CN117439240B

CN117439240B - Intelligent control method, system and storage medium of wireless charger

Info

Publication number: CN117439240B
Application number: CN202311764857.6A
Authority: CN
Inventors: 柯美顺; 刘俐; 何思桦; 谢武林
Original assignee: Shenzhen Meishunhe Electronics Co ltd
Current assignee: Shenzhen Meishunhe Electronics Co ltd
Priority date: 2023-12-21
Filing date: 2023-12-21
Publication date: 2024-03-01
Anticipated expiration: 2043-12-21
Also published as: CN117439240A

Abstract

The invention discloses an intelligent control method, a system and a storage medium of a wireless charger, comprising the following steps: protocol communication and charging mode adaptation are carried out on the target charging equipment, and identity verification is carried out according to the adapted charging mode; acquiring user charging history information, analyzing user charging preference according to the user charging history information, and constructing a user portrait; acquiring real-time charging monitoring information, and carrying out user charging demand analysis by combining the user portrait to acquire demand analysis information; acquiring battery monitoring information of the charging equipment, evaluating the battery state, and formulating an adaptive charging strategy; and detecting abnormal events according to the monitoring information of the battery of the charging equipment, and carrying out abnormal early warning according to the detection result. The method is close to the use habit of the user, so that the use experience of the user is improved, abnormal events are monitored, and the charging safety and quality are guaranteed.

Description

Intelligent control method, system and storage medium of wireless charger

Technical Field

The invention relates to the technical field of intelligent control of wireless chargers, in particular to an intelligent control method, an intelligent control system and a storage medium of a wireless charger.

Background

With the popularity of mobile devices and the rise of electric vehicles, there is an increasing demand for wireless charging technology. Conventional wireless charging systems present challenges such as low charging efficiency, slow charging speed, and inadequate suitability for charging different devices and battery types. Current chargers often lack the ability to actively sense device status and user demand, resulting in energy waste and reduced charging efficiency, while being unable to sense abnormal events and control in advance.

Therefore, how to implement intelligent control of the wireless charger through technologies such as data analysis and intelligent sensing is an important problem. Through the intelligent control method, interoperability between the charger and the charging equipment is enhanced, the battery state, the charging requirement and the charging environment of the equipment are perceived in real time, and charging power, frequency and adjustment are carried out, so that the charging efficiency is improved to the greatest extent, the service life of the battery is prolonged, and the charging safety of the equipment is ensured.

Disclosure of Invention

The invention overcomes the defects of the prior art, and provides an intelligent control method, an intelligent control system and a storage medium of a wireless charger, which aim to improve the use experience of users and ensure the charging safety.

In order to achieve the above object, a first aspect of the present invention provides an intelligent control method for a wireless charger, including:

protocol communication and charging mode adaptation are carried out on the target charging equipment, and identity verification is carried out according to the adapted charging mode;

acquiring user charging history information, analyzing user charging preference according to the user charging history information, and constructing a user portrait;

acquiring real-time charging monitoring information, and carrying out user charging demand analysis by combining the user portrait to acquire demand analysis information;

acquiring battery monitoring information of the charging equipment, evaluating the battery state, and formulating an adaptive charging strategy;

and detecting abnormal events according to the monitoring information of the battery of the charging equipment, and carrying out abnormal early warning according to the detection result.

In this scheme, carry out protocol communication and charge mode adaptation to target charging equipment to carry out authentication according to adaptation charge mode, specifically include:

transmitting a query instruction to target charging equipment based on radio frequency to obtain feedback instruction information, wherein the feedback instruction information comprises maximum power supported by the charging equipment, and allowable voltage and current ranges;

presetting a charging mode judgment threshold, and judging the feedback instruction information and the charging mode judgment threshold to obtain adaptive charging mode information;

Generating an identity verification instruction according to the adaptive charging mode information, and sending the identity verification instruction to a charging equipment end for identity verification to obtain verification feedback information;

an identity verification mechanism is established, wherein the identity verification mechanism comprises identity verification keys of all charging equipment supported by target wireless charging equipment, and verification feedback information is judged through the identity verification mechanism to obtain identity verification result information;

and carrying out charging mode adaptation according to the adaptation charging mode information and the identity verification result information, and adjusting the output parameters of the wireless charging equipment to the corresponding range.

In this scheme, obtain user's historical information that charges, carry out user's preference analysis and build user's portrait according to user's historical information that charges, specifically include:

acquiring user charging history information, including user charging frequency, charging time, charging mode, charging electric quantity and charging place;

presetting a time division rule, carrying out user charging time preference analysis by combining user charging history information, and dividing the user charging time according to the time division rule to obtain charging time division information;

extracting charging frequency, charging duration and charging electric quantity of each time period of a user according to the charging time division information, registering the frequency, the duration and the electric quantity, so that each charging corresponds to corresponding charging duration and electric quantity before and after charging, and registration information is obtained;

Establishing a duration-electric quantity-frequency correlation matrix according to the registration information, and analyzing whether the user charging preference is less-times more charging or more-times less charging through the correlation matrix to obtain first charging preference analysis information;

extracting user charging mode information according to the user charging history information, and analyzing charging mode preference according to the use frequency of various charging modes to obtain second charging preference analysis information;

extracting user charging place information and user charging time information through the user charging history information, and analyzing charging place preference of each time period by combining the charging time division information to obtain third charging preference analysis information;

and constructing a user preference portrait according to the first charging preference analysis information, the second charging preference analysis information and the third charging preference analysis information.

In this scheme, acquire real-time charge monitoring information, combine the user portrait to carry out user's charge demand analysis, specifically include:

acquiring real-time charging monitoring information, including: the charger outputs parameter information, charging equipment electric quantity information, charging time information and charging position information;

acquiring user preference images, extracting real-time charging time and place of a user through real-time charging monitoring information, and carrying out matching analysis by combining the user preference images to judge charging preference of the current time and place of the user so as to obtain charging preference judging information;

Extracting real-time electric quantity of the charging equipment through the real-time charging monitoring information, judging with a preset threshold value, and dividing the electric quantity of the equipment to be charged into different electric quantity grades to obtain electric quantity grade judging information;

acquiring characteristic information of various charging demands based on big data retrieval to form a comparison data set;

presetting demand labels, calculating the mahalanobis distance between each demand label and a comparison data set, performing correlation analysis as a correlation degree, and dividing corresponding features into corresponding demand labels according to correlation analysis results to obtain division result information;

constructing a demand analysis model based on a decision tree algorithm, constructing a training data set by dividing result information, setting the highest priority analysis weight for the electric quantity grade, and performing deep learning and training on the demand analysis model;

and inputting the electric quantity grade judgment information and the charging preference judgment information into a demand analysis module to perform demand analysis, so as to obtain demand analysis information.

In this scheme, the battery state evaluation is performed and an adaptive charging policy is formulated, which specifically includes:

acquiring charging equipment battery monitoring information, wherein the charging equipment battery monitoring information comprises: historical battery monitoring information and real-time battery monitoring information;

Extracting characteristics of historical battery monitoring information, and extracting characteristics of voltage and current, charging and discharging time, cycle times and temperature of historical charging to obtain historical battery monitoring characteristic information;

performing capacity attenuation rate analysis according to the historical battery monitoring characteristic information, drawing a capacity analysis chart through charge and discharge time characteristics, voltage characteristics and current characteristics, and calculating the actual capacity of the battery by combining an ampere-hour integration method to obtain actual capacity information;

acquiring initial capacity information of a target battery, calculating the actual capacity information and the initial capacity to obtain a capacity difference value, and calculating the ratio of the capacity difference value to the initial capacity to serve as a capacity attenuation rate to obtain capacity attenuation rate information;

performing internal resistance change analysis according to the historical battery monitoring characteristic information, calculating the internal resistance of the battery according to the charging voltage and current characteristics of the battery and combining an ohm law, and drawing an internal resistance change trend chart;

constructing a charging temperature map according to the historical battery monitoring characteristic information, calculating the average temperature of historical charging through the charging temperature map, and judging with a preset threshold value to obtain temperature analysis information;

constructing a battery condition evaluation model based on a random forest, and inputting the actual capacity information, the capacity attenuation rate information, the internal resistance change trend graph and the temperature analysis information into the battery condition evaluation model for evaluation;

Extracting features of various information input into a battery condition evaluation model, randomly selecting a plurality of features to construct a plurality of project decision trees, distributing different evaluation weights, integrating the prediction results of all the project decision trees and carrying out result regression by using an average method to obtain battery condition evaluation information;

and presetting a charging protection strategy, carrying out charging protection strategy adaptation according to the battery condition evaluation information, obtaining an adaptive charging strategy, and carrying out charging protection.

In this scheme, according to the monitoring information of battery of charging equipment carries out abnormal event and thermal runaway monitoring, carries out unusual early warning according to the testing result, specifically includes:

acquiring monitoring information of a battery of the charging equipment, and acquiring real-time battery monitoring information through the monitoring information of the battery of the charging equipment;

acquiring capacity attenuation rate information and battery service time information, and performing capacity attenuation rate analysis to obtain capacity attenuation rate analysis information;

acquiring safe charging temperature information, detecting abnormal temperature based on an abnormal value detection algorithm, calculating a difference value between the abnormal temperature and the safe charging temperature, counting abnormal times and drawing a temperature change curve to obtain abnormal temperature analysis information;

acquiring real-time charging mode information, calculating the capacity increment of the battery in unit time through real-time battery monitoring information, and calculating the ratio of the capacity increment to time to serve as the capacity increment rate to obtain charging effect analysis information;

Constructing an abnormal event monitoring model, acquiring various abnormal event incentive characteristic information based on big data retrieval, and forming a training data set to train the abnormal event monitoring model;

and inputting the capacity attenuation rate analysis information, the abnormal temperature analysis information and the charging effect analysis information into the abnormal event monitoring model for analysis to obtain abnormal event detection information, and carrying out charging abnormal early warning according to the abnormal event detection information.

In this scheme, according to the monitoring information of battery of charging equipment carries out abnormal event and thermal runaway monitoring, carries out unusual early warning according to the testing result, still includes:

acquiring different degrees of thermal runaway examples based on big data retrieval, dividing according to degrees, and extracting characteristic information of the different degrees of thermal runaway examples to form a thermal runaway comparison data set;

constructing a thermal runaway monitoring model, and inputting real-time battery monitoring information into the thermal runaway monitoring model for analysis, wherein the thermal runaway monitoring model comprises: the device comprises an equivalent circuit module, a battery thermal analysis module, a battery aging analysis module and a parameter analysis module;

calculating real-time voltage, current, SOC, surface temperature and internal resistance through the real-time battery monitoring information combined with an equivalent circuit module to obtain first analysis information, wherein the first analysis information is used as an input parameter of a battery thermal analysis module to perform battery thermal analysis;

Calculating internal resistance heat generation information of the battery through the first analysis information, calculating temperature distribution of each part inside the battery through a heat conduction equation, constructing a heat distribution diagram, and inputting the first analysis information into a battery aging analysis module to calculate capacity attenuation rate and internal resistance of the battery;

parameter identification is carried out through a parameter analysis module, a particle swarm optimization algorithm and a recursive least square method are introduced to carry out parameter identification, and various parameters obtained through calculation are optimized to obtain parameter identification information;

performing thermal runaway monitoring according to the parameter identification information, judging the parameter identification information and a preset threshold value, and detecting abnormal data according to a judging result to obtain abnormal data detection information;

performing similarity calculation on the abnormal data detection information and the thermal runaway comparison data set, and judging with a preset threshold value to obtain thermal runaway monitoring information;

and judging abnormal temperature conditions in the charging process through the thermal runaway monitoring information, carrying out thermal runaway early warning, and stopping power supply until the temperature is recovered to be normal.

The second aspect of the present invention provides an intelligent control system for a wireless charger, the system comprising: the intelligent control system comprises a memory and a processor, wherein the memory contains an intelligent control method program of a wireless charger, and the intelligent control method program of the wireless charger realizes the following steps when being executed by the processor:

The third aspect of the present invention further provides a computer readable storage medium, wherein the computer readable storage medium includes an intelligent control method program suitable for a wireless charger, and when the intelligent control method program suitable for the wireless charger is executed by a processor, the steps of the intelligent control method of the wireless charger described in any one of the above are implemented.

Drawings

In order to more clearly illustrate the technical solutions of embodiments or examples of the present invention, the drawings that are required to be used in the embodiments or examples of the present invention will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive efforts for those skilled in the art.

Fig. 1 is a flowchart of an intelligent control method of a wireless charger according to an embodiment of the present invention;

FIG. 2 is a flow chart of wireless charging safety monitoring according to an embodiment of the present invention;

FIG. 3 is a block diagram of an intelligent control system of a wireless charger according to an embodiment of the present invention;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.

as shown in fig. 1, the present invention provides a flow chart of an intelligent control method of a wireless charger, including:

s102, carrying out protocol communication and charging mode adaptation on target charging equipment, and carrying out identity verification according to the adapted charging mode;

It should be noted that, for the wireless charging device, at the first time of charging, the model of the device to be charged and the supported charging mode are queried, so that the supportable charging mode is determined according to the capability of the charging device. For the step of identity verification, because the protocols of the large mobile equipment terminals on the market for charging are different, the large mobile equipment terminals all have own exclusive fast charging protocols, and when charging is carried out, the identity verification is carried out, so that whether the identity of the equipment to be charged accords with the charging mode supported by the wireless charging equipment is judged, the situation that the equipment supports a certain charging mode is avoided, but the identity verification is not the corresponding equipment, so that the situation that charging accidents possibly happen is caused, and the charging equipment can be regulated and output according to the equipment characteristics and the identity in the charging process, so that the charging efficiency is improved, and the equipment safety is ensured.

S104, acquiring user charging history information, analyzing user charging preference according to the user charging history information, and constructing a user portrait;

It should be noted that, first, a time division rule is preset, and user charging time preference analysis is performed in combination with user charging history information. And obtaining charging time division information by dividing the charging time of the user, and reflecting the charging preference of the user in different time periods. And then, extracting the charging frequency, the charging duration and the charging electric quantity of each time period of the user according to the charging time division information. Through registering the frequency, the duration and the electric quantity, the corresponding charging duration and the electric quantity before and after charging can be ensured. Based on the registration information, an association matrix of duration, electric quantity and frequency is established, and the charging preference of the user is revealed through analysis of the association matrix, whether the charging preference is smaller in number of times and larger in each charging amount or larger in number of times and smaller in each charging amount. Meanwhile, the charging mode information of the user is extracted, the using frequency of various charging modes is analyzed, and the charging mode habit of the user when the user performs charging behavior is known, such as the most rapid charging habit or the normal rapid charging. And then, analyzing the user charging place information and the user charging time information, and combining the charging time division information to obtain third charging preference analysis information, namely charging place preference of the user in different time periods. Finally, based on the first charging preference analysis information, the second charging preference analysis information and the third charging preference analysis information, a user preference portrait is constructed, comprehensive and deep information about the charging preference of the user is provided, for example, the user prefers to charge in an office in 9-18 points during working days, and is used to adopt a quick charging mode and is used to multiple charging behaviors with less charging, so that a corresponding charging strategy is better set according to the charging habit of the user.

S106, acquiring real-time charging monitoring information, and carrying out user charging demand analysis by combining the user portrait to obtain demand analysis information;

It should be noted that, the requirement analysis is performed on the user, the charging preference, the electric quantity state level and the real-time charging monitoring data of the user are comprehensively considered, and an intelligent requirement analysis result is provided so as to better meet the personalized charging requirement of the user. An example of a user's demand analysis is as follows: when the electric quantity of the mobile phone of the user is lower than a certain threshold value, such as 20%, the requirement of the user is obvious to charge quickly, so that the charging equipment and the fastest charging mode of the equipment to be charged are matched preferentially for charging no matter what mode the user is accustomed to, and the charging requirement of the user is met; when the electric quantity is normal, the charging habit of the user is considered, and whether the user is used to perform battery protection on the target equipment is judged, and the requirement at the moment is that the battery of the user equipment is protected while the user equipment is charged quickly.

S108, acquiring monitoring information of a battery of the charging equipment, evaluating the state of the battery, and formulating an adaptive charging strategy;

The method comprises the steps of calculating capacity attenuation rate of target charging equipment through historical battery monitoring information of the target charging equipment, and performing internal resistance change analysis and temperature change analysis; and judging the battery capacity change of the target equipment through the capacity attenuation rate, carrying out battery condition assessment by combining the internal resistance change and the temperature change, judging the health condition of the battery at the current moment, judging whether the battery needs to be protected through the health condition of the battery, and adopting a corresponding charging protection strategy to carry out battery protection, such as reducing the charging rate, so as to effectively protect the battery and improve the service life and the performance of the battery.

S110, detecting abnormal events according to the battery monitoring information of the charging equipment, and carrying out abnormal early warning according to the detection result;

inputting the capacity attenuation rate analysis information, the abnormal temperature analysis information and the charging effect analysis information into the abnormal event monitoring model for analysis to obtain abnormal event detection information, and carrying out charging abnormal early warning according to the abnormal event detection information;

It should be noted that, first, the abnormal event detection is performed on the charging device, the abnormal capacity change is evaluated by analyzing the capacity fading rate, whether the battery of the charging device has a problem is determined, whether the charging temperature is too high or not is determined by analyzing the charging temperature, whether the charging rate needs to be reduced or not is determined, the charging temperature is controlled, and whether the normal charging rate in unit time is reached is determined by analyzing the charging effect, so that whether the problem occurs in the charging device or the device to be charged is analyzed. Further, a thermal runaway monitoring model is constructed, and the model comprises an equivalent circuit module, a battery thermal analysis module, a battery aging analysis module and a parameter analysis module. The equivalent circuit module, the battery thermal analysis module and the battery aging analysis module are in parameter coupling relation, and parameter identification, optimization, updating and analysis are carried out through the parameter analysis module. And calculating real-time voltage, current, SOC, surface temperature and internal resistance through the real-time battery monitoring information and the equivalent circuit module to obtain first analysis information. This information is used as input to the battery thermal analysis module for battery thermal analysis and to construct thermal profiles. And carrying out parameter identification through a parameter analysis module, and introducing a particle swarm optimization algorithm and a recursive least square method to carry out parameter identification. And updating the equivalent circuit module, the battery thermal analysis module and the battery aging analysis module by optimizing the obtained parameters. And performing thermal runaway monitoring according to the parameter identification information, judging the parameter identification information and a preset threshold value, and detecting abnormal data to obtain abnormal data detection information. And (3) carrying out similarity calculation on the abnormal data detection information and the thermal runaway comparison data set, judging the thermal runaway condition, and carrying out thermal runaway early warning so as to ensure that the temperature in the charging process is in a normal range.

It should be noted that, the equivalent circuit module, the battery thermal analysis module, the battery aging analysis module and the parameter analysis module are respectively functional sub-models in the thermal runaway monitoring model, and are respectively an equivalent circuit sub-model, a battery thermal analysis sub-model, a battery aging analysis sub-model and a parameter analysis sub-model. The equivalent circuit model is also called a semi-empirical model, and is a model constructed based on the response of external parameters such as the voltage and current of the battery. Common types are Thevenin model, dual polarized (Dual Polarization, DP) model, multi-order RC model, etc. The battery thermal model is a mathematical model that describes the temperature change of the battery during charge and discharge. The model considers thermal processes such as heat generation, heat conduction, heat radiation and the like to predict the temperature distribution of each point inside the battery. The main components of the thermal model comprise a heat generation module, a heat conduction module, a heat radiation module and a temperature distribution module. By solving the heat conduction equation, the temperature response of the battery under different working conditions can be calculated. The battery aging model is used to describe a mathematical model that gradually reduces battery performance over time. In the use process of the battery, the performance of the battery can be irreversibly changed due to chemical reaction, charge-discharge cycle and other reasons, so that the battery capacity is reduced, the internal resistance is increased and other aging phenomena are caused. The battery aging model expresses the aging process of the battery in the form of a mathematical equation by considering these aging mechanisms. A capacity fade model and an internal resistance increase model are typically included. The capacity fade model describes the decrease in battery capacity over time, while the internal resistance increase model represents the trend of increase in internal impedance of the battery. By parameterizing the working conditions, temperature, charge-discharge cycle times and other factors of the battery, the service life and performance decay trend of the battery are predicted. The essence of parameter identification is an optimization problem, an optimal value or an optimal relation is found in a region with a solution through experimental data and parameter relation, and is substituted into an established model, so that a model fitting value is maximally close to a true value, and the state of a battery is accurately calculated and estimated.

as shown in fig. 2, the present invention provides a wireless charging safety monitoring flowchart, comprising:

s202, carrying out protocol communication and equipment identity authentication, and matching corresponding charging modes;

s204, calculating the capacity attenuation rate, monitoring abnormal temperature change during charging, and analyzing the real-time charging effect;

s206, analyzing abnormal events, analyzing whether charging abnormal events exist, and performing charging abnormal early warning and control;

s208, monitoring thermal runaway of the charging equipment according to the real-time charging monitoring information, and monitoring abnormal data of the battery;

s210, performing thermal runaway analysis according to abnormal data of the battery, and analyzing whether abnormal temperature conditions occur;

and S212, carrying out charge control and abnormality early warning based on the thermal runaway analysis result.

Further, acquiring real-time electric quantity information of the equipment to be charged and travel distance information of the user, judging the real-time electric quantity information and a preset threshold value, and analyzing the charging requirement of the user to obtain analysis information of the charging requirement of the user; analyzing big data according to the travel distance information of the user, judging the chargeable duration, and obtaining chargeable duration information; performing charging mode adaptation by combining the user charging demand analysis information and the chargeable duration information to obtain charging mode adaptation information; if the charging mode adaptation information is for normal charging, acquiring user preference portrait and battery condition evaluation information to formulate a charging protection strategy for battery charging protection, and prolonging the service life of the battery; if the charging mode adaptation information is quick charging, monitoring the charging equipment in real time to acquire charging monitoring information; carrying out temperature change analysis of charging equipment according to the charging monitoring information, and constructing a temperature change diagram; analyzing the average temperature, the highest temperature and the temperature duration time during charging through the temperature change diagram, judging with a preset threshold value, judging whether cooling control is needed or not, and obtaining cooling control analysis information; if the cooling control analysis information is for cooling control, acquiring air outlet information of a vehicle-mounted cooling system and mounting position information of wireless charging equipment; carrying out air port matching according to the installation position information and the air outlet information, and judging the air outlet direction according to the installation position information to obtain air port matching information; formulating a cooling strategy according to the cooling control analysis information and the tuyere adaptation information, and formulating cooling air outlet temperature and air speed through real-time temperature of the charging equipment to obtain cooling control strategy information; acquiring a real-time temperature change diagram of the cooled charging equipment, calculating average temperature and temperature duration after cooling, and judging cooling effect to obtain cooling effect judgment information; if the cooling effect judging information is that cooling is successful, judging whether the charging temperature of the charging equipment is too low, and dynamically adjusting a cooling strategy according to a judging result; if the cooling effect judging information is that cooling fails, continuously increasing the air outlet speed and the temperature of the air outlet to charge and cool; when adopting the cooling strategy, if still can not successfully cool down after increasing cooling intensity, then carry out the thermal runaway monitoring, carry out early warning and stop charging according to the thermal runaway monitoring result, protection equipment safety.

Fig. 3 is a block diagram 3 of an intelligent control system of a wireless charger according to an embodiment of the present invention, where the system includes: the intelligent control method comprises a memory 31 and a processor 32, wherein the memory 31 contains an intelligent control method program of a wireless charger, and the intelligent control method program of the wireless charger realizes the following steps when being executed by the processor 32:

An embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium includes an intelligent control method program applicable to a wireless charger, where the intelligent control method program applicable to a wireless charger implements the steps of the intelligent control method of a wireless charger described in any one of the above when executed by a processor.

It should be noted that the present invention provides an intelligent control method, system and storage medium for a wireless charger, firstly, parameter information supported by a device is obtained through communication with a target charging device, and an adapted charging mode is determined, so as to ensure that output parameters of the charger are in a suitable range. The charging preference portrait of the user is constructed by registering and performing associated matrix analysis on information such as charging frequency, duration, electric quantity and place, the requirement analysis is performed, the real-time charging time and place of the user are extracted by real-time charging monitoring information, and the charging preference of the user is judged by performing matching analysis in combination with the portrait of the user. Meanwhile, the real-time electric quantity of the charging equipment is judged and divided into different electric quantity grades, so that the charging requirement of a user is judged. And extracting characteristics through historical battery monitoring information, and analyzing the capacity decay rate, the internal resistance change and the charging temperature, so as to evaluate the health state of the battery. And analyzing the capacity fading rate, the abnormal temperature and the charging effect through the real-time battery monitoring information, and constructing an abnormal event monitoring and thermal runaway monitoring model. And judging the abnormal condition in the charging process, performing early warning and power failure operation, and ensuring the safety of the charging process. Through comprehensive information analysis and model construction, intelligent matching of the charging equipment and a user is realized, charging efficiency and safety are improved, and personalized charging requirements of the user are met.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An intelligent control method of a wireless charger is characterized by comprising the following steps:

detecting abnormal events according to the battery monitoring information of the charging equipment, and carrying out abnormal early warning according to the detection result;

the method comprises the steps of obtaining user charging history information, analyzing user charging preference according to the user charging history information, and constructing a user portrait, and specifically comprises the following steps:

Constructing a user preference portrait according to the first charging preference analysis information, the second charging preference analysis information and the third charging preference analysis information;

the acquisition of real-time charging monitoring information is combined with user portrait to perform user charging demand analysis, and specifically comprises the following steps:

and inputting the electric quantity grade judgment information and the charging preference judgment information into a demand analysis model to perform demand analysis, so as to obtain demand analysis information.

2. The intelligent control method of a wireless charger according to claim 1, wherein the performing protocol communication and charging mode adaptation on the target charging device, and performing identity verification according to the adapted charging mode, specifically includes:

3. The intelligent control method of a wireless charger according to claim 1, wherein the performing the battery state evaluation and formulating the adaptive charging policy specifically includes:

4. The intelligent control method of a wireless charger according to claim 1, wherein the monitoring of abnormal events and thermal runaway is performed according to the battery monitoring information of the charging device, and the abnormal early warning is performed according to the detection result, specifically comprising:

5. The intelligent control method of a wireless charger according to claim 1, wherein the monitoring of abnormal events and thermal runaway is performed according to the battery monitoring information of the charging device, and the abnormal early warning is performed according to the detection result, further comprising:

6. An intelligent control system for a wireless charger, the system comprising: the intelligent control system comprises a memory and a processor, wherein the memory contains an intelligent control method program of a wireless charger, and the intelligent control method program of the wireless charger realizes the following steps when being executed by the processor:

7. The intelligent control system of a wireless charger according to claim 6, wherein the performing protocol communication and charging mode adaptation on the target charging device, and performing identity verification according to the adapted charging mode, specifically comprises:

8. A computer readable storage medium, characterized in that the computer readable storage medium comprises an intelligent control method program suitable for a wireless charger, which when executed by a processor, implements the steps of the intelligent control method of a wireless charger according to any one of claims 1 to 5.