CN117543780B - Charging power self-adaptive adjustment method for intelligent equipment - Google Patents
Charging power self-adaptive adjustment method for intelligent equipment 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
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/00714—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery charging or discharging current
- H02J7/00716—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery charging or discharging current in response to integrated charge or discharge current
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
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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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Abstract
The invention relates to the technical field of data processing, in particular to a charging power self-adaptive adjustment method for intelligent equipment, which comprises the following steps: acquiring a charging data sequence of the intelligent equipment; acquiring the abnormality degree of charging current data in each battery charging data set in a charging data sequence of the intelligent equipment; acquiring the real anomaly of the charging current data in each battery charging data set according to the anomaly correction value of the charging current data in each battery charging data set; and according to the real abnormal degree of the charging current data in each battery charging data set, the charging power of the intelligent device is adaptively adjusted. The invention avoids the influence of the addition of abnormal current data on the prediction result, thereby achieving the self-adaptive adjustment of the charging power of the intelligent equipment.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a charging power self-adaptive adjustment method for intelligent equipment.
Background
The popularity and development of intelligent devices have led to an increasing demand for charging devices, where the charging power of the devices is often an important parameter. However, there is also a difference in charging power due to the difference in charging devices. If the charging equipment cannot be adaptively adjusted according to the charging requirement of the equipment, the charging power may be insufficient or too high, so that the charging speed and the charging efficiency of the equipment are affected, and even the equipment is overheated and damaged; the intelligent device charging power self-adaptive adjustment is an important technology, and can effectively improve the charging efficiency of the device, reduce the charging time, reduce the charging temperature of the device and protect the safety and stability of the device. In future development, with the continuous development of intelligent devices, the intelligent device charging power self-adaptive adjustment technology will also be applied more, and a better and safer charging experience is brought to people.
In existing research, many scholars have adopted different algorithms and strategies to achieve adaptive adjustment of charging power. Among these, a more common method is a method based on current prediction of a smart device battery. Since the current and voltage determine the electrical power of the smart device and the voltage is typically constant during charging of the smart device, the effect of adjusting the electrical power is achieved by optimizing the current of the charging device. And carrying out self-adaptive adjustment on the charging current of the intelligent equipment according to the predicted current data, thereby achieving the effect of self-adaptive adjustment on the charging power.
Among these, the more common prediction algorithm is the algorithm of EWMA exponential weighted average prediction. And the EWMA model adds all the historical data to the prediction calculation when predicting. However, in the process of charging the intelligent device, abnormal current data is generated in the process of charging the intelligent device due to different charging environments or charging devices, and when the abnormal current data is added into the EWMA model for current prediction, larger deviation of a prediction result is caused. Therefore, normal current data is required to participate in prediction calculation, and the influence of the addition of abnormal current data on a prediction result is avoided.
Disclosure of Invention
In order to solve the above problems, the present invention provides a charging power adaptive adjustment method for an intelligent device, the method comprising:
acquiring a charging data sequence of the intelligent equipment; the charging data sequence of the intelligent device comprises a plurality of battery charging data sets, wherein each battery charging data set comprises charging current data, battery electric quantity data and battery temperature data;
recording the charging current data in all battery charging data sets as integral charging current data; acquiring the abnormality degree of the charging current data in each battery charging data set according to the difference between the charging current data in each battery charging data set and the overall charging current data and the battery temperature data;
recording the battery temperature data in all battery charging data sets as integral battery temperature data, and acquiring an abnormality correction value of charging current data in each battery charging data set according to the difference between the battery temperature data and the integral battery temperature data in each battery charging data set and the battery electric quantity data; acquiring the true anomaly degree of the charging current data in each battery charging data set according to the anomaly degree correction value and the anomaly degree of the charging current data in each battery charging data set;
clustering the charging data sequence of the intelligent device according to the real anomaly degree of the charging current data in each battery charging data set, and forming a cluster sequence by a plurality of obtained clusters; obtaining all normal clusters in the cluster sequence according to the difference of the real abnormal degree among the clusters in the cluster sequence; and adaptively adjusting the charging power of the intelligent equipment according to all the normal clustering clusters.
Preferably, the method for obtaining the abnormality degree of the charging current data in each battery charging dataset according to the difference between the charging current data and the overall charging current data in each battery charging dataset and the battery temperature data includes the following specific steps:
acquiring the first charge data sequence of the intelligent deviceA target sequence of individual battery charge datasets; acquiring the +.f in the charging data sequence of the intelligent device>The degree of difference of the charging current data of the individual battery charging data sets and all battery charging data sets; acquiring the +.f in the charging data sequence of the intelligent device>Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of the individual battery charging datasets; will be->The battery charging data set is input into the EWMA model to obtain the +.>Predictive values for the individual battery charge datasets; then +.>The degree of abnormality calculation expression of the charging current data in the individual battery charging data set is:
in the method, in the process of the invention,representing +.>Abnormality degree of charging current data in the individual battery charging data set; />Representing +.>The degree of difference of the charging current data of the individual battery charging data sets and all battery charging data sets; />Representing +.>Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of the individual battery charging datasets; />Representing +.>Charging current data within the individual battery charging data sets; />Representing predicted values of all battery charging datasets in a charging dataset sequence of the intelligent device; />Representing a linear normalization function; />The representation takes absolute value.
Preferably, the first step in the charging data sequence of the intelligent device is obtainedThe target sequence of the battery charging data sets comprises the following specific methods:
associating a charging data sequence of the intelligent device with the firstAll battery charging data sets having the same charging current data among the individual battery charging data sets as +.>A target sequence of battery charge datasets.
Preferably, the first step in the charging data sequence of the intelligent device is obtainedThe difference degree of the charging current data of each battery charging data set and all battery charging data sets comprises the following specific methods:
charging data sequence of intelligent deviceThe absolute value of the difference between the charging current data in the individual battery charging data set and the average value of the charging current data in all battery charging data sets in the charging data sequence of the intelligent device is used as +.>The degree of difference in the charging current data of the individual battery charging dataset from all battery charging datasets.
Preferably, the first step in the charging data sequence of the intelligent device is obtainedBattery charging data set and +.>The specific formula of the difference degree of the battery temperature data in all the battery charging data sets in the target sequence of the battery charging data sets is as follows:
in the method, in the process of the invention,representing +.>Battery temperature data within the individual battery charge data sets;representing +.>The number of all battery charging datasets in the target sequence of individual battery charging datasets; />Representing +.>The target sequence of the individual battery charge data set +.>Battery temperature data within the individual battery charge data sets.
Preferably, the obtaining the abnormality correction value of the charging current data in each battery charging dataset according to the difference between the battery temperature data and the overall battery temperature data in each battery charging dataset and the battery power data includes the following specific steps:
acquiring the rationality of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment, and then the first battery charging data sequence of the intelligent equipmentThe method for calculating the correction value of the degree of abnormality of the charging current data in each battery charging data set comprises the following steps:
in the method, in the process of the invention,representing +.>An abnormality correction value for the charging current data in the individual battery charging dataset; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />Representing a total number of all battery charging data sets in a charging data sequence of the smart device; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />The representation takes absolute value.
Preferably, the specific formula for obtaining the rationality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device is as follows:
in the method, in the process of the invention,representing +.>Charging current data within the individual battery charging data sets;representing +.>Battery charge data within the individual battery charge data set; />Representing +.>Battery temperature data within the individual battery charge data sets; />Representing the minimum value of battery temperature data within all battery charge data sets in the charge data sequence of the smart device.
Preferably, the obtaining the true anomaly degree of the charging current data in each battery charging data set according to the anomaly degree correction value and the anomaly degree of the charging current data in each battery charging data set includes the following specific steps:
and taking the product of the abnormality correction value of the charging current data in each battery charging data set in the charging data sequence of the intelligent device and the abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device as the true abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device.
Preferably, the method for clustering the charging data sequence of the intelligent device according to the real anomaly degree of the charging current data in each battery charging data set includes the following specific steps:
clustering the real anomaly of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment by using iterative self-organizing analysis clustering to obtain a plurality of clustering clusters; the method comprises the steps of obtaining the real abnormal degree average value of each cluster, and sequencing a plurality of clusters according to the size from large to small through the real abnormal degree average value of each cluster to obtain a cluster sequence.
Preferably, the method for obtaining all normal clusters in the cluster sequence according to the difference of the real abnormal degrees among the clusters in the cluster sequence comprises the following specific steps:
for any two adjacent clusters of the cluster sequence, taking the ratio of the real abnormal degree mean value of the first cluster to the real abnormal degree mean value of the second cluster in the two adjacent clusters as the real abnormal degree difference of the first cluster to obtain the real abnormal degree difference of all clusters in the cluster sequence; and for any cluster in the cluster sequence, if the real abnormal degree average value of the cluster is smaller than the maximum value of the real abnormal degree differences of all clusters in the cluster sequence, marking the cluster as a normal cluster, and further obtaining all normal clusters in the cluster sequence.
The technical scheme of the invention has the beneficial effects that: according to the difference between the battery temperature data and the whole battery temperature data in each battery charging dataset and the battery electric quantity data, obtaining an abnormality correction value of charging current data in each battery charging dataset, and according to the abnormality and the abnormality correction value of the charging current data in each battery charging dataset, obtaining the real abnormality of the charging current data in each battery charging dataset; screening a charging data sequence of the intelligent device according to the real anomaly degree of the charging current data in each battery charging data set to obtain all normal current data; and all normal current data are participated in the prediction calculation, so that the influence of the addition of abnormal current data on a prediction result is avoided, the prediction result is inaccurate, and the self-adaptive adjustment of the charging power of the intelligent equipment is further realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of steps of a method for adaptively adjusting charging power of an intelligent device according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific embodiments, structures, features and effects of the charging power adaptive adjustment method for intelligent devices according to the present 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 charging power self-adaptive adjustment method for intelligent equipment provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for adaptively adjusting charging power of an intelligent device according to an embodiment of the present invention is shown, where the method includes the following steps:
step S001: and acquiring a charging data sequence of the intelligent equipment.
It should be noted that, during the charging process of the smart device, excessive current may cause the battery to generate heat, thereby causing damage to the battery. A smaller current results in lower charging efficiency and greater energy loss. It is therefore necessary to adaptively adjust the charging power of the smart device during the charging process.
In the intelligent device charging process, the intelligent device and the charger are communicated through a power line communication PLC. Taking the charging process of the smart phone as an example, when the smart phone is connected to the charger, the smart phone sends a special string of signals to the charger through an internal power management chip, and the serial of signals includes various charging requirements of the smart phone, such as charging current, charging voltage, battery state and the like. After the charger receives the signals, the output current and the voltage of the charger can be adjusted according to the content of the signals so as to meet the charging requirement of the mobile phone. Meanwhile, the charger also can send feedback signals to the mobile phone at regular intervals, such as the current charging current, charging voltage, battery temperature and the like, and a battery management system on the smart phone can record the feedback signals and analyze the feedback signals to adjust the charging strategy of the smart phone so as to carry out self-adaptive adjustment of charging power.
Specifically, in order to implement the charging power self-adaptive adjustment method for the intelligent device provided in this embodiment, firstly, a charging data sequence of the intelligent device needs to be collected, and the specific process is as follows:
in this embodiment, an intelligent device is taken as an example of a smart phone to be described; collecting three data types, namely charging current data of the intelligent equipment, battery power data of the intelligent equipment and battery temperature data of the intelligent equipment, in sequence every 1 minute as a sampling time, and collecting for 24 hours; and taking three data of charging current data of the intelligent device, battery power data of the intelligent device and battery temperature data of the intelligent device at each sampling moment as a charging data sequence of the intelligent device.
The battery power data in this embodiment refers to a percentage of a remaining power in a battery, and the charging data sequence of the intelligent device includes a plurality of battery charging data sets, where each battery charging data set includes charging current data, battery power data and battery temperature data.
For example, when the battery power is 80, it means that the remaining power in the battery is 80%.
So far, the charging data sequence of the intelligent equipment is obtained through the method.
Step S002: and acquiring the abnormality degree of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment.
It should be noted that, since the mobile phone charging process is usually constant voltage charging, the magnitude of the charging current has a certain relationship with the charging temperature. The higher the charging temperature, the smaller the charging current will be, usually to protect the battery life of the mobile phone. The degree of abnormality of the charging current is thus calculated by analyzing the relationship between the charging temperature and the charging current.
Specifically, for the first in the charging data sequence of the intelligent deviceBattery charging data set, charging data sequence of intelligent device and +.>All battery charging data sets having the same charging current data among the individual battery charging data sets as +.>A target sequence of individual battery charge datasets; will be->The battery charging data set is input into the EWMA model to obtain the +.>Predictive values for the individual battery charge datasets; then +.>Personal battery charge dataThe abnormality degree calculation expression of the intra-set charging current data is:
in the method, in the process of the invention,representing +.>Abnormality degree of charging current data in the individual battery charging data set; />Representing +.>The degree of difference of the charging current data of the individual battery charging data sets and all battery charging data sets; />Representing +.>Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of the individual battery charging datasets; />Representing +.>Charging current data within the individual battery charging data sets; />Representing the average value of charging current data in all battery charging data sets in a charging data sequence of the intelligent device; />Representing predicted values of all battery charging datasets in a charging dataset sequence of the intelligent device; />Representing +.>Battery temperature data within the individual battery charge data sets; />Representing +.>The number of all battery charging datasets in the target sequence of individual battery charging datasets; />Representing +.>The target sequence of the individual battery charge data set +.>Battery temperature data within the individual battery charge data sets; />Representing a linear normalization function; />The representation takes absolute value.
It should be noted that the number of the substrates,indicate->Degree of difference of charging current data of individual battery charging data set and all battery charging data sets, +.>Indicate->The degree of difference between the individual battery charge data set and the charge current data predicted by the EWMA model is +.>Is based on->The smaller the value of (2), the description of +.>The smaller the degree of abnormality of the charging current data in the individual battery charging data sets; the battery charging temperature also determines +.>Abnormality degree of charge current data in individual battery charge data set,/->Indicate->Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of individual battery charging datasets, the larger this value, the description +.>The greater the degree of abnormality of the charging current data within the individual battery charging data sets.
The predicted value of the data obtained by the EWMA model is in the prior art, and the embodiment is not described herein in detail.
So far, the abnormality degree of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment is obtained through the method.
Step S003: and acquiring the true anomaly of the charging current data in each battery charging data set according to the anomaly correction value of the charging current data in each battery charging data set.
It should be noted that, in addition to the decrease of the charging current caused by the battery temperature during the charging process, the decrease of the charging current caused by the continuous increase of the battery power of the smart phone device, so that the correction coefficient of each charging current data needs to be calculated to obtain the true anomaly of each charging current data.
1. And acquiring an abnormality correction value of charging current data in each battery charging data set in the charging data sequence of the intelligent device.
It should be noted that, during the charging process of the smart phone device, the battery power is continuously increased along with the progress of the charging process. As the battery charge increases, the internal resistance of the battery increases gradually, and since the charging process of the mobile phone device is usually constant voltage, in order to ensure that the battery is full, and simultaneously reduce the excessive pressure on the battery as much as possible, the service life of the battery is prolonged, the overheat risk is reduced, and the charging current value is usually reduced. According to this feature, it is also necessary to correct the degree of abnormality of each charging current data.
Specifically, the first in the charging data sequence of the intelligent deviceThe method for calculating the correction value of the degree of abnormality of the charging current data in each battery charging data set comprises the following steps:
in the method, in the process of the invention,representing +.>An abnormality correction value for the charging current data in the individual battery charging dataset; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />Representing a total number of all battery charging data sets in a charging data sequence of the smart device; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />Representing +.>Charging current data within the individual battery charging data sets; />Representing +.>Battery charge data within the individual battery charge data set; />Representing +.>Battery temperature data within the individual battery charge data sets; />Representing a minimum value of battery temperature data in all battery charging data sets in a charging data sequence of the intelligent device; />The representation takes absolute value.
It should be noted that 100 represents a maximum value of battery power of the intelligent device, that is, a percentage of remaining battery power; when (when)When the value of (2) is larger, the larger the charged electric quantity of the battery is, the smaller the corresponding charging current is, so that the charging current data and the battery electric quantity data are in inverse proportion; when the battery temperature data is larger, the battery temperature is larger, and the corresponding charging current is smaller, so that the battery temperature data and the charging current data are in inverse proportion; />Indicate->The difference in the mean value of the charge current data rationality of the individual battery charge data set and the other battery charge data sets, the greater this value is indicative of the +.>The greater the correction value of the degree of abnormality of the charging current data in each battery charging data set.
So far, the correction value of the degree of abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device is obtained.
2. And acquiring the real anomaly of the charging current data in each battery charging data set in the charging data sequence of the intelligent device.
Specifically, the product of the abnormality correction value of the charging current data in each battery charging data set in the charging data sequence of the intelligent device and the abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device is used as the true abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device.
So far, the real anomaly degree of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment is obtained through the method.
Step S004: and according to the real abnormal degree of the charging current data in each battery charging data set, the charging power of the intelligent device is adaptively adjusted.
Specifically, clustering real abnormal degrees of charging current data in each battery charging data set in a charging data sequence of the intelligent equipment by using iterative self-organizing analysis clustering to obtain a plurality of clustering clusters; the method comprises the steps of obtaining the real abnormal degree average value of each cluster, and sequencing a plurality of clusters according to the size from large to small through the real abnormal degree average value of each cluster to obtain a cluster sequence.
For any two adjacent clusters of the cluster sequence, taking the ratio of the real abnormal degree mean value of the first cluster to the real abnormal degree mean value of the second cluster in the two adjacent clusters as the real abnormal degree difference of the first cluster to obtain the real abnormal degree difference of all clusters in the cluster sequence; and for any cluster in the cluster sequence, if the real abnormal degree average value of the cluster is smaller than the maximum value of the real abnormal degree differences of all clusters in the cluster sequence, marking the cluster as a normal cluster, and further obtaining all normal clusters in the cluster sequence.
Presetting a parameterWherein the present embodiment is +.>To describe the example, the present embodiment is not particularly limited, wherein +.>Depending on the particular implementation.
Further, each battery charging data set corresponding to all normal clustering clusters is recorded as a target battery charging data set, and the average value of all the target battery charging data sets is input into an EWMA model to obtain a current data predicted value of the intelligent device.
The average value of all target battery charging data sets is recorded as the actual current data of the intelligent equipment, if the absolute value of the difference value between the charging current data of the actual current data of the intelligent equipment and the current data predicted value of the intelligent equipment is larger thanThe charging current of the intelligent equipment is adjusted, and the specific adjustment value calculating method comprises the following steps:
in the method, in the process of the invention,representing the adjusted charging current value of the intelligent equipment; />Charging current data representing actual current data of the smart device; />Representing a predicted value of current data of the intelligent device; />Representing the current adjustment coefficient of the intelligent equipment; />The representation takes absolute value.
The charging current value adjusted by the intelligent equipment is input into a sending signal packet of the intelligent mobile phone, then signals are sent to the charger in an electric power communication PLC alternating current mode, the charger adjusts the charging current according to the received signals, the charging current is adjusted to be the charging current value adjusted by the intelligent equipment, and the effect of self-adaptive adjustment of the charging power of the intelligent equipment is achieved.
This embodiment is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The self-adaptive adjustment method for the charging power of the intelligent equipment is characterized by comprising the following steps of:
acquiring a charging data sequence of the intelligent equipment; the charging data sequence of the intelligent device comprises a plurality of battery charging data sets, wherein each battery charging data set comprises charging current data, battery electric quantity data and battery temperature data;
recording the charging current data in all battery charging data sets as integral charging current data; acquiring the abnormality degree of the charging current data in each battery charging data set according to the difference between the charging current data in each battery charging data set and the overall charging current data and the battery temperature data;
recording the battery temperature data in all battery charging data sets as integral battery temperature data, and acquiring an abnormality correction value of charging current data in each battery charging data set according to the difference between the battery temperature data and the integral battery temperature data in each battery charging data set and the battery electric quantity data; acquiring the true anomaly degree of the charging current data in each battery charging data set according to the anomaly degree correction value and the anomaly degree of the charging current data in each battery charging data set;
clustering the charging data sequence of the intelligent device according to the real anomaly degree of the charging current data in each battery charging data set, and forming a cluster sequence by a plurality of obtained clusters; obtaining all normal clusters in the cluster sequence according to the difference of the real abnormal degree among the clusters in the cluster sequence; according to all normal clustering clusters, the charging power of the intelligent equipment is adaptively adjusted;
the method for acquiring the abnormality degree of the charging current data in each battery charging data set according to the difference between the charging current data and the whole charging current data in each battery charging data set and the battery temperature data comprises the following specific steps:
acquiring the first charge data sequence of the intelligent deviceA target sequence of individual battery charge datasets; acquiring the +.f in the charging data sequence of the intelligent device>The degree of difference of the charging current data of the individual battery charging data sets and all battery charging data sets; acquiring the +.f in the charging data sequence of the intelligent device>Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of the individual battery charging datasets; will be->The battery charging data set is input into the EWMA model to obtain the +.>Predictive values for the individual battery charge datasets; then +.>The degree of abnormality calculation expression of the charging current data in the individual battery charging data set is:
in the method, in the process of the invention,representing +.>Abnormality degree of charging current data in the individual battery charging data set; />Representing +.>The degree of difference of the charging current data of the individual battery charging data sets and all battery charging data sets; />Representing +.>Battery charging data set and +.>The degree of difference of the battery temperature data in all battery charging datasets in the target sequence of the individual battery charging datasets; />Representation intelligenceThe charging data sequence of the device +.>Charging current data within the individual battery charging data sets; />Representing predicted values of all battery charging datasets in a charging dataset sequence of the intelligent device; />Representing a linear normalization function; />The representation takes absolute value;
the method for obtaining the abnormality correction value of the charging current data in each battery charging data set according to the difference between the battery temperature data and the whole battery temperature data in each battery charging data set and the battery electric quantity data comprises the following specific steps:
acquiring the rationality of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment, and then the first battery charging data sequence of the intelligent equipmentThe method for calculating the correction value of the degree of abnormality of the charging current data in each battery charging data set comprises the following steps:
in the method, in the process of the invention,representing +.>An abnormality correction value for the charging current data in the individual battery charging dataset; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />Representing a total number of all battery charging data sets in a charging data sequence of the smart device; />Representing +.>The rationality of the charging current data within the individual battery charging dataset; />The representation takes absolute value.
2. The method for adaptively adjusting charging power of an intelligent device according to claim 1, wherein the acquiring the charging data sequence of the intelligent device is characterized in thatThe target sequence of the battery charging data sets comprises the following specific methods:
associating a charging data sequence of the intelligent device with the firstAll battery charging data sets having the same charging current data among the individual battery charging data sets as +.>A target sequence of battery charge datasets.
3. The method for adaptively adjusting charging power of an intelligent device according to claim 1, wherein the acquiring the charging data sequence of the intelligent device is characterized in thatThe difference degree of the charging current data of each battery charging data set and all battery charging data sets comprises the following specific methods:
charging data sequence of intelligent deviceThe absolute value of the difference between the charging current data in the individual battery charging data set and the average value of the charging current data in all battery charging data sets in the charging data sequence of the intelligent device is used as +.>The degree of difference in the charging current data of the individual battery charging dataset from all battery charging datasets.
4. The method for adaptively adjusting charging power of an intelligent device according to claim 1, wherein the acquiring the charging data sequence of the intelligent device is characterized in thatBattery charging data set and +.>The specific formula of the difference degree of the battery temperature data in all the battery charging data sets in the target sequence of the battery charging data sets is as follows:
in the method, in the process of the invention,representing +.>Battery temperature data within the individual battery charge data sets; />Representing +.>The number of all battery charging datasets in the target sequence of individual battery charging datasets; />Representing +.>The target sequence of the individual battery charge data set +.>Battery temperature data within the individual battery charge data sets.
5. The method for adaptively adjusting the charging power of the intelligent device according to claim 1, wherein the specific formula for obtaining the rationality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device is:
in the method, in the process of the invention,representing +.>Charging current data within the individual battery charging data sets; />Representing +.>Battery charge data within the individual battery charge data set; />Representing +.>Battery temperature data within the individual battery charge data sets; />Representing the minimum value of battery temperature data within all battery charge data sets in the charge data sequence of the smart device.
6. The method for adaptively adjusting the charging power of the intelligent device according to claim 1, wherein the obtaining the true anomaly of the charging current data in each battery charging dataset according to the anomaly correction value and the anomaly of the charging current data in each battery charging dataset comprises the following specific steps:
and taking the product of the abnormality correction value of the charging current data in each battery charging data set in the charging data sequence of the intelligent device and the abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device as the true abnormality of the charging current data in each battery charging data set in the charging data sequence of the intelligent device.
7. The method for adaptively adjusting the charging power of the intelligent device according to claim 1, wherein the clustering the charging data sequence of the intelligent device according to the real anomaly of the charging current data in each battery charging data set, and forming a plurality of obtained clusters into a cluster sequence comprises the following specific steps:
clustering the real anomaly of the charging current data in each battery charging data set in the charging data sequence of the intelligent equipment by using iterative self-organizing analysis clustering to obtain a plurality of clustering clusters; the method comprises the steps of obtaining the real abnormal degree average value of each cluster, and sequencing a plurality of clusters according to the size from large to small through the real abnormal degree average value of each cluster to obtain a cluster sequence.
8. The method for adaptively adjusting the charging power of the intelligent device according to claim 1, wherein the specific method for obtaining all normal clusters in the cluster sequence according to the difference of real outliers among clusters in the cluster sequence comprises the following steps:
for any two adjacent clusters of the cluster sequence, taking the ratio of the real abnormal degree mean value of the first cluster to the real abnormal degree mean value of the second cluster in the two adjacent clusters as the real abnormal degree difference of the first cluster to obtain the real abnormal degree difference of all clusters in the cluster sequence; and for any cluster in the cluster sequence, if the real abnormal degree average value of the cluster is smaller than the maximum value of the real abnormal degree differences of all clusters in the cluster sequence, marking the cluster as a normal cluster, and further obtaining all normal clusters in the cluster sequence.
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