Intelligent management system for automatic charging and endurance optimization of hotel robot

Abstract

The invention provides an intelligent management system for automatic charging and endurance optimization of hotel robots. The intelligent management system comprises a positioning module, a posture adjustment module, a charging monitoring module, a charging control module, a battery management module, a charging strategy optimization module, an energy consumption balance task distribution module and a path planning module. Through the positioning and posture adjusting module, the robot can be accurately connected with a charging station in a complex hotel, and it is ensured that charging is started accurately and stably; multi-element monitoring is combined with precise charging control, regulation and control are performed according to battery characteristics, the service life is prolonged, and endurance is guaranteed; modules such as fine battery management, charging strategy optimization and the like are linked, charging and task assignment are reasonably arranged, energy consumption is reduced, efficiency is improved, and efficient operation of hotels is enabled.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of automatic charging and battery life optimization of hotel robots, and in particular to an intelligent management system for automatic charging and battery life optimization of hotel robots. Background Art

[0002] With the development of hotel intelligence, robots are widely used, but traditional automatic charging and endurance management technologies have many problems. On the one hand, automatic charging relies on physical contact and is easily disturbed by the environment, with inaccurate charging position positioning, rigid posture adjustment, single state confirmation, and rough end conditions; on the other hand, endurance optimization lacks fine management, battery capacity and life prediction deviation is large, charging strategy is inflexible, task planning does not take energy consumption into consideration, path planning has high energy consumption, and real-time monitoring and dynamic adjustment lag behind. In this context, a new intelligent management system is urgently needed to improve the operating efficiency of hotel robots.

[0003] Traditional hotel robot automatic charging and intelligent management technology for battery life optimization. In the automatic charging technology link, the charging position is inaccurate, the calibration accuracy is poor, and the robot is easily unable to dock due to interference from the ground and obstacles, and cannot be recharged in time, affecting the execution of subsequent tasks; the charging posture adjustment is inflexible and relies on manual intervention, which not only reduces efficiency but also increases labor costs; the charging status detection is single, and it is difficult to detect abnormalities such as battery overheating, which poses a safety hazard, and may also shorten the battery life due to the inability to handle the fault in time; the charging end condition is not well set, and overcharging or undercharging will frequently damage the battery and reduce the overall performance of the robot. In terms of battery life optimization, the battery management is extensive, the capacity and life prediction are inaccurate, and the aging battery is not replaced, which can easily interrupt the robot's work. Idle and uncharged, and long charging for urgent tasks will delay the timeliness of the task; task planning ignores energy consumption, and uneven distribution leads to the exhaustion of some robots and idle waste of some robots. The high energy consumption of path planning greatly reduces the battery life; real-time monitoring and dynamic adjustment are weak, energy consumption abnormalities are difficult to warn, and the adjustment is delayed when the power or task is urgent, which cannot guarantee the continuous and stable operation of the robot, reducing the quality of hotel services.

[0004] Therefore, it is necessary to design an intelligent management system for automatic charging and battery life optimization of hotel robots to solve the existing intelligent management technology for automatic charging and battery life optimization of hotel robots. The charging position positioning is easily affected by uneven ground and obstacles, the calibration accuracy is poor, and carpet wrinkles and temporary items can hinder the precise docking of robots; the charging posture adjustment is mechanically rigid, lacks intelligent dynamic adaptation capabilities, and manual help is required for a slight deviation in the interface; the charging status relies only on simple voltage and current detection, which is difficult to monitor the entire process and handle abnormalities, and cannot detect hidden dangers such as battery overheating; the charging end condition setting is simple, often causing overcharging or undercharging, which damages the battery life and robot performance. In terms of battery life optimization, battery management is extensive, capacity and life prediction are inaccurate, aging batteries are difficult to replace in time, and charging strategies are improper during idle or emergency tasks; task planning does not weigh energy consumption, and the allocation of tasks focuses on timeliness and not power consumption. The energy consumption of path planning is insufficient, and it is easy to choose a short but high-consumption route; real-time monitoring and dynamic adjustment are weak, data acquisition and analysis are one-sided, and it is difficult to warn of abnormal energy consumption. When the power is in short supply or the task is urgent, the work and task arrangement adjustment are delayed. Summary of the invention

[0005] In view of this, the present invention proposes an intelligent management system for automatic charging and battery life optimization of hotel robots, aiming to solve the problems of the existing intelligent management technology for automatic charging and battery life optimization of hotel robots in the automatic charging technology link, such as inaccurate charging position positioning and poor calibration accuracy. The robot may easily fail to dock due to interference from the ground and obstacles, and cannot be recharged in time, affecting the execution of subsequent tasks; the charging posture adjustment is inflexible and relies on manual intervention, which not only reduces efficiency but also increases labor costs; the charging status detection is single, and it is difficult to detect abnormalities such as battery overheating, which poses a safety hazard and may also shorten the battery life due to the inability to handle faults in time; the charging end conditions are not well set, and over- or under-charging will frequently damage the battery and reduce the overall performance of the robot. In terms of battery life optimization, battery management is extensive, capacity and life predictions are inaccurate, and aging batteries that are not replaced can easily interrupt the robot's work. Not charging when idle and charging for urgent tasks will delay the task effectiveness; task planning ignores energy consumption, and uneven distribution causes some robots to run out of power and some to be idle and wasted. The high energy consumption of path planning greatly reduces the battery life; real-time monitoring and dynamic adjustment are weak, energy consumption anomalies are difficult to warn, and adjustments are delayed when power or tasks are urgent, which cannot guarantee the continuous and stable operation of the robot, thereby reducing the quality of hotel services.

[0006] In one aspect, the present invention proposes an intelligent management system for automatic charging and battery life optimization of hotel robots, comprising:

[0007] The positioning module uses a laser radar sensor and a visual recognition sensor to model the hotel environment and locate the charging location. The positioning module monitors the ground conditions and obstacle information in real time and plans the optimal route through an intelligent algorithm;

[0008] A posture adjustment module, equipped with a mechanical arm and an adaptive docking device, which dynamically senses the position and angle deviation of the charging interface in combination with visual feedback and automatically adjusts the posture;

[0009] The charging monitoring module is used to monitor the voltage, current, temperature and internal resistance of the battery, and uses intelligent data analysis algorithms to monitor changes in the charging process in real time. When an abnormality occurs, it initiates an early warning and takes protective measures;

[0010] The charging control module uses charging curve control technology to set the charging end conditions and dynamically adjust the charging power according to the characteristics and real-time status of the battery;

[0011] The battery management module uses a big data analysis platform and machine learning algorithms to combine the robot's historical operating data, real-time workload, and ambient temperature to predict battery capacity and remaining service life and plan battery replacement plans;

[0012] The charging strategy module is used to track the robot's work tasks and idle time in real time, and decide the best time and duration for charging based on the battery level, task urgency, and expected idle time.

[0013] The energy-balanced task allocation module uses the energy consumption evaluation model to allocate tasks based on task priority, task completion time, and energy consumption estimation during task execution;

[0014] Path planning module, using autonomous navigation system and energy optimization algorithm to plan low-energy and high-efficiency travel routes;

[0015] The positioning module provides the posture adjustment module with the location information of the charging seat, and the posture adjustment module responds according to the positioning information, and the positioning module and the posture adjustment module complete the docking action; the charging monitoring module transmits the charging data collected in real time to the charging control module, and the charging control module adjusts the charging strategy according to the monitoring data; the battery management module shares the battery capacity and life prediction data with the optimized charging strategy module, and the optimized charging strategy module formulates a charging plan based on the shared data; the energy consumption balancing task allocation module arranges tasks according to the energy consumption information of each path fed back by the path planning module, and the path planning module optimizes the path in real time according to the task allocation results.

[0016] Furthermore, the positioning module utilizes the high-precision ranging and scanning functions of the lidar to construct a three-dimensional spatial map of the hotel and update the map information in real time. The positioning module utilizes the visual recognition sensor to capture image details through a high-definition camera, identifies various obstacles such as carpet wrinkles, temporarily placed tables and chairs, and trash cans, and classifies and locates the obstacles in combination with a deep learning algorithm. The positioning module fuses the data of the lidar sensor and the visual recognition sensor and inputs them into the intelligent path planning algorithm. The intelligent path planning algorithm plans an optimal route for the robot in the hotel based on the Di jkstra algorithm, which avoids all obstacles and reaches the charging station at the fastest speed.

[0017] Furthermore, the posture adjustment module is equipped with an encoder at the joint of the robotic arm to provide real-time feedback of the position and posture information of the robotic arm. When the robot approaches the charging base, the built-in proximity sensor of the adaptive docking device senses the approximate position of the charging interface and guides the initial movement of the robotic arm. As the distance gets closer, the built-in pressure sensor of the adaptive docking device detects the pressure changes when the robotic arm contacts the charging base, combines the high-definition image of the charging interface transmitted by the visual recognition sensor, and uses image recognition and feature extraction technology to analyze the position deviation and angle deviation of the charging interface. The posture adjustment module uses a control algorithm to drive the robotic arm to perform real-time posture adjustment.

[0018] Furthermore, the charging monitoring module uses a thermistor to monitor the battery temperature in real time and capture the temperature change trend of the battery during the charging process. The charging monitoring module uses a four-wire measurement method to measure the battery internal resistance in real time; the charging monitoring module uses big data and machine learning models to build an intelligent data analysis algorithm, and performs real-time analysis on the collected data, using cluster analysis and association rule mining. When an abnormality occurs, an early warning is initiated through sound and light alarms and push notifications, and the charging monitoring module automatically cuts off the charging circuit and adjusts the charging parameters.

[0019] Furthermore, the charging control module grasps the real-time status of the battery by real-time identification of model parameters. In the constant current charging stage, the charging control module dynamically adjusts the charging current according to the current remaining capacity, temperature and aging degree of the battery; after entering the constant voltage charging stage, the charging control module regulates the charging voltage to control the charging cut-off voltage accuracy within ±0.01V; the charging control module interacts with the battery management system in real time through a two-way communication interface, and dynamically adjusts the charging power according to the battery feedback information.

[0020] Furthermore, the battery management module collects historical operating data of the robot, which includes the start and end time of each task, mileage, load weight, ambient temperature and battery power consumption. The battery management module uses the regression algorithm and time series prediction model in machine learning to estimate the battery capacity in real time, and plans the battery replacement plan in advance based on the prediction results.

[0021] Furthermore, the optimized charging strategy module utilizes wireless communication technology to connect with the hotel room management system and the front desk reception system in real time to obtain robot task allocation information, passenger flow in each area and expected idle time distribution. The optimized charging strategy module utilizes an intelligent decision-making algorithm to comprehensively consider the current battery power, task urgency and expected idle time. The optimized charging strategy module utilizes dynamic programming to automatically dispatch the robot to a nearby idle charging station to replenish power in time when the robot is idle and the power is in a medium or low range; when an emergency task occurs and the key robot is low in power, a high-power fast charging station is preferentially allocated to it.

[0022] Furthermore, the energy consumption balance task allocation module constructs an energy consumption evaluation model in the task allocation link, and assigns weights to each factor according to factors such as task type, task distance, terrain and road conditions of the execution path, and the current battery power status of the robot, and calculates the estimated energy consumption value of each task. When allocating tasks, a distributed optimization algorithm based on multi-robot collaboration is used to allocate tasks with task priority as the primary constraint, completion time as the secondary constraint, and energy consumption balance as the optimization goal;

[0023] The path planning module utilizes the robot's existing autonomous navigation system and energy consumption optimization algorithm, and uses a heuristic search strategy to plan a long but low-energy route for the robot based on road conditions, robot movement speed, and the impact of start-stop frequency on energy consumption.

[0024] Compared with the prior art, the beneficial effect of the present invention lies in that the intelligent management system for automatic charging and battery life optimization of the hotel robot of the present invention, through the cooperation of the positioning module and the posture adjustment module, allows the robot to accurately find and dock the charging station in a complex hotel environment, with centimeter-level positioning and millimeter-level posture adjustment accuracy, which greatly improves the accuracy and stability of the charging start link; the multi-element charging status monitoring module conducts real-time and all-round monitoring, keenly captures various abnormalities, and combines with the precise charging control module to accurately regulate the charging and discharging according to the battery characteristics, effectively avoiding battery bulging, shortened life and insufficient charging, extending the battery life and ensuring battery life; the battery management module uses big data and Deep learning predicts battery status and plans replacement in advance to avoid embarrassing power outages and safeguard hotel service continuity. The charging strategy module is optimized to work in conjunction with the hotel’s task scheduling to intelligently arrange charging based on the robot’s battery level and task status, with more charging during idle time and fast charging during emergencies, shortening emergency task response time by more than 50%. The energy-aware task allocation and energy-saving path planning modules work together to reasonably assign tasks and plan low-energy paths, reducing group energy consumption by 15% to 20%. All modules work closely together to comprehensively improve charging efficiency and battery life, reducing service interruptions due to power issues by more than 95% and improving overall operating efficiency by 30% to 40%, enabling efficient hotel operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0026] Figure 1 This is a functional block diagram of an intelligent management system for automatic charging and battery life optimization of a hotel robot according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to be able to fully convey the scope of the present invention to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the implementation regulations.

[0028] Reference Figure 1 As shown, in some embodiments of the present application, an intelligent management system for automatic charging and battery life optimization of a hotel robot includes:

[0029] The positioning module uses lidar sensors and visual recognition sensors to model the hotel environment and locate the charging location. The positioning module monitors the ground conditions and obstacle information in real time and plans the optimal route through intelligent algorithms;

[0030] The posture adjustment module is equipped with a robotic arm and an adaptive docking device. The posture adjustment module dynamically senses the position and angle deviation of the charging interface in combination with visual feedback and automatically adjusts the posture;

[0031] The charging monitoring module monitors the voltage, current, temperature and internal resistance of the battery, and uses intelligent data analysis algorithms to monitor changes in the charging process in real time. When an abnormality occurs, it initiates an early warning and takes protective measures;

[0032] The charging control module adopts the charging curve control technology. The charging control module sets the charging end conditions and dynamically adjusts the charging power according to the characteristics and real-time status of the battery;

[0033] The battery management module uses a big data analysis platform and machine learning algorithms to combine the robot's historical operating data, real-time workload, and ambient temperature to predict battery capacity and remaining service life and plan battery replacement plans;

[0034] Optimize the charging strategy module to track the robot's work tasks and idle time in real time, and decide the best time and duration for charging based on the battery level, task urgency, and expected idle time;

[0035] The energy-balanced task allocation module uses the energy consumption evaluation model to allocate tasks based on task priority, task completion time, and energy consumption estimation during task execution;

[0036] The path planning module uses the autonomous navigation system and energy optimization algorithm to enable the robot to plan a low-energy and high-efficiency route.

[0037] It is understandable that each module works together. The high-precision positioning and posture adjustment module enables the robot to accurately dock with the charging station in a complex hotel, ensuring accurate and stable charging start; multi-dimensional monitoring is combined with precise charging control, which is regulated according to battery characteristics to extend life and ensure battery life; fine battery management, optimized charging strategy and other modules are linked to reasonably arrange charging and tasks, reduce energy consumption, improve efficiency, and enable efficient hotel operations.

[0038] Specifically, the positioning module uses the high-precision ranging and scanning functions of the laser radar to build a three-dimensional spatial map of the hotel, and updates the map information in real time to reflect the dynamic changes in the environment. The visual recognition technology captures image details through high-definition cameras, accurately identifies various obstacles such as carpet wrinkles, temporarily placed tables and chairs, and trash cans, and combines deep learning algorithms to classify and locate these objects. The data after multi-sensor fusion is input into the intelligent path planning algorithm, which is based on an improved version of the A* algorithm or the Di jkstra algorithm, fully considering the kinematic constraints and dynamic characteristics of the robot, and planning an optimal route for the robot in a complex hotel environment that avoids all obstacles and reaches the charging station at the fastest speed. The positioning error is strictly controlled within the range of ±5 mm to ensure that the robot can accurately dock and charge every time.

[0039] It is understandable that the positioning module uses high-precision laser radar to measure distance and scan, build and update the hotel's three-dimensional map in real time, and uses visual recognition technology and deep learning algorithms to accurately identify various obstacles and classify and locate them, integrate multi-sensor data, and rely on improved A* or Di jkstra algorithms. It fully considers the robot's movement and dynamic characteristics to plan the optimal route, and accurately controls the positioning error within ±5 mm, allowing the robot to quickly and accurately reach the charging station. It not only greatly improves the charging efficiency and ensures the robot's continuous and stable operation, but also enables it to move freely in complex hotel environments and effectively avoid collisions, reducing manpower and material resources for hotel operations, and helping hotels reduce costs and increase efficiency in all aspects.

[0040] Specifically, the robot arm of the posture adjustment module is made of lightweight high-strength alloy material, has multiple degrees of freedom, can flexibly rotate and translate in three-dimensional space, and is equipped with high-precision encoders at its joints, which can provide real-time feedback on the position and posture information of the robot arm. The adaptive docking device has built-in pressure sensors and proximity sensors. When the robot approaches the charging base, the proximity sensor first senses the approximate position of the charging interface and guides the robot arm to move initially; as the distance gets closer, the pressure sensor detects the slight pressure changes when the robot arm contacts the charging base, combined with the high-definition image of the charging interface transmitted by the visual feedback system, and uses image recognition and feature extraction technology to accurately analyze the position deviation (accurate to ±0.1 degrees, ±1 mm) and angle deviation of the charging interface. The control algorithm drives the robot arm to perform intelligent and refined real-time posture adjustment based on this, achieving close and seamless docking with the charging interface, and the docking success rate is increased to more than 98%, with basically no human intervention required.

[0041] It is understandable that the equipped robotic arm is made of lightweight and high-strength alloy, has multiple degrees of freedom, can rotate and translate freely in three-dimensional space, and high-precision encoders at the joints can grasp the position and posture in real time; the adaptive docking device complements it, and the two are combined with the visual feedback system to dynamically sense the position and angle deviation of the charging port. When the robot approaches the charging base, the proximity sensor first locks the approximate position to assist the robotic arm in its initial position. As it approaches, the pressure sensor cooperates with the high-definition image transmitted by the visual feedback, and relies on image recognition and feature extraction technology to keenly capture tiny changes, accurately analyze deviations to ±0.1 degrees and ±1 mm, and then automatically perform intelligent and refined posture adjustments to achieve close and seamless docking with the charging port, raising the docking success rate to more than 98%, greatly reducing human intervention, ensuring efficient charging and stable battery life of the robot, and effectively guaranteeing the continuity and efficiency of hotel operation services.

[0042] Specifically, the charging monitoring module adds a high-precision thermistor to monitor the battery temperature in real time based on the original voltage and current detection circuit. Its measurement accuracy can reach ±0.5℃, which can keenly capture the temperature change trend of the battery during the charging process. At the same time, the battery internal resistance monitoring module is connected using a four-wire measurement method to measure the battery internal resistance in real time with a resolution of up to 0.1 milliohms, accurately reflecting the changes in the internal state of the battery. The intelligent data analysis algorithm is built based on big data and machine learning models, and performs real-time analysis on the collected multi-dimensional data. It uses cluster analysis, association rule mining and other means. Once abnormal temperature rises (such as exceeding 45℃), internal resistance suddenly increases (exceeding the normal range by 20%), and voltage and current fluctuations are found, the warning is immediately activated through sound and light alarms, push notifications, etc., and the charging circuit is automatically cut off or the charging parameters are adjusted to ensure the safe and stable charging process.

[0043] It is understandable that the charging monitoring module can accurately monitor key indicators such as battery voltage, current, temperature and internal resistance, and use intelligent data analysis algorithms to monitor every change in the charging process in real time. When the robot is charging, once the battery has abnormal conditions such as sudden voltage change, abnormal current, excessive temperature or increased internal resistance, the module will quickly activate the early warning mechanism and immediately take corresponding protective measures such as reducing the charging power and cutting off the charging circuit to effectively avoid dangerous conditions such as battery overheating, bulging and even fire and explosion. It not only greatly extends the battery life, ensures that the robot can complete charging safely, stably and efficiently every time, maintains a good endurance state, but also eliminates worries for the hotel's daily operations and ensures that hotel services are not affected in all aspects.

[0044] Specifically, the charging control module deeply studies the characteristics of the lithium battery used by the robot, establishes a battery equivalent circuit model, and accurately grasps the real-time status of the battery by identifying the model parameters in real time. Based on this, an optimized version of the constant current-constant voltage (CC-CV) charging mode is adopted. In the constant current charging stage, the charging current is dynamically adjusted according to the current remaining capacity, temperature and aging degree of the battery to ensure that the battery is charged at the best rate and avoid lithium precipitation and other phenomena that damage the battery life; after entering the constant voltage charging stage, the charging voltage is finely controlled to control the charging cut-off voltage accuracy within ±0.01V to prevent overcharging. At the same time, through the two-way communication interface, it interacts with the battery management system in real time, and dynamically adjusts the charging power according to the battery feedback information, while ensuring that the battery life is extended by more than 20%, the charging time is shortened by 15%, ensuring that the robot is always fully charged and in the best working condition.

[0045] It is understandable that the charging control module adopts advanced charging curve control technology, which can intelligently set the most appropriate charging end conditions according to the current characteristics and real-time status of the battery to avoid overcharging or undercharging. At the same time, the charging power can be adjusted dynamically. When the battery power is low, it can quickly replenish the power with a higher power. As the power gradually becomes saturated, the power is reduced in time and the charging is smooth, which not only ensures the charging efficiency but also protects the health of the battery. In this way, not only the overall charging time of the robot is greatly shortened, the frequency of its use is increased, and efficient services can be provided to guests at any time, but also the battery cycle life can be further extended, the cost of battery replacement can be reduced, and many conveniences can be brought to the daily operation of the hotel, and the automatic charging and endurance management of the hotel robot can be fully optimized.

[0046] Specifically, the battery management module collects a large amount of historical data accumulated by the robot over a long period of time, including the start and end time of each task, mileage, load weight, ambient temperature, and corresponding battery power consumption, and uses big data analysis platforms (such as Hive, Spark, etc.) for in-depth mining. Combining the regression algorithms in machine learning (such as linear regression, support vector regression, etc.) with time series prediction models (such as ARI MA, LSTM, etc.), the battery capacity is estimated in real time by comprehensively considering factors such as the robot's current real-time workload and ambient temperature change trends. The prediction accuracy can reach within ±5%, and the remaining service life prediction error is controlled within ±10%. Based on the accurate prediction results, the battery replacement plan is planned at least one week in advance, and the intelligent inventory management system is used to ensure that the new battery is in place on time before the battery ages to the critical value, effectively avoiding the embarrassing situation of the robot suddenly running out of power during the execution of the task.

[0047] It is understandable that the battery management module relies on the big data analysis platform and machine learning algorithms to deeply integrate the robot's past operating data, current real-time workload, and ambient temperature and other multi-dimensional information to accurately predict the battery capacity and remaining service life. Through such a comprehensive and intelligent analysis, on the one hand, it can provide early insight into the trend of battery performance changes, provide a basis for the hotel to reasonably plan the robot's work tasks, and avoid "dropping the chain" during service due to insufficient battery power; on the other hand, it can be used to plan the battery replacement plan in an orderly manner and replace it in time before the end of the battery life, which can ensure that the robot always maintains a good battery life and provides guests with uninterrupted and high-quality services at any time, and optimize the battery procurement cost and replacement nodes to avoid resource waste, fundamentally ensuring the scientificity and efficiency of the hotel robot's automatic charging and battery life management, and helping the hotel operate smoothly and worry-free.

[0048] Specifically, the charging strategy optimization module uses wireless communication technology (such as Wi-Fi6, Bluetooth 5.0, etc.) to connect with the hotel room management system, front desk reception system, etc. in real time to obtain robot task allocation information, passenger flow in each area, and expected idle time distribution data. Through the built-in intelligent decision-making algorithm, the current battery power (divided into three power intervals of high, medium and low), the urgency of the task (divided into three levels of emergency, ordinary and non-emergency) and the expected idle time (accurate to minutes) are comprehensively considered. Dynamic programming or greedy algorithms are used to automatically dispatch robots to nearby idle charging stations to replenish power in time when the robot is idle and the power is in the medium and low range; when encountering an emergency task and the key robot is low in power, a high-power fast charging station is assigned to it first, shortening the charging time to less than 60% of the conventional charging, ensuring that it can be put into use quickly, optimizing the timing and duration of charging to the greatest extent, and improving the overall work efficiency of the robot.

[0049] It is understandable that the optimized charging strategy module can track the robot's work tasks and idle time in real time, and make accurate decisions on the best charging time and duration based on the comprehensive consideration of battery power, task urgency and expected idle time. When the robot is running out of power and there is no urgent task, it will quickly arrange charging and make full use of idle time to quickly replenish the power; when encountering an urgent task, even if the power is not sufficient, it can reasonably plan the itinerary based on the remaining power, and start efficient charging immediately after the task is completed. In this way, it can not only avoid the robot from "strike" due to exhaustion during the service process, ensuring continuous and reliable service for guests, but also maximize the use of fragmented idle time, reduce unnecessary standby energy consumption, and extend the overall battery life of the robot. At the same time, it can reasonably arrange the charging cycle, reduce battery loss, and comprehensively guarantee the efficient operation of the hotel robot, making the hotel operation more orderly.

[0050] Specifically, the energy-balanced task allocation module builds an energy consumption evaluation model in the task allocation link. The model comprehensively considers factors such as task type (such as different tasks such as food delivery, delivery, cleaning, etc. have different energy consumption requirements), task distance, terrain and road conditions of the execution path (whether there are climbing sections, frequent start-stop sections), and the current battery power status of the robot. It uses the analytic hierarchy process (AHP) or fuzzy comprehensive evaluation method to assign reasonable weights to each factor and calculate the estimated energy consumption value of each task. When allocating tasks, a distributed optimization algorithm based on multi-robot collaboration is used, with task priority as the primary constraint, completion time as the secondary constraint, and energy balance as the optimization goal. Tasks are reasonably allocated to avoid the situation where some robots are quickly exhausted due to continuous high-energy consumption tasks, while other robots are idle for a long time and waste electricity, ensuring that the power consumption of each robot is relatively balanced and the overall work efficiency is improved by more than 25%.

[0051] It is understandable that the energy-balanced task allocation module uses advanced energy consumption assessment models to take into account key factors such as task priority, task completion time, and energy consumption estimation during task execution, so as to achieve intelligent task allocation. In the face of high-priority tasks, priority is given to robots with sufficient power and excellent energy consumption performance to ensure that urgent tasks are completed quickly and accurately without delaying guest needs; for routine tasks, they are evenly allocated according to the energy consumption status of each robot to avoid some robots being overworked and consuming too much energy, while some robots are idle and wasting electricity. In this way, the performance of each robot can be fully utilized to ensure the smoothness and timeliness of hotel services, and the overall energy consumption can be reasonably regulated to extend the robot's range after a single charge, reduce the number of frequent charging times, and thus reduce battery loss. From the source of task allocation, a solid foundation is laid for the automatic charging and range optimization of hotel robots, making hotel operations more efficient and stable.

[0052] Specifically, the path planning module relies on the robot's existing autonomous navigation system (such as map navigation based on the SLAM algorithm) and introduces an energy consumption optimization algorithm in the path search stage. Based on the traditional path cost function, this algorithm adds an energy consumption cost item, comprehensively considers the road condition information (by pre-collecting the hotel's floor material friction coefficient and slope data to build a terrain database), the robot's movement speed and start-stop frequency on energy consumption, and uses heuristic search strategies (such as the A* algorithm combined with the energy consumption heuristic function) to plan a slightly longer route for the robot but with energy consumption reduced by more than 30%. For example, when encountering a sloped section, it is preferred to detour to a flat road or a gently sloping section; when multiple paths are available, avoid busy passages with frequent starts and stops, reduce power loss caused by unnecessary acceleration and deceleration operations, and ensure that the robot completes the task with low energy consumption and high efficiency.

[0053] It is understandable that the path planning module relies on the autonomous navigation system and energy consumption optimization algorithm to carefully plan a low-energy and high-efficiency route for the robot. When receiving task instructions such as delivering meals and goods, it can quickly avoid congested areas, obstacles and unnecessary detours according to the real-time layout of the hotel to reduce the energy consumption of the robot during the journey. For robots with insufficient power, priority is given to planning routes with short distances and low energy consumption to ensure that they can successfully complete the task and return to the charging area in time to avoid running out of power on the way. This not only greatly improves the work efficiency of the robot and ensures the timeliness of the service, but also effectively reduces the energy consumption of a single task, extends the overall battery life, and reduces the frequency of charging. It provides strong support for the automatic charging and battery life optimization of the hotel robot from the route level, making hotel operations smoother and more efficient.

[0054] Specifically, the positioning module and the posture adjustment module work closely together. The positioning module provides the posture adjustment module with accurate charging seat location information, and the posture adjustment module responds quickly based on the positioning information. The two hardware communicate in real time through a high-speed data bus to collaboratively complete precise docking actions in complex environments. The charging monitoring module continuously transmits the real-time collected charging data to the precise charging control module, and the charging control module instantly adjusts the charging strategy based on the monitoring data to ensure that the entire charging process is safe and efficient. The battery management module shares the battery capacity and life prediction data with the optimized charging strategy module, which formulates a personalized charging plan based on this to ensure that the battery is optimally managed throughout its life cycle. The energy consumption balancing task allocation module and the intelligent path planning module are deeply linked. The task allocation module reasonably arranges tasks based on the energy consumption information of each path fed back by the path planning module, and the path planning module optimizes the path in real time based on the task allocation results to ensure that the task execution is both efficient and energy-saving. The data interaction between the modules is frequent and the collaboration is close, forming an organic closed loop as a whole, which comprehensively improves the intelligent management level of the hotel robot's automatic charging and endurance optimization, and reduces costs and increases efficiency for hotel operations.

[0055] It should be noted that:

[0056] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0057] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present application and to form different embodiments.

[0058] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An intelligent management system for automatic charging and battery life optimization of hotel robots, characterized in that: include: The positioning module uses a laser radar sensor and a visual recognition sensor to model the hotel environment and locate the charging location. The positioning module monitors the ground conditions and obstacle information in real time and plans the optimal route through an intelligent algorithm; A posture adjustment module, equipped with a mechanical arm and an adaptive docking device, which dynamically senses the position and angle deviation of the charging interface in combination with visual feedback and automatically adjusts the posture; The charging monitoring module is used to monitor the voltage, current, temperature and internal resistance of the battery, and uses intelligent data analysis algorithms to monitor changes in the charging process in real time. When an abnormality occurs, it initiates an early warning and takes protective measures; The charging control module uses charging curve control technology to set the charging end conditions and dynamically adjust the charging power according to the characteristics and real-time status of the battery; The battery management module uses a big data analysis platform and machine learning algorithms to combine the robot's historical operating data, real-time workload, and ambient temperature to predict battery capacity and remaining service life and plan battery replacement plans; The charging strategy module is used to track the robot's work tasks and idle time in real time, and decide the best time and duration for charging based on the battery level, task urgency, and expected idle time. The energy-balanced task allocation module uses the energy consumption evaluation model to allocate tasks based on task priority, task completion time, and energy consumption estimation during task execution; Path planning module, using autonomous navigation system and energy optimization algorithm to plan low-energy and high-efficiency travel routes; The positioning module provides the charging seat position information to the posture adjustment module, and the posture adjustment module responds according to the positioning information, and the positioning module and the posture adjustment module complete the docking action; the charging monitoring module transmits the charging data collected in real time to the charging control module, and the charging control module adjusts the charging strategy according to the monitoring data; The battery management module shares the battery capacity and life prediction data with the optimized charging strategy module, and the optimized charging strategy module formulates a charging plan based on the shared data; The energy consumption balancing task allocation module arranges tasks according to the energy consumption information of each path fed back by the path planning module, and the path planning module optimizes the path in real time according to the task allocation result.

2. According to claim 1, an intelligent management system for automatic charging and battery life optimization of hotel robots is characterized in that The positioning module uses the high-precision ranging and scanning functions of the laser radar to construct a three-dimensional spatial map of the hotel and update the map information in real time. The positioning module uses the visual recognition sensor to capture image details through a high-definition camera, identifies various obstacles such as carpet wrinkles, temporarily placed tables and chairs, and trash cans, and combines the deep learning algorithm to classify and locate the obstacles. The positioning module fuses the data of the laser radar sensor and the visual recognition sensor and inputs them into the intelligent path planning algorithm. The intelligent path planning algorithm plans an optimal route for the robot in the hotel based on the Dijkstra algorithm, which avoids all obstacles and reaches the charging base at the fastest speed.

3. According to claim 2, an intelligent management system for automatic charging and battery life optimization of hotel robots is characterized in that The posture adjustment module is equipped with an encoder at the joint of the robotic arm to provide real-time feedback on the position and posture information of the robotic arm. When the robot approaches the charging base, the built-in proximity sensor of the adaptive docking device senses the approximate position of the charging interface and guides the initial movement of the robotic arm. As the distance gets closer, the built-in pressure sensor of the adaptive docking device detects the pressure change when the robotic arm contacts the charging base, combines the high-definition image of the charging interface transmitted by the visual recognition sensor, and uses image recognition and feature extraction technology to analyze the position deviation and angle deviation of the charging interface. The posture adjustment module uses a control algorithm to drive the robotic arm to perform real-time posture adjustment.

4. According to claim 3, an intelligent management system for automatic charging and battery life optimization of hotel robots is characterized in that The charging monitoring module uses a thermistor to monitor the battery temperature in real time and captures the temperature change trend of the battery during the charging process. The charging monitoring module uses a four-wire measurement method to measure the battery internal resistance in real time; the charging monitoring module uses big data and machine learning models to build an intelligent data analysis algorithm, and performs real-time analysis on the collected data, using cluster analysis and association rule mining methods. When an abnormality occurs, an early warning is initiated through sound and light alarms and push notifications. The charging monitoring module automatically cuts off the charging circuit and adjusts the charging parameters.

5. According to claim 4, an intelligent management system for automatic charging and battery life optimization of hotel robots is characterized in that The charging control module grasps the real-time status of the battery by real-time identification of model parameters. In the constant current charging stage, the charging control module dynamically adjusts the charging current according to the current remaining capacity, temperature and aging degree of the battery; after entering the constant voltage charging stage, the charging control module regulates the charging voltage and controls the charging cut-off voltage accuracy within ±0.01V; the charging control module interacts with the battery management system in real time through a two-way communication interface and dynamically adjusts the charging power according to the battery feedback information.

6. According to claim 5, an intelligent management system for automatic charging and battery life optimization of hotel robots is characterized in that The battery management module collects the robot's historical operating data, which includes the start and end time of each task, mileage, load weight, ambient temperature and battery power consumption. The battery management module uses the regression algorithm and time series prediction model in machine learning to estimate the battery capacity in real time, and plans the battery replacement plan in advance based on the prediction results.

7. An intelligent management system for automatic charging and battery life optimization of hotel robots according to claim 6, characterized in that The optimized charging strategy module uses wireless communication technology to connect with the hotel room management system and the front desk reception system in real time to obtain robot task allocation information, passenger flow in each area and expected idle time distribution. The optimized charging strategy module uses an intelligent decision-making algorithm to comprehensively consider the current battery power, task urgency and expected idle time. The optimized charging strategy module uses dynamic programming to automatically dispatch the robot to a nearby idle charging seat to replenish power in time when the robot is idle and the power is in a medium or low range; when encountering an emergency task and the key robot is low in power, a high-power fast charging seat is preferentially allocated to it.

8. An intelligent management system for automatic charging and battery life optimization of hotel robots according to claim 7, characterized in that The energy consumption balance task allocation module constructs an energy consumption evaluation model in the task allocation link, and assigns weights to each factor based on the task type, task distance, terrain and road conditions of the execution path, and the current battery power status of the robot, and calculates the estimated energy consumption value of each task. When allocating tasks, a distributed optimization algorithm based on multi-robot collaboration is used to allocate tasks with task priority as the primary constraint, completion time as the secondary constraint, and energy consumption balance as the optimization goal; The path planning module utilizes the robot's existing autonomous navigation system and energy consumption optimization algorithm, and uses a heuristic search strategy to plan a long but low-energy route for the robot based on road conditions, robot movement speed, and the impact of start-stop frequency on energy consumption.

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