Intelligent monitoring method and system for battery operation data based on cloud computing

Abstract

The present invention relates to the field of data supervision technology, and specifically to a method and system for intelligent supervision of battery operation data based on cloud computing. The supervision method comprises the following steps: obtaining the continuous use frequency and actual power of each battery in history, calculating the health factor of each battery, and estimating the remaining life of each battery; obtaining the power consumption of a user's power consumption record according to the user's historical battery usage each time; classifying all power consumption records into levels, and predicting the user's next predicted power consumption; obtaining the most recent continuous unused times and the most recent continuous used times of each selectable battery, and calculating the priority value of each selectable battery; when the predicted power consumption is greater than the real-time power of each battery, calculating the remaining demand after the user selects a battery; analyzing whether there is an abnormality when the battery is charged; and calculating the new priority value of each battery according to the remaining demand and the usage of the remaining batteries.

Description

Technical Field

[0001] The present invention relates to the technical field of data supervision, and in particular to a method and system for intelligent supervision of battery operation data based on cloud computing. Background Art

[0002] The cycle life of the battery will be affected by the number and frequency of charge and discharge. Too frequent charge and discharge will seriously affect the battery's service life. Occasionally putting the battery aside for a while can help slow down this loss, but too long of an idle time will also cause the battery to over-discharge, which will also affect the battery's service life. Because different batteries have different charging data and charging rates, users always give priority to faster batteries when choosing batteries and will not choose slower batteries, which will affect the service life of both batteries. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for intelligent monitoring of battery operation data based on cloud computing to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a battery operation data intelligent supervision method based on cloud computing, the supervision method comprising the following steps:

[0005] Step S100: setting a charge and discharge process of the battery as a use record of the battery; obtaining the continuous use frequency and actual power of each battery according to the historical use record of each battery, and calculating the health factor of each battery; estimating the remaining life of each battery according to the average power and average charging time when the battery sends an abnormal reminder in the historical use record;

[0006] Step S200: The process from when the user starts to use the battery to when the user stops using the battery is set as a power consumption record of the user; the power consumption of the user's power consumption record is obtained according to the battery consumption of each time in the user's history and the time interval between two adjacent battery uses; all power consumption records are classified into levels according to the power consumption, and according to the number of power consumption records in each level, the level to which the user's next power consumption belongs is predicted, and the predicted power consumption of the user's next time is calculated;

[0007] Step S300: Compare the predicted power consumption with the real-time power consumption of each battery to select selectable batteries; obtain the number of times each selectable battery has been continuously unused and the number of times each selectable battery has been continuously used, calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery;

[0008] Step S400: When the predicted power consumption is greater than the real-time power of each battery, calculate the remaining demand after the user selects a battery; according to the discharge time after the user selects a battery, obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time, and analyze whether there is an abnormality during battery charging; calculate the new priority value of each battery according to the remaining demand and the usage of the remaining batteries.

[0009] Furthermore, step S100 includes the following steps:

[0010] Step S101: Obtain the history of all battery usage records and set the Batteries The usage record is ; Record battery The number of times the record is used is , select any one of the primary batteries The usage record of the battery Is the last usage record of the battery If it is not a battery, If the battery is Use the record as a target record; get the battery The number of times the usage record is used as the target record , calculate the battery Continuous use frequency ; When the battery in two consecutive usage records is the same, it means that the same battery has been used continuously. Continuous use of the battery will affect the battery life. Calculating the continuous use frequency is conducive to the subsequent prediction of the remaining life of the battery;

[0011] Step S102: Obtaining a battery The actual power after the most recent charge is , according to the formula: ,in, is the theoretical capacity of the battery, and the battery is calculated The degree of life decay ; Set battery The health factor is , according to the formula:

[0012]

[0013] Calculate the battery Health Factors ; The higher the degree of life decay, the lower the health factor of the battery; the higher the frequency of continuous use, the lower the health factor of the battery; and the health factor is also between 0 and 1;

[0014] Step S103: Obtain the actual power and charging time of each battery when it sends an abnormal reminder in history, where The actual battery capacity for sending abnormal reminders is , charging time is , calculate the average battery power when the battery sends an abnormal reminder and average charging time ,in, The number of batteries that send abnormal reminders; according to the formula:

[0015]

[0016] Calculate the first Batteries Remaining life .

[0017] Further, step S200 includes the following steps:

[0018] Step S201: Get the user's history The battery usage after using the battery for the first time is , set the The battery used is , and the actual power is ;when When extracting the user Use the battery from the first The time interval between battery use , set a continuous use time difference of ,like , then get the Battery usage after using the battery , the user First use of battery and The battery usage is combined into one electricity consumption record of the user and the electricity consumption of the electricity consumption record is ; Sometimes the user's power consumption exceeds the power of one battery, so it is necessary to determine the interval between two battery usage records. If the interval is short, it means that the user has used the battery continuously, and the total discharge of the two batteries is the user's power consumption at one time;

[0019] Step S202: Obtain the power consumption of each power consumption record of the user, and set the The electricity consumption recorded is ; Divide the electricity consumption recorded into level, The power consumption threshold for each level is ,in, is the total number of electricity consumption records of historical users, then the power consumption range of the pth level is ;

[0020] Step S203: Set the The number of electricity consumption records at each level is ; Get the user's most recent The electricity consumption recorded in the electricity consumption records, among which, The first electricity consumption record The number of electricity consumption records at each level is , according to the formula:

[0021]

[0022] Calculate the probability that the user's next electricity consumption record belongs to the pth level ; Based on the proportion of the number of times the user has used electricity at each level in history, an overall proportion can be determined. The approximate number of times the electricity has been used at each level in recent times can be inferred. Then, the remaining number can be obtained by subtracting the number of times each level has been recorded, which is equivalent to the probability of each level.

[0023] Step S204: Set the probability that the power consumption belongs to the pth level to be the largest, and obtain the pth level. The power consumption of each power consumption record in each level is calculated according to the formula:

[0024]

[0025] in, For the The first level The electricity consumption of the electricity consumption record is calculated to obtain the user's next predicted electricity consumption. .

[0026] Further, step S300 includes the following steps:

[0027] Step S301: Set the user's next predicted power consumption to , read the real-time power of each battery, among which, The real-time power of the battery is ,like , then the As optional batteries; when there is no battery whose real-time power consumption is greater than the predicted power consumption, all batteries are regarded as optional batteries;

[0028] Step S302: Set the The battery is optional. Select the battery that the user used most recently. The power consumption record of the battery is obtained The last consecutive number of times the battery has not been used is ; to use the The battery power consumption record is used as the base point to obtain the The number of times the battery has been used continuously recently ; According to the formula:

[0029]

[0030] in, For the The charging time required to fully charge a battery. For the Remaining battery life , For the The number of times a battery is used is calculated. The priority value of each battery i ; Sort each optional battery according to the priority value from large to small as the selection priority order of the optional batteries;

[0031] The charging time and remaining life required to complete battery charging are both parameters. The larger the value, the healthier the battery is, and the higher the priority will be. The number of times the battery has not been used recently indicates that the battery has not been used for a long time. In order to avoid being left idle for too long, the priority should be increased. The number of times the battery has been used recently indicates that the battery has been used continuously for many times recently. Multiple uses will affect the life of the battery, and the priority should be lowered. At the same time, the number of times the battery has been used recently should be based on the number of times the battery has not been used recently, because the longer the battery has not been used, the lower the impact of previous continuous use will be. Therefore, it is more reasonable to use the number of times the battery has been used recently as an index of the number of times the battery has not been used recently. The setting of these parameters ensures that the final priority value is more accurate.

[0032] Furthermore, step S400 includes the following steps:

[0033] Step S401: Set the user to select the Batteries, and The real-time power of the battery is , get the Battery life ,in, For the The discharge rate of the battery; the predicted power consumption is , the user's remaining demand is ;

[0034] Step S402: Get the The remaining batteries The charging rate of a battery is , get the After the battery is discharged The theoretical charge capacity of a battery is ; Set the The real-time power of the battery is , No. The actual capacity of the battery after the last charge is ,like , then After the battery is discharged Theoretical capacity of a battery ,like , then Theoretical capacity of a battery ;

[0035] Step S403: Get the After the battery is discharged The actual battery capacity ,when When Each battery sends an abnormal reminder;

[0036] Step S404: Get the The number of times the battery has not been used in a row and the number of recent consecutive uses , according to the formula:

[0037]

[0038] Among them, δ h For the The priority value of each battery, For the The number of times a battery is used is calculated. After the first battery is discharged, New priority value for each battery .

[0039] In order to better implement the above method, a battery operation data intelligent supervision system is also proposed. The supervision system includes a life prediction module, a power consumption prediction module, a priority setting module and an adjustment module;

[0040] The life prediction module is used to set a battery charge and discharge process as a battery usage record; obtain the continuous use frequency and actual power of each battery based on the historical usage record of each battery, and calculate the health factor of each battery; estimate the remaining life of each battery based on the average power and average charging time when the battery sends an abnormal reminder in the historical usage record;

[0041] The power consumption prediction module is used to set the process from the user starting to use the battery to the user stopping using the battery as a power consumption record of the user; obtain the power consumption of the user's power consumption record according to the user's historical battery usage and the time interval between two adjacent battery uses; classify all power consumption records according to the power consumption, predict the level of the user's next power consumption according to the number of power consumption records in each level, and calculate the user's next predicted power consumption;

[0042] The priority setting module is used to compare the predicted power consumption with the real-time power consumption of each battery to select the selectable batteries; obtain the latest consecutive unused times and the latest consecutive used times of each selectable battery to calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery;

[0043] The adjustment module is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time according to the discharge time after the user selects a battery, and analyze whether there is an abnormality during battery charging; calculate the new priority value of each battery according to the remaining demand and the usage of each remaining battery.

[0044] Furthermore, the life prediction module includes a health factor unit and a life calculation unit;

[0045] The health factor unit is used to obtain the continuous usage frequency and actual power of each battery based on the historical usage records of each battery, and calculate the health factor of each battery; the life calculation unit is used to estimate the remaining life of each battery based on the average power and average charging time when the battery sends abnormal reminders in the historical usage records.

[0046] Further, the power consumption prediction module includes a power consumption calculation unit and a level prediction unit;

[0047] The power consumption calculation unit is used to obtain the power consumption of the user's power consumption record based on the user's historical battery usage and the time interval between two adjacent battery uses; the level prediction unit is used to classify all power consumption records according to the power consumption, predict the level of the user's next power consumption according to the number of power consumption records in each level, and calculate the user's next predicted power consumption.

[0048] Further, the priority setting module includes a screening unit and a priority value calculation unit;

[0049] The screening unit is used to compare the predicted power consumption with the real-time power consumption of each battery to screen out the selectable batteries; the priority value calculation unit is used to obtain the most recent consecutive unused times and the most recent consecutive used times of each selectable battery to calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery.

[0050] Further, the adjustment module includes an abnormality determination unit and a new priority value setting unit;

[0051] The abnormality judgment unit is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time according to the discharge time after the user selects a battery, and analyze whether there is an abnormality during battery charging; the new priority value setting unit is used to calculate the new priority value of each battery according to the remaining demand and the usage of each remaining battery.

[0052] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention takes into account both the reduction in battery life caused by too frequent use and the natural discharge of the battery caused by long-term non-use, which affects the battery life, so that the battery can be used in a timely but infrequent manner, thereby effectively improving the battery life; (2) The present invention accurately predicts the remaining life of each battery, thereby facilitating the consideration of the battery life issue when subsequently prioritizing the batteries, reasonably setting the priority order of batteries with different lifespans, and thus increasing the battery life to a certain extent; (3) The present invention predicts the user's future power consumption based on the user's historical power consumption, thereby providing the most suitable battery, reasonably arranging the battery usage frequency, and increasing the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a schematic diagram of the steps of the intelligent supervision method of battery operation data based on cloud computing;

[0055] Figure 2 It is a structural diagram of the battery operation data intelligent supervision system based on cloud computing. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1 to Figure 2 The present invention provides a technical solution: a battery operation data intelligent supervision method based on cloud computing, the supervision method comprises the following steps:

[0058] Step S100: setting a charge and discharge process of the battery as a use record of the battery; obtaining the continuous use frequency and actual power of each battery according to the historical use record of each battery, and calculating the health factor of each battery; estimating the remaining life of each battery according to the average power and average charging time when the battery sends an abnormal reminder in the historical use record;

[0059] Wherein, step S100 includes the following steps:

[0060] Step S101: Obtain the history of all battery usage records and set the Batteries The usage record is ; Record battery The number of times the record is used is , select any one of the primary batteries Use records to determine the battery Is the last usage record of the battery If it is not a battery, If the battery usage record is Use the record as a target record; get the battery The number of times the usage record is used as the target record , calculate the battery Continuous use frequency ;

[0061] Step S102: Obtaining a battery The actual power after the most recent charge is , according to the formula: ,in, is the theoretical capacity of the battery, and the battery is calculated The degree of life decay ; Set battery The health factor is , according to the formula:

[0062]

[0063] Calculate the battery Health Factors ;

[0064] Step S103: Obtain the actual power and charging time of each battery when it sends an abnormal reminder in history, where The actual battery capacity for sending abnormal reminders is , charging time is , calculate the average battery power when the battery sends an abnormal reminder and average charging time ,in, The number of batteries that send abnormal reminders; according to the formula:

[0065]

[0066] Calculate the first Batteries Remaining life .

[0067] Step S200: The process from when the user starts to use the battery to when the user stops using the battery is set as a power consumption record of the user; the power consumption of the user's power consumption record is obtained according to the battery consumption of each time in the user's history and the time interval between two adjacent battery uses; all power consumption records are classified into levels according to the power consumption, and according to the number of power consumption records in each level, the level to which the user's next power consumption belongs is predicted, and the predicted power consumption of the user's next time is calculated;

[0068] Wherein, step S200 includes the following steps:

[0069] Step S201: Get the user's history The battery usage after using the battery for the first time is , set the The battery used is , and the actual power is ;when When extracting the user Use the battery from the first The time interval between battery use , set a continuous use time difference of ,like , then get the Battery usage after using the battery , the user First use of battery and The battery usage is combined into one electricity consumption record of the user and the electricity consumption of the electricity consumption record is ;

[0070] Step S202: Obtain the power consumption of each power consumption record of the user, and set the The electricity consumption recorded is ; Divide the electricity consumption recorded into level, The power consumption threshold for each level is ,in, is the total number of electricity consumption records of historical users, then the power consumption range of the pth level is ;

[0071] Step S203: Set the The number of electricity consumption records at each level is ; Get the user's most recent The electricity consumption recorded in the electricity consumption records, among which, The first electricity consumption record The number of electricity consumption records at each level is , according to the formula:

[0072]

[0073] Calculate the probability that the user's next electricity consumption record belongs to the pth level ;

[0074] Step S204: Set the probability that the power consumption belongs to the pth level to be the largest, and obtain the pth level. The power consumption of each power consumption record in each level is calculated according to the formula:

[0075]

[0076] in, For the The first level The electricity consumption of the electricity consumption record is calculated to obtain the user's next predicted electricity consumption. .

[0077] Step S300: Compare the predicted power consumption with the real-time power consumption of each battery to select selectable batteries; obtain the number of times each selectable battery has been continuously unused and the number of times each selectable battery has been continuously used, calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery;

[0078] Wherein, step S300 includes the following steps:

[0079] Step S301: Set the user's next predicted power consumption to , read the real-time power of each battery, among which, The real-time power of the battery is ,like , then the As optional batteries; when there is no battery whose real-time power consumption is greater than the predicted power consumption, all batteries are regarded as optional batteries;

[0080] Step S302: Set the The battery is optional. Select the battery that the user used most recently. The power consumption record of the battery is obtained The last consecutive number of times the battery has not been used is ; to use the The battery power consumption record is used as the base point to obtain the The number of times the battery has been used continuously recently ; According to the formula:

[0081]

[0082] in, For the The charging time required to fully charge a battery. For the Remaining battery life , For the The number of times a battery is used is calculated. The priority value of each battery i ; Sort each optional battery according to the priority value from large to small as the selection priority order of the optional batteries.

[0083] Step S400: when the predicted power consumption is greater than the real-time power of each battery, the remaining demand after the user selects a battery is calculated; according to the discharge time after the user selects a battery, the theoretical charge capacity and actual power of the remaining batteries after the discharge time are obtained, and whether there is an abnormality during battery charging is analyzed; according to the remaining demand and the usage of the remaining batteries, the new priority value of each battery is calculated;

[0084] Wherein, step S400 includes the following steps:

[0085] Step S401: Set the user to select the Batteries, and The real-time power of the battery is , get the Battery life ,in, For the The discharge rate of the battery; the predicted power consumption is , the user's remaining demand is ;

[0086] Step S402: Get the The remaining batteries The charging rate of a battery is , get the After the battery is discharged The theoretical charge capacity of a battery is ; Set the The real-time power of the battery is , No. The actual capacity of the battery after the last charge is ,like , then After the battery is discharged Theoretical capacity of a battery ,like , then Theoretical capacity of a battery ;

[0087] Step S403: Get the After the battery is discharged The actual battery capacity ,when When Each battery sends an abnormal reminder;

[0088] Step S404: Get the The number of times the battery has not been used in a row and the number of recent consecutive uses , according to the formula:

[0089]

[0090] Among them, δ h For the The priority value of each battery, For the The number of times a battery is used is calculated. After the first battery is discharged, New priority value for each battery .

[0091] A cloud computing-based intelligent monitoring system for battery operation data, which includes a life prediction module, a power consumption prediction module, a priority setting module, and an adjustment module;

[0092] The life prediction module is used to set a battery charge and discharge process as a battery usage record; obtain the continuous use frequency and actual power of each battery based on the historical usage record of each battery, and calculate the health factor of each battery; estimate the remaining life of each battery based on the average power and average charging time when the battery sends an abnormal reminder in the historical usage record;

[0093] The power consumption prediction module is used to set the process from the user starting to use the battery to the user stopping using the battery as a power consumption record of the user; obtain the power consumption of the user's power consumption record according to the user's historical battery usage and the time interval between two adjacent battery uses; classify all power consumption records according to the power consumption, predict the level of the user's next power consumption according to the number of power consumption records in each level, and calculate the user's next predicted power consumption;

[0094] The priority setting module is used to compare the predicted power consumption with the real-time power consumption of each battery to select the selectable batteries; obtain the latest consecutive unused times and the latest consecutive used times of each selectable battery to calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery;

[0095] The adjustment module is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time according to the discharge time after the user selects a battery, and analyze whether there is an abnormality during battery charging; calculate the new priority value of each battery according to the remaining demand and the usage of each remaining battery.

[0096] Among them, the life prediction module includes a health factor unit and a life calculation unit;

[0097] The health factor unit is used to obtain the continuous usage frequency and actual power of each battery based on the historical usage records of each battery, and calculate the health factor of each battery; the life calculation unit is used to estimate the remaining life of each battery based on the average power and average charging time when the battery sends abnormal reminders in the historical usage records.

[0098] Among them, the power consumption prediction module includes a power consumption calculation unit and a level prediction unit;

[0099] The power consumption calculation unit is used to obtain the power consumption of the user's power consumption record based on the user's historical battery usage and the time interval between two adjacent battery uses; the level prediction unit is used to classify all power consumption records according to the power consumption, predict the level of the user's next power consumption according to the number of power consumption records in each level, and calculate the user's next predicted power consumption.

[0100] Among them, the priority setting module includes a screening unit and a priority value calculation unit;

[0101] The screening unit is used to compare the predicted power consumption with the real-time power consumption of each battery to screen out the selectable batteries; the priority value calculation unit is used to obtain the most recent consecutive unused times and the most recent consecutive used times of each selectable battery to calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery.

[0102] Among them, the adjustment module includes an abnormality judgment unit and a new priority value setting unit;

[0103] The abnormality judgment unit is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time according to the discharge time after the user selects a battery, and analyze whether there is an abnormality during battery charging; the new priority value setting unit is used to calculate the new priority value of each battery according to the remaining demand and the usage of each remaining battery.

[0104] Suppose there are three batteries in total, and the batteries have been used 10 times in history, with the order of use being: 1233211223. The first battery has been used 3 times, and the target record is 2 times; the second battery has been used 4 times, and the target record is 3 times; the third battery has been used 3 times, and the target record is 2 times; therefore, the continuous use frequencies of the three batteries are 66.7%, 75%, and 66.7% respectively;

[0105] Get the actual power of the first battery after the last charge is 98, the theoretical capacity of the battery is 100, and get the degree of life attenuation ; According to the formula: The health factor of the first battery is 55.3%. Based on the actual power and charging time of each battery when it sends an abnormal reminder, the average power of the battery when it sends an abnormal reminder is calculated. , average charging time , according to the formula: , the remaining life of the first battery is calculated to be 966.44h; similarly, the actual power of the second and third batteries is 98, so the remaining life of the second battery is 1152h, and the remaining life of the third battery is 966.44h;

[0106] Get the user's 10 historical electricity usage records, which can be divided into 3 levels, of which 5 times are in level 1, 4 times are in level 2, and 1 time is in level 3; get the user's most recent 3 electricity usage records, of which 2 times are level 1 and 1 time is level 2; according to the formula:

[0107]

[0108] The probability of getting level 1 is -0.5, the probability of level 2 is 0.2, and the probability of level 3 is 0.3. Therefore, it is predicted that the user's next power consumption will belong to level 3. The historical power consumption of level 3 is 95, so the user's predicted power consumption is 95.

[0109] Since the power of the three batteries is 98, all three batteries are optional; because the order of battery use is: 1233211223; according to the formula: ,in, , , , , , , so the priority value for the first battery is Similarly, the priority values ​​of the remaining two batteries can be calculated and sorted from large to small according to the priority values, which is the battery selection priority order.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligently monitoring battery operation data based on cloud computing, characterized in that: The supervision method comprises the following steps: Step S100: setting a charge and discharge process of the battery as a use record of the battery; obtaining the continuous use frequency and actual power of each battery according to the historical use record of each battery, and calculating the health factor of each battery; estimating the remaining life of each battery according to the average power and average charging time when the battery sends an abnormal reminder in the historical use record; Step S200: The process from when the user starts to use the battery to when the user stops using the battery is set as a power consumption record of the user; the power consumption of the user's power consumption record is obtained according to the battery consumption of each time in the user's history and the time interval between two adjacent battery uses; all power consumption records are classified into levels according to the power consumption, and according to the number of power consumption records in each level, the level to which the user's next power consumption belongs is predicted, and the predicted power consumption of the user's next time is calculated; Step S300: Compare the predicted power consumption with the real-time power consumption of each battery to select selectable batteries; obtain the number of times each selectable battery has been continuously unused and the number of times each selectable battery has been continuously used, calculate the priority value of each selectable battery; and determine the selection priority order of each selectable battery; Step S400: when the predicted power consumption is greater than the real-time power of each battery, the remaining demand after the user selects a battery is calculated; according to the discharge time after the user selects a battery, the theoretical charge capacity and actual power of the remaining batteries after the discharge time are obtained, and whether there is an abnormality during battery charging is analyzed; according to the remaining demand and the usage of the remaining batteries, the new priority value of each battery is calculated; The step S100 includes the following steps: Step S101: Obtain the history of all battery usage records and set the Batteries The usage record is ; Record battery The number of times the record is used is , select any one of the primary batteries The usage record of the battery Is the last usage record of the battery If it is not a battery, If the battery is Use the record as a target record; get the battery The number of times the usage record is used as the target record , calculate the battery Continuous use frequency ; Step S102: Obtaining a battery The actual power after the most recent charge is , according to the formula: ,in, is the theoretical capacity of the battery, and the battery is calculated The degree of life decay ; Set battery The health factor is , according to the formula: ， Calculate the battery Health Factors ; Step S103: Obtain the actual power and charging time of each battery when it sends an abnormal reminder in history, where The actual battery capacity for sending abnormal reminders is , charging time is , calculate the average battery power when the battery sends an abnormal reminder and average charging time ,in, The number of batteries that send abnormal reminders; according to the formula: ， Calculate the first Batteries Remaining life ; The step S300 includes the following steps: Step S301: Set the user's next predicted power consumption to , read the real-time power of each battery, among which, The real-time power of the battery is ,like , then the As optional batteries; when there is no battery whose real-time power consumption is greater than the predicted power consumption, all batteries are regarded as optional batteries; Step S302: Set the The battery is optional. Select the battery that the user used most recently. The power consumption record of the battery is obtained The last consecutive number of times the battery has not been used is ; to use the The battery power consumption record is used as the base point to obtain the The number of times the battery has been used continuously recently ; According to the formula: ， in, For the The charging time required to fully charge a battery. For the Remaining battery life , For the The number of times a battery is used is calculated. The priority value of each battery i ; Sort each optional battery according to the priority value from large to small as the selection priority order of the optional batteries.

2. The method for intelligently monitoring battery operation data based on cloud computing according to claim 1, characterized in that: The step S200 includes the following steps: Step S201: Get the user's history The battery usage after using the battery for the first time is , set the The battery used is , and the actual power is ;when When extracting the user Use the battery from the first The time interval between battery use , set a continuous use time difference of ,like , then get the Battery usage after using the battery , the user First use of battery and The battery usage is combined into one electricity consumption record of the user and the electricity consumption of the electricity consumption record is ; Step S202: Obtain the power consumption of each power consumption record of the user, and set the The electricity consumption recorded is ; Divide the electricity consumption recorded into level, The power consumption threshold for each level is ,in, is the total number of electricity consumption records of historical users, then the power consumption range of the pth level is ; Step S203: Set the The number of electricity consumption records at each level is ; Get the user's most recent The electricity consumption recorded in the electricity consumption records, among which, The first electricity consumption record The number of electricity consumption records at each level is , according to the formula: ， Calculate the probability that the user's next electricity consumption record belongs to the pth level ; Step S204: Set the probability that the power consumption belongs to the pth level to be the largest, and obtain the pth level. The power consumption of each power consumption record in each level is calculated according to the formula: ， in, For the The first level The electricity consumption of the electricity consumption record is calculated to obtain the user's next predicted electricity consumption. .

3. The method for intelligently monitoring battery operation data based on cloud computing according to claim 2 is characterized in that: The step S400 includes the following steps: Step S401: Set the user to select the Batteries, and The real-time power of the battery is , get the Battery life ,in, For the The discharge rate of the battery; the predicted power consumption is , the user's remaining demand is ; Step S402: Get the The remaining batteries The charging rate of a battery is , get the After the battery is discharged The theoretical charge capacity of a battery is ; Set the The real-time power of the battery is , No. The actual capacity of the battery after the last charge is ,like , then After the battery is discharged Theoretical capacity of a battery ,like , then Theoretical capacity of a battery ; Step S403: Get the After the battery is discharged The actual battery capacity ,when When Each battery sends an abnormal reminder; Step S404: Get the The number of times the battery has not been used in a row and the number of recent consecutive uses , according to the formula: ， Among them, δ h For the The priority value of each battery, For the The number of times a battery is used is calculated. After the first battery is discharged, New priority value for each battery .

4. A battery operation data intelligent supervision system applied to the battery operation data intelligent supervision method based on cloud computing according to any one of claims 1 to 3, characterized in that: The supervision system includes a life prediction module, a power consumption prediction module, a priority setting module and an adjustment module; The life prediction module is used to set a charge and discharge process of the battery as a use record of the battery; obtain the continuous use frequency and actual power of each battery according to the historical use record of each battery, and calculate the health factor of each battery; and estimate the remaining life of each battery according to the average power and average charging time when the battery sends an abnormal reminder in the historical use record; The power consumption prediction module is used to set the process from the user starting to use the battery to the user stopping using the battery as a power consumption record of the user; obtain the power consumption of the user's power consumption record according to the user's historical battery usage and the time interval between two adjacent battery uses; classify all power consumption records according to the power consumption, predict the level of the user's next power consumption according to the number of power consumption records in each level, and calculate the user's next predicted power consumption; The priority setting module is used to compare the predicted power consumption with the real-time power consumption of each battery to select the selectable batteries; obtain the latest continuous unused times and the latest continuous used times of each selectable battery, and calculate the priority value of each selectable battery; and determine the selection priority of each optional battery; The adjustment module is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time according to the discharge time after the user selects a battery, and analyze whether there is an abnormality during battery charging; According to the remaining demand and the usage of the remaining batteries, the new priority value of each battery is calculated.

5. The battery operation data intelligent monitoring system according to claim 4 is characterized in that: The life prediction module includes a health factor unit and a life calculation unit; The health factor unit is used to obtain the continuous usage frequency and actual power of each battery according to the historical usage records of each battery, and calculate the health factor of each battery; the life calculation unit is used to estimate the remaining life of each battery according to the average power and average charging time when the battery sends abnormal reminders in the historical usage records.

6. The battery operation data intelligent monitoring system according to claim 4 is characterized in that: The power consumption prediction module includes a power consumption calculation unit and a level prediction unit; The power consumption calculation unit is used to obtain the power consumption of a user's power consumption record based on the user's historical battery usage and the time interval between two adjacent battery uses; the level prediction unit is used to classify all power consumption records according to the power consumption, predict the level to which the user's next power consumption belongs according to the number of power consumption records in each level, and calculate the user's next predicted power consumption.

7. The battery operation data intelligent monitoring system according to claim 4 is characterized in that: The priority setting module includes a screening unit and a priority value calculation unit; The screening unit is used to compare the predicted power consumption with the real-time power consumption of each battery, and screen out selectable batteries; The priority value calculation unit is used to obtain the most recent consecutive unused times and the most recent consecutive used times of each optional battery, and calculate the priority value of each optional battery; And determine the selection priority of each optional battery.

8. The battery operation data intelligent monitoring system according to claim 4 is characterized in that: The adjustment module includes an abnormality judgment unit and a new priority value setting unit; The abnormality judgment unit is used to calculate the remaining demand after the user selects a battery when the predicted power consumption is greater than the real-time power of each battery; according to the discharge time after the user selects a battery, obtain the theoretical charge capacity and actual power of the remaining batteries after the discharge time, and analyze whether there is an abnormality during battery charging; The new priority value setting unit is used to calculate the new priority value of each battery according to the remaining demand and the usage of each other battery.

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