CN118249467B - Intelligent watch wireless charging method based on temperature control - Google Patents
Intelligent watch wireless charging method based on temperature control Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
- H02J7/007194—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature of the battery
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
- H02J50/10—Circuit arrangements or systems for wireless supply or distribution of electric power using inductive coupling
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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Abstract
The invention relates to the technical field of charging data processing, in particular to a wireless charging method of an intelligent watch based on temperature control. According to the method, according to the position characteristics and the local temperature distribution of each data point in the data cluster, the data abnormality evaluation of each data point in the data cluster is obtained; obtaining cluster abnormality evaluation of the data cluster; acquiring a state anomaly factor at the current moment according to the time characteristic, the temperature characteristic and the cluster anomaly evaluation distribution in the history range at the current moment; constructing a system state vector at the current moment, and obtaining a system state vector at the next moment by using a UKF algorithm; adjusting temperature data in a system state vector at the next moment to obtain weighted temperature data at the next moment; and identifying the charging state. According to the invention, the charging state is accurately identified by obtaining the accurate predicted value of the temperature data.
Description
Technical Field
The invention relates to the technical field of charging data processing, in particular to a wireless charging method of an intelligent watch based on temperature control.
Background
In the wireless charging process of the intelligent watch, heat generated by the charging coil and the battery can influence the charging efficiency and the safety, if the temperature is too high, the charging efficiency can be reduced, the battery can be damaged, and even the safety problem is caused. The monitoring index change state in the charging process is generally sensed by adopting fixed threshold means such as over-temperature protection, but the abnormal situation or the fast approaching abnormal situation in the charging process can not be timely identified due to the fact that the actual data change situation of charging is not combined, and the damage of the battery can be caused; therefore, the change condition of temperature data in the charging process of the smart watch needs to be analyzed in time to identify the charging state.
In the prior art, the UKF algorithm is used for predicting the charging state at the next moment, so that the problem of untimely identification can be effectively solved; however, due to the influence of factors such as aging, temperature change and power supply fluctuation of the battery, the traditional UKF algorithm prediction result ignores the condition that the charging state changes rapidly, and the uncertainty of the actual charging state can not be fully reflected, so that deviation exists between the prediction result at the next moment and the actual state, and the accuracy of the charging state identification is poor.
Disclosure of Invention
In order to solve the technical problem of poor accuracy of charge state identification caused by prediction deviation in the prior art, the invention aims to provide a temperature control-based intelligent watch wireless charging method, which adopts the following technical scheme:
The invention provides a temperature control-based intelligent watch wireless charging method, which comprises the following steps:
Acquiring temperature data and accumulated electric quantity data of intelligent equipment charging at each moment in a history range of the current moment to form data points in a sample space;
Clustering all data points in a sample space to obtain a plurality of data clustering clusters; for any data cluster, according to the position characteristics and the local temperature distribution of each data point in the data cluster, obtaining the data anomaly evaluation of each data point in the data cluster; obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of the data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points;
Acquiring a state anomaly factor at the current moment according to the time characteristic, the temperature change characteristic and the cluster anomaly evaluation distribution of the data cluster in the history range at the current moment; constructing a system state vector according to the temperature data and the accumulated electric quantity data at the current moment, and obtaining the system state vector at the next moment by using a UKF algorithm; adjusting temperature data in a system state vector at the next moment according to the state anomaly factor at the current moment to obtain weighted temperature data at the next moment;
And identifying the charging state according to the weighted temperature data at the next moment.
Further, the data anomaly evaluation acquisition method includes:
In the data clustering cluster, calculating the relative distance between each data point and the central data point, and normalizing the relative distance to be used as a position characteristic; obtaining the temperature dispersion degree of each data point according to the local temperature distribution of each data point;
Obtaining data anomaly evaluation according to the position characteristics and the temperature dispersion degree of each data point;
The data anomaly evaluation is positively correlated with the location feature and the degree of temperature dispersion.
Further, the method for acquiring the temperature dispersion degree comprises the following steps:
Calculating variances of all temperature data in the data cluster as a first discrete coefficient; calculating the difference of temperature data between each data point of the data cluster and the corresponding nearest other data cluster data points to obtain a second discrete coefficient;
obtaining a temperature discrete degree according to the first discrete coefficient and the second discrete coefficient;
the temperature discrete degree is positively correlated with the first discrete coefficient; the degree of temperature dispersion is inversely related to the second coefficient of dispersion.
Further, the cluster anomaly evaluation acquisition method comprises the following steps:
A PCA algorithm is adopted to obtain a plurality of main directions of the data cluster;
Obtaining cluster abnormal evaluation according to an obtaining formula of the cluster abnormal evaluation, wherein the obtaining formula of the cluster abnormal evaluation is as follows:
; wherein, Representing clusters of dataCluster anomaly evaluation of (2); Representing clusters of data Is the largest main direction of (a); Representing clusters of data Is the smallest principal direction of (2); Representing clusters of data The number of internal data points; Representing the number of all data points in the sample space; Representing clusters of data A number of data points; Representing clusters of data Middle (f)Data point data abnormity evaluation; Representing clusters of data Middle (f)Adjacent data points to a data pointIs evaluated for data anomalies; Representing clusters of data Middle (f)Data points; Represent the first The minimum relative distance of the data point to the maximum principal direction; For clustering data The mode length of the smallest main direction of (a); Representing modulo symbols.
Further, the method for acquiring the state anomaly factor comprises the following steps:
obtaining an abnormal factor according to an obtaining formula of the state abnormal factor, wherein the obtaining formula of the state abnormal factor is as follows:
; wherein, Indicating the current timeStatus anomaly factors of (2); Representing the current time; Indicating the charging start time; representing a preset temperature safety threshold; Indicating the current time Temperature data of (2); Indicating the time from the start of charging To the current timeThe number of data clusters in the range; Represent the first Evaluating cluster abnormality of the data cluster; Indicating the time from the start of charging To the current timeThe mean value of cluster abnormal evaluation of all data clusters in the range; Indicating the time from the start of charging To the current timeStandard deviation of cluster anomaly evaluation of all data clusters in the range.
Further, the method for acquiring weighted temperature data includes:
Carrying out normalized mapping on the state anomaly factors at the current moment to obtain weighting factors;
Obtaining weighted temperature data of the next moment according to the weighting factors and the temperature data in the system state vector of the next moment;
the temperature data and the weighting factors in the system state vector at the next moment are positively correlated with the weighted temperature data.
Further, the identifying of the state of charge is based on the weighted temperature data at the next time instant:
And if the weighted temperature data at the next moment is larger than the preset temperature safety threshold, judging that the charging state corresponding to the current moment is abnormal.
Further, the method for acquiring the data cluster comprises the following steps:
And clustering all data points in the sample space based on an ISODATA clustering algorithm to obtain a plurality of data clustering clusters.
Further, the method for obtaining the relative distance is to calculate the Euclidean distance.
Further, a softsign function is used for normalization mapping.
The invention has the following beneficial effects:
According to the invention, all data points in a sample space are clustered to obtain a plurality of data clustering clusters, so that state changes of the watch in the charging process are accurately known, and the data points in different states are distinguished through clustering; for any data cluster, according to the position characteristics and the local temperature distribution of each data point in the data cluster, obtaining data anomaly evaluation of each data point in the data cluster, evaluating the anomaly degree of each data point in the cluster where the data point is located, and analyzing the anomaly state of the intelligent watch in the charging process; obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points, and judging the abnormal condition of the current cluster more accurately; acquiring a state anomaly factor at the current moment according to the time characteristic, the temperature characteristic and cluster anomaly evaluation distribution of the data cluster in the history range at the current moment; constructing a system state vector according to the temperature data and the accumulated electric quantity data at the current moment, obtaining the system state vector at the next moment by using a UKF algorithm, and evaluating and predicting the charging state of the intelligent watch in real time; according to the state anomaly factor at the current moment, temperature data in a system state vector at the next moment are adjusted to obtain weighted temperature data at the next moment, and temperature prediction deviation caused by the anomaly is corrected; and identifying the charging state. According to the invention, the charging state is accurately identified by obtaining the accurate predicted value of the temperature data.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a wireless charging method for a smart watch based on temperature control according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of a temperature control-based intelligent watch wireless charging method according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent watch wireless charging method based on temperature control provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a wireless charging method for a smart watch based on temperature control according to an embodiment of the present invention is shown, and the specific method includes:
step S1: and acquiring temperature data and accumulated electric quantity data of the intelligent device charged at each moment in a history range at the current moment to form data points in a sample space.
In the embodiment of the invention, in order to timely identify the abnormal situation in the charging process, the state change of the intelligent equipment in the charging process is more accurately known by collecting detailed data, so that the regulation and control of the charging process are facilitated; firstly, acquiring monitoring data in a charging process through a sensor integrated in the intelligent equipment, namely acquiring temperature data and accumulated electric quantity data of the intelligent equipment at each moment in a history range of the current moment to form data points in a sample space; and constructing a two-dimensional rectangular coordinate system by taking the temperature data as a horizontal axis and the accumulated electric quantity data as a vertical axis, and marking data points determined by the temperature data and the accumulated electric quantity data at the same moment in the two-dimensional rectangular coordinate system to form a sample space.
It should be noted that, in one embodiment of the present invention, the intelligent device for analysis is a smart watch; calculating the product of the real-time current at each moment and the interval between the corresponding moment and the previous adjacent moment to obtain the electric quantity at each moment interval, and accumulating the electric quantity at all moment intervals to obtain accumulated electric quantity data at each moment; the history range of the current moment is the time length in the charging process, namely the range from the charging starting moment to the current moment; the time interval is 0.5s; in other embodiments of the present invention, the time interval may be specifically set according to specific situations, which is not limited and described herein.
Step S2: clustering all data points in a sample space to obtain a plurality of data clustering clusters; for any data cluster, according to the position characteristics and the local temperature distribution of each data point in the data cluster, obtaining the data anomaly evaluation of each data point in the data cluster; and obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points.
Clustering is a method capable of clustering similar data points, and helps to understand the inherent structure and distribution of data; by grouping the data points into different clusters, the different patterns and behaviors of the data are more easily understood and analyzed. All data points in the sample space are clustered to obtain a plurality of data clusters.
Preferably, in one embodiment of the present invention, the method for acquiring a data cluster includes:
And clustering all data points in the sample space based on an ISODATA clustering algorithm to obtain a plurality of data clustering clusters.
The ISODATA clustering algorithm is an iterative clustering algorithm, and is used for classifying all data points into a plurality of clusters according to the similarity, so that the difference between the data points in the same cluster is minimum, the distance difference between different clusters is maximum, and the cluster division is continuously optimized in each iteration to achieve the optimal clustering result. It should be noted that the specific ISODATA clustering algorithm is a technical means well known to those skilled in the art, and will not be described herein.
The data points in the same data cluster have similarity in characteristic space and temperature distribution, and the points with larger difference with other data points in the data cluster can be identified for subsequent analysis by comparing the position characteristics of the data points with the local temperature distribution; the position characteristics of the data points reflect the distance and distribution of the data points relative to the center data points of the data cluster, and if one data point is too far from the center of the cluster where the data point is located, the data point is more likely to be abnormal; the local temperature distribution describes the temperature change condition around the data point, can identify whether the data point is similar to the data points in other clusters in the temperature characteristic, and judges the abnormal condition of the data point; and therefore, for any data cluster, according to the position characteristic and the local temperature distribution of each data point in the data cluster, obtaining the data abnormality evaluation of each data point in the data cluster.
Preferably, in one embodiment of the present invention, the method for acquiring data anomaly evaluation includes:
In the data clustering cluster, calculating the relative distance between each data point and the central data point, and normalizing the relative distance to be used as a position characteristic; obtaining the temperature dispersion degree of each data point according to the local temperature distribution of each data point; obtaining data anomaly evaluation according to the position characteristics and the temperature discrete degree of each data point; the data anomaly evaluation is positively correlated with the position characteristics and the degree of temperature dispersion.
Preferably, in one embodiment of the present invention, the method for acquiring the degree of temperature dispersion includes:
Calculating variances of all temperature data in the data cluster as a first discrete coefficient; calculating the difference of temperature data between each data point of the data cluster and the corresponding nearest other data cluster data points to obtain a second discrete coefficient; obtaining a temperature discrete degree according to the first discrete coefficient and the second discrete coefficient; the temperature discrete degree is positively correlated with the first discrete coefficient; the degree of temperature dispersion is inversely related to the second dispersion coefficient.
In one embodiment of the invention, the formula for data anomaly evaluation is expressed as:
；
Wherein, Representing clusters of dataData pointsIs evaluated for data anomalies; Representing clusters of data Data pointsWith a central data pointThe relative distance between them; Representing clusters of data All data points and central data pointThe maximum relative distance between the two; Representing clusters of data All data points and central data pointA minimum relative distance therebetween; representing data points Temperature data of (2); Representing a current data point Adjacent nearest other data clustersData pointsTemperature data of (2); Represented as clusters of data Variance of all temperature data in the inner.
In the formula for evaluating the abnormality of the data,Representing clusters of computed dataData pointsWith a central data pointThe difference between the relative distance and the minimum relative distance is compared with the value of the difference between the maximum and the minimum relative distance, i.e. the data clusterData pointsWith a central data pointThe relative distance between the two clusters is normalized, and the larger the normalized value is, the data clusterData pointsWith a central data pointThe larger the relative distance between the two data points is, the farther the data points are from the positions of the central data points of the data cluster, the more obvious the temperature change amplitude is, and the larger the data abnormal evaluation is; indicating the degree of temperature dispersion, Avoiding the denominator of the formula to be 0,Representing data cluster data points for a second discrete coefficientClustering data points with corresponding adjacent nearest other dataThe larger the difference between the temperature data, the similar variation amplitude does not exist in the corresponding two data points; the smaller the difference, the greater the likelihood that the corresponding two data points are similarly changed, the more likely the data points are to have a wrong classification problem; if the variance of the temperature in the data cluster where the data point is located is larger, the fluctuation characteristic is larger, the difference between the data points in the cluster is larger, the temperature difference between the data point and the data point corresponding to the other nearest data cluster is smaller, and the temperature dispersion degree of the data point is larger, so that the data point is more likely to be a different data point.
It should be noted that, in one embodiment of the present invention, the method for obtaining the relative distance is to calculate the euclidean distance; in other embodiments of the present invention, the practitioner may also use other calculation methods to quantify the relative distance between the data points, and the specific means are well known to those skilled in the art, and will not be described in detail herein.
It should be noted that, in other embodiments of the present invention, the positive and negative correlation is constructed by subtracting or adding other basic mathematical operations, and specific means are technical means well known to those skilled in the art, and are not described herein.
The number of data points is an important index for evaluating the size of the cluster, the clusters with different sizes possibly reflect different data distribution modes and densities, the larger cluster possibly contains more normal data points, the smaller cluster is possibly more easily influenced by noise or abnormal values, and when the cluster is evaluated abnormal, the influence of the number of data points on abnormal evaluation needs to be considered, so that the excessively high abnormal evaluation on the smaller cluster is avoided; the data abnormal evaluation difference between adjacent data points can reflect the local change and fluctuation condition of the data, and a larger difference can mean that the data has larger change or abnormality in the area, so that the overall abnormal degree of the cluster can be evaluated more accurately; the distribution trend characteristics of the data points describe the distribution form and trend of the data in the cluster, different distribution trends possibly reflect the internal rules and structures of the data, are more beneficial to analysis of outliers or irregular distribution, and can have important influence on cluster abnormal evaluation. And obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points.
Preferably, in one embodiment of the present invention, the method for acquiring cluster abnormality evaluation includes:
A PCA algorithm is adopted to obtain a plurality of main directions of the data cluster;
Obtaining cluster abnormal evaluation according to an obtaining formula of the cluster abnormal evaluation, wherein the obtaining formula of the cluster abnormal evaluation is as follows:
；
Wherein, Representing clusters of dataCluster anomaly evaluation of (2); Representing clusters of data Is the largest main direction of (a); Representing clusters of data Is the smallest principal direction of (2); Representing clusters of data The number of internal data points; Representing the number of all data points in the sample space; Representing clusters of data A number of data points; Representing clusters of data Middle (f)Data point data abnormity evaluation; Representing clusters of data Middle (f)Nearest neighbor data points of data pointsIs evaluated for data anomalies; Representing clusters of data Middle (f)Data points; Represent the first The minimum relative distance of the data point to the maximum principal direction; For clustering data The mode length of the smallest main direction of (a); Representing modulo symbols.
In the formula for cluster anomaly evaluation,Representing clusters of data over all data points in sample spaceThe larger the value is, the fewer the number of data points in the data cluster is, and the more abnormality is likely to exist in the cluster; Represent the first Data point data anomaly evaluation and nearest neighbor data pointDifferences in data anomaly evaluation of (a), i.eData point data anomaly evaluation and sumData points with minimum relative distance between data pointsDifferences in data anomaly evaluation of (2); the larger the difference is, the lower the similarity between the data points is, and the larger the difference is, the more charge abnormal state is likely to occur; the maximum principal direction represents the direction in which the temperature data changes the most,Represent the firstClustering data on minimum relative distance ratio between data point and maximum main directionThe larger the ratio, the larger the minimum relative distance, the larger the value of the module length of the minimum principal direction, which indicates the firstThe less similar the trend of the data point and the maximum main direction is, the more likely the data point is not in the change state represented by the main direction, and the more likely the data point is in an abnormal change state in the charging process; the smaller the ratio, the smaller the minimum relative distance, indicating the firstThe more likely a data point tends to be similar to the maximum principal direction, the less likely it is to belong to the state of change represented by the principal direction, and the less likely it is for anomalies.
In one embodiment of the present invention, the firstThe method for acquiring the minimum relative distance from the data point to the maximum main direction comprises the following steps: the method comprises the steps of obtaining a projection point of a data point in the maximum main direction, and calculating Euclidean distance between the data point and the projection point, namely the minimum relative distance; the PCA algorithm is a well known technique to those skilled in the art and will not be described in detail herein.
In other embodiments of the present inventionThe difference of the difference can be used to represent the data clusterThe more the number of the internal data points is, the smaller the cluster abnormal evaluation is; Other basic mathematical operations such as addition can be used to construct the positive and negative correlation, and specific means are well known to those skilled in the art, and will not be described here.
Step S3: acquiring a state anomaly factor at the current moment according to the time characteristic, the temperature change characteristic and the cluster anomaly evaluation distribution of the data cluster in the history range at the current moment; constructing a system state vector according to the temperature data and the accumulated electric quantity data at the current moment, and obtaining the system state vector at the next moment by using a UKF algorithm; and adjusting the temperature data in the system state vector at the next moment according to the state anomaly factor at the current moment to obtain weighted temperature data at the next moment.
The charging process is a time-dependent process, the state of the battery can change along with the charging time, and the historical time characteristics can reveal the stages and the change trend in the charging process; in the charging process, chemical reaction can occur in the battery to generate heat, so that the temperature of the battery changes, if the temperature of the battery is too high, the problem that the charging speed is too high in the charging process can be possibly caused, and the abnormal conditions can be found and processed in time by monitoring the temperature change, so that the safety and the effectiveness of the charging process are ensured; the cluster abnormal evaluation distribution reflects the abnormal degree of the data points in different clusters, is beneficial to identifying the data points which are inconsistent with the normal charging mode, and can be used for more clearly knowing the change of the charging state; the time characteristics, the temperature change characteristics and the cluster abnormal evaluation distribution of the data cluster are comprehensively considered, so that the state of the charging process is comprehensively evaluated and abnormal conditions are timely found; therefore, the state anomaly factors at the current moment are obtained according to the time characteristics, the temperature change characteristics and the cluster anomaly evaluation distribution of the data clusters in the history range at the current moment.
Preferably, in one embodiment of the present invention, the method for acquiring the state anomaly factor includes:
obtaining an abnormal factor according to an obtaining formula of the state abnormal factor, wherein the obtaining formula of the state abnormal factor is as follows:
；
Wherein, Indicating the current timeStatus anomaly factors of (2); Representing the current time; Indicating the charging start time; representing a preset temperature safety threshold; Indicating the current time Temperature data of (2); Indicating the time from the start of charging To the current timeThe number of data clusters in the range; Represent the first Evaluating cluster abnormality of the data cluster; Indicating the time from the start of charging To the current timeThe mean value of cluster abnormal evaluation of all data clusters in the range; Indicating the time from the start of charging To the current timeStandard deviation of cluster anomaly evaluation of all data clusters in the range.
In the acquisition formula of the state anomaly factor,The difference between the current time and the charging start time is represented, and the larger the difference is, the longer the charging duration is; Indicating a preset temperature safety threshold and the current moment The smaller the difference value is, the larger the temperature data is, the more the temperature data approaches to or is larger than a preset temperature safety threshold value, and the more possible abnormal charging condition at the current moment is indicated; avoiding the formula denominator being 0, the smaller the value, The larger the size of the container,The larger the charging process time is, the larger the charging temperature data is, the more abnormal conditions are likely to occur; Calculating the deviation degree of cluster abnormal evaluation of all data clusters relative to the overall mean value in the history range of the current moment; the greater the deviation degree is, the less similar the cluster abnormal evaluation is, namely the more obvious the transition between charging states in the charging process is, the lower the similarity between the charging states is, the greater the possibility of abnormality is, the more the charging states need to be regulated and controlled, and the abnormal factors of the charging states are increased.
It should be noted that, in the embodiment of the present invention, if the temperature data is too high and is greater than the preset temperature safety threshold, the occurrence of a safety accident is avoided, and the heat dissipation state needs to be directly forced to enter, and no subsequent consideration needs to be performed, so that the analysis of the state anomaly factor is only applicable to the case that the temperature data is less than or equal to the preset temperature safety threshold. The size of the preset temperature safety threshold can be specifically set by an implementer according to the performance and actual situation of the battery, and the preset temperature safety threshold is not limited and described in detail herein.
It should be noted that, in other embodiments of the present invention, the positive-negative correlation may be constructed according to other basic mathematical operations such as adding the two parts, and specific means are technical means known to those skilled in the art, and will not be described herein.
The state anomaly factor at the current moment reflects some dynamic processes or external interference in the charging process, and influences the temperature state at the next moment; the temperature data of the next moment is predicted more accurately by considering the state anomaly factors of the current moment; the weighted temperature data at the next moment can be obtained by adjustment, so that the system can be better adapted to the actual working state of the system, and the performance of the system is optimized; and therefore, the temperature data in the system state vector at the next moment is adjusted according to the state abnormality factor at the current moment, and the weighted temperature data at the next moment is obtained.
Preferably, in one embodiment of the present invention, the method for acquiring weighted temperature data includes:
Carrying out normalized mapping on the state anomaly factors at the current moment to obtain weighting factors; obtaining weighted temperature data of the next moment according to the weighting factors and the temperature data in the system state vector of the next moment; the temperature data and the weighting factors in the system state vector at the next moment are positively correlated with the weighted temperature data. In one embodiment of the invention, the formula for weighting the temperature data is:
；
Wherein, Weighted temperature data representing a next time instant; Temperature data in the system state vector representing the next time; representing the weighting factors.
In the formula for weighting the temperature data, the larger the weighting factor is, the more abnormal conditions are likely to occur, the more the temperature data in the system state vector at the next moment is required to be enlarged,The larger the temperature data is, the larger the weighted temperature data at the next time is, and the higher the temperature may be.
It should be noted that, in one embodiment of the present invention, a softsign function is used to normalize and map the state anomaly factor at the current time, and map the state anomaly factor onto the (-1, 1) range; the softsign functions are well known to those skilled in the art, and are not described in detail herein.
It should be noted that, in other embodiments of the present invention, the positive-negative correlation may be constructed according to other basic mathematical operations such as adding the temperature data and the weighting factors, and specific means are technical means known to those skilled in the art, and will not be described herein.
Step S4: and identifying the charging state according to the weighted temperature data.
The temperature of the battery can directly influence the charging efficiency and performance of the battery, and the charging speed can be reduced or even the battery is damaged due to the fact that the temperature is too high or too low; therefore, the actual charging state of the battery can be known more accurately through weighting the temperature data, and potential charging risks can be found in time, so that the charging process is optimized. The state of charge is identified based on the weighted temperature data.
Preferably, in one embodiment of the present invention, identifying the state of charge according to the weighted temperature data of the next moment in time includes:
And if the weighted temperature data at the next moment is larger than the preset temperature safety threshold, judging that the charging state corresponding to the current moment is abnormal.
It should be noted that, in another embodiment of the present invention, after the state of charge at the current time is analyzed, the charging process may be regulated to prevent the battery from being damaged or a safety accident from occurring: the intelligent watch can reduce energy conversion and loss by reducing charging power, adjust the working state of a charging power chip, reduce screen brightness, automatically pause high power consumption, prompt users to reduce heat generation by vibration, sound or warning information on a screen and other modes, so that the temperature is in a normal range.
In summary, according to the position characteristics and the local temperature distribution of each data point in the data cluster, the abnormal data evaluation of each data point in the data cluster is obtained; obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points; acquiring a state anomaly factor of the current moment according to the time characteristic, the temperature characteristic and the cluster anomaly evaluation distribution in the current moment range; constructing a system state vector according to the temperature data and the accumulated electric quantity data at the current moment, and obtaining the system state vector at the next moment by using a UKF algorithm; adjusting temperature data in a system state vector at the next moment to obtain weighted temperature data at the next moment; and identifying the charging state. According to the invention, the charging state is accurately identified by obtaining the accurate predicted value of the temperature data.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (9)
1. A temperature control-based wireless charging method for a smart watch, the method comprising:
Acquiring temperature data and accumulated electric quantity data of intelligent equipment charging at each moment in a history range of the current moment to form data points in a sample space;
Clustering all data points in a sample space to obtain a plurality of data clustering clusters; for any data cluster, according to the position characteristics and the local temperature distribution of each data point in the data cluster, obtaining the data anomaly evaluation of each data point in the data cluster; obtaining cluster abnormal evaluation of the data cluster according to the number of data points in the data cluster, the difference of the data abnormal evaluation between adjacent data points and the distribution trend characteristics of the data points;
Acquiring a state anomaly factor at the current moment according to the time characteristic, the temperature change characteristic and the cluster anomaly evaluation distribution of the data cluster in the history range at the current moment; constructing a system state vector according to the temperature data and the accumulated electric quantity data at the current moment, and obtaining the system state vector at the next moment by using a UKF algorithm; adjusting temperature data in a system state vector at the next moment according to the state anomaly factor at the current moment to obtain weighted temperature data at the next moment;
identifying a charging state according to the weighted temperature data at the next moment;
The method for acquiring the state anomaly factors comprises the following steps:
obtaining an abnormal factor according to an obtaining formula of the state abnormal factor, wherein the obtaining formula of the state abnormal factor is as follows:
; wherein, Indicating the current timeStatus anomaly factors of (2); Representing the current time; Indicating the charging start time; representing a preset temperature safety threshold; Indicating the current time Temperature data of (2); Indicating the time from the start of charging To the current timeThe number of data clusters in the range; Represent the first Evaluating cluster abnormality of the data cluster; Indicating the time from the start of charging To the current timeThe mean value of cluster abnormal evaluation of all data clusters in the range; Indicating the time from the start of charging To the current timeStandard deviation of cluster anomaly evaluation of all data clusters in the range.
2. The wireless charging method of the smart watch based on temperature control according to claim 1, wherein the method for acquiring the data anomaly evaluation comprises the following steps:
In the data clustering cluster, calculating the relative distance between each data point and the central data point, and normalizing the relative distance to be used as a position characteristic; obtaining the temperature dispersion degree of each data point according to the local temperature distribution of each data point;
Obtaining data anomaly evaluation according to the position characteristics and the temperature dispersion degree of each data point;
The data anomaly evaluation is positively correlated with the location feature and the degree of temperature dispersion.
3. The method for wirelessly charging a smart watch based on temperature control according to claim 2, wherein the method for obtaining the degree of temperature dispersion comprises:
Calculating variances of all temperature data in the data cluster as a first discrete coefficient; calculating the difference of temperature data between each data point of the data cluster and the corresponding nearest other data cluster data points to obtain a second discrete coefficient;
obtaining a temperature discrete degree according to the first discrete coefficient and the second discrete coefficient;
the temperature discrete degree is positively correlated with the first discrete coefficient; the degree of temperature dispersion is inversely related to the second coefficient of dispersion.
4. The method for wirelessly charging the smart watch based on temperature control according to claim 1, wherein the method for acquiring cluster anomaly evaluation comprises the following steps:
A PCA algorithm is adopted to obtain a plurality of main directions of the data cluster;
Obtaining cluster abnormal evaluation according to an obtaining formula of the cluster abnormal evaluation, wherein the obtaining formula of the cluster abnormal evaluation is as follows:
; wherein, Representing clusters of dataCluster anomaly evaluation of (2); Representing clusters of data Is the largest main direction of (a); Representing clusters of data Is the smallest principal direction of (2); Representing clusters of data The number of internal data points; Representing the number of all data points in the sample space; Representing clusters of data A number of data points; Representing clusters of data Middle (f)Data point data abnormity evaluation; Representing clusters of data Middle (f)Adjacent data points to a data pointIs evaluated for data anomalies; Representing clusters of data Middle (f)Data points; Represent the first The minimum relative distance of the data point to the maximum principal direction; For clustering data The mode length of the smallest main direction of (a); Representing modulo symbols.
5. The method for wireless charging of a smart watch based on temperature control of claim 1, wherein the method for obtaining weighted temperature data comprises:
Carrying out normalized mapping on the state anomaly factors at the current moment to obtain weighting factors;
Obtaining weighted temperature data of the next moment according to the weighting factors and the temperature data in the system state vector of the next moment;
the temperature data and the weighting factors in the system state vector at the next moment are positively correlated with the weighted temperature data.
6. The method for wireless charging of a smart watch based on temperature control of claim 1, wherein the state of charge is identified based on the weighted temperature data at the next time:
And if the weighted temperature data at the next moment is larger than the preset temperature safety threshold, judging that the charging state corresponding to the current moment is abnormal.
7. The method for wirelessly charging a smart watch based on temperature control of claim 1, wherein the method for acquiring the data cluster comprises:
And clustering all data points in the sample space based on an ISODATA clustering algorithm to obtain a plurality of data clustering clusters.
8. The method for wirelessly charging a smart watch based on temperature control of claim 2, wherein the method for obtaining the relative distance is to calculate euclidean distance.
9. The temperature control-based wireless charging method for the smart watch of claim 5, wherein a softsign function is used for normalization mapping.
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