Charging control method and system of electronic equipment

Abstract

The invention relates to the technical field of battery charging control, in particular to a charging control method and system for electronic equipment. The method comprises the following steps: counting each historical charging process in a historical database under a constant current charging process, dividing the historical charging process into historical time periods through time period division, and analyzing a comprehensive evaluation coefficient of each historical time period under a current constant current value; for the real-time charging process, the time period similar weight of each historical time period for the real-time time period can be determined in a time period matching mode, the optimal degree of the real-time time period for each constant current value can be determined by performing weighted fusion on the comprehensive evaluation coefficients, and then the charging current in the current real-time time period is determined. According to the invention, data comparison is carried out through big data analysis and a data collaborative filtering mode, dynamic adjustment of the constant current in the charging process of the electronic equipment is realized, and on the basis of ensuring the charging efficiency, the charging safety is improved, and the cycle life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging control, and particularly to a charging control method and system for an electronic device. Background Art

[0002] With the rapid development of mobile communication, intelligent terminals, wearable devices, and electric vehicles, lithium-ion batteries have become the mainstream power supply solution. To meet the growing demands of users for charging speed and battery life, electronic devices generally adopt the constant current-constant voltage (CC-CV) mode for battery charging management. This charging strategy is widely used in the charging systems of various electronic products due to its advantages such as simple structure, reliable control, and high efficiency. However, this mode generally adopts a fixed maximum charging current during the constant current stage and fails to dynamically adjust according to the changes in the internal impedance and temperature rise trend of the battery during the charging process. Especially in the later stage of the constant current stage, as the battery voltage gradually increases and the internal resistance increases, the battery heating phenomenon becomes significantly aggravated. If the maximum current is still maintained for charging, it is easy to cause the battery temperature to be too high, which not only affects the safety of the charging process but also accelerates battery aging and affects its cycle life. Summary of the Invention

[0003] In order to solve the technical problem that in the existing battery charging process, a constant current is used for a long time for charging, resulting in battery damage, the purpose of the present invention is to provide a charging control method and system for an electronic device. The specific technical solutions adopted are as follows: The present invention proposes a charging control method for an electronic device, and the method includes: Statistical charging information in each historical charging process of the electronic device battery during the constant current charging process in the historical database, where the charging information at least includes temperature, internal resistance, and state of charge; Dividing each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information; for each historical time period, obtaining a comprehensive evaluation coefficient of each historical time period according to the temperature and state of charge changes of the battery within the historical time period; Determining the real-time time period corresponding to the real-time moment in the real-time charging process; obtaining the time period similarity weight according to the difference in charging information between the real-time time period and each historical time period; for each constant current value in the constant current charging process, based on the time period similarity weight, performing weighted fusion on the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value, obtaining the preference degree of each constant current value for the real-time time period; determining the charging current corresponding to the real-time time period according to the preference degree.

[0004] Further, the dividing each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information includes: Preset the length of the time period according to the range of the state of charge, and the length of the time period is negatively correlated with the magnitude of the state of charge. Based on the size of the time period, divide each historical charging process into an initial time period to obtain multiple initial historical time periods in each historical charging process; In each historical charging process, according to the changes in temperature and internal resistance at each moment, obtain the degree of battery fluctuation at each moment. Based on the degree of battery fluctuation, screen out the segmentation points, and segment the initial historical time period based on the segmentation points to obtain all historical time periods of each historical charging process.

[0005] Further, the method for obtaining the degree of battery fluctuation includes: For each moment, search forward and backward by a preset search length respectively to obtain the forward analysis range and the backward analysis range of each moment; For any one dimension between temperature and internal resistance, take the mean value of the first-order differences in each analysis range of the dimension as the change characteristic of the dimension in each analysis range; normalize the difference between the change characteristics between the backward analysis range and the forward analysis range to obtain the initial degree of fluctuation of the dimension; Between the two dimensions of temperature and internal resistance, select the maximum initial degree of fluctuation as the degree of battery fluctuation at each moment.

[0006] Further, the method for obtaining the comprehensive evaluation coefficient includes: Obtain the temperature evaluation coefficient according to the size relationship between the average battery temperature in the historical time period and the preset safe temperature range; Obtain the state of charge growth rate in the historical time period; take the product of the state of charge growth rate and the temperature evaluation coefficient as the comprehensive evaluation coefficient.

[0007] Further, the method for obtaining the temperature evaluation coefficient includes: If the average battery temperature is less than the lower limit of the safe temperature range, perform a negative correlation mapping on the difference between the lower limit of the safe temperature range and the average battery temperature to obtain the temperature evaluation coefficient; If the average battery temperature is within the safe temperature range, assign the temperature evaluation coefficient to the positive integer 1; If the average battery temperature is greater than the upper limit of the safe temperature range, perform a negative correlation mapping on the difference between the average battery temperature and the upper limit of the safe temperature range to obtain the temperature evaluation coefficient.

[0008] Further, the method for obtaining the time period similarity weight includes: Taking temperature, internal resistance, state of charge, and battery health state as the dimensions to be analyzed respectively, obtaining the DTW distance of the data sequence of the dimension to be analyzed between the real-time time period and the historical time period, and taking the ratio of the DTW distance to the number of matching data pairs in the DTW matching process as the dimension difference under the dimension to be analyzed; After accumulating the dimension differences of all dimensions to be analyzed, perform a negative correlation mapping to obtain the time period similarity weight.

[0009] Further, the method of weighted fusion is weighted averaging.

[0010] Further, determining the charging current corresponding to the real-time time period according to the preference degree includes: Selecting the constant current value with the largest preference degree as the corresponding charging current.

[0011] Further, determining the charging current corresponding to the real-time time period according to the preference degree further includes: Taking the ratio of the preset maximum battery voltage to the internal resistance of the battery at the real-time moment as the maximum charging current; between the maximum charging current and the constant current value with the largest preference degree, selecting the minimum current value as the charging current.

[0012] The present invention also provides a charging control system for an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the charging control methods for an electronic device.

[0013] The present invention has the following beneficial effects: The present invention uses the big data analysis method to count each historical charging process under the constant current charging process in the historical database. The present invention aims to dynamically adjust the constant current along with the progress of the battery on the basis of the constant current charging process. Therefore, for the historical charging process, it can be divided into individual historical time periods through time period division, and then analyze the comprehensive evaluation coefficient of each historical time period under the current constant current value. Furthermore, for the real-time charging process, the time period similarity weight of each historical time period for the real-time time period can be determined through the time period matching method. By weighted fusion of the comprehensive evaluation coefficients, the preference degree of the real-time time period for each constant current value can be determined, and then the charging current under the current real-time time period can be determined. The present invention realizes the dynamic adjustment of the magnitude of the constant current during the charging process of the electronic device through big data analysis and data collaborative filtering methods, can effectively reduce the temperature rise rate during the charging process, inhibit heat accumulation, and can also avoid the risk of battery overheating and life degradation caused by mismatched current values. On the basis of ensuring the charging efficiency, it improves the charging safety and extends the battery cycle life. Description of the Drawings

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 Flowchart of a charging control method for an electronic device provided by an embodiment of the present invention; Figure 2 Schematic diagram of the relationship between the length of a time period and the state of charge provided by an embodiment of the present invention; Figure 3 Schematic diagram of the comprehensive rating coefficient provided by an embodiment of the present invention. Detailed implementation manners

[0016] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a charging control method and system for an electronic device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0018] The following specifically describes the specific solutions of a charging control method and system for an electronic device provided by the present invention in conjunction with the drawings.

[0019] Please refer to Figure 1 , which shows a flowchart of a charging control method for an electronic device provided by an embodiment of the present invention. The method includes: Step S1: Statistically analyze the charging information in each historical charging process of the battery of the electronic device during the constant current charging process in the historical database.

[0020] During the charging process of existing electronic devices, the battery management unit can collect the charging information of the battery in real time during each charging process. In the embodiments of the present invention, the charging information at least includes temperature, internal resistance, and state of charge, and may also include voltage, current, charging time node, ambient temperature, charge amount, etc. These charging information can be recorded in the local storage device or cloud database of the device in chronological order to form a historical database. In the embodiments of the present invention, during a new charging process, based on the charging information reflected in the historical charging process under constant current charging, a statistical analysis method is used to determine the charging current during the real-time charging process.

[0021] In the embodiments of the present invention, the charging information collection frequency can be set to once per minute.

[0022] Step S2: Divide each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information; for each historical time period, obtain the comprehensive evaluation coefficient of each historical time period according to the temperature and state of charge changes of the battery within the historical time period.

[0023] In the traditional constant current-constant voltage (CC-CV) charging mode, the initial stage is usually set to the maximum allowable charging current to improve the charging efficiency. However, as the charging process progresses, the internal impedance of the battery gradually increases, resulting in an increase in the heat accumulation per unit time. Excessive temperature rise not only affects charging safety but also may accelerate battery aging and reduce the cycle life. Therefore, the embodiments of the present invention aim to propose an improved strategy based on the traditional CC-CV mode. In the later stage of the constant current stage, when it is detected that the battery heating trend is significantly enhanced, the constant current is dynamically reduced to suppress the heat generation. While maintaining the charging efficiency as much as possible, the temperature rise is effectively reduced, thereby improving the safety of the charging process and extending the battery service life. Considering that during the constant current charging stage, the battery terminal voltage gradually increases with charging, and the internal temperature and internal resistance of the battery do not change linearly but have obvious stage characteristics. Especially in the middle and later stages, the internal resistance increases rapidly and the heating speed increases significantly. If only the entire constant current stage is used as an overall analysis unit, it is easy to mask the abnormal trends at key time points and affect the accuracy of the control strategy. Therefore, when performing statistical analysis on the historical charging process in the embodiments of the present invention, it is first necessary to divide each historical charging process into multiple historical time periods based on the fluctuations and changes of the charging information, that is, each historical time period can be regarded as a charging stage. Through the fluctuations and changes of multi-dimensional charging information, the charging state of the historical charging process can be effectively divided, ensuring that the battery state within a historical time period is relatively unified, and thus can be used for subsequent collaborative comparison analysis.

[0024] Preferably, in an embodiment of the present invention, considering that the charging state of the battery has a relatively close relationship with the SoC (state of charge), and the state of charge can be regarded as the battery power. In the medium and high SoC stages, the temperature rise will be more sensitive and intense. Therefore, the embodiment of the present invention initially segments based on the level of the state of charge, and further analyzes the fluctuations and changes of other charging information based on the initial segmentation result, specifically including: Set a preset time period length according to the range of the state of charge, and the time period length has a negative correlation with the size of the state of charge. That is, in the interval with a higher state of charge, the corresponding time period length is smaller. Please refer to Figure 2 , which shows a schematic diagram of the relationship between the size of the time period length and the size of the state of charge provided by an embodiment of the present invention. In the interval of 0% - 50% of the state of charge, the time period length is one segment every 5 minutes; in the interval of 50% - 80%, the time period length is one segment every 3 minutes; in the interval of 80% - 95%, the time period length is one segment every 1 minute; in the interval of 95% - 100%, the time period length is one segment every 30s. That is, when the state of charge is low, the heat generation is less and the change of the state of charge is slow, which is suitable for coarse-grained monitoring. As the state of charge increases, the time period length after division needs to be gradually reduced.

[0025] Based on the size of the time period, perform an initial time period division on each historical charging process to obtain multiple initial historical time periods in each historical charging process.

[0026] Further considering the changes in other dimensions of charging information, among the charging information, temperature and internal resistance are relatively intuitive information that can characterize the charging state. If there is an obvious fluctuation change in the battery temperature or battery internal resistance at a certain time point, it means that this time point is a heat generation critical point, and the battery charging stage enters the next stage after this time point. Therefore, in each historical charging process, according to the changes in temperature and internal resistance at each moment, obtain the battery fluctuation degree at each moment, filter out the segmentation points based on the battery fluctuation degree, and segment the initial historical time periods based on the segmentation points to obtain all the historical time periods of each historical charging process.

[0027] Further, in an embodiment of the present invention, the method for obtaining the battery fluctuation degree includes: For each moment, search forward and backward according to the preset search length respectively to obtain the forward analysis range and the backward analysis range of each moment. In the embodiment of the present invention, the search length is set to 5, that is, with each moment as the center, the 5 time points forward form the forward analysis range, and the 5 time points backward form the backward analysis range.

[0028] For any dimension between temperature and internal resistance, the mean of the first-order differences in each analysis range under the dimension is used as the change characteristic of the dimension in each analysis range; the difference in the change characteristics between the backward analysis range and the forward analysis range is normalized to obtain the initial fluctuation degree under the dimension. The greater the initial fluctuation degree, it indicates that during the historical charging process, obvious temperature or internal resistance changes occurred after this moment, so this moment is more likely to be used as the segmentation point moment.

[0029] Between the two dimensions of temperature and internal resistance, the maximum initial fluctuation degree is selected as the battery fluctuation degree at each moment.

[0030] It should be noted that the normalization process in the embodiments of the present invention can use range normalization. Set the battery fluctuation degree threshold to 0.85. If the battery fluctuation degree is greater than the battery fluctuation degree threshold, then it is determined that this moment is the segmentation point.

[0031] After obtaining each historical time period in the historical charging process, each time period can be regarded as a charging stage. Therefore, the charging information within each historical time period can be analyzed to determine the charging efficiency of the current constant current within the historical time period, and further the charging efficiency corresponding to the constant current value in this charging stage can be quantified. The battery charging temperature is an intuitive data affecting the battery life. Therefore, in the embodiments of the present invention, the comprehensive evaluation coefficient of each historical time period is obtained according to the temperature and state of charge change of the battery within the historical time period. That is, the more normal the battery temperature is and the greater the change in the state of charge, it indicates that more electricity has been charged to the battery during this historical time period, and the battery temperature remains within a safe temperature range. Then, the charging efficiency is high and the safety is also high at this time, so the comprehensive evaluation coefficient is larger.

[0032] Preferably, in the embodiments of the present invention, the method for obtaining the comprehensive evaluation coefficient includes: Obtain the temperature evaluation coefficient according to the relationship between the mean battery temperature within the historical time period and the preset safe temperature range. That is, the closer the mean battery temperature is to the safe temperature range, the greater the temperature evaluation coefficient.

[0033] Obtain the state of charge growth rate within the historical time period; the product of the state of charge growth rate and the temperature evaluation coefficient is used as the comprehensive evaluation coefficient. That is, the greater the state of charge growth rate, it indicates that the charging efficiency is greater, and at the same time, the greater the temperature evaluation coefficient, it indicates that the charging is safer, so the comprehensive evaluation coefficient is larger.

[0034] In the embodiments of the present invention, in order to more intuitively quantify the comprehensive evaluation coefficient, after calculating the comprehensive evaluation coefficient, it is also necessary to normalize it.

[0035] It should be noted that the state of charge growth rate is the ratio of the state of charge at the end of the historical time period minus the state of charge at the start time to the length of the historical time period.

[0036] Furthermore, the method for obtaining the temperature evaluation coefficient includes: If the average battery temperature is less than the lower limit of the safe temperature range, then the difference between the lower limit of the safe temperature range and the average battery temperature is negatively correlated and mapped to obtain the temperature evaluation coefficient; If the average battery temperature is within the safe temperature range, then the temperature evaluation coefficient is assigned the positive integer 1; If the average battery temperature is greater than the upper limit of the safe temperature range, then the difference between the average battery temperature and the upper limit of the safe temperature range is negatively correlated and mapped to obtain the temperature evaluation coefficient.

[0037] It should be noted that in the embodiments of the present invention, the negative correlation mapping is in the form of a reciprocal.

[0038] Please refer to Figure 3 , which shows the schematic diagram of the comprehensive rating coefficient provided by an embodiment of the present invention. It should be noted that Figure 3 the abscissa in is the charging time period number, that is, the number of the historical time period. Since the charging processes with multiple different constant current values are integrated and displayed, the numbering result is the combined result of different historical charging processes, resulting in no scoring results for some historical charging processes under certain numbers, that is, the 0.00 points shown in the table.

[0039] Step S3: Determine the real-time time period corresponding to the real-time moment in the real-time charging process; obtain the time period similarity weight according to the difference in charging information between the real-time time period and each historical time period; for each constant current value in the constant current charging process, based on the time period similarity weight, weight and fuse the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value to obtain the preference degree of each constant current value for the real-time time period; determine the charging current corresponding to the real-time time period according to the preference degree.

[0040] After the data quantization of the historical charging process is completed, the real-time time period in the real-time charging process can be used to perform collaborative comparison and analysis on each historical time period. It should be noted that the division method of the real-time time period is the same as that of the historical time period. The time sequence range from the real-time moment to the start time is used as the local charging process, and the division rule of the historical time period can be used to determine the real-time time period corresponding to the real-time moment.

[0041] Embodiments of the present invention are based on the idea of collaborative filtering. By analyzing the time period similarity weight between the real-time time period and the historical time period, the comprehensive evaluation coefficients of the historical time period under each constant current value are weighted and fused. The preference degree of each constant current value for the real-time time period can be obtained. According to the preference degree, the charging current corresponding to the real-time time period at the real time can be determined.

[0042] Preferably, in the embodiments of the present invention, the method for obtaining the time period similarity weight includes: Taking temperature, internal resistance, state of charge, and battery health state as the dimensions to be analyzed respectively, obtaining the DTW distance of the data sequences of the dimensions to be analyzed between the real-time time period and the historical time period, and taking the ratio of the DTW distance to the number of matching data pairs in the DTW matching process as the dimension difference under the dimension to be analyzed.

[0043] After accumulating the dimension differences of all dimensions to be analyzed, perform a negative correlation mapping to obtain the time period similarity weight. In the embodiments of the present invention, the negative correlation mapping of the accumulated value of the dimension differences can also adopt the reciprocal form, which will not be elaborated here.

[0044] In the embodiments of the present invention, the method of weighted fusion is weighted averaging. That is, for each historical time period, multiply its corresponding time period similarity weight by the comprehensive evaluation coefficient, and then obtain the average value of the multiplication results of all historical time periods.

[0045] In one embodiment of the present invention, the constant current value with the largest preference degree can be directly used as the corresponding charging current.

[0046] Additionally, in other embodiments of the present invention, considering that the resistance values are different at different times, in order to prevent the charging current from being too large and causing the voltage to exceed the maximum voltage, the ratio of the preset maximum battery voltage to the internal resistance of the battery at the real time is used as the maximum charging current; between the maximum charging current and the constant current value with the largest preference degree, the minimum current value is selected as the charging current.

[0047] In summary, the present invention uses big data analysis methods to count each historical charging process in the historical database during the constant current charging process. For the historical charging process, it can be divided into individual historical time periods through time period division, and then the comprehensive evaluation coefficient of each historical time period at the current constant current value is analyzed. For the real-time charging process, the time period similarity weight of each historical time period for the real-time time period can be determined through time period matching. By weighted fusion of the comprehensive evaluation coefficients, the preference degree of the real-time time period for each constant current value can be determined, and then the charging current at the current real-time time period can be determined. The present invention realizes the dynamic adjustment of the magnitude of the constant current during the charging process of the electronic device through big data analysis and data collaborative filtering, can effectively reduce the temperature rise rate during the charging process, suppress heat accumulation, and can also avoid the risk of battery overheating and life degradation caused by mismatched current values. On the basis of ensuring the charging efficiency, the charging safety is improved and the battery cycle life is extended.

[0048] The present invention also provides a charging control system for an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the charging control methods for an electronic device are implemented.

[0049] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A charging control method for an electronic device, characterized in that, The method includes: Counting the charging information in each historical charging process of the electronic device battery during constant current charging in the historical database, where the charging information at least includes temperature, internal resistance, and state of charge; Dividing each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information; for each historical time period, obtaining a comprehensive evaluation coefficient for each historical time period according to the temperature and the change of the state of charge of the battery within the historical time period; Determining the real-time time period corresponding to the real-time moment during the real-time charging process; obtaining the time period similarity weight according to the difference in charging information between the real-time time period and each historical time period; for each constant current value during constant current charging, based on the time period similarity weight, weighted fusion of the comprehensive evaluation coefficients of all historical time periods corresponding to the constant current value is performed to obtain the preference degree of each constant current value for the real-time time period; determining the charging current corresponding to the real-time time period according to the preference degree.

2. The charging control method of an electronic device according to claim 1, characterized in that, The dividing of each historical charging process into multiple historical time periods according to the fluctuations and changes of the charging information includes: Presetting the time period length according to the range of the state of charge, where the time period length has a negative correlation with the size of the state of charge, and based on the size of the time period, initial time period division is performed on each historical charging process to obtain multiple initial historical time periods in each historical charging process; In each historical charging process, according to the changes in temperature and internal resistance at each moment, the battery fluctuation degree at each moment is obtained, segment points are selected based on the battery fluctuation degree, and the initial historical time periods are segmented based on the segment points to obtain all historical time periods of each historical charging process.

3. The charging control method of an electronic device according to claim 2, wherein The method for obtaining the battery fluctuation degree includes: For each moment, search forward and backward according to a preset search length respectively to obtain the forward analysis range and the backward analysis range of each moment; For any one dimension between temperature and internal resistance, taking the mean value of the first-order differences within each analysis range in this dimension as the change feature of this dimension within each analysis range; normalizing the difference between the change features between the backward analysis range and the forward analysis range to obtain the initial fluctuation degree in this dimension; Between the two dimensions of temperature and internal resistance, selecting the maximum initial fluctuation degree as the battery fluctuation degree at each moment.

4. The charging control method of an electronic device according to claim 1, characterized in that, The method for obtaining the comprehensive evaluation coefficient includes: Obtaining a temperature evaluation coefficient according to the relationship between the mean value of the battery temperature within the historical time period and the preset safe temperature range; Obtaining the growth rate of the state of charge within the historical time period; taking the product of the growth rate of the state of charge and the temperature evaluation coefficient as the comprehensive evaluation coefficient.

5. A charging control method for an electronic device according to claim 4, characterized in that, The method for obtaining the temperature evaluation coefficient includes: If the mean value of the battery temperature is less than the lower limit of the safe temperature range, performing a negative correlation mapping on the difference between the lower limit of the safe temperature range and the mean value of the battery temperature to obtain the temperature evaluation coefficient; If the mean value of the battery temperature is within the safe temperature range, assigning the temperature evaluation coefficient as the positive integer 1; If the average battery temperature is greater than the upper limit of the safe temperature range, the difference between the average battery temperature and the upper limit of the safe temperature range is negatively correlated and mapped to obtain a temperature evaluation coefficient.

6. The charging control method of an electronic device according to claim 1, wherein The method for obtaining the similarity weight of the time period includes: Taking temperature, internal resistance, state of charge, and battery health state as the dimensions to be analyzed respectively, obtaining the DTW distance of the data sequences of the dimensions to be analyzed between the real-time time period and the historical time period, and taking the ratio of the DTW distance to the number of matching data pairs in the DTW matching process as the dimension difference under the dimension to be analyzed; After accumulating the dimension differences of all dimensions to be analyzed, perform negative correlation mapping to obtain the similarity weight of the time period.

7. The charging control method of an electronic device according to claim 1, characterized in that The method of weighted fusion is weighted averaging.

8. The charging control method of an electronic device according to claim 1, wherein Determining the charging current corresponding to the real-time time period according to the preference degree includes: Selecting the constant current value with the largest preference degree as the corresponding charging current.

9. A charging control method for an electronic device according to claim 1, characterized in that Determining the charging current corresponding to the real-time time period according to the preference degree further includes: Taking the ratio of the preset maximum battery voltage to the internal resistance of the battery at the real-time moment as the maximum charging current; between the maximum charging current and the constant current value with the largest preference degree, selecting the minimum current value as the charging current.

10. A charging control system for an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the charging control method of an electronic device according to any one of claims 1 to 9.

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