Mobile robot charging warning method, device, equipment and readable storage medium

Abstract

The application provides a charging early warning method, device and equipment for a mobile robot, and a readable storage medium. According to historical charging key index parameters of different models of mobile robots, corresponding battery standard models are respectively established. According to different battery standard models, corresponding thresholds of charging key indexes of the corresponding mobile robots are respectively determined, and the thresholds corresponding to different charging key indexes of different models of mobile robots are dynamically adjusted in combination with a current environment and a battery aging coefficient. According to a relationship between current different charging key index parameters of different models of mobile robots and corresponding dynamic thresholds, and a change trend of the current different charging key index parameters of different models of mobile robots, the mobile robot is subjected to linkage charging early warning. The application scheme realizes unified management of charging of mobile robots of different manufacturers, and through a charging early warning method combining threshold early warning and change trend early warning, more accurate early warning can be realized, and charging failure can be early discovered and early warned.

Description

Technical Field

[0001] This invention belongs to the technical field of mobile robot charging, and particularly relates to a charging early warning method, device, equipment, and readable storage medium for mobile robots. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Warehousing is a crucial component of modern logistics, playing a vital role in the logistics system. In warehouse management, the introduction of numerous AMR (Autonomous Mobile Robot) devices can shorten sorting cycles by 30% to 50%. Especially during peak periods such as e-commerce promotions, the response speed of AMR devices can be more than twice that of manual labor, saving not only labor costs but also reducing energy consumption by 40% and cargo damage rate by over 99%.

[0004] However, current AMR devices come from different suppliers, and the battery systems of AMR devices lack unified management and charging history data, which increases the difficulty of battery maintenance and management. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a charging early warning method, device, equipment, and readable storage medium for mobile robots, enabling unified management of charging for mobile robots from different manufacturers. By combining threshold early warning and trend warning, a more accurate early warning can be achieved.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a charging warning method for a mobile robot, comprising: Based on the key historical charging parameters of different mobile robot models, corresponding battery standard models were established. Based on different battery standard models, the threshold values ​​corresponding to the key charging indicators of the mobile robot are determined respectively, and the threshold values ​​corresponding to different key charging indicators of different mobile robot models are dynamically adjusted in combination with the current environment and battery aging coefficient. Based on the relationship between different key charging indicators and corresponding dynamic thresholds for different mobile robot models, as well as the changing trends of different key charging indicators for different mobile robot models, a coordinated charging early warning system is implemented for the mobile robots.

[0007] Optionally, based on the key historical charging performance parameters of different mobile robot models, corresponding battery standard models can be established, specifically as follows: Collect key charging parameters for different models of mobile robots; Analyze the complete charging records of different mobile robot models for each charge, calculate the charging efficiency of different mobile robot models for each charge, and determine the standard charging efficiency range of different mobile robot models. Based on the standard reference range of key charging indicators for different mobile robot models and the standard charging efficiency range for different mobile robot models, we establish the initial battery standard models corresponding to different mobile robot models. Based on the maximum and minimum values ​​of each charging key indicator within the current preset historical time period for different mobile robot models, and combined with the standard reference range of each charging key indicator in the corresponding initial battery standard model, the standard reference range of each charging key indicator is updated to obtain the updated battery standard model for different mobile robot models.

[0008] Optionally, the key charging indicators include current, voltage, and temperature.

[0009] Optionally, the threshold values ​​corresponding to different key charging indicators for different mobile robot models can be dynamically adjusted based on the current environment and battery aging coefficient, specifically: The current threshold of the mobile robot is dynamically adjusted based on the battery aging coefficient, temperature compensation coefficient, and the difference between the current battery temperature and the reference temperature of the mobile robot. The temperature threshold of the mobile robot is dynamically adjusted based on the current reference temperature limit of the mobile robot, the difference between the current ambient temperature and the reference ambient temperature, and the influence factor of battery health status on temperature. The voltage threshold of the mobile robot is dynamically adjusted based on the influence factor of battery health status on voltage, temperature compensation coefficient, reference voltage threshold of the mobile robot, and battery temperature rise deviation of the mobile robot.

[0010] Optionally, the changing trends of different key charging indicator parameters for different models of mobile robots are determined, specifically as follows: Based on the preset warning thresholds corresponding to the first and second derivatives of different key charging indicators, and combined with the first and second derivatives of different key charging indicator parameters calculated at different times, it is determined whether the current key charging indicator parameters of the mobile robot show an abnormal trend.

[0011] Optionally, the method further includes: determining whether to issue a charging warning to the mobile robot based on the relationship between the Pearson correlation coefficient of the current battery temperature of different models of mobile robots and the battery charging efficiency in the corresponding battery standard model, and a preset correlation coefficient threshold.

[0012] Optionally, it also includes: using the key charging indicator parameters of the mobile robot as nodes, determining the causal dependency relationship of the key indicator parameters through historical data to form a parameter causal network; and tracing back according to the parameter causal network based on the abnormal situation of the key charging indicator parameters, calculating the causal contribution of each node, and locating the root cause of the abnormality.

[0013] In a second aspect, the present invention provides a charging warning device for a mobile robot, comprising: The module is used to build corresponding battery standard models based on the key historical charging parameters of different mobile robot models. The threshold dynamic adjustment module is used to determine the thresholds corresponding to the key charging indicators of the mobile robot according to different battery standard models, and dynamically adjust the thresholds corresponding to different key charging indicators of different models of mobile robots in combination with the current environment and battery aging coefficient. The linkage early warning module is used to provide linkage early warning for mobile robots based on the relationship between different current key charging indicators and corresponding dynamic thresholds of different models of mobile robots, as well as the changing trends of different current key charging indicators of different models of mobile robots.

[0014] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0015] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0016] The above one or more technical solutions have the following beneficial effects: In this invention, corresponding battery standard models are established based on the historical charging key indicator parameters of different mobile robot models. Thresholds corresponding to the charging key indicators of each mobile robot are determined according to the different battery standard models, and the thresholds corresponding to different charging key indicators of different mobile robot models are dynamically adjusted in conjunction with the current environment and battery aging coefficient. Based on the relationship between the current charging key indicator parameters of different mobile robot models and their corresponding dynamic thresholds, as well as the changing trends of the current charging key indicator parameters of different mobile robot models, a linked charging early warning system is implemented for the mobile robots. This invention achieves unified management of charging for mobile robots from different manufacturers. By combining threshold-based early warning with trend-based early warning, a more accurate early warning system can be achieved, enabling early detection and warning of charging faults.

[0017] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 This is a flowchart of the charging early warning method for a mobile robot in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of constructing a standard battery model in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the dynamic adjustment of thresholds corresponding to key charging indicators in an embodiment of the present invention. Figure 4 This is a schematic diagram of a charging warning device for a mobile robot in an embodiment of the present invention; Figure 5 This is a structural block diagram of the computer device in an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] First, the mobile robot involved in the charging early warning method for the mobile robot provided in this embodiment will be described in detail.

[0024] Mobile robots, or AMRs, are automated guided vehicles, unmanned automated transport vehicles used for automated transportation and handling in factories, warehouses, and other locations. AMRs are typically equipped with various sensors and navigation devices, allowing them to move independently within the workplace according to pre-set paths and behavioral rules, completing designated tasks. AMRs can be customized to suit the layout and needs of the workplace; common types include forklift-type, vehicle-type, and trailer-type. Applications of AMRs include, but are not limited to, raw material transportation, semi-finished product handling, finished product transportation, warehouse management, and production line material replenishment. With the development of the logistics industry and the advancement of automation technology, warehouse AMR logistics vehicles are increasingly widely used in warehousing and logistics, becoming one of the important tools for improving production efficiency and intelligence.

[0025] A battery is a device that converts stored chemical energy into electrical energy. A battery consists of one or more battery cells, each containing a positive electrode, a negative electrode, and an electrolyte. When the battery is connected to an external circuit, ions in the electrolyte move from the negative electrode to the positive electrode under the influence of an electric field, while electrons flow from the negative electrode to the positive electrode through the external circuit, thus generating an electric current.

[0026] Figure 1 This is a flowchart of the charging warning method for a mobile robot provided in this embodiment, including: S101: Establish corresponding battery standard models based on the key historical charging parameters of different mobile robot models.

[0027] Since mobile robots from different manufacturers have different models, by monitoring the key charging parameters of each AMR device in real time, a dedicated model is established for the battery of each AMR model, enabling centralized management of different AMR devices.

[0028] S102: Determine the threshold values ​​corresponding to the key charging indicators of the mobile robot based on different battery standard models, and dynamically adjust the threshold values ​​corresponding to different key charging indicators of different mobile robot models in combination with the current environment and battery aging coefficient.

[0029] S103: Based on the relationship between different charging key indicator parameters and corresponding dynamic thresholds of different mobile robot models, as well as the changing trends of different charging key indicator parameters of different mobile robot models, a linkage charging early warning is provided for the mobile robot.

[0030] This embodiment establishes corresponding battery standard models based on historical charging key indicator parameters for different mobile robot models. Based on these models, threshold values ​​for each charging key indicator of the mobile robot are determined, and these threshold values ​​are dynamically adjusted according to the current environment and battery aging coefficient. Based on the relationship between the current charging key indicator parameters and the corresponding dynamic thresholds for different mobile robot models, as well as the changing trends of these parameters, a linked charging early warning system is implemented for the mobile robots. This embodiment achieves unified management of charging for mobile robots from different manufacturers. By combining threshold-based and trend-based early warning methods, more accurate warnings can be achieved, enabling early detection and alerting of charging faults.

[0031] like Figure 2 As shown, in S101, corresponding battery standard models are established based on the key historical charging index parameters of different mobile robot models, specifically including: S201: Collect key charging parameters for different models of mobile robots.

[0032] Optional, key charging parameters for mobile robots include changes in charging voltage, battery temperature, charging current, and charging capacity.

[0033] S202: Analyze the complete charging records of different mobile robot models for each charging session, calculate the charging efficiency of different mobile robot models for each charging session, and determine the standard charging efficiency range for different mobile robot models.

[0034] In this embodiment, since the AMR charging action is a continuous process, this process is not a fixed time. During the production process of different demand scenarios, the AMR charging time will also change dynamically. Based on the real-time status data reported by each AMR, the start and end times of each charging are automatically analyzed, and a file is established for each AMR, recording in detail the equipment supplier, key component brand, threshold range of operating parameters, and other key parameters of each AMR.

[0035] By dynamically monitoring the changes in key charging data reported in real time by each AMR, such as charging voltage, battery temperature, charging current, and charging capacity, each charging process can be dynamically monitored. By analyzing the complete historical records of each charging by the AMR, the charging efficiency for each charging can be calculated, and the standard charging efficiency range can be determined based on the calculated charging efficiency for each charging.

[0036] As one possible implementation, for each complete charging process of the AMR, the key charging indicator parameters are filtered to identify charging events that are completed normally without errors, excluding events with abnormal interruptions, BMS errors, or severely missing data. The average and standard deviation of the charging efficiency for qualified charging events are then calculated, based on the standard charging efficiency range: [ μ- N*σ, μ + M*σ ], where μ is the mean and σ is the standard deviation. N and M The value is determined based on business tolerance, such as... N=2, M=3.

[0037] S203: Based on the standard reference range of each key charging indicator for different mobile robot models, and the standard charging efficiency range for different mobile robot models, establish the initial battery standard model corresponding to each mobile robot model.

[0038] By using the values ​​of key parameters of different AMRs as standard reference indicators, and by establishing reasonable ranges of variation for each indicator through long-term historical data collection, an initial standard model is established for the battery of each AMR device.

[0039] S204: Based on the maximum and minimum values ​​of each charging key indicator within the current preset historical time period for different mobile robot models, and combined with the standard reference range of each charging key indicator in the corresponding initial battery standard model, update the standard reference range of each charging key indicator to obtain the updated battery standard model for different mobile robot models.

[0040] In this embodiment, the status history of the past six months is collected, and the maximum and minimum values ​​of the corresponding charging key indicators within this interval are statistically analyzed. Combined with the normal working range of the standard indicators of AMR, the range of change of the charging key indicators is set, which serves as the reasonable range of change of the charging key indicators.

[0041] Optionally, a data update is performed automatically at the end of each month, using the latest data to produce a new version of the AMR battery standard model. By comparing with historical versions of the standard charging model, the changing trends of key parameters of the AMR battery can be analyzed.

[0042] As one possible implementation, over the past six months, a monitoring system connected to the charging equipment collects key charging indicator data at fixed time intervals (e.g., every 10 minutes). These key charging indicators may include charging voltage, charging current, charging power, battery temperature, etc. To ensure the accuracy and completeness of the data, the monitoring system should have high-precision sensors and reliable data transmission and storage capabilities. The collected charging data is organized chronologically to form a status history record. Each record should include the collection time and the corresponding values ​​of each key charging indicator. For example, at 10:00 AM on January 1, 2024, the charging voltage is X volts, the charging current is Y amperes, the charging power is Z kilowatts, and the battery temperature is T degrees Celsius. The organized status history record is analyzed to determine the maximum and minimum values ​​for each key charging indicator.

[0043] By combining the calculated maximum and minimum values ​​of key charging indicators over the past six months with the normal operating range of AMR standard reference indicators, a reasonable range of variation for key charging indicators is set.

[0044] For example, if the maximum charging voltage recorded in the past six months is 235 volts and the minimum is 205 volts, the standard normal operating range for AMR charging voltage is 200 volts - 240 volts. Considering practical situations and a certain safety margin, a reasonable range for charging voltage variation can be set to 195 - 245 volts, with the lower limit slightly lower than the standard lower limit by 5 volts and the upper limit slightly higher than the standard upper limit by 5 volts, to accommodate possible short-term fluctuations.

[0045] Assuming the maximum charging current measured in the past six months is 28 amps and the minimum is 6 amps, and the standard normal operating range for AMR charging current is 5-30 amps, then the reasonable range for charging current variation can be set to 4-32 amps. The lower limit is 1 amp lower than the standard lower limit, and the upper limit is 2 amps higher than the standard upper limit, which takes into account the extreme values ​​that have actually occurred while ensuring that it is within a reasonable safety range.

[0046] In this embodiment S102, as Figure 3 As shown, the threshold values ​​for key charging indicators of mobile robots are determined according to different battery standard models. These threshold values ​​are then dynamically adjusted for different mobile robot models based on the current environment and battery aging coefficient. Specifically, this includes: S301: The current threshold of the mobile robot is dynamically adjusted based on the battery aging coefficient, temperature compensation coefficient, and the difference between the current battery temperature and the reference temperature of the mobile robot.

[0047] In this embodiment, the current threshold adjustment formula is:

[0048] in, To adjust the current threshold, Here, α is the initial current threshold, α is the temperature compensation coefficient, and ΔT is the difference between the current battery temperature and the reference temperature. This represents the battery aging factor.

[0049] S302: The temperature threshold of the mobile robot is dynamically adjusted based on the current reference temperature limit of the mobile robot, the difference between the current ambient temperature and the reference ambient temperature, and the influence factor of battery health status on temperature.

[0050] In this embodiment, the dynamic correction of the temperature threshold needs to comprehensively consider factors such as ambient temperature, battery aging coefficient (SOH), and humidity. The specific formula can be expressed as follows:

[0051] in, This is the reference temperature limit, such as the maximum allowable temperature stated in the battery manufacturer's trademark; This is the ambient temperature compensation coefficient, used to correct the impact of ambient temperature on heat dissipation efficiency. For example, when the ambient temperature increases, the threshold needs to be reduced. This is the difference between the current ambient temperature and the reference ambient temperature, i.e. , The current ambient temperature, i.e., the real-time environmental measurement value. The reference ambient temperature is used as a base, such as the preset reference temperature of 25°C. Factors affecting battery health status include temperature, such as when SOH decreases. <1, the threshold is further reduced. This is the humidity compensation coefficient. When the ambient humidity exceeds the safe range, the threshold is dynamically lowered. For example, when the humidity is >80%, an additional correction is triggered.

[0052] S303: The voltage threshold of the mobile robot is dynamically adjusted based on the influence factor of battery health status on voltage, temperature compensation coefficient, reference voltage threshold of the mobile robot, and battery temperature rise deviation of the mobile robot.

[0053] In this embodiment, the voltage threshold adjustment needs to consider battery aging (SOH), internal resistance changes, and temperature-related effects, as shown in the formula:

[0054] in, The adjusted voltage threshold; This is the internal resistance compensation coefficient, where the internal resistance is... As batteries age, voltage drop increases, necessitating a reduction in the upper voltage limit. Factors affecting battery health state on voltage, for example, a 10% decrease in SOH (State of Health). Reduce by 0.05; This is the temperature compensation coefficient; For battery temperature rise deviation, = This represents the actual temperature of the battery.

[0055] In this embodiment S103, based on the relationship between different charging key indicator parameters of different mobile robot models and their corresponding dynamic thresholds, as well as the changing trends of different charging key indicator parameters of different mobile robot models, a linked charging warning is issued for the mobile robot, specifically as follows: If different charging key indicator parameters of different mobile robot models exceed the corresponding dynamic thresholds, a charging warning will be triggered. Based on the preset warning thresholds corresponding to the first and second derivatives of different key charging indicators, and combined with the first and second derivatives of different key charging indicator parameters calculated at different times, it is determined whether the current key charging indicator parameters of the mobile robot show an abnormal trend. If an abnormal trend is observed, a charging warning is issued.

[0056] In this embodiment, early detection of dynamic evolution is achieved by identifying abnormal change trends of parameters through time series analysis and issuing an early warning before the dynamic threshold is exceeded. Abnormal trends such as voltage drop are identified through first derivative analysis and are triggered 5 to 15 seconds earlier than the threshold.

[0057] The trend early warning method is achieved by using time series analysis based on mathematical models. The core of this method is to identify abnormal change trends in key charging indicators by calculating derivatives.

[0058] Set the time series data as [ The monitored values ​​at any given time are illustrated using voltage as an example: First derivative (instantaneous rate of change):

[0059] Second derivative (changing acceleration):

[0060] Warning trigger conditions:

[0061] in = -0.5V / s, k=5~15 seconds.

[0062] This embodiment also includes: determining whether to issue a charging warning to the mobile robot based on the relationship between the Pearson correlation coefficient of the current battery temperature of different models of mobile robots and the corresponding battery charging efficiency in the battery standard model, and the preset correlation coefficient threshold.

[0063] As one possible implementation method, the correlation coefficient between battery temperature and charging efficiency is calculated as follows:

[0064]

[0065]

[0066]

[0067] in, For the first i Battery temperature values ​​at each sampling point; For the first i Charging efficiency (%) at each sampling point; Represents the mean of the temperature series. ; ; This represents the mean of the efficiency sequence; n Indicates the sequence length; For the first j The charging efficiency value of each sampling point.

[0068] As one implementation method, determining whether to issue a charging warning for the mobile robot specifically involves:

[0069] In this embodiment, early warning is provided by using the correlation coefficient between temperature and charging efficiency, which has significant advantages over traditional single-parameter threshold monitoring. It can detect hidden faults in advance, quantify performance degradation, and reduce false alarm rate.

[0070] In this embodiment, the method further includes: using the key charging index parameters of the mobile robot as nodes, determining the causal dependency relationship of the key index parameters through historical data to form a parameter causal network; and tracing back based on the abnormal situation of the key charging index parameters according to the parameter causal network, calculating the causal contribution of each node, and locating the root cause of the abnormality.

[0071] Specifically, based on the key charging parameters of the mobile robot, historical data is used to determine the causal dependencies of the key parameters by employing linear non-Gaussian models or conditional independence tests, combined with expert knowledge, and using different key charging parameters as nodes, thus forming a parameter causal network. Based on historical data, conditional probabilities, such as the probability value of P (electrolyte decomposition | temperature anomaly), are calculated to determine the edge weights of causal transmission links such as temperature anomaly → electrolyte decomposition → increased internal resistance → voltage fluctuation.

[0072] Starting from abnormal parameters, trace back along the parameter causal network to possible upstream nodes (increased internal resistance → electrolyte decomposition → abnormal temperature), calculate the causal contribution of each node, such as calculating the intervention effect through a structural causal model (SCM), and then locate the root cause. Each root cause corresponds to an early warning prompt. For example, if the charging current is normal, but the current fluctuation frequency is strongly correlated with the vibration sensor data, an early warning of "loose contactor" will be issued; or if the insulation resistance decreases by 3 times when the ambient humidity is >80%, an early warning of "leakage risk in humid environment" will be triggered.

[0073] When constructing the parameter causal network, the key charging parameters are not limited to voltage, current and temperature, but can also include SOH, internal resistance, charging efficiency, ambient temperature and humidity, vibration data, insulation resistance, current fluctuation frequency, etc.

[0074] This embodiment forms a parameter causal network by using the key charging indicators of the mobile robot as nodes. When the key charging indicators are abnormal, the parameter causal network can be used for tracing and locating, realizing a complete closed loop of data collection → intelligent analysis → dynamic decision-making. It achieves proactive prediction from "single parameter exceeding the threshold" to "multi-parameter root cause localization", significantly improving the accuracy of AMR battery safety management.

[0075] like Figure 4 As shown, this embodiment also provides a charging warning device for a mobile robot, including: The module is used to build corresponding battery standard models based on the key historical charging parameters of different mobile robot models. The threshold dynamic adjustment module is used to determine the thresholds corresponding to the key charging indicators of the mobile robot according to different battery standard models, and dynamically adjust the thresholds corresponding to different key charging indicators of different models of mobile robots in combination with the current environment and battery aging coefficient. The linkage early warning module is used to provide linkage early warning for mobile robots based on the relationship between different current key charging indicators and corresponding dynamic thresholds of different models of mobile robots, as well as the changing trends of different current key charging indicators of different models of mobile robots.

[0076] Figure 5This illustration shows a structural block diagram of a computer device 600 provided in an exemplary embodiment of this application. The computer device 600 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 600 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names. Optionally, the computer device 600 can also be implemented as a mobile device, such as a vehicle-mounted terminal or other portable smart terminal.

[0077] Typically, computer device 600 includes a processor 601 and a memory 602.

[0078] Processor 601 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 601 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 601 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 601 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 601 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0079] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 602 are used to store at least one instruction, which is executed by the processor 601 to implement the model training method or behavior encoding method provided in the method embodiments of this application.

[0080] In some embodiments, the computer device 600 may also optionally include a peripheral device interface 603 and at least one peripheral device. The processor 601, memory 602, and peripheral device interface 603 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 603 via a bus, signal line, or circuit board. For example, the peripheral device may include at least one of the following: a radio frequency circuit 604, a display screen 605, a camera assembly 606, an audio circuit 607, a positioning assembly 615, and a power supply 608.

[0081] Peripheral interface 603 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 601 and memory 602. In some embodiments, processor 601, memory 602 and peripheral interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 601, memory 602 and peripheral interface 603 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0082] The radio frequency (RF) circuit 604 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 604 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 604 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 604 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 604 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 604 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0083] Display screen 605 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 605 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 601 for processing. In this case, display screen 605 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 605, disposed on the front panel of computer device 600; in other embodiments, there may be at least two display screens, disposed on different surfaces of computer device 600 or in a folded design; in still other embodiments, display screen 605 may be a flexible display screen, disposed on a curved or folded surface of computer device 600. Furthermore, display screen 605 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 605 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0084] The camera assembly 606 is used to acquire images or videos. Optionally, the camera assembly 606 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 606 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0085] The audio circuit 607 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to the processor 601 for processing, or to the radio frequency circuit 604 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location within the computer device 600. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 601 or the radio frequency circuit 604 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 607 may also include a headphone jack.

[0086] The positioning component 615 is used to calculate the current geographic location of the device 600 in order to enable navigation or LBS (Location Based Service). The positioning component 615 can be a positioning component based on the US GPS (Global Positioning System) or the Chinese BeiDou system.

[0087] Power supply 608 is used to supply power to the various components in computer device 600. Power supply 608 can be alternating current, direct current, a disposable battery, or a rechargeable battery. When power supply 608 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0088] In some embodiments, the computer device 600 further includes one or more sensors 609. The one or more sensors 609 include, but are not limited to, an accelerometer 610, a gyroscope 611, a pressure sensor 612, an optical sensor 613, and a proximity sensor 614.

[0089] Accelerometer 610 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 600. For example, accelerometer 610 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 601 can control display screen 605 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 610. Accelerometer 610 can also be used for games or for acquiring user motion data.

[0090] The gyroscope sensor 611 can detect the orientation and rotation angle of the computer device 600. The gyroscope sensor 611 can work in conjunction with the accelerometer sensor 610 to acquire the user's 3D movements on the computer device 600. Based on the data acquired by the gyroscope sensor 611, the processor 601 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0091] The pressure sensor 612 can be disposed on the side bezel of the computer device 600 and / or on the lower layer of the display screen 605. When the pressure sensor 612 is disposed on the side bezel of the computer device 600, it can detect the user's grip signal on the computer device 600, and the processor 601 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 612. When the pressure sensor 612 is disposed on the lower layer of the display screen 605, the processor 601 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 605. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0092] An optical sensor 613 is used to collect ambient light intensity. In one embodiment, the processor 601 can control the display brightness of the display screen 605 based on the ambient light intensity collected by the optical sensor 613. For example, when the ambient light intensity is high, the display brightness of the display screen 605 is increased; when the ambient light intensity is low, the display brightness of the display screen 605 is decreased. In another embodiment, the processor 601 can also dynamically adjust the shooting parameters of the camera assembly 606 based on the ambient light intensity collected by the optical sensor 613.

[0093] A proximity sensor 614, also known as a distance sensor, is typically mounted on the front panel of the computer device 600. The proximity sensor 614 is used to detect the distance between the user and the front of the computer device 600. In one embodiment, when the proximity sensor 614 detects that the distance between the user and the front of the computer device 600 is gradually decreasing, the processor 601 controls the display screen 605 to switch from a screen-on state to a screen-off state; when the proximity sensor 614 detects that the distance between the user and the front of the computer device 600 is gradually increasing, the processor 601 controls the display screen 605 to switch from a screen-off state to a screen-on state.

[0094] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the computer device 600, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0095] This application also provides a computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the charging warning method for a mobile robot provided in the above method embodiments.

[0096] This application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the charging warning method for a mobile robot provided in the above-described method embodiments.

[0097] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0098] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A charging early warning method for a mobile robot, characterized in that, include: Based on the key historical charging parameters of different mobile robot models, corresponding battery standard models were established. Based on different battery standard models, the threshold values ​​corresponding to the key charging indicators of the mobile robot are determined respectively, and the threshold values ​​corresponding to different key charging indicators of different mobile robot models are dynamically adjusted in combination with the current environment and battery aging coefficient. Based on the relationship between different key charging indicators and corresponding dynamic thresholds for different mobile robot models, as well as the changing trends of different key charging indicators for different mobile robot models, a coordinated charging early warning system is implemented for the mobile robots.

2. The charging early warning method for a mobile robot as described in claim 1, characterized in that, Based on the key historical charging performance parameters of different mobile robot models, corresponding standard battery models are established, specifically as follows: Collect key charging parameters for different models of mobile robots; Analyze the complete charging records of different mobile robot models for each charge, calculate the charging efficiency of different mobile robot models for each charge, and determine the standard charging efficiency range of different mobile robot models. Based on the standard reference range of key charging indicators for different mobile robot models and the standard charging efficiency range for different mobile robot models, we establish the initial battery standard models corresponding to different mobile robot models. Based on the maximum and minimum values ​​of each charging key indicator within the current preset historical time period for different mobile robot models, and combined with the standard reference range of each charging key indicator in the corresponding initial battery standard model, the standard reference range of each charging key indicator is updated to obtain the updated battery standard model for different mobile robot models.

3. The charging early warning method for a mobile robot as described in claim 1, characterized in that, The key charging parameters include current, temperature, and voltage.

4. The charging early warning method for a mobile robot as described in claim 3, characterized in that, The threshold values ​​corresponding to different key charging indicators for different mobile robot models are dynamically adjusted based on the current environment and battery aging coefficient, specifically as follows: The current threshold of the mobile robot is dynamically adjusted based on the battery aging coefficient, temperature compensation coefficient, and the difference between the current battery temperature and the reference temperature of the mobile robot. The temperature threshold of the mobile robot is dynamically adjusted based on the current reference temperature limit of the mobile robot, the difference between the current ambient temperature and the reference ambient temperature, and the influence factor of battery health status on temperature. The voltage threshold of the mobile robot is dynamically adjusted based on the influence factor of battery health status on voltage, temperature compensation coefficient, reference voltage threshold of the mobile robot, and battery temperature rise deviation of the mobile robot.

5. The charging early warning method for a mobile robot as described in claim 1, characterized in that, The changing trends of different charging key indicator parameters for different models of mobile robots have been determined, specifically as follows: Based on the preset warning thresholds corresponding to the first and second derivatives of different key charging indicators, and combined with the first and second derivatives of different key charging indicator parameters calculated at different times, it is determined whether the current key charging indicator parameters of the mobile robot show an abnormal trend.

6. The charging early warning method for a mobile robot as described in claim 1, characterized in that, The method further includes: determining whether to issue a charging warning to the mobile robot based on the relationship between the Pearson correlation coefficient of the current battery temperature of different models of mobile robots and the battery charging efficiency in the corresponding battery standard model, and a preset correlation coefficient threshold.

7. The charging warning method for a mobile robot as described in any one of claims 1-6, characterized in that, Also includes: Using the key charging parameters of mobile robots as nodes, the causal dependencies of the key parameters are determined through historical data, forming a parameter causal network. Based on the abnormalities in key charging parameters, the causal network of these parameters is used to trace the cause and effect, calculate the causal contribution of each node, and locate the root cause of the anomaly.

8. A charging early warning device for a mobile robot, characterized in that, include: The module is used to build corresponding battery standard models based on the key historical charging parameters of different mobile robot models. The threshold dynamic adjustment module is used to determine the thresholds corresponding to the key charging indicators of the mobile robot according to different battery standard models, and dynamically adjust the thresholds corresponding to different key charging indicators of different models of mobile robots in combination with the current environment and battery aging coefficient. The linkage early warning module is used to provide linkage early warning for mobile robots based on the relationship between different current key charging indicators and corresponding dynamic thresholds of different models of mobile robots, as well as the changing trends of different current key charging indicators of different models of mobile robots.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-7.

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