Charging device dormancy control method and system based on ambient light monitoring

Abstract

The invention relates to the technical field of charging control, and particularly discloses a charging device sleep control method and system based on ambient light monitoring, and the method comprises the steps: collecting an ambient light parameter and an ambient image of a target space range of a space where a charging device is located in real time; based on the use scene recognition model, carrying out use scene recognition on the newly collected environment image of the target space range to obtain a current use scene of the charging device; performing ambient light dynamic classification on the latest acquired ambient light parameters of the target space range based on the dynamic ambient light classification model, and outputting the ambient light level of the target space range in real time; dynamically adjusting a power output curve of the charging device based on the current use scene of the charging device, and starting a hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device; intelligent control over the charging device is achieved, and the energy utilization efficiency and the working stability of the charging device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging control, and particularly to a charging device sleep control method and system based on ambient light monitoring. Background Art

[0002] In today's society, the widespread use of electronic devices has made charging devices an indispensable part of daily life and work. With the growing demand for energy conservation and device performance optimization, the intelligent control of charging devices has received increasing attention. As an important factor in the surrounding environment, ambient light has a potential impact on the working state of charging devices. In past research and practice, there have been some attempts to apply ambient light monitoring to the control of charging devices. Early methods usually simply made rough control adjustments based on the brightness of ambient light. For example, the charging power was reduced in dim ambient light to save energy. Currently, intelligent charging devices generally use ambient light sensing technology to achieve energy-saving control. A typical solution is a light-controlled charging system that detects the ambient brightness through a photoresistor and triggers the standby mode when the illuminance is lower than a set threshold. A study published in IEEE Transactions on Power Electronics in 2022 proposed a multi-level light intensity determination algorithm, dividing three brightness intervals corresponding to different working states. The development of existing technologies shows the following characteristics: First, the acquisition of ambient light parameters has evolved from a single optoelectronic sensor to multi-spectral detection technologies, such as using an RGB three-channel light sensor chip to obtain color temperature parameters. Second, the sleep control strategy has evolved from a fixed threshold to dynamic adjustment, such as the proposed adaptive threshold algorithm based on historical light intensity data. Third, some high-end devices have begun to integrate human infrared sensors. For example, the Apple MagSafe charger extends the activation time through motion detection. In terms of system architecture, the mainstream solution uses a hardware architecture with a microcontroller directly connected to a light sensor, such as the BQ25621 power management chip of TI Company with a built-in light sensor interface module. Existing literature shows that the charging control technology based on ambient light has formed a complete technical route including data acquisition, threshold determination, and state switching, and has been commercially applied in fields such as mobile devices and electric vehicle charging piles.

[0003] However, the static threshold determination mechanism fails to fully consider different usage scenarios and the complex variation characteristics of ambient light. It is difficult to adapt to a dynamic light environment. For example, in existing systems, a fixed illuminance threshold (such as 200 lux) is used to trigger dormancy, unable to distinguish the scene differences between natural light gradual change and artificial light source mutation, resulting in frequent false triggers. Traditional light intensity grading does not consider scene semantic features. For example, the projection scene in a meeting room and the low illuminance environment in a bedroom at night have completely different device usage requirements. The lack of scene perception ability in existing technologies leads to inaccurate dormancy decisions. With the progress of technology, some charging devices have begun to introduce more complex sensors and algorithms to obtain more accurate ambient light information. However, these methods still have limitations in practical applications. For example, they fail to effectively integrate ambient light data with the device usage scenario, or are not accurate and comprehensive enough in classifying and evaluating ambient light. There is a lack of dynamic coupling between the dormancy strategy and power output. Existing solutions use a fixed dormancy delay time (such as 30 minutes), without optimizing the energy consumption control curve according to real-time charging power requirements, resulting in energy waste or a decline in user experience. At the same time, there is still much room for improvement in the existing charging device control strategy in dynamically adapting to environmental changes and user needs, unable to finely adjust the working state of the charging device according to real-time ambient light and specific usage scenarios to achieve the best energy utilization efficiency and device performance.

[0004] Therefore, the present invention proposes a dormancy control method and system for a charging device based on ambient light monitoring. Summary of the Invention

[0005] The present invention provides a dormancy control method and system for a charging device based on ambient light monitoring, including: real-time collecting ambient light parameters and ambient images, ensuring the timeliness and comprehensiveness of the data, providing an accurate basis for subsequent analysis and control. Obtaining the current usage scenario through a usage scenario recognition model, considering scene semantic features, being able to targetedly adjust the working state of the charging device, improving adaptability, and having the scene perception ability resulting in more accurate dormancy decisions. Classifying the ambient light parameters using a dynamic ambient light classification model, and real-time outputting the ambient light level, achieving an accurate assessment of the ambient light condition. Adjusting the power output curve based on the current usage scenario, optimizing the energy consumption control curve according to real-time charging power requirements, making the energy more conserved or the user experience improved. And starting a hierarchical dormancy mechanism in combination with the ambient light level, effectively saving energy, extending the service life of the charging device, and being able to adapt to a dynamic light environment. Effectively integrating ambient light data with the device usage scenario, through dynamically coupling the dormancy strategy and power output, realizing the intelligent control of the charging device, and improving the energy utilization efficiency and working stability of the charging device.

[0006] The present invention provides a dormancy control method for a charging device based on ambient light monitoring, including:

[0007] S1: Collect the ambient light parameters and ambient images of the target space range where the charging device is located in real time;

[0008] S2: Based on the usage scenario recognition model, recognize the usage scenario of the target space range for the latest collected ambient image to obtain the current usage scenario of the charging device;

[0009] S3: Based on the dynamic ambient light classification model, perform dynamic classification of the ambient light parameters of the target space range for the latest collection, and output the ambient light level of the target space range in real time;

[0010] S4: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start the hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

[0011] Preferably, for the charging device sleep control method based on ambient light monitoring, S1: Collect the ambient light parameters and ambient images of the target space range where the charging device is located in real time, including:

[0012] Collect the ambient light parameters of the target space range where the charging device is located in real time based on the multispectral sensor array;

[0013] Collect the ambient image of the target space range where the charging device is located in real time based on the high-resolution imaging device.

[0014] Preferably, for the charging device sleep control method based on ambient light monitoring, S2: Based on the usage scenario recognition model, recognize the usage scenario of the target space range for the latest collected ambient image to obtain the current usage scenario of the charging device, including:

[0015] Establish a usage scenario recognition model;

[0016] Input the latest collected ambient image of the target space range into the usage scenario recognition model to recognize the current usage scenario of the charging device.

[0017] Preferably, for the charging device sleep control method based on ambient light monitoring, S3: Based on the dynamic ambient light classification model, perform dynamic classification of the ambient light parameters of the target space range for the latest collection, and output the ambient light level of the target space range in real time, including:

[0018] Establish a dynamic ambient light classification model;

[0019] Input the latest collected ambient light parameters of the target space range into the dynamic ambient light classification model to output the ambient light level of the target space range.

[0020] Preferably, for the charging device sleep control method based on ambient light monitoring, S4: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start a hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device, including:

[0021] Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device and the state of charge of the energy storage unit;

[0022] When it is determined based on the ambient light level of the target space range and the power output curve of the charging device that the charging device meets the determination conditions of any sleep level in the hierarchical sleep mechanism, then start the sleep mechanism of the corresponding sleep level.

[0023] Preferably, for the charging device sleep control method based on ambient light monitoring, dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device and the state of charge of the energy storage unit, including:

[0024] Perform dimensionality reduction processing on the multi-dimensional features of each usage scenario to obtain the comprehensive scenario feature value of each usage scenario:

[0025]

[0026] In the formula, F is the comprehensive scenario feature value of the currently calculated usage scenario, dimensionless; n is the total number of feature dimensions of the multi-dimensional features of the currently calculated usage scenario; α i is the weight coefficient of the i-th feature value in the multi-dimensional features of the currently calculated usage scenario, dimensionless; x i is the i-th feature value in the multi-dimensional features of the currently calculated usage scenario, dimensionless; is the mean value of the i-th feature value in the multi-dimensional features of all usage scenarios, dimensionless; is the standard deviation of the i-th feature value in the multi-dimensional features of all usage scenarios, dimensionless;

[0027] Generate the state of charge function of the energy storage unit based on the initial state of charge, rated capacity and open circuit voltage of the energy storage unit:

[0028]

[0029] In the formula, SOC(t) is the state of charge of the energy storage unit at time t, dimensionless; SOC(t 0 ) is the state of charge of the energy storage unit at the initial time t 0 ; C is the rated capacity of the energy storage unit, in ampere-hours; V(τ) is the terminal voltage of the energy storage unit at time τ, in volts; I(τ) is the charging current of the energy storage unit at time τ, in amperes; R(τ) is the internal resistance of the energy storage unit at time τ, in ohms; EOCV U(τ) is the open-circuit voltage of the energy storage unit at time τ;

[0030] Determine the current state of charge of the energy storage unit based on the state-of-charge function of the energy storage unit;

[0031] Generate an adjusted power output curve of the charging device based on the comprehensive scenario eigenvalue of the current usage scenario of the charging device and the current state of charge of the energy storage unit.

[0032] Preferably, for the charging device sleep control method based on ambient light monitoring, generating an adjusted power output curve of the charging device based on the comprehensive scenario eigenvalue of the current usage scenario of the charging device and the current state of charge of the energy storage unit, includes:

[0033] Generate an adjustment function ω(SOC) of the charge state on the output power of the charging device based on the maximum state of charge of the energy storage unit, two safety state-of-charge grading values, and the current state of charge of the energy storage unit:

[0034]

[0035] In the formula, SOC is the current state of charge of the energy storage unit, SOC threshold1 is the first safety state-of-charge grading value of the energy storage unit, η 1 is the adjustment coefficient in the low state of charge, SOC max is the maximum state of charge of the energy storage unit, SOC threshold2 is the second safety state-of-charge grading value of the energy storage unit, η 2 is the adjustment coefficient in the high state of charge;

[0036] Generate an adjusted power output curve of the charging device based on the initial power output curve of the charging device, the adjustment function of the charge state on the output power of the charging device, and the comprehensive scenario eigenvalue of the current usage scenario of the charging device.

[0037] Preferably, for the charging device sleep control method based on ambient light monitoring, generating an adjusted power output curve of the charging device based on the initial power output curve of the charging device, the adjustment function of the charge state on the output power of the charging device, and the comprehensive scenario eigenvalue of the current usage scenario of the charging device, includes:

[0038] Set the initial power output curve of the charging device as:

[0039] P 0 (t) = V max · [I max - k · (t - t CC )]

[0040] In the formula, P 0P(t) is the initial power output value of the charging device at time t when the charging device outputs power according to the initial power output curve, with the unit of watt; V max is the constant charging voltage, with the unit of volt; I max is the maximum charging current at which the charging device operates, with the unit of ampere; k is the current decline coefficient, with the unit of ampere / second, and t CC is the end time when the charging device outputs power according to the initial power output curve;

[0041] Based on the initial power output curve of the charging device, the adjustment function of the output power of the charging device according to the state of charge, and the comprehensive scenario eigenvalue of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated:

[0042]

[0043] In the formula, P(t) is the adjusted power output curve of the charging device, e is the natural constant and its value is 2.71828, λ is the rate at which the influence factor of the usage scenario on the output power of the charging device changes with the comprehensive scenario eigenvalue of the current usage scenario, and μ is the preset critical comprehensive scenario eigenvalue.

[0044] Preferably, the charging device dormancy control method based on ambient light monitoring further includes:

[0045] During the hierarchical dormancy process, the state of charge of the energy storage unit is synchronously monitored;

[0046] When the state of charge of the energy storage unit is lower than the preset state of charge threshold, a wake-up compensation mechanism is triggered.

[0047] The present invention provides a charging device dormancy control system based on ambient light monitoring for executing any one of the above-mentioned charging device dormancy control methods based on ambient light monitoring, including:

[0048] An environmental data acquisition module for real-time acquiring the ambient light parameters and environmental images of the target space range where the charging device is located;

[0049] A usage scenario recognition module for recognizing the current usage scenario of the charging device based on a usage scenario recognition model for the latest acquired environmental image of the target space range;

[0050] An ambient light level judgment module for dynamically classifying the ambient light parameters of the latest acquired target space range based on a dynamic ambient light classification model and real-time outputting the ambient light level of the target space range;

[0051] The hierarchical sleep control module is used to dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start the hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

[0052] The beneficial effects of the present invention compared with the prior art are as follows: Real-time collection of ambient light parameters and ambient images ensures the timeliness and comprehensiveness of data, providing an accurate basis for subsequent analysis and control. Obtaining the current usage scenario through the usage scenario recognition model, considering the semantic features of the scenario, can targetedly adjust the working state of the charging device, improve adaptability, and the ability to perceive the scenario leads to more accurate sleep decisions. Using the dynamic ambient light classification model to classify ambient light parameters and output the ambient light level in real time realizes an accurate assessment of the ambient light condition. Adjusting the power output curve based on the current usage scenario and optimizing the energy consumption control curve according to the real-time charging power demand makes the energy more conserved or the user experience improved. And starting the hierarchical sleep mechanism in combination with the ambient light level effectively saves energy, extends the service life of the charging device, and can adapt to the dynamic light environment. Effectively integrating ambient light data with the usage scenario of the device, through dynamically coupling the sleep strategy with the power output, realizes the intelligent control of the charging device, improves the energy utilization efficiency and working stability of the charging device.

[0053] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0054] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0055] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0056] Figure 1 It is a flowchart of a method for controlling the sleep of a charging device based on ambient light monitoring in an embodiment of the present invention;

[0057] Figure 2 It is a flowchart of another method for controlling the sleep of a charging device based on ambient light monitoring in an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of a system for controlling the sleep of a charging device based on ambient light monitoring in an embodiment of the present invention. Detailed Embodiments

[0059] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] The present invention provides a method for controlling the dormancy of a charging device based on ambient light monitoring. Referring to Figure 1 , it includes:

[0062] S1: Real-time collect the ambient light parameters and ambient images of the target space range where the charging device is located;

[0063] S2: Based on the usage scenario recognition model, perform usage scenario recognition on the latest collected ambient image of the target space range to obtain the current usage scenario of the charging device;

[0064] S3: Based on the dynamic ambient light classification model, perform dynamic classification of the ambient light parameters of the latest collected target space range, and real-time output the ambient light level of the target space range;

[0065] S4: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start a hierarchical dormancy mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

[0066] In this embodiment, the charging device refers to a device that can provide electrical energy for an electronic device.

[0067] In this embodiment, the target space range refers to a specific space area where the charging device is located, and is a specific range for collecting ambient light parameters and ambient images.

[0068] In this embodiment, the ambient light parameters are quantitative indicators describing the characteristics of light in the environment, such as light intensity, color temperature, etc.

[0069] In this embodiment, the ambient image is a visual image record of the space environment where the charging device is located.

[0070] In this embodiment, the usage scenario recognition model is a mathematical model that determines the current usage scenario of the charging device by analyzing the ambient image.

[0071] In this embodiment, the current usage scenario of the charging device refers to the specific situation where the charging device is being used, such as indoor office, outdoors, dark night situation, etc.

[0072] In this embodiment, the dynamic ambient light classification model is a model that can dynamically classify the ambient light conditions according to the real-time collected ambient light parameters.

[0073] In this embodiment, the ambient light level of the target space range is a hierarchical assessment of the ambient light condition within the target space range, such as strong light, weak light, etc.

[0074] In this embodiment, the power output curve of the charging device describes the curve of the output power of the charging device changing with time or other variables.

[0075] In this embodiment, the hierarchical sleep mechanism is a mechanism that divides the sleep state of the charging device into different levels according to different conditions and controls it according to the corresponding levels.

[0076] The beneficial effects of the above technologies are as follows: Real-time collection of ambient light parameters and ambient images ensures the timeliness and comprehensiveness of data, providing an accurate basis for subsequent analysis and control. Obtaining the current usage scenario through the use of the scenario recognition model, considering the semantic features of the scenario, can adjust the working state of the charging device targeted, improve adaptability, and the ability to perceive the scenario leads to more accurate sleep decisions. Using the dynamic ambient light classification model to classify ambient light parameters and output the ambient light level in real time realizes an accurate assessment of the ambient light condition. Adjusting the power output curve based on the current usage scenario and optimizing the energy consumption control curve according to the real-time charging power demand makes the energy more conserved or the user experience improved. And starting the hierarchical sleep mechanism in combination with the ambient light level effectively saves energy, extends the service life of the charging device, and can adapt to the dynamic light environment. Effectively integrating ambient light data with the usage scenario of the device, through dynamically coupling the sleep strategy with the power output, realizes the intelligent control of the charging device, improves the energy utilization efficiency and working stability of the charging device.

[0077] Embodiment 2:

[0078] Based on the charging device sleep control method based on ambient light monitoring in Embodiment 1, S1: Real-time collect the ambient light parameters and ambient images of the target space range where the charging device is located, including:

[0079] Real-time collect the ambient light parameters of the target space range where the charging device is located based on the multi-spectral sensor array;

[0080] Real-time collect the ambient images of the target space range where the charging device is located based on the high-resolution imaging device. The following are the explanations of these two terms:

[0081] In this embodiment, the multi-spectral sensor array is a set composed of multiple sensors that can detect different spectral bands, which can obtain richer and more comprehensive spectral information, improving the accuracy and fineness of ambient light monitoring.

[0082] In this embodiment, the high-resolution imaging device is a device that can capture images with high clarity and rich details, which helps to clearly capture environmental details and provide high-quality image data for accurately identifying the usage scenario.

[0083] The beneficial effects of the above technical solutions are as follows: By using the multispectral sensor array to collect ambient light parameters, more comprehensive and accurate spectral information can be obtained, improving the accuracy of ambient light monitoring. Using the high-resolution imaging device to collect environmental images helps to clearly capture environmental details and provide high-quality image data for the accurate identification of the usage scenario. The cooperation of the multispectral sensor array and the high-resolution imaging device ensures the real-time and accurate collection of ambient light parameters and environmental images. It provides a reliable data basis for subsequent usage scenario identification, ambient light classification, and sleep control. It improves the quality and effect of ambient light monitoring and enhances the reliability and effectiveness of the sleep control of the charging device.

[0084] Embodiment 3:

[0085] Based on the charging device sleep control method based on ambient light monitoring in Embodiment 1, S2: Based on the usage scenario recognition model, perform usage scenario recognition on the environmental image of the latest collected target space range to obtain the current usage scenario of the charging device, including:

[0086] Establish a usage scenario recognition model;

[0087] Input the environmental image of the latest collected target space range into the usage scenario recognition model to identify the current usage scenario of the charging device.

[0088] In this embodiment, establishing a usage scenario recognition model refers to constructing a mathematical model through a series of steps such as data collection, feature extraction, algorithm selection, and training optimization, which can accurately determine the current usage scenario of the charging device (such as indoor office, outdoor mobile, etc.) based on the input environmental image. This process may involve analyzing and learning a large number of sample data of different usage scenarios to determine the characteristic patterns of different scenarios, and training and validating the model through appropriate machine learning or deep learning algorithms, and finally obtaining a model that can effectively identify various usage scenarios.

[0089] The beneficial effects of the above technical solutions are as follows: By establishing a usage scenario recognition model, an effective tool and method are provided for accurately identifying the usage scenarios of charging devices. Inputting the collected environmental images into the model for recognition realizes automated and intelligent scenario judgment, improving the recognition efficiency and accuracy. It can quickly adapt to different environmental images and accurately determine the current usage scenario, such as indoor office, outdoor mobile, etc. This provides a key basis for subsequent adjustment of the power output curve and sleep control according to the usage scenario. It enables the charging device to be flexibly controlled and adjusted according to the actual usage scenario, enhancing the user experience and energy utilization efficiency.

[0090] Embodiment 4:

[0091] Based on the charging device sleep control method using ambient light monitoring in Embodiment 1, S3: Perform dynamic ambient light classification on the ambient light parameters of the latest collected target space range based on the dynamic ambient light classification model, and output the ambient light level of the target space range in real time, including:

[0092] Establish a dynamic ambient light classification model;

[0093] Input the ambient light parameters of the latest collected target space range into the dynamic ambient light classification model, and output the ambient light level of the target space range.

[0094] In this embodiment, establishing a dynamic ambient light classification model means constructing a mathematical model through a series of operations that can dynamically and accurately classify ambient light conditions (such as strong light, weak light, medium light, etc.) based on real-time collected ambient light parameters. This usually includes collecting a large amount of parameter data under different ambient light conditions, extracting key features, selecting suitable classification algorithms (such as decision trees, support vector machines, etc.), training and debugging the model, and using verification data to evaluate the accuracy and reliability of the model to ensure that the model can accurately classify the changing ambient light in real time and effectively.

[0095] The beneficial effects of the above technical solutions are as follows: Establishing a dynamic ambient light classification model provides a reliable model basis for the accurate classification of ambient light. Inputting the collected ambient light parameters into the model for classification can obtain the ambient light level in real time and quickly. The dynamic classification model can adapt to the continuous change of ambient light, improving the timeliness and accuracy of classification. The accurately output ambient light level provides an important basis for subsequent power adjustment and sleep control of the charging device. It helps to achieve more refined and intelligent control of the charging device, achieving the goals of energy conservation and performance optimization. It improves the efficiency and quality of ambient light classification and enhances the scientificity and rationality of the charging device control strategy.

[0096] Embodiment 5:

[0097] Based on Embodiment 1, for the charging device sleep control method based on ambient light monitoring, S4: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start a hierarchical sleep mechanism based on the ambient light level in the target space range and the power output curve of the charging device, including:

[0098] Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device and the state of charge of the energy storage unit;

[0099] When it is determined that the charging device meets the determination conditions of any sleep level in the hierarchical sleep mechanism based on the ambient light level in the target space range and the power output curve of the charging device, then start the sleep mechanism corresponding to the sleep level.

[0100] In this embodiment, the determination condition of any sleep level refers to the specific criteria or rules set to distinguish different degrees of sleep states. For example, when the ambient light level is lower than a certain value and the power output of the charging device is lower than another specific value, it meets the determination conditions of a specific sleep level.

[0101] In this embodiment, the sleep level is different levels divided for the sleep state of the charging device according to different criteria, such as light sleep, deep sleep, etc. Each level corresponds to different device states and energy consumption levels.

[0102] In this embodiment, starting the sleep mechanism corresponding to the sleep level means that when it is determined that the charging device meets the conditions of a specific sleep level, a series of control operations specified by that level are executed to make the charging device enter the corresponding sleep state.

[0103] In this embodiment, determining that the charging device meets the determination conditions of any sleep level in the hierarchical sleep mechanism based on the ambient light level in the target space range and the power output curve of the charging device comprehensively considers the intensity of the ambient light and the power output of the charging device, and compares these two factors with the determination criteria of each pre-set sleep level to determine whether the charging device meets the requirements of a specific sleep level.

[0104] The beneficial effects of the above technical solutions are as follows: Dynamically adjust the power output curve according to the current usage scenario and the state of charge of the energy storage unit, making the power output of the charging device more in line with the actual needs and optimizing the charging effect. By combining the ambient light level and the power output curve to determine the sleep level, more accurate sleep control is achieved, avoiding unnecessary energy consumption. The hierarchical sleep mechanism can flexibly start the corresponding level according to different conditions, improving the pertinence and refinement degree of sleep control. It helps to extend the service life of the charging device, reduce the operating cost, and improve the energy utilization efficiency. Make the sleep control of the charging device more intelligent and efficient, enhancing the comprehensive performance and user experience of the charging device.

[0105] Example 6:

[0106] Based on the charging device dormancy control method for ambient light monitoring in Example 5, the power output curve of the charging device is dynamically adjusted based on the current usage scenario of the charging device and the state of charge of the energy storage unit, including:

[0107] Perform dimensionality reduction processing on the multi-dimensional features of each usage scenario to obtain the comprehensive scenario feature value of each usage scenario:

[0108]

[0109] In the formula, F is the comprehensive scenario feature value of the currently calculated usage scenario, dimensionless; n is the total number of feature dimensions of the multi-dimensional features of the currently calculated usage scenario; α i is the weight coefficient of the i-th feature value in the multi-dimensional features of the currently calculated usage scenario, dimensionless; x i is the i-th feature value in the multi-dimensional features of the currently calculated usage scenario, dimensionless; is the mean value of the i-th feature value in the multi-dimensional features of all usage scenarios, dimensionless; is the standard deviation of the i-th feature value in the multi-dimensional features of all usage scenarios, dimensionless;

[0110] Generate the state of charge function of the energy storage unit based on the initial state of charge, rated capacity, and open circuit voltage of the energy storage unit:

[0111]

[0112] In the formula, SOC(t) is the state of charge of the energy storage unit at time t, dimensionless; SOC(t 0 ) is the state of charge of the energy storage unit at the initial time t 0 ; C is the rated capacity of the energy storage unit, in ampere-hours; V(τ) is the terminal voltage of the energy storage unit at time τ, in volts; I(τ) is the charging current of the energy storage unit at time τ, in amperes; R(τ) is the internal resistance of the energy storage unit at time τ, in ohms; E OCV (τ) is the open circuit voltage of the energy storage unit at time τ;

[0113] Determine the current state of charge of the energy storage unit based on the state of charge function of the energy storage unit;

[0114] Generate the adjusted power output curve of the charging device based on the comprehensive scenario feature value of the current usage scenario of the charging device and the current state of charge of the energy storage unit.

[0115] In this embodiment, the energy storage unit refers to a component in the charging device for storing electrical energy, such as a battery, etc.

[0116] In this embodiment, the comprehensive scenario feature value of each usage scenario is a value obtained by calculating and integrating the features of multiple dimensions of each usage scenario, which can generally represent the characteristics of this scenario.

[0117] In this embodiment, the weight coefficient of the feature value is a value assigned to each feature value, which is used to represent the importance degree of this feature value in the comprehensive calculation.

[0118] In this embodiment, the initial moment refers to the time point when a specific calculation or observation process starts.

[0119] In this embodiment, the open-circuit voltage of the energy storage unit at moment τ is the terminal voltage of the battery when the battery is not connected to an external circuit at a specific moment τ.

[0120] In this embodiment, determining the current state of charge of the energy storage unit based on the state-of-charge function of the energy storage unit is to substitute the current moment into the state-of-charge function for calculation and analysis, and obtain the relative proportion of the stored electricity of the energy storage unit at the current moment.

[0121] The beneficial effects of the above technical solutions are as follows: Dimensionality reduction processing is performed on the multi-dimensional features of the usage scenario to obtain the comprehensive scenario feature value, which simplifies data processing and improves calculation efficiency. Generating the state-of-charge function of the energy storage unit can accurately reflect the change of the state of charge of the energy storage unit, providing a reliable basis for power adjustment. Determining the current state of charge of the energy storage unit helps to more accurately grasp its power situation, so as to reasonably adjust the power output of the charging device. Considering the comprehensive feature value of the usage scenario and the current state of charge of the energy storage unit to generate the power output curve makes the power output of the charging device more reasonable and optimized. It improves the scientificity and accuracy of the power adjustment of the charging device, realizing more efficient energy utilization and more stable charging performance.

[0122] Embodiment 7:

[0123] On the basis of Embodiment 6, a charging device sleep control method based on ambient light monitoring, based on the comprehensive scenario feature value of the current usage scenario of the charging device and the current state of charge of the energy storage unit, generates an adjusted power output curve of the charging device, including:

[0124] Based on the maximum state of charge of the energy storage unit, two safety state-of-charge grading values, and the current state of charge of the energy storage unit, generate an adjustment function ω(SOC) of the state of charge on the output power of the charging device:

[0125]

[0126] In the formula, SOC is the current state of charge of the energy storage unit, SOC threshold1η is the first safety state of charge grading value for the energy storage unit 1 SOC is the adjustment coefficient in the low state of charge max SOC is the maximum state of charge of the energy storage unit threshold2 η is the second safety state of charge grading value for the energy storage unit 2 is the adjustment coefficient in the high state of charge;

[0127] Based on the initial power output curve of the charging device, the adjustment function of the output power of the charging device according to the state of charge, and the comprehensive scenario characteristic value of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated.

[0128] In this embodiment, the maximum state of charge of the energy storage unit refers to the limit state where the energy storage unit can be fully charged.

[0129] In this embodiment, the two safety state of charge grading values are two boundary values for dividing the power range set to ensure the safe and effective operation of the energy storage unit.

[0130] In this embodiment, the adjustment coefficient in the low state of charge is a coefficient used to adjust the output power of the charging device when the energy storage unit is at a low power level.

[0131] In this embodiment, the adjustment coefficient in the high state of charge is a coefficient used to adjust the output power of the charging device when the energy storage unit is at a high power level.

[0132] The beneficial effects of the above technical solutions are as follows: generating an adjustment function of the output power of the charging device according to the state of charge, which can perform targeted power adjustment according to different states of charge of the energy storage unit. Considering the maximum state of charge and the two safety state of charge grading values, it ensures the safety and reasonableness of the power output of the charging device at different power stages. Combining the initial power output curve, the adjustment function and the comprehensive scenario characteristic value to generate the adjusted power output curve, which synthesizes various factors and makes the power output curve more in line with the actual needs. It helps to improve the stability and reliability of the charging device and extend the service life of the energy storage unit. It realizes the fine control of the power output of the charging device, optimizes the performance of the charging device and the energy utilization efficiency.

[0133] Embodiment 8:

[0134] Based on the charging device sleep control method based on ambient light monitoring in Embodiment 7, based on the initial power output curve of the charging device, the adjustment function of the output power of the charging device according to the state of charge, and the comprehensive scenario characteristic value of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated, including:

[0135] Set the initial power output curve of the charging device as:

[0136] P 0 (t) = V max ·[I max -k·(t - t CC )]

[0137] In the formula, P 0 (t) is the initial power output value of the charging device at time t when the charging device outputs power according to the initial power output curve, and the unit is watt; V max is the constant charging voltage, and the unit is volt; I max is the maximum charging current at which the charging device operates, and the unit is ampere; k is the current decline coefficient, and the unit is ampere / second, t CC is the end time when the charging device outputs power according to the initial power output curve;

[0138] Based on the initial power output curve of the charging device, the adjustment function of the output power of the charging device according to the state of charge, and the comprehensive scenario eigenvalue of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated:

[0139]

[0140] In the formula, P(t) is the adjusted power output curve of the charging device, e is the natural constant and its value is 2.71828, λ is the rate at which the influence factor of the usage scenario on the output power of the charging device changes with the comprehensive scenario eigenvalue of the current usage scenario, μ is the preset critical comprehensive scenario eigenvalue, and λ and μ are parameters obtained through analyzing a large amount of charging data in different scenarios and training with machine learning algorithms.

[0141] In this embodiment, the constant charging voltage refers to the voltage value that remains unchanged during the constant voltage charging process of the charging device (that is, the process in which the charging device outputs power according to the initial power output curve).

[0142] In this embodiment, the current decline coefficient is a parameter used to describe the reduction rate of the charging current over time or other factors, and it determines the speed at which the current drops during the constant voltage stage.

[0143] In this embodiment, the preset critical comprehensive scenario eigenvalue is a threshold of the comprehensive scenario eigenvalue set in advance and is used as an important reference standard in relevant calculations or judgments.

[0144] The beneficial effects of the above technical solutions are as follows: A clear initial power output curve is set, providing a benchmark for subsequent adjustments and facilitating comparison and analysis. By comprehensively considering the adjustment function of the state of charge, the comprehensive scenario characteristic value of the current usage scenario, and the initial power output curve, the adjusted power output curve generated is more accurate and practical. Introducing the natural constant and influence factor can more sensitively reflect the impact of the usage scenario on power output, making the adjustment more flexible and adaptive. Adjusting the power output curve according to different usage scenarios and states of charge improves the adaptability and energy utilization efficiency of the charging device. It makes the power output curve of the charging device more scientific and reasonable, meets diverse usage requirements, and simultaneously improves the performance and reliability of the charging device.

[0145] Embodiment 9:

[0146] Based on the charging device dormancy control method using ambient light monitoring on the basis of Embodiment 1, with reference to Figure 2 , it further includes:

[0147] During the hierarchical dormancy process, synchronously monitor the state of charge of the energy storage unit;

[0148] When the state of charge of the energy storage unit is lower than the preset state of charge threshold, trigger the wake-up compensation mechanism.

[0149] In this embodiment, synchronously monitoring the state of charge of the energy storage unit during the hierarchical dormancy process means that when the charging device is in different levels of dormancy states, the remaining power situation of the energy storage unit is simultaneously detected and tracked in real time.

[0150] In this embodiment, the preset state of charge threshold is a specific value set in advance regarding the state of charge of the energy storage unit, which is used to compare with the actually monitored state of charge, so as to make corresponding decisions.

[0151] In this embodiment, the wake-up compensation mechanism is a mechanism triggered when the state of charge of the energy storage unit reaches or is lower than a certain level, which is used to restore the normal working state of the charging device and supplement the power.

[0152] The beneficial effects of the above technical solutions are as follows: Synchronously monitoring the state of charge of the energy storage unit during the hierarchical dormancy process can keep track of its power situation in real time, ensuring the safety and reliability of the dormancy strategy. Triggering the wake-up compensation mechanism when the state of charge is lower than the preset state of charge threshold effectively avoids over-discharging of the energy storage unit and protects the battery life. This mechanism improves the energy management efficiency of the charging device in the dormancy state, reducing unnecessary energy losses. It ensures that the charging device can resume normal operation in a timely manner when needed, without affecting the user's usage requirements. It improves the dormancy control strategy of the charging device and enhances the stability and usability of the system.

[0153] Embodiment 10:

[0154] The present invention provides a charging device sleep control system based on ambient light monitoring, which is used to execute the charging device sleep control method described in any one of Embodiments 1 to 9, with reference to Figure 3 , and includes:

[0155] An ambient data acquisition module, which is used to collect the ambient light parameters and ambient images of the target space range where the charging device is located in real time;

[0156] A usage scenario recognition module, which is used to perform usage scenario recognition on the latest acquired ambient image of the target space range based on a usage scenario recognition model to obtain the current usage scenario of the charging device;

[0157] An ambient light level judgment module, which is used to perform dynamic classification of the ambient light parameters of the latest acquired target space range based on a dynamic ambient light classification model and output the ambient light level of the target space range in real time;

[0158] A hierarchical sleep control module, which is used to dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start a hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

[0159] The beneficial effects of the above technologies are as follows: The real-time collection of ambient light parameters and ambient images ensures the timeliness and comprehensiveness of the data, providing an accurate basis for subsequent analysis and control. By obtaining the current usage scenario through the usage scenario recognition model, considering the semantic features of the scenario, it can adjust the working state of the charging device targeted, improve adaptability, and the ability to perceive the scenario leads to more accurate sleep decisions. Using the dynamic ambient light classification model to classify the ambient light parameters and output the ambient light level in real time realizes an accurate assessment of the ambient light condition. Adjusting the power output curve based on the current usage scenario and optimizing the energy consumption control curve according to the real-time charging power demand make the energy more conserved or the user experience improved. And starting a hierarchical sleep mechanism in combination with the ambient light level effectively saves energy, prolongs the service life of the charging device, and can adapt to the dynamic light environment. Effectively integrating the ambient light data with the usage scenario of the device, through dynamically coupling the sleep strategy with the power output, realizes the intelligent control of the charging device, improves the energy utilization efficiency and working stability of the charging device.

[0160] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A charging device sleep control method based on ambient light monitoring, characterized in that: include: S1: real-time collection of ambient light parameters and ambient images of the target spatial range of the space where the charging device is located; S2: performing usage scene recognition on the latest collected environment image of the target space range based on the usage scene recognition model to obtain the current usage scene of the charging device; S3: Based on the dynamic ambient light classification model, the latest collected ambient light parameters of the target space range are dynamically classified, and the ambient light level of the target space range is output in real time; S4: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and start the graded sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

2. The charging device sleep control method based on ambient light monitoring according to claim 1, characterized in that: S1: Real-time collection of ambient light parameters and ambient images of the target space range where the charging device is located, including: Based on a multi-spectral sensor array, ambient light parameters of a target spatial range of the space where the charging device is located are collected in real time; Based on a high-resolution camera device, an environmental image of the target spatial range of the space where the charging device is located is collected in real time.

3. The charging device sleep control method based on ambient light monitoring according to claim 1, characterized in that: S2: Based on the usage scene recognition model, the usage scene recognition is performed on the latest collected environment image of the target space range to obtain the current usage scene of the charging device, including: Establish a usage scenario recognition model; The latest collected environment image of the target space range is input into the usage scene recognition model to identify the current usage scene of the charging device.

4. The charging device sleep control method based on ambient light monitoring according to claim 1, characterized in that: S3: Based on the dynamic ambient light classification model, the latest collected ambient light parameters of the target space range are dynamically classified, and the ambient light level of the target space range is output in real time, including: Establish a dynamic ambient light classification model; The latest acquired ambient light parameters of the target space range are input into the dynamic ambient light classification model, and the ambient light level of the target space range is output.

5. The charging device sleep control method based on ambient light monitoring according to claim 1, characterized in that: S4: dynamically adjusting the power output curve of the charging device based on the current usage scenario of the charging device, and starting a hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device, including: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device and the charge state of the energy storage unit; When it is determined based on the ambient light level of the target space range and the power output curve of the charging device that the charging device meets the determination conditions of any sleep level in the graded sleep mechanism, the sleep mechanism of the corresponding sleep level is started.

6. The charging device sleep control method based on ambient light monitoring according to claim 5, characterized in that: Dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device and the charge state of the energy storage unit, including: Perform dimensionality reduction processing on the multi-dimensional features of each usage scenario to obtain the comprehensive scenario feature value of each usage scenario: Where F is the comprehensive scenario feature value of the current calculated usage scenario, dimensionless; n is the total number of feature dimensions of the multidimensional features of the current calculated usage scenario; α i is the weight coefficient of the i-th eigenvalue in the multidimensional features of the current usage scenario, dimensionless; x i is the i-th eigenvalue in the multidimensional features of the currently calculated usage scenario, dimensionless; is the mean of the i-th eigenvalues ​​in the multidimensional features of all usage scenarios, dimensionless; is the standard deviation of the i-th eigenvalue in the multidimensional features of all usage scenarios, dimensionless; Based on the initial state of charge, rated capacity and open circuit voltage of the energy storage unit, the state of charge function of the energy storage unit is generated: In the formula, SOC(t) is the state of charge of the energy storage unit at time t, dimensionless; SOC(t0) is the state of charge of the energy storage unit at the initial time t0, dimensionless; C is the rated capacity of the energy storage unit, in ampere-hours; V(τ) is the terminal voltage of the energy storage unit at time τ, in volts; I(τ) is the charging current of the energy storage unit at time τ, in amperes; R(τ) is the internal resistance of the energy storage unit at time τ, in ohms; E OCV (τ) is the open circuit voltage of the energy storage unit at time τ; Determining a current state of charge of the energy storage unit based on the state of charge function of the energy storage unit; Based on the comprehensive scenario characteristic value of the current usage scenario of the charging device and the current charge state of the energy storage unit, an adjusted power output curve of the charging device is generated.

7. The charging device sleep control method based on ambient light monitoring according to claim 6, characterized in that: Based on the comprehensive scenario characteristic value of the current usage scenario of the charging device and the current charge state of the energy storage unit, an adjusted power output curve of the charging device is generated, including: Based on the maximum state of charge of the energy storage unit, the two safe state of charge classification values, and the current state of charge of the energy storage unit, an adjustment function ω(SOC) of the state of charge to the output power of the charging device is generated: In the formula, SOC is the current state of charge of the energy storage unit, SOC threshold1 is the first safe state of charge classification value of the energy storage unit, η1 is the adjustment coefficient at low state of charge, SOC max is the maximum state of charge of the energy storage unit, SOC threshold2 is the second safe state of charge classification value of the energy storage unit, and η2 is the adjustment coefficient at the high state of charge; Based on the initial power output curve of the charging device, the adjustment function of the state of charge on the output power of the charging device, and the comprehensive scenario characteristic value of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated.

8. The charging device sleep control method based on ambient light monitoring according to claim 7, characterized in that: Based on the initial power output curve of the charging device, the adjustment function of the state of charge on the output power of the charging device, and the comprehensive scenario characteristic value of the current use scenario of the charging device, an adjusted power output curve of the charging device is generated, including: The initial power output curve of the charging device is set to: P0(t)=V max ·[I max -k·(t-t CC )] Where P0(t) is the initial power output value of the charging device at time t when the charging device outputs power according to the initial power output curve, in watts; V max is the constant charging voltage in volts; I max is the maximum charging current of the charging device, in amperes; k is the current drop coefficient, in amperes / second, t CC The time when the charging device ends power output according to the initial power output curve; Based on the initial power output curve of the charging device, the adjustment function of the state of charge on the output power of the charging device, and the comprehensive scenario characteristic value of the current usage scenario of the charging device, an adjusted power output curve of the charging device is generated: Wherein, P(t) is the adjusted power output curve of the charging device, e is a natural constant and its value is 2.71828, λ is the rate at which the influence factor of the usage scenario on the output power of the charging device changes with the comprehensive scenario characteristic value of the current usage scenario, and μ is the preset critical comprehensive scenario characteristic value.

9. The charging device sleep control method based on ambient light monitoring according to claim 1, characterized in that: Also includes: Synchronously monitor the state of charge of the energy storage unit during hierarchical dormancy; When the state of charge of the energy storage unit is lower than the preset state of charge threshold, the wake-up compensation mechanism is triggered.

10. A charging device sleep control system based on ambient light monitoring, characterized in that: The method for controlling the sleep state of a charging device based on ambient light monitoring according to any one of claims 1 to 9 comprises: An environmental data acquisition module, used to collect in real time the ambient light parameters and environmental images of the target spatial range of the space where the charging device is located; A usage scene recognition module is used to perform usage scene recognition on the latest collected environment image of the target space range based on the usage scene recognition model to obtain the current usage scene of the charging device; An ambient light level judgment module is used to dynamically classify the latest collected ambient light parameters of the target space range based on a dynamic ambient light classification model, and output the ambient light level of the target space range in real time; The hierarchical sleep control module is used to dynamically adjust the power output curve of the charging device based on the current usage scenario of the charging device, and to start the hierarchical sleep mechanism based on the ambient light level of the target space range and the power output curve of the charging device.

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