] Step S1: Data acquisition and preprocessing

[0040] The robot's built-in sensors (GPS, inertial navigation unit) collect the user's operating location coordinates (latitude and longitude) and timestamp data in real time;

[0041] Process continuous data in segments at fixed time intervals (e.g., 10 minutes) to generate a structured location time series dataset;

[0042] Eliminate outliers such as coordinates outside the geofence range and timestamp logic errors;

[0043] Z-score standardization is performed on features such as residence time and movement speed to eliminate dimensional differences.

[0044] Step S2: Effective behavior recognition and feature extraction

[0045] Count the duration of stay at each location. If it exceeds a threshold (e.g., 30 seconds), mark it as a valid usage point (e.g., shelf area, charging station).

[0046] Count the number of visits to effective usage points within a unit time (such as 24 hours) to generate usage frequency distribution characteristics;

[0047] Extract features such as the mean, variance, visit interval, movement speed, and spatial density of the dwell time to construct a multidimensional feature vector: .

[0048] Step S3: Behavior pattern classification (combination of clustering and supervised learning)

[0049] Use the K-means algorithm and preset initial categories (e.g., 5 categories: operation, inspection, charging, standby, and abnormal);

[0050] Calculate the Euclidean distance between the feature vector and the cluster center to group similar behaviors into the same group;

[0051] Calculate the silhouette coefficient within the group. If it is lower than the threshold (such as 0.7), adjust the number of cluster centers or distance weights and reclassify.

[0052] Use manually annotated data to train a random forest classifier (parameters: n_estimators=100, max_depth=10).

[0053] Step S4: Model training and validation

[0054] Divide historical data into training set and validation set in a ratio of 7:3;

[0055] Merge the pseudo labels generated by clustering with manually annotated data to enhance the generalization ability of the model;

[0056] Accuracy (target ≥85%): ;

[0057] Focus on missed abnormal patterns (such as charging interruptions);

[0058] Measures behavioral consistency within a group.

[0059] Step S5: Dynamic update and anomaly detection

[0060] The random forest model is updated with the partial_fit method based on the newly added data every day, and the cluster centers are adjusted.

[0061] Calculate the anomaly score using the Isolation Forest algorithm: ;

[0062] If the score exceeds the threshold (such as 0.7), manual review or automatic labeling is triggered.

[0063] Step S6: Model output and application

[0064] Output a classification result table containing timestamps, location coordinates, and behavior pattern identifiers (such as Work_A01);

[0065] Use heat maps to display high-frequency operation areas and statistically analyze pattern distribution (e.g., operations accounting for 60%) to assist in operation and maintenance decision-making.

[0066] The classification results are input into the temperature-behavior mapping model to dynamically adjust the temperature control and charging and discharging strategies.

[0067] It should be noted that the setting of the number of cluster centers in K-means cluster analysis directly affects the classification accuracy. The system initially sets the number of cluster centers to 5, corresponding to the five behavioral modes of operation, inspection, charging, standby, and abnormality. During the calculation process, the Euclidean distance between the behavioral feature vector of shelf A area and the operation mode cluster center is 0.23, and the distance to the inspection mode center is 0.67, so it is classified into the operation mode group. When the similarity index within a group is only 0.45, which is lower than the preset threshold of 0.7, the system automatically adjusts the number of cluster centers to 7 to achieve a more refined division of behavioral patterns.

[0068] For example, after the mapping table is established, the location coordinates 114.052, 22.552 correspond to the behavior pattern identifier "Work_A01," and 114.048, 22.548 corresponds to "Patrol_B02." Statistics from the feature description library show that the work mode contains 1,200 user behavior samples, with an average dwell time of 42 seconds and a visit frequency of 0.8 times per hour. The inspection mode contains 800 samples, with an average dwell time of 15 seconds and a movement speed of 1.2 meters per second. This classification system enables the robot to accurately identify its working status based on its current location and behavior characteristics, optimizing task scheduling strategies.

[0069] Step S103, based on the user behavior pattern of the behavior classification result, combined with the ambient temperature data in the robot's original data set, a regression analysis method is used to construct an association mapping relationship between temperature and behavior pattern. If the ambient temperature data exceeds the preset temperature threshold range, the association mapping relationship is weighted and a temperature-behavior mapping model of temperature-behavior association is obtained.

[0070] The behavioral pattern identifier data from the behavior classification results is obtained from the robot behavior log database and sorted by millisecond timestamp. Ambient temperature parameter records within the same timestamp range are extracted from the sensor database, and missing data are filled using nearest neighbor interpolation within 5 milliseconds before and after. The two columns of data are merged to generate a raw data correspondence table containing three columns: timestamp, behavioral pattern identifier, and temperature parameter. The 25th and 75th percentiles of the temperature parameter are calculated, with the lower threshold set at the 25th percentile minus 1.5 times the interquartile range, and the upper threshold set at the 75th percentile plus 1.5 times the interquartile range. The raw data correspondence table is traversed. If the temperature parameter is less than the lower threshold or greater than the upper threshold, an anomaly column is added to the row and assigned a value of 1; otherwise, it is assigned a value of 0. Scikit-learn's LinearRegression function is used to fit the one-hot encoding matrix of the temperature parameter and the behavioral pattern identifier to obtain the initial regression coefficients. For each data point, if the anomaly flag is 1, the weight of the point is set to 0.5; otherwise, the weight remains 1.0. The weighted least squares method is used to recalculate the regression coefficients to generate the final mapping model.

[0071] For example, when analyzing the correlation between robot behavior logs and ambient temperature parameters, we can start from the perspective of data acquisition and preprocessing. After extracting behavior pattern identifier data from the log database, it is sorted by millisecond timestamp to ensure time series continuity. For example, a log record from an industrial warehouse robot shows that the behavior pattern identifier for timestamps from 10:00:00.000 to 10:00:01.000 is "Work_A01," indicating work mode. During the same period, ambient temperature parameters extracted from the sensor database may contain missing values. In this case, the nearest neighbor interpolation method within the previous and next 5 milliseconds is used to ensure data integrity. For example, if the temperature data at 10:00:00.500 is missing, the temperature value of 22.3 degrees Celsius at 10:00:00.495 is used as the filler value. This method maintains the temporal consistency of the data and lays the foundation for subsequent analysis.

[0072] For example, in the data merging and anomaly detection phase, after the raw data corresponding table is generated, it contains three columns: timestamp, behavior pattern identifier, and temperature parameter. Taking a certain time point 10:00:00.000 as an example, the behavior pattern identifier is "Work_A01" and the temperature parameter is 23.5 degrees Celsius. The 25% quantile of the calculated temperature parameter is 20.0 degrees Celsius, the 75% quantile is 25.0 degrees Celsius, the interquartile range is 5.0 degrees Celsius, the lower threshold is 12.5 degrees Celsius, and the upper threshold is 32.5 degrees Celsius. If the temperature at a certain time point is 35.0 degrees Celsius, which exceeds the upper limit, it is marked as an anomaly and assigned a value of 1. This anomaly marking method helps to quickly screen out environmental anomalies that may affect robot behavior, providing more reliable data support for subsequent modeling.

[0073] For example, during the process of building a regression model and adjusting weights, using linear regression to fit a one-hot encoding matrix of temperature parameters and behavioral pattern identifiers can initially reveal the impact of environmental factors on behavioral patterns. Suppose the initial regression coefficients show that for every 1-degree Celsius increase in temperature, the probability of operating mode increases by 0.02, while the probability of inspection mode decreases by 0.01. For data points marked as abnormal with a value of 1, the weight is set to 0.5 to reduce their impact on the model. For example, if the temperature at a certain point in time is abnormally 35.0 degrees Celsius, adjusting the weight to 0.5 will weaken its impact on the final model. After recalculating the weighted regression coefficients, the resulting mapping model can more accurately reflect the probability distribution of behavioral patterns under normal conditions. This weighting method effectively improves the robustness of the model, especially in scenarios with large environmental fluctuations such as industrial warehouses.

[0074] For example, from a business perspective, industrial warehouse robots must maintain stable behavior in varying temperature environments. Temperature anomalies can cause the robot's operating efficiency to decline or its inspection path to deviate. The aforementioned anomaly marking and weighted regression method can promptly identify potential risk points and provide data support for subsequent task scheduling. For example, if the temperature consistently exceeds an upper threshold, the system can prioritize relocating the robot to a cooler area. This approach not only protects the equipment but also optimizes overall operational efficiency.

[0075] In step S104, the current ambient temperature data and the user behavior pattern are dynamically matched through the temperature-behavior mapping model, and a preset decision tree algorithm is used to preliminarily calculate the temperature control strategy to determine whether the current temperature needs to be adjusted, thereby obtaining a first control instruction.

[0076] Specifically, a temperature-behavior mapping model is used to compare ambient temperature data and user behavior patterns in real time, determining the degree of match between the two and determining a preliminary matching result. Based on the preliminary matching result, feature extraction is performed on the corresponding relationship between the ambient temperature data and the user behavior pattern. A decision tree algorithm is then used to infer a control strategy, resulting in a policy calculation result. Based on the policy calculation result, the current ambient temperature data is analyzed to determine whether it deviates from a preset threshold range. If so, a corresponding temperature adjustment signal is generated, and a set of adjustment signals is determined. Using the set of adjustment signals, a secondary verification is performed on the user behavior pattern at the current ambient temperature to determine the degree of correlation between the behavior pattern and the temperature adjustment signal, generating verified correlation data. Based on the verified correlation data, if the correlation falls below the preset matching standard, the temperature adjustment signal is corrected to obtain a corrected adjustment signal, and the final control instruction is determined. Using the final control instruction, the changing trend of the ambient temperature data is continuously monitored, and feedback on the behavior pattern underlying the changing trend is obtained, resulting in dynamic monitoring results. According to the dynamic monitoring results, if the change trend shows that the ambient temperature data continues to deviate from the preset threshold, the control instructions are iteratively updated to obtain the updated instruction set and determine the basis for subsequent control.

[0077] For example, in the case of an industrial warehouse robot controlling ambient temperature, the application of a temperature-behavior mapping model can begin by comparing ambient temperature data with user behavior patterns in real time. For example, if the ambient temperature at a certain point in time is 28.5 degrees Celsius and the behavior pattern indicates high-intensity work, the model comparison will find an 85% match, initially determining it to be within the normal range. This comparison method helps quickly determine whether the current environment is suitable for a specific behavior pattern.

[0078] For example, during feature extraction and the decision tree algorithm's inference of control strategies, in-depth analysis can be performed on the correspondence between temperature and behavioral patterns. Assuming the extracted features include the temperature change rate and the duration of the operation, the decision tree algorithm can infer that when the temperature exceeds 30.0 degrees Celsius and the operation duration exceeds two hours, the operation intensity should be reduced. This inference method can provide a clear basis for subsequent decision-making on control measures.

[0079] For example, if the ambient temperature deviates from a preset threshold, assuming the preset threshold range is 18.0 to 30.0 degrees Celsius, and the current temperature is 31.2 degrees Celsius, a temperature adjustment signal is generated, recommending lowering the ambient temperature to 29.0 degrees Celsius. This signal generation mechanism enables timely response to abnormal conditions and ensures the stability of the robot's operating environment.

[0080] For example, during the secondary verification of the correlation between the behavior pattern and the temperature adjustment signal, suppose the adjustment signal recommends a temperature reduction, but the behavior pattern indicates that the robot is still operating at a high load. The correlation is only 60%, below the preset standard of 80%. In this case, the adjustment signal needs to be corrected, perhaps adjusting the temperature reduction from 2.0 degrees Celsius to 3.0 degrees Celsius to better suit the current behavior requirements. This verification and correction mechanism can improve the accuracy of control.

[0081] For example, if the continuous monitoring of the final control command reveals a continuous upward trend in temperature, from 29.0°C to 32.0°C within 24 hours, the control command will be iteratively updated, perhaps including a forced pause to prevent equipment overheating. This dynamic monitoring and command update approach effectively responds to environmental changes and ensures long-term operational reliability.

[0082] For example, in an expansion solution, historical temperature data and behavioral pattern feedback can be combined to optimize the logic for generating control commands. For example, if the temperature frequently exceeded 30.0°C over the past week, resulting in a 10% drop in robot efficiency, an early warning mechanism could be set up to automatically adjust task allocation when the temperature approaches the threshold. This approach can further improve system adaptability and reduce potential risks.

[0083] It's important to note that all of the aforementioned steps revolve around temperature control for industrial warehouse robots, aiming to form a closed-loop control system through multi-dimensional analysis and real-time feedback. This design not only addresses sudden environmental changes but also improves overall operational efficiency through continuous optimization, providing strong support for equipment management and task scheduling in industrial scenarios.

[0084] Step S105: Analyze the matching degree between the current battery status data and the temperature control requirement according to the first control instruction. If the remaining power in the battery status data is lower than the preset power threshold, adjust the dynamic charge and discharge strategy first and determine the charge and discharge adjustment plan.

[0085] Specifically, a first control instruction collects battery status data in real time, performs a preliminary analysis of the remaining charge compared to a preset threshold, and obtains a charge status assessment result. Based on the charge status assessment result, if the remaining charge is below the preset threshold, a secondary check is conducted on the temperature control requirements. A pre-established decision tree algorithm is used to analyze the applicability of the dynamic strategy and determine the priority adjustment direction. Based on the priority adjustment direction, parameters are configured based on the specific charge and discharge adjustment requirements, and the adjustment range under the dynamic strategy is obtained, resulting in a preliminary charge and discharge adjustment plan. Based on the preliminary charge and discharge adjustment plan, the change trend of the battery status data after adjustment is analyzed, and a stability index under the change trend is obtained to determine the feasibility of the adjustment plan. Based on the stability index, if the change trend indicates that the charge data fluctuation exceeds a preset range, the adjustment plan is optimized, the optimized strategy parameters are obtained, and a final charge and discharge adjustment plan is determined. Based on the final charge and discharge adjustment plan, the degree of match between temperature control and battery status is continuously monitored, and matching change data is obtained to obtain a dynamic control basis. Based on the dynamic control basis, if the matching degree falls below a preset standard, the control instructions are iteratively adjusted to obtain an adjusted instruction set and determine the subsequent strategy execution direction.

[0086] For example, in an industrial warehouse robot, real-time battery status data can be collected through built-in sensors that record power data every minute and, combined with historical operating records, form a complete power profile. For example, if the remaining battery level is 35% during a certain period, while the preset threshold is 40%, initial analysis indicates that the battery level is low and requires further attention.

[0087] For example, if the battery level falls below a preset threshold, a secondary check on the need for temperature control can be implemented by analyzing the relationship between the current ambient temperature and the robot's operating load. Assuming the ambient temperature is 29.5 degrees Celsius and the robot is operating at a medium-to-high load, the decision tree algorithm will prioritize whether to lower the temperature to reduce power consumption, prioritizing cooling.

[0088] For example, after determining the priority adjustment direction, the charging rate or discharge limit can be set through parameter configuration to meet the specific needs of charge and discharge adjustments. Assuming the current power consumption rate is fast, the system configures the charging rate to 80% of the standard value to avoid the impact of excessive charging on battery life, while limiting the discharge power to ensure smooth operation.

[0089] For example, when analyzing power data trends for a preliminary charge and discharge adjustment plan, you can observe whether the power level stabilizes after the adjustment. Assuming that the power level increases from 35% to 38% within one hour after the adjustment, and the stability indicator shows a fluctuation range of less than 2%, the plan is initially considered feasible. If the fluctuation exceeds the preset range, such as 5%, the adjustment plan needs to be optimized, perhaps by reducing the charging rate or adjusting the operating task.

[0090] For example, when optimizing a solution, policy parameters can be adjusted to balance power recovery with operational needs. Assuming the optimized charging rate is reduced to 70% of the standard value, and non-essential robot tasks are reduced, the power fluctuation range is reduced to 1.5%, and the final solution is confirmed.

[0091] For example, continuous monitoring of the matching degree between temperature control and battery status can be achieved by comparing the changes in the two data every hour. If the monitoring finds that the battery recovery speed after temperature control is slow, and the matching degree is only 65%, which is lower than the preset standard of 75%, the control instructions need to be adjusted, perhaps further reducing the temperature or suspending some tasks.

[0092] For example, during iterative adjustments to control instructions, the instruction set can be dynamically updated based on the matching degree change data. Assuming that after adjustment, the temperature drops to 28.0 degrees Celsius, the battery recovery rate increases, and the matching degree reaches 80%. Subsequent policy execution can continue to prioritize energy conservation, ensuring the stability of the robot's operation and extending its battery life. This approach effectively addresses dynamic changes in power and temperature, ensuring long-term and efficient operation of the device.

[0093] In step S106, a genetic algorithm is used to jointly optimize the temperature control strategy and the charge and discharge strategy for the charge and discharge adjustment scheme, and the coordinated control parameters of the two are calculated in real time. If the deviation value of the coordinated control parameter is greater than the preset deviation threshold, the parameter is iteratively corrected to obtain a second control instruction.

[0094] Specifically, to address the joint optimization requirements of the charge-discharge strategy and temperature control, a genetic algorithm is used to perform a preliminary parameter matching analysis between the two. By calculating relevant indicators for coordinated control, an initial set of control parameters is obtained. Based on this initial set of control parameters, if the deviation of the coordinated control parameters exceeds a preset deviation threshold, the parameters are initially corrected to obtain an adjusted parameter combination based on data fluctuations obtained during real-time calculations. Using this adjusted parameter combination, the dynamic changes in strategy matching are continuously monitored, and matching data after the parameter adjustments is obtained to determine whether the matching meets the preset criteria. Based on the matching data analysis, if the matching does not meet the preset criteria, the control instructions are iterated a second time. The information processing stage classifies the causes of the deviation and determines the corrected instruction direction. Based on this corrected instruction direction, state feedback data from the strategy execution process is obtained based on the specific execution requirements of the charge-discharge strategy to determine whether the feedback data falls within the preset stability range. Based on the analysis of the state feedback data, if the stability does not meet the preset range, the parameter adjustment range is fine-tuned. The information processing stage filters the fluctuation data to obtain the final set of control instructions. Through the final set of control instructions, the synergistic effect of temperature control and charging and discharging strategies is continuously tracked, the dynamic change data of the matching between the two is obtained, and the basis for subsequent strategy optimization is determined.

[0095] Specifically, the parameters of the genetic algorithm are set as follows: the population size is set to 100, which has been verified through multiple experiments to achieve a balance between computational efficiency and diversity; the crossover rate adopts a dynamic adjustment strategy, with an initial value of 0.8. If the fitness does not improve for five consecutive generations, it is reduced to 0.6; the mutation rate is initially set to 0.05, and is increased to 0.1 when the population diversity is lower than the preset threshold;

[0096] The fitness function in the genetic algorithm is composed of the following three core indicators: ;

[0097] Temperature control deviation index : ;

[0098] Parameter definition: : The absolute deviation between the actual temperature and the target temperature (unit: °C). For example, if the target temperature is 25 °C and the actual temperature is 26.5 °C, then .

[0099] : The maximum temperature deviation allowed by the system (such as ), that is, the allowable temperature range is . : The extreme temperature deviation that the system can tolerate (such as 3°C). If it exceeds this value, it will be considered a serious abnormality. Formula logic explanation:

[0100] Performance within the allowable deviation range: When the actual temperature deviation is within the allowable range (i.e. ), the numerator is 0, then , indicating that the temperature control is fully up to standard.

[0101] Exceeding the allowed deviation but not reaching the threshold: When the actual deviation exceeds the allowed range but does not reach the threshold (i.e. ), points will be deducted in proportion to the excess. For example: , , the actual deviation is 2℃, then: ;

[0102] Reaching or exceeding the threshold: When the actual deviation hour, , indicating a serious failure of temperature control.

[0103] Power fluctuation index : ;

[0104] : Standard deviation of battery charge fluctuation (unit: %).

[0105] Strategy Stability Index : ;

[0106] in, : No. Stability score of the strategy execution (based on historical data fitting); : preset stability target value; : Stability tolerance threshold.

[0107] Weight coefficient: (temperature control priority), (power stability), (Strategy stability).

[0108] For example, in industrial warehouse robot operations, genetic algorithms can be used to analyze parameter matching to optimize the combined charging and discharging strategies and temperature control. The basic principle of genetic algorithms is to simulate biological evolution to select highly adaptable parameter combinations, making them suitable for complex scenarios involving multivariable collaborative optimization.

[0109] Specifically, assuming that during the operation of the robot, the charging rate in the initial parameter set is set to 90% of the standard value, and the temperature control target is 28.0 degrees Celsius, through algorithm iteration, the relevant indicators of the coordinated control of the two are calculated, and it is found that the correlation between temperature and power consumption is weak and needs further adjustment.

[0110] For example, in response to real-time data fluctuations, if the deviation of the collaborative control parameters exceeds the preset threshold, such as the actual temperature fluctuation to 30.0 degrees Celsius, which exceeds the target value by 2.0 degrees Celsius, the parameters need to be initially corrected.

[0111] In one possible implementation, the charging rate can be reduced to 85% of the standard value, while the temperature control intensity is adjusted and the operating frequency of the cooling device is increased to obtain an adjusted parameter combination. This approach can effectively balance the dynamic changes in power and temperature.

[0112] For example, when continuously monitoring the dynamic changes of strategy matching, assuming that the adjusted matching degree data is 72%, which does not meet the preset standard of 80%, a second iteration is required.

[0113] Specifically, by analyzing the causes of the deviation, it was found that the high ambient humidity might have caused poor cooling effect, and then the corrected instruction direction was determined to increase dehumidification measures and at the same time fine-tune the charging rate to 80% of the standard value.

[0114] For example, when obtaining status feedback data for the execution requirements of the charging and discharging strategy, if the feedback shows that the power fluctuation range is 3%, which exceeds the preset stability range of 2%, the parameter adjustment range needs to be fine-tuned.

[0115] In one possible implementation, by filtering the fluctuating data and eliminating outliers caused by short-term task peaks, the charging rate is ultimately stabilized at 78% of the standard value, forming the final set of control instructions.

[0116] For example, the synergistic effect of temperature control and charging and discharging strategies can be continuously tracked. If the dynamic change data shows that the matching degree between the two has increased to 82%, exceeding the preset standard, it can be used as a basis for subsequent strategy optimization.

[0117] Specifically, we can further explore the possibility of reducing the frequency of temperature control during low-load periods to save energy. This continuous optimization approach can ensure the smooth operation of the robot while extending battery life and improving overall efficiency.

[0118] In step S107, a specific control signal is issued to the temperature control execution unit and the charge and discharge execution unit of the robot battery system through the second control instruction, and a closed-loop feedback mechanism is used to monitor the execution results in real time to determine whether the execution effect reaches the preset target and obtain execution feedback data.

[0119] Specifically, a control signal is sent to the temperature control execution unit and the charge and discharge execution unit of the battery system through the second control instruction; a closed-loop feedback mechanism is used to monitor the execution result and obtain preliminary status data; based on the preliminary status data, a deviation of the execution result is analyzed; if the deviation exceeds a preset target threshold, the source of the deviation is identified and the deviation category is determined;

[0120] Through the deviation category, the control signal parameters are adjusted and stability verification is performed to obtain the adjusted signal set; based on the adjusted signal set, the state changes are continuously tracked and execution feedback data is obtained; if the synergistic effect of the execution feedback data does not reach the preset target, the fluctuation data is filtered twice to obtain the optimized state information and determine the final control signal combination.

[0121] For example, in the scenario of industrial warehouse robot operation, the coordinated control of the battery system's temperature regulation and charging and discharging strategy can be refined through specific implementation methods to refine the process of command issuance and feedback adjustment.

[0122] Regarding the link of sending control signals to the temperature control execution unit and the charge and discharge execution unit, assuming that the initial control signal sets the charging rate to 75% of the standard value and the temperature target to 27.5 degrees Celsius, after the signal is sent, the execution unit will adjust the equipment operating status according to the instructions.

[0123] It should be noted that the design of the control signal needs to take into account the device response time and environmental interference to ensure that the instructions can be executed accurately.

[0124] For example, when a closed-loop feedback mechanism is used to monitor execution results, the battery temperature and power change data can be collected in real time through sensors to obtain preliminary status data.

[0125] Assume that monitoring finds that the actual temperature value is 28.8 degrees Celsius, which exceeds the target value by 1.3 degrees Celsius. At the same time, the power fluctuation range is 2.5%, which exceeds the expected value by 0.5%. Further analysis of the source of the deviation is required.

[0126] The principle of closed-loop feedback is to continuously compare actual values with target values, forming the basis for dynamic adjustments.

[0127] For example, when analyzing execution result deviations and determining deviation categories, if it is found that temperature deviations are mainly caused by ambient heat accumulation, and power fluctuations are related to sudden changes in task load, the deviation categories can be divided into two categories: environmental factors and load factors.

[0128] Considering environmental factors, you can consider increasing the operating time of cooling equipment;

[0129] Based on load factors, the charging rate needs to be adjusted to smooth out changes in power.

[0130] This classification helps to pinpoint the root cause of the problem.

[0131] For example, when adjusting the control signal parameters and performing stability verification, assuming that the charging rate is reduced to 70% of the standard value and the cooling equipment frequency is increased, after verification, it is found that the temperature fluctuation range is reduced to 0.8 degrees Celsius and the power fluctuation is reduced to 1.8%, indicating that the adjustment direction is reasonable.

[0132] The purpose of stability checking is to ensure that parameter adjustments do not introduce new instabilities.

[0133] For example, when continuously tracking status changes and obtaining execution feedback data, if it is found that the synergy effect does not reach the preset target, such as the temperature and power matching degree is only 68%, which is lower than the target value of 75%, further optimization is required.

[0134] Continuous tracking of feedback data can timely reflect the dynamic changes in system operation and provide a basis for subsequent adjustments.

[0135] For example, when performing secondary filtering on the fluctuation data and obtaining optimized status information, abnormal fluctuation values caused by short-term task peaks can be eliminated. Assuming that the temperature fluctuation range is reduced to 0.5 degrees Celsius after filtering and the power fluctuation is stabilized at 1.5%, the final control signal combination can be determined accordingly, such as locking the charging rate at 68% of the standard value.

[0136] The significance of secondary filtering is to improve data reliability and ensure the accuracy of control signals.

[0137] This multi-link coordinated adjustment method can effectively improve the operating stability of the battery system, extend the service life of the equipment, and optimize energy consumption distribution.

[0138] In step S108, the parameters of the self-optimizing temperature control algorithm are dynamically adjusted based on the execution feedback data, and the weights and thresholds of the algorithm are updated using an online learning method. If the error value in the feedback data exceeds the preset error range, the algorithm parameters are recalculated to determine the parameter set.

[0139] The first execution feedback data is analyzed to analyze the temperature control algorithm's operational performance. Information processing is used to filter out outliers in the data, generating preliminary processed feedback information. Based on this preliminary processed feedback information, deviations are identified to determine the parameter adjustments required for the temperature control algorithm. If the deviation exceeds the preset standard, a data comparison step locates the source of the deviation and determines the deviation category. Based on the deviation category information, an online learning method is used to dynamically update the temperature control algorithm's weight thresholds. The updated parameters are then stability-verified through information processing to obtain the adjusted parameter set. Using this adjusted parameter set, the operating status of the temperature control system is monitored in real time. A closed-loop feedback mechanism is used to continuously track changes in the parameters after execution, generating updated status data. Based on this updated status data, data comparison is performed to determine the optimization effect of the temperature control algorithm. If the coordinated performance does not meet the preset standard, the information processing step performs a secondary filtering of data fluctuations to obtain the optimized status information. Using this optimized status information, the operating parameters of the temperature control algorithm are fine-tuned. Simulation verification tools are used to conduct a feasibility analysis of the fine-tuned parameters and determine the final parameter combination. Based on the final parameter combination, the long-term operating status of the temperature control system is continuously monitored, and a closed-loop feedback mechanism is used to dynamically track the execution results to obtain a long-term operating status record.

[0140] Specifically, the online learning method is used to dynamically update the weight threshold of the temperature control algorithm. The update formula is: ;

[0141] in, For the The weight parameter vector of the iteration (such as temperature control weight, charge and discharge rate weight, etc.); The learning rate is dynamically adjusted, with an initial value of 0.01. attenuation( ); is the gradient of the loss function, and the loss function is defined as: ;

[0142] in, Encode input feature vectors (such as ambient temperature, battery level, and user behavior patterns);

[0143] is the target output (such as ideal temperature control amount);

[0144] For the model prediction output, the linear form is: ;

[0145] is the L2 regularization coefficient (default value 0.01) to prevent overfitting. is the sparsification coefficient (default value 0.001), through Promote weight sparsity and reduce redundant parameters.

[0146] When applied, obtain the ambient temperature from the sensor network , battery level , User behavior pattern coding , construct the input feature vector .

[0147] Target output Generate according to preset temperature control strategy (e.g. ).

[0148] Calculate predicted values ;

[0149] Calculate the loss gradient: ;

[0150] Update weights: ;

[0151] Abnormal data processing:

[0152] If you enter data If the temperature exceeds the reasonable range (such as temperature > 50°C or battery < 5%), the update will be suspended and an abnormal alarm will be triggered;

[0153] The sliding window method (window size = 50 sets of data) is used to filter short-term fluctuations and retain long-term trend data.

[0154] Weight convergence judgment:

[0155] When the weight changes When , the model is determined to have converged and the iteration is stopped.

[0156] For example, in the temperature control management of an industrial warehouse robot battery system, the analysis of the first execution feedback data can be carefully analyzed from multiple perspectives. Regarding data parsing and outlier filtering, assume that after a robot has been running for a long time, the feedback data contains temperature fluctuations and abnormal peak information. In one possible implementation, the data is initially screened through the information processing step, and sudden temperature increases caused by brief overloads are identified as abnormal and eliminated, resulting in stable preliminary feedback information, such as a stable average temperature of 30.5 degrees Celsius.

[0157] For example, to identify and locate the source of deviations, preliminary feedback can be compared against pre-set standards. For example, suppose the preset temperature target is 28.0 degrees Celsius, and the actual average is 30.5 degrees Celsius, a deviation of 1.5 degrees Celsius above the standard. Through data comparison, analysis reveals that the deviation may be due to a delayed response in the cooling system, and the deviation is classified as insufficient internal execution efficiency. This step is crucial in identifying the root cause of the problem and providing direction for subsequent adjustments.

[0158] For example, in the dynamic updating of parameters, online learning methods can be used to adjust the weight thresholds of the temperature control algorithm based on deviation categories. For example, if the initial weights favor low-frequency response, they are adjusted to high-frequency response mode. The stability of the updated parameters is verified through information processing to ensure that they do not cause system oscillations, resulting in the adjusted parameter set. This adjustment helps improve the adaptability of the temperature control system.

[0159] For example, real-time monitoring and closed-loop feedback mechanisms can continuously track the operating status of the temperature control system based on the adjusted parameter set. Suppose the updated status data shows that the temperature has dropped to 29.2°C, indicating that the initial adjustments have been effective. This is where the closed-loop feedback mechanism comes into play, dynamically tracking the system to ensure it operates within a reasonable range and reduce the risk of cumulative deviations.

[0160] For example, during the optimization comparison and secondary filtering phase, if the temperature control algorithm's collaborative performance falls short of expectations—for example, if temperature stability is only 70%, below the preset 80%—a secondary filtering of data fluctuations is necessary. Assuming the fluctuations are due to external airflow interference, the resulting optimization status information provides a reliable basis for subsequent fine-tuning. This process helps improve data accuracy.

[0161] For example, for parameter fine-tuning and simulation verification, based on optimization status information, algorithm parameters can be fine-tuned, such as slightly increasing the response frequency by 5%. The feasibility of the adjustments is analyzed using simulation verification tools, confirming that the temperature can be stabilized at around 28.3 degrees Celsius, and finalizing the parameter combination. This step ensures the rationality and feasibility of the adjustments.

[0162] For example, in long-term operating status monitoring, a closed-loop feedback mechanism dynamically tracks the execution results of the final parameter combination. Assume that long-term data shows that the temperature remains between 28.0 and 28.2 degrees Celsius, indicating stable system operation. This continuous monitoring method helps to promptly identify potential problems and ensure the stability and durability of the battery system.

[0163] In step S109, based on the parameter set and in combination with the environmental adaptability module, the adaptability of the algorithm under different ambient temperatures and location information is verified, and the stability of the algorithm under extreme environments is evaluated using a simulation test method to determine whether the algorithm meets the requirements of battery life protection and obtain the final optimization control model.

[0164] Specifically, the environmental adaptation module collects data to obtain a second parameter dataset under different temperature variations and position information, constructs an initial simulation environment, and generates preliminary environmental response data. Based on this preliminary environmental response data, a simulation test method is used to simulate temperature variations and position information fluctuations in extreme environments. The algorithm verification module then conducts multi-scenario testing to determine the algorithm's performance under each scenario. A stability assessment index is extracted from the algorithm's performance. If the stability assessment index falls below the threshold, the second parameter is dynamically adjusted to obtain the adjusted parameter response data. Simulation testing is then repeated with the environmental adaptation module based on this adjusted parameter response data to verify the algorithm's adaptability under extreme environments and determine whether it meets the predetermined stability criteria. If the stability criteria are not met, the battery life protection requirement module analyzes the impact of the current parameters on battery life and generates life protection correlation data. Based on this life protection correlation data, a support vector machine algorithm is used to adjust the optimization control strategy, construct a more adaptable control logic, and determine the final optimized control model. Using this final optimized control model, temperature variations and position information in different environments are continuously monitored to obtain real-time feedback data, completing the dynamic verification of the model.

[0165] In the environmental adaptability verification stage, SVM is used to perform secondary optimization on the parameter set generated by online learning:

[0166] Input data:

[0167] Feature vector: ambient temperature, humidity, location coordinates, battery aging coefficient;

[0168] Label: Stability score of the parameter set (0-1, based on historical data).

[0169] Support vector machine model training:

[0170] Kernel function: Gaussian kernel (RBF), kernel parameter σ=0.5;

[0171] Optimization goal: maximize classification interval, penalty factor C=1.0;

[0172] Decision function: ;

[0173] in, .

[0174] Parameter adjustment rules:

[0175] If the SVM prediction stability score is <0.7, the online learning weight Perform normalization: ;

[0176] If the score is ≥ 0.7, the current parameter set is directly applied.

[0177] For example, in the area of temperature control management for robot battery systems, data collection for the environmental adaptation module can start with different temperature changes and position information to build an initial simulation environment. Consider an industrial warehouse scenario where a robot needs to operate within a temperature range of -10°C to 40°C, and its position information frequently changes due to the warehouse's shelf layout. During the data collection phase, temperature and position data at key points during robot operation are recorded to form a secondary parameter dataset, providing the foundation for subsequent simulation testing.

[0178] For example, simulation testing methods for preliminary environmental response data can simulate temperature fluctuations and position fluctuations in extreme environments.

[0179] In one possible implementation, the temperature is set to an extreme high of 45.0 degrees Celsius, and the location information is simulated as a rapid switching path to observe the algorithm's performance under high load. Through multi-scenario testing, stability assessment indicators for the algorithm's operation are extracted, such as response delay time and temperature control deviation. If the stability indicator is found to be below the preset threshold, such as a response delay exceeding 2.0 seconds, the second parameter is dynamically adjusted.

[0180] For example, during dynamic parameter adjustment and resimulation testing, the adjusted parameter response data can be verified in conjunction with the environmental adaptation module. Assuming the adjusted parameters narrow the temperature control range to plus or minus 0.5 degrees Celsius, resimulation testing will show that temperature fluctuations in extreme environments are controlled within 1.0 degrees Celsius, approaching the predetermined stability standard. This approach helps improve the algorithm's adaptability to complex environments.

[0181] For example, if stability standards are not met, the battery lifespan protection requirement module analyzes the impact of parameters on battery lifespan. Assuming that current parameters cause the battery to operate excessively at high temperatures, the resulting lifespan protection correlation data indicates a potential 10% reduction in battery lifespan. This analysis provides important guidance for subsequent optimization.

[0182] For example, in optimizing control strategy adjustments, a support vector machine algorithm can be used to build a more robust control logic. Assume that through algorithmic analysis, temperature control is prioritized while optimizing energy consumption distribution during position switching, ultimately forming an optimized control model. This approach can better balance temperature control and lifespan protection requirements.

[0183] For example, continuous monitoring and dynamic verification of the final optimized control model can be achieved by collecting real-time temperature and position feedback data under different environments. Assuming that, during actual operation, the temperature remains stable at around 28.0 degrees Celsius and energy consumption fluctuations during position switching are less than 5%, the model's reliability in dynamic environments has been verified. This continuous monitoring approach ensures the long-term stable operation of the system.

[0184] The description of the above embodiments is only used to help understand the technical solutions and core ideas of the present invention; ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robot battery intelligent temperature control method based on ambient temperature and user behavior, characterized in that: include: Acquiring position information data and ambient temperature data, and performing synchronous calibration and noise suppression processing on the position information data and the ambient temperature data to obtain an original data set; According to the location information data in the original data set, combined with a pre-established user behavior habit model, the user behavior pattern is analyzed and classified to obtain a behavior classification result; based on the behavior classification result and the ambient temperature data in the original data set, a correlation mapping relationship between temperature and behavior pattern is constructed to obtain a temperature-behavior mapping model; the current ambient temperature data and the user behavior pattern are dynamically matched through the temperature-behavior mapping model to generate a first control instruction; according to the first control instruction, the charge and discharge strategy is adjusted in combination with the battery status data to obtain a charge and discharge adjustment plan; the charge and discharge adjustment plan is jointly optimized to generate a second control instruction; the robot battery system is controlled and monitored in real time through the second control instruction to obtain execution feedback data; the algorithm parameters are dynamically adjusted according to the execution feedback data to obtain a parameter set; the environmental adaptability of the parameter set is verified to generate a final optimization control model.

2. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method of acquiring position information data and ambient temperature data, performing synchronous calibration and noise suppression processing on the position information data and the ambient temperature data to obtain an original data set specifically includes: collecting original position information data and original ambient temperature data in the robot's operating environment; performing segmented processing on the original position information data and the original ambient temperature data using a preset time interval, and performing preliminary filtering on the data in each time period to obtain an intermediate data set; performing synchronous calibration on the intermediate data set, aligning the timestamp deviation, and if the timestamp deviation exceeds a preset threshold, performing interpolation adjustment on the data to obtain a calibration data set; based on the calibration data set, using noise suppression technology to smooth the position information data and the ambient temperature data, eliminating abnormal fluctuation points, and obtaining the original data set; using the original data set to provide a data basis for subsequent analysis, obtaining a stability index after data processing, and judging whether the processing result meets the preset standard; if the stability index does not meet the preset standard, adjusting the parameters of the noise suppression technology, reprocessing the data, and obtaining an updated data set.

3. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method is based on the location information data in the original data set, combined with a pre-established user behavior habit model, analyzing and classifying user behavior patterns to obtain behavior classification results, specifically including: extracting location coordinate information and corresponding timestamp data from the original data set, segmenting the location coordinate information through a time window partitioning method to obtain a location time series data set; based on the location time series data set, counting the user's stay time and number of visits at each spatial location, if the stay time exceeds a preset threshold, it is marked as a valid usage point, and the usage frequency distribution characteristics are calculated; using the user behavior habit model to perform pattern matching analysis on the usage frequency distribution characteristics to generate a behavior feature vector matrix; classifying the behavior feature vector matrix through a cluster analysis algorithm, calculating the distance between the data point and the cluster center, and obtaining a preliminary clustering grouping result; for the preliminary clustering grouping result, calculating the behavior pattern similarity index within the group, if the similarity index is lower than the preset threshold, adjusting the clustering parameters for reclassification to obtain the behavior classification result.

4. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method constructs an association mapping relationship between temperature and behavior pattern for the behavior classification results and the ambient temperature data in the original data set to obtain a temperature-behavior mapping model, which specifically includes: obtaining behavior pattern identifier data from the behavior classification results and sorting them by timestamp; extracting ambient temperature parameter records with corresponding timestamps from the original data set, and if data is missing, using nearest neighbor interpolation to complete it; merging the behavior pattern identifier data and the ambient temperature parameter records to generate an original data correspondence table; calculating the upper and lower threshold ranges of the ambient temperature parameter records, and marking them as abnormal if they exceed the range; fitting the association between the ambient temperature parameter records and the behavior pattern identifier data through a regression analysis method, and if they are marked as abnormal, adjusting the weights and recalculating the regression coefficients to obtain the temperature-behavior mapping model.

5. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method of dynamically matching the current ambient temperature data and the user behavior pattern through the temperature-behavior mapping model to generate a first control instruction specifically includes: comparing the current ambient temperature data and the user behavior pattern through the temperature-behavior mapping model to obtain a matching degree and determine a preliminary matching result; based on the preliminary matching result, extracting corresponding features of the ambient temperature data and the user behavior pattern, and using a decision tree algorithm to infer a control strategy to obtain a strategy calculation result; based on the strategy calculation result, judging whether the current ambient temperature data deviates from a preset threshold range, and if so, generating a temperature adjustment signal set; performing a secondary check on the user behavior pattern through the temperature adjustment signal set to obtain correlation degree data; if the correlation degree data is lower than the preset matching standard, correcting the temperature adjustment signal set to obtain the final first control instruction; based on the first control instruction, continuously monitoring the changing trend of the ambient temperature data to obtain dynamic monitoring results.

6. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method of adjusting the charge and discharge strategy according to the first control instruction and the battery status data to obtain a charge and discharge adjustment plan specifically includes: collecting the remaining power information in the battery status data through the first control instruction, comparing it with the preset threshold value, and obtaining a power status evaluation result; based on the power status evaluation result, if the remaining power is lower than the preset threshold value, verifying the temperature control demand, using the decision tree algorithm to analyze the applicability of the dynamic strategy, and determining the priority adjustment direction; configuring the charge and discharge adjustment parameters through the priority adjustment direction, obtaining the adjustment range, and obtaining a preliminary charge and discharge adjustment plan; analyzing the changing trend of the battery status data according to the preliminary charge and discharge adjustment plan, and obtaining a stability index; if the stability index shows a fluctuation beyond the preset range, optimizing the adjustment plan parameters to obtain the final charge and discharge adjustment plan; through the charge and discharge adjustment plan, continuously monitoring the matching degree between temperature control and battery status to obtain a basis for dynamic control.

7. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The joint optimization of the charge and discharge adjustment scheme to generate the second control instruction specifically includes: using a genetic algorithm to perform parameter matching analysis on the charge and discharge adjustment scheme and the temperature control strategy, calculating the collaborative control index, and obtaining an initial control parameter set; based on the initial control parameter set, if the collaborative control parameter deviation exceeds a preset deviation threshold, performing a preliminary correction to obtain an adjusted parameter combination; through the adjusted parameter combination, monitoring strategy matching changes and obtaining matching degree data; if the matching degree data does not meet the preset standard, performing a secondary iterative process to classify the cause of the deviation and determine the corrected instruction direction; through the corrected instruction direction, obtaining strategy execution status feedback data to determine whether it meets the preset stability range; if the stability does not reach the preset range, fine-tuning the parameter amplitude, filtering the fluctuation data, and obtaining the final second control instruction.

8. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The robot battery system is controlled and monitored in real time through the second control instruction to obtain execution feedback data, specifically including: sending a control signal to the temperature control execution unit and the charge and discharge execution unit of the battery system through the second control instruction; using a closed-loop feedback mechanism to monitor the execution results and obtain preliminary status data; analyzing the execution result deviation based on the preliminary status data, and if the deviation exceeds the preset target threshold, identifying the source of the deviation and determining the deviation category; adjusting the control signal parameters based on the deviation category, performing stability verification, and obtaining an adjusted signal set; based on the adjusted signal set, continuously tracking state changes and obtaining execution feedback data; if the synergistic effect of the execution feedback data does not reach the preset target, performing a secondary filtering on the fluctuation data to obtain optimized state information and determine the final control signal combination.

9. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The method of dynamically adjusting the algorithm parameters according to the execution feedback data to obtain a parameter set specifically includes: analyzing the operating performance of the temperature control algorithm through the execution feedback data, filtering outliers, and obtaining preliminary feedback information; identifying the parameter adjustment deviation according to the preliminary feedback information, and if the deviation exceeds the preset standard, locating the source of the deviation and determining the deviation category information; using the deviation category information, updating the weight threshold of the temperature control algorithm using an online learning method, performing a stability check, and obtaining an adjusted parameter set; monitoring the state changes of the temperature control system according to the adjusted parameter set, and obtaining updated state data; if the coordinated performance of the updated state data does not meet the preset standard, performing a secondary filtering on the fluctuation data to obtain optimized state information; fine-tuning the operating parameters through the optimized state information, performing simulation verification, and determining the final parameter set.

10. The robot battery intelligent temperature control method based on ambient temperature and user behavior according to claim 1, characterized in that: The environmental adaptability verification of the parameter set to generate a final optimized control model specifically includes: collecting the parameter set data under different temperature changes and position information through the environmental adaptation module, constructing a simulation environment, and obtaining preliminary environmental response data; simulating fluctuations in extreme environments based on the preliminary environmental response data, performing multi-scenario testing on the algorithm verification module, and determining operating performance; extracting a stability assessment index from the operating performance, and if it is lower than a preset threshold, dynamically adjusting the parameter set to obtain adjusted parameter response data; re-performing simulation tests based on the adjusted parameter response data to verify adaptability; if the adaptability does not meet the preset standard, analyzing the battery life protection correlation data, using the support vector machine algorithm to adjust the control strategy, and determining the final optimized control model; and continuously monitoring environmental changes through the final optimized control model to obtain real-time feedback data.

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