A magnetic charging treasure temperature intelligent monitoring method, system and medium

Abstract

The present application provides a method, system and medium for intelligent temperature monitoring of a magnetic power bank. The method includes: obtaining the heat dissipation performance characteristic data of the magnetic power bank, calculating the heat dissipation performance evaluation index, obtaining the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, extracting the maximum allowable charging power according to the charging protocol information, obtaining the temperature control evaluation power according to the comparison between the maximum allowable charging power and the real-time charging power, and then calculating the predicted temperature in combination with the charging time and the heat dissipation performance evaluation index, obtaining the real-time ambient temperature, calculating the predicted temperature influencing factor, correcting the predicted temperature, obtaining the corrected predicted temperature, processing it with the real-time temperature, obtaining the charging temperature control level, and obtaining the charging control plan according to the temperature control level; thereby, the intelligent monitoring of the temperature of the magnetic power bank is realized through the calculation and threshold comparison of the heat dissipation performance evaluation index, the corrected predicted temperature and the real-time temperature.

Description

Technical Field

[0001] The present application relates to the technical field of power bank temperature monitoring, and more specifically, to a method, system and medium for intelligently monitoring the temperature of a magnetic power bank. Background Art

[0002] Magnetic power banks are free from the constraints of data cables. You only need to attach the power bank to the back of the mobile phone to charge it, which is convenient to use. However, in the process of charging electronic devices and charging itself, heat will be generated due to factors such as current transmission, internal chemical reactions of the battery, and circuit conversion. If the temperature is too high, it will not only affect the charging efficiency and battery life of the power bank, but may also pose a threat to the safety of the user. At present, there is a problem that intelligent analysis and accurate evaluation cannot be performed based on the charging equipment, charging environment and heat dissipation performance.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method, system and medium for intelligent monitoring of the temperature of a magnetic power bank, which can realize intelligent monitoring of the temperature of a magnetic power bank through calculation and threshold comparison of heat dissipation performance evaluation index, corrected predicted temperature and real-time temperature.

[0005] The present application also provides a method for intelligently monitoring the temperature of a magnetic power bank, comprising the following steps:

[0006] Acquire and process the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank;

[0007] Obtain the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extract the maximum allowable charging power based on the charging protocol information;

[0008] Processing is performed according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing is performed according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data;

[0009] Obtain the real-time ambient temperature of the magnetic power bank, process according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correct the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature;

[0010] Obtain the real-time temperature corresponding to the charging time data of the magnetic power bank, and process the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level;

[0011] A charging control solution for the magnetic power bank is obtained according to the temperature control level.

[0012] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the step of acquiring and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank includes:

[0013] Obtain the heat dissipation performance characteristic data of the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material, and the heat dissipation efficiency of the heat sink;

[0014] The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank.

[0015] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the processing is performed according to the maximum allowable charging power and the real-time charging power to obtain the temperature control evaluation power, and the temperature control evaluation power is processed in combination with the charging time data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain the predicted temperature corresponding to the charging time data, including:

[0016] comparing the real-time charging power with the maximum allowed charging power;

[0017] If the real-time charging power is less than or equal to the maximum charging power, the real-time charging power is the temperature control evaluation power;

[0018] If the real-time charging power is greater than the maximum charging power, the maximum charging power is the temperature control evaluation power;

[0019] The temperature control evaluation power and charging time data as well as the heat dissipation performance evaluation index and battery material conductivity data are input into a preset charging temperature prediction model for processing to obtain a predicted temperature corresponding to the charging time data.

[0020] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the obtaining of the real-time ambient temperature of the magnetic power bank, processing according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correcting the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature, includes:

[0021] Get the real-time ambient temperature of the magnetic charging treasure;

[0022] Comparing the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature deviation rate;

[0023] Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to the range to which the threshold comparison result belongs;

[0024] According to the ambient temperature deviation level, a mapping table of the relationship between the preset ambient temperature deviation level and the predicted temperature impact factor is searched to obtain the corresponding predicted temperature impact factor;

[0025] The predicted temperature is corrected according to the predicted temperature influence factor to obtain a corrected predicted temperature.

[0026] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the ambient temperature deviation rate is compared with a preset ambient temperature deviation rate threshold, and the ambient temperature deviation level is determined according to the range to which the threshold comparison result belongs, including:

[0027] Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold;

[0028] If the ambient temperature deviation rate is less than or equal to the preset ambient temperature deviation rate threshold, it corresponds to the normal ambient deviation level;

[0029] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is lower than the preset standard ambient temperature, then it corresponds to the low temperature ambient deviation level;

[0030] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is greater than the preset standard ambient temperature, a high temperature ambient deviation level is corresponding.

[0031] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the real-time temperature corresponding to the charging time data of the magnetic power bank is obtained, and the charging temperature control level is obtained according to the real-time temperature and the corrected predicted temperature, including:

[0032] Get the real-time temperature corresponding to the charging time data of the magnetic power bank;

[0033] Comparing the corrected predicted temperature with the real-time temperature to obtain a predicted temperature deviation rate corresponding to the charging duration data;

[0034] Comparing the predicted temperature deviation rate with a preset predicted temperature deviation rate threshold, and obtaining a charging temperature control level according to a range to which the threshold comparison result belongs;

[0035] The charging temperature control level includes primary temperature control, secondary temperature control or tertiary temperature control.

[0036] Optionally, in the magnetic power bank temperature intelligent monitoring method described in the present application, the step of obtaining the charging control scheme of the magnetic power bank according to the temperature control level includes:

[0037] If the charging temperature control level is level one, a first charging control scheme is generated without adjusting the charging current or charging power;

[0038] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is lower than the real-time temperature, a second charging control scheme is generated, including reducing the charging current or reducing the charging power;

[0039] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is greater than the real-time temperature, a third charging control scheme is generated, including increasing the charging current to level 1 or increasing the charging power to level 1;

[0040] If the charging temperature control level is level three, and the corrected predicted temperature is greater than the real-time temperature, a fourth charging control scheme is generated, including increasing the charging current to level two or increasing the charging power to level two;

[0041] If the charging temperature control level is level three temperature control, and the corrected predicted temperature is lower than the real-time temperature, a fifth charging control scheme is generated to stop charging.

[0042] In a second aspect, the present application provides a magnetic power bank temperature intelligent monitoring system, the system comprising: a memory and a processor, the memory comprising a program of a magnetic power bank temperature intelligent monitoring method, the program of the magnetic power bank temperature intelligent monitoring method when executed by the processor implements the following steps:

[0043] Acquire and process the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank;

[0044] Obtain the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extract the maximum allowable charging power based on the charging protocol information;

[0045] Processing is performed according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing is performed according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data;

[0046] Obtain the real-time ambient temperature of the magnetic power bank, process according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correct the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature;

[0047] Obtain the real-time temperature corresponding to the charging time data of the magnetic power bank, and process the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level;

[0048] A charging control solution for the magnetic power bank is obtained according to the temperature control level.

[0049] Optionally, in the magnetic power bank temperature intelligent monitoring system described in the present application, the step of acquiring and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank includes:

[0050] Obtain the heat dissipation performance characteristic data of the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material, and the heat dissipation efficiency of the heat sink;

[0051] The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank.

[0052] In a third aspect, the present application also provides a computer-readable storage medium, which stores a program for a method for intelligently monitoring the temperature of a magnetic power bank. When the program for the method for intelligently monitoring the temperature of a magnetic power bank is executed by a processor, the steps of the method for intelligently monitoring the temperature of a magnetic power bank as described in any one of the above items are implemented.

[0053] From the above, it can be seen that the present application provides a method, system and medium for intelligent monitoring of the temperature of a magnetic power bank, which realizes intelligent monitoring of the temperature of a magnetic power bank through calculation and threshold comparison of a heat dissipation performance evaluation index, a corrected predicted temperature and a real-time temperature.

[0054] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flow chart of a method for intelligently monitoring the temperature of a magnetic power bank provided in an embodiment of the present application;

[0057] Figure 2 A flow chart of obtaining a heat dissipation performance evaluation index of a magnetic power bank in a method for intelligently monitoring the temperature of a magnetic power bank provided in an embodiment of the present application;

[0058] Figure 3 A flow chart of obtaining a predicted temperature corresponding to charging time data in a method for intelligently monitoring the temperature of a magnetic power bank provided in an embodiment of the present application;

[0059] Figure 4 A flowchart of obtaining the corrected predicted temperature of a magnetic power bank intelligent temperature monitoring method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0062] Please refer to Figure 1 , Figure 1 The flowchart of a magnetic power bank temperature intelligent monitoring method in some embodiments of the present application. The magnetic power bank temperature intelligent monitoring method is used in terminal devices, such as computers, mobile phone terminals, etc. The magnetic power bank temperature intelligent monitoring method includes the following steps:

[0063] S11, obtaining and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain a heat dissipation performance evaluation index of the magnetic power bank;

[0064] S12, obtaining the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extracting the maximum allowable charging power according to the charging protocol information;

[0065] S13, processing according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data;

[0066] S14, obtaining the real-time ambient temperature of the magnetic power bank, performing processing according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correcting the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature;

[0067] S15, obtaining the real-time temperature corresponding to the charging time data of the magnetic power bank, and processing the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level;

[0068] S16. Obtaining a charging control solution for the magnetic power bank according to the temperature control level.

[0069] It should be noted that in order to realize intelligent monitoring of the temperature of the magnetic power bank, the heat dissipation performance of the magnetic power bank is first evaluated through the heat dissipation performance characteristic data, and then the charging protocol information and battery material conductivity data of the magnetic power bank are obtained according to the power bank model. The maximum allowable charging power is extracted according to the charging protocol information and compared with the real-time charging power to determine which of the real-time charging power or the maximum allowable charging power is the temperature control evaluation power. Then, the predicted temperature corresponding to the charging time data of the magnetic power bank is obtained by combining the battery material conductivity data and the charging time data obtained by the preset sensor. Different ambient temperatures will have a greater impact on the charging temperature of the magnetic power bank. The real-time ambient temperature of the magnetic power bank is obtained, and the predicted temperature influence factor is calculated. The predicted temperature is corrected according to the predicted temperature influence factor to obtain the corrected predicted temperature. Then the real-time temperature corresponding to the charging time data of the magnetic power bank is obtained, and the predicted temperature is compared with the real-time temperature to obtain the charging temperature control level. The intelligent monitoring of the temperature of the magnetic power bank is realized, and then the charging control scheme of the magnetic power bank is obtained.

[0070] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of a method for intelligently monitoring the temperature of a magnetic power bank in some embodiments of the present application to obtain a heat dissipation performance evaluation index of the magnetic power bank. According to an embodiment of the present invention, the heat dissipation performance characteristic data of the magnetic power bank is obtained and processed to obtain the heat dissipation performance evaluation index of the magnetic power bank, including:

[0071] S21. Obtaining heat dissipation performance characteristic data of the magnetic power bank, including thermal conductivity data of the magnetic material, thermal conductivity data of the magnetic isolation sheet material, thermal conductivity data of the shell material, and heat dissipation efficiency of the heat sink;

[0072] S22, inputting the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink into a preset heat dissipation performance evaluation model for processing, and obtaining the heat dissipation performance evaluation index of the magnetic power bank.

[0073] It should be noted that in order to accurately evaluate the heat dissipation performance of the magnetic power bank, the heat dissipation performance characteristic data of the key heat dissipation parts in the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink, are obtained according to the model information of the power bank. The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank;

[0074] The heat dissipation performance evaluation index calculation formula of the magnetic charging treasure is:

[0075] ;

[0076] in, It is the heat dissipation performance evaluation index of the magnetic charging treasure. , , , They are the thermal conductivity data of magnetic material, the thermal conductivity data of magnetic isolation sheet material, the thermal conductivity data of shell material and the heat dissipation efficiency of heat sink. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0077] Please refer to Figure 3 , Figure 3 It is a flow chart of obtaining the predicted temperature corresponding to the charging time data of a magnetic power bank temperature intelligent monitoring method in some embodiments of the present application. According to an embodiment of the present invention, the processing according to the maximum allowable charging power and the real-time charging power to obtain the temperature control evaluation power, and the processing according to the temperature control evaluation power combined with the charging time data and the battery material conductivity data and the heat dissipation performance evaluation index to obtain the predicted temperature corresponding to the charging time data includes:

[0078] S31, comparing the real-time charging power with the maximum allowable charging power;

[0079] S32: If the real-time charging power is less than or equal to the maximum charging power, the real-time charging power is the temperature control evaluation power;

[0080] S33. If the real-time charging power is greater than the maximum charging power, the maximum charging power is the temperature control evaluation power;

[0081] S34, inputting the temperature control evaluation power and charging time data as well as the heat dissipation performance evaluation index and battery material conductivity data into a preset charging temperature prediction model for processing, and obtaining a predicted temperature corresponding to the charging time data.

[0082] It should be noted that in order to obtain the predicted temperature corresponding to different charging times of the magnetic power bank, the real-time charging power of the magnetic power bank is first compared with the maximum allowable charging power of the charging protocol. If the real-time charging power is less than or equal to the maximum charging power, the real-time charging power is the temperature control evaluation power; if the real-time charging power is greater than the maximum charging power and exceeds the power allowed by the charging protocol, the maximum charging power is the temperature control evaluation power, and the determined temperature control evaluation power, charging time data, heat dissipation performance evaluation index and battery material conductivity data are input into the preset charging temperature prediction model for processing to obtain the predicted temperature corresponding to the charging time data;

[0083] The calculation formula for the predicted temperature in the charging temperature prediction model is:

[0084] ;

[0085] in, To predict the temperature, , , , They are temperature control evaluation power, charging time data, heat dissipation performance evaluation index and battery material conductivity data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0086] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a corrected predicted temperature of a magnetic power bank temperature intelligent monitoring method in some embodiments of the present application. According to an embodiment of the present invention, obtaining the real-time ambient temperature of the magnetic power bank, processing according to the real-time ambient temperature, obtaining a predicted temperature influence factor, correcting the predicted temperature according to the predicted temperature influence factor, and obtaining a corrected predicted temperature includes:

[0087] S41, obtaining the real-time ambient temperature of the magnetic power bank;

[0088] S42, comparing the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature deviation rate;

[0089] S43, performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining an ambient temperature deviation level according to a range to which the threshold comparison result belongs;

[0090] S44, querying a preset mapping table of the relationship between the ambient temperature deviation level and the predicted temperature impact factor according to the ambient temperature deviation level, and obtaining a corresponding predicted temperature impact factor;

[0091] S45. Correct the predicted temperature according to the predicted temperature influencing factor to obtain a corrected predicted temperature.

[0092] It should be noted that different charging environment temperatures have a great impact on the temperature change of the magnetic power bank. In order to further accurately evaluate the predicted temperature, the real-time ambient temperature is obtained based on the preset temperature sensor, and the error value is eliminated and compared with the preset standard ambient temperature to obtain the ambient temperature deviation rate. For example, the real-time ambient temperature is 10°C and the preset standard ambient temperature is 25°C. That is, the ambient temperature deviation rate, the ambient temperature deviation rate is compared with the preset ambient temperature deviation rate threshold, the ambient temperature deviation level is determined according to the range to which the threshold comparison result belongs, and the preset ambient temperature deviation level and the predicted temperature influence factor relationship mapping table is queried according to the obtained ambient temperature deviation level to obtain the corresponding predicted temperature influence factor, wherein the preset ambient temperature deviation level and the predicted temperature influence factor relationship mapping table is obtained by obtaining the ambient temperature deviation level of a large number of historical samples and the corresponding predicted temperature influence factor analysis, and finally the predicted temperature is corrected according to the obtained predicted temperature influence factor to obtain the corrected predicted temperature;

[0093] The modified predicted temperature calculation formula is:

[0094] ;

[0095] in, To correct the predicted temperature, , are the predicted temperature influencing factor and the predicted temperature, respectively. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0096] According to an embodiment of the present invention, performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to a range to which the threshold comparison result belongs, includes:

[0097] Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold;

[0098] If the ambient temperature deviation rate is less than or equal to the preset ambient temperature deviation rate threshold, it corresponds to the normal ambient deviation level;

[0099] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is lower than the preset standard ambient temperature, then it corresponds to the low temperature ambient deviation level;

[0100] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is greater than the preset standard ambient temperature, a high temperature ambient deviation level is corresponding.

[0101] It should be noted that, in order to determine the ambient temperature deviation level, the obtained ambient temperature deviation rate is compared with the preset ambient temperature deviation rate threshold. In this embodiment, the preset ambient temperature deviation rate threshold is set to (0, 0.4], [0.4, 1], corresponding to the normal ambient deviation level, the low temperature ambient deviation level or the high temperature ambient deviation level; for example, if the obtained ambient temperature deviation rate is 0.3, it corresponds to the normal ambient deviation level; if the obtained ambient temperature deviation rate is 0.5, and the real-time ambient temperature is less than the preset standard ambient temperature, it corresponds to the low temperature ambient deviation level; if the obtained ambient temperature deviation rate is 0.5, and the real-time ambient temperature is greater than the preset standard ambient temperature, it corresponds to the high temperature ambient deviation level.

[0102] According to an embodiment of the present invention, the real-time temperature corresponding to the charging time data of the magnetic power bank is obtained, and the charging temperature control level is obtained by processing the real-time temperature and the corrected predicted temperature, including:

[0103] Get the real-time temperature corresponding to the charging time data of the magnetic power bank;

[0104] Comparing the corrected predicted temperature with the real-time temperature to obtain a predicted temperature deviation rate corresponding to the charging duration data;

[0105] Comparing the predicted temperature deviation rate with a preset predicted temperature deviation rate threshold, and obtaining a charging temperature control level according to a range to which the threshold comparison result belongs;

[0106] The charging temperature control level includes primary temperature control, secondary temperature control or tertiary temperature control.

[0107] It should be noted that in order to more accurately monitor the temperature of the magnetic power bank, the real-time temperature corresponding to the charging time data is obtained based on the preset sensor, and the corrected predicted temperature is compared with the real-time temperature to obtain the predicted temperature deviation rate corresponding to the charging time data. For example, if the corrected predicted temperature is 25 degrees Celsius and the real-time temperature is 28 degrees Celsius, then That is, the predicted temperature deviation rate is compared with the preset predicted temperature deviation rate threshold. In this embodiment, the preset predicted temperature deviation rate threshold is set to (0, 0.2], [0.2, 0.5], (0.5, 1], corresponding to the first level temperature control, the second level temperature control or the third level temperature control, respectively. For example, the obtained predicted temperature deviation rate is 0.12, which corresponds to the first level temperature control.

[0108] According to an embodiment of the present invention, the charging control scheme of the magnetic power bank obtained according to the temperature control level includes:

[0109] If the charging temperature control level is level one, a first charging control scheme is generated without adjusting the charging current or charging power;

[0110] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is lower than the real-time temperature, a second charging control scheme is generated, including reducing the charging current or reducing the charging power;

[0111] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is greater than the real-time temperature, a third charging control scheme is generated, including increasing the charging current to level 1 or increasing the charging power to level 1;

[0112] If the charging temperature control level is level three, and the corrected predicted temperature is greater than the real-time temperature, a fourth charging control scheme is generated, including increasing the charging current to level two or increasing the charging power to level two;

[0113] If the charging temperature control level is level three temperature control, and the corrected predicted temperature is less than the real-time temperature, a fifth charging control scheme is generated to stop charging.

[0114] It should be noted that in order to realize intelligent control after intelligent monitoring of the temperature of the magnetic power bank, the temperature control level of the predicted temperature deviation rate is first determined, and then the relationship between the corrected predicted temperature and the real-time temperature is determined, and finally the charging control plan is determined.

[0115] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0116] The method of obtaining the real-time temperature corresponding to the charging time data of the magnetic power bank also includes:

[0117] Get the real-time temperature of the area corresponding to the charging time data of the magnetic power bank, including battery temperature, circuit board temperature, magnetic component temperature and shell temperature;

[0118] The battery temperature, circuit board temperature, magnetic component temperature and housing temperature are input into a preset real-time temperature evaluation model for processing to obtain the real-time temperature.

[0119] It should be noted that the magnetic power bank has many components, and the heating conditions of each part are different during charging. Monitoring the temperature of a certain position alone cannot indicate the real temperature. Based on multiple preset sensors, the battery temperature, circuit board temperature, magnetic component temperature and shell temperature are obtained, and the battery temperature, circuit board temperature, magnetic component temperature and shell temperature are input into the preset real-time temperature evaluation model for processing to obtain the real-time temperature;

[0120] The real-time temperature calculation formula is:

[0121] ;

[0122] in, Real-time temperature, , , , They are the battery temperature, circuit board temperature, magnetic component temperature and shell temperature. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0123] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0124] Obtain design parameter data of magnetic components, including coil turns, wire diameter, and electromagnetic conversion efficiency;

[0125] Get the battery internal resistance and discharge time data of the magnetic power bank;

[0126] Input the coil turns, wire diameter, electromagnetic conversion efficiency, battery internal resistance and discharge duration data into a preset discharge heat generation evaluation model for processing to obtain a discharge heat generation evaluation index;

[0127] Comparing the discharge heating evaluation index with a preset discharge heating evaluation threshold;

[0128] If it is less than or equal to the preset discharge heating assessment threshold, no adjustment is made;

[0129] If it is greater than the preset discharge heating assessment threshold, an early warning is output and the magnetic power bank is controlled to stop discharging.

[0130] It should be noted that when the magnetic power bank is charging a mobile phone, heating may occur due to electromagnetic conversion, so temperature monitoring should be carried out in a timely manner. First, the design parameter data of the magnetic component including the number of coil turns, wire diameter and electromagnetic conversion efficiency should be obtained, and then the battery internal resistance is obtained according to the model information of the power bank. The discharge time data is obtained based on the preset sensor, and the number of coil turns, wire diameter, electromagnetic conversion efficiency, battery internal resistance and discharge time data are input into the preset discharge heating evaluation model for processing to obtain the discharge heating evaluation index.

[0131] The calculation formula of the discharge heat evaluation index in the discharge heat evaluation model is:

[0132] ;

[0133] in, is the discharge heating evaluation index, , , Coil turns, wire diameter and electromagnetic conversion efficiency, , They are the battery internal resistance and discharge time data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0134] The present invention also discloses a magnetic power bank temperature intelligent monitoring system, comprising a memory and a processor, wherein the memory comprises a magnetic power bank temperature intelligent monitoring method program, and when the magnetic power bank temperature intelligent monitoring method program is executed by the processor, the following steps are implemented:

[0135] Acquire and process the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank;

[0136] Obtain the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extract the maximum allowable charging power based on the charging protocol information;

[0137] Processing is performed according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing is performed according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data;

[0138] Obtain the real-time ambient temperature of the magnetic power bank, process according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correct the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature;

[0139] Obtain the real-time temperature corresponding to the charging time data of the magnetic power bank, and process the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level;

[0140] A charging control solution for the magnetic power bank is obtained according to the temperature control level.

[0141] It should be noted that in order to realize intelligent monitoring of the temperature of the magnetic power bank, the heat dissipation performance of the magnetic power bank is first evaluated through the heat dissipation performance characteristic data, and then the charging protocol information and battery material conductivity data of the magnetic power bank are obtained according to the power bank model. The maximum allowable charging power is extracted according to the charging protocol information and compared with the real-time charging power to determine which of the real-time charging power or the maximum allowable charging power is the temperature control evaluation power. Then, the predicted temperature corresponding to the charging time data of the magnetic power bank is obtained by combining the battery material conductivity data and the charging time data obtained by the preset sensor. Different ambient temperatures will have a greater impact on the charging temperature of the magnetic power bank. The real-time ambient temperature of the magnetic power bank is obtained, and the predicted temperature influence factor is calculated. The predicted temperature is corrected according to the predicted temperature influence factor to obtain the corrected predicted temperature. Then the real-time temperature corresponding to the charging time data of the magnetic power bank is obtained, and the predicted temperature is compared with the real-time temperature to obtain the charging temperature control level. The intelligent monitoring of the temperature of the magnetic power bank is realized, and then the charging control scheme of the magnetic power bank is obtained.

[0142] According to an embodiment of the present invention, the step of acquiring and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank includes:

[0143] Obtain the heat dissipation performance characteristic data of the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material, and the heat dissipation efficiency of the heat sink;

[0144] The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank.

[0145] It should be noted that in order to accurately evaluate the heat dissipation performance of the magnetic power bank, the heat dissipation performance characteristic data of the key heat dissipation parts in the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink, are obtained according to the model information of the power bank. The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank;

[0146] The heat dissipation performance evaluation index calculation formula of the magnetic charging treasure is:

[0147] ;

[0148] in, It is the heat dissipation performance evaluation index of the magnetic charging treasure. , , , They are the thermal conductivity data of magnetic material, the thermal conductivity data of magnetic isolation sheet material, the thermal conductivity data of shell material and the heat dissipation efficiency of heat sink. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0149] According to an embodiment of the present invention, the processing according to the maximum allowable charging power and the real-time charging power to obtain the temperature control evaluation power, and the processing according to the temperature control evaluation power combined with the charging time data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain the predicted temperature corresponding to the charging time data, includes:

[0150] comparing the real-time charging power with the maximum allowed charging power;

[0151] If the real-time charging power is less than or equal to the maximum charging power, the real-time charging power is the temperature control evaluation power;

[0152] If the real-time charging power is greater than the maximum charging power, the maximum charging power is the temperature control evaluation power;

[0153] The temperature control evaluation power and charging time data as well as the heat dissipation performance evaluation index and battery material conductivity data are input into a preset charging temperature prediction model for processing to obtain a predicted temperature corresponding to the charging time data.

[0154] It should be noted that in order to obtain the predicted temperature corresponding to different charging times of the magnetic power bank, the real-time charging power of the magnetic power bank is first compared with the maximum allowable charging power of the charging protocol. If the real-time charging power is less than or equal to the maximum charging power, the real-time charging power is the temperature control evaluation power; if the real-time charging power is greater than the maximum charging power and exceeds the power allowed by the charging protocol, the maximum charging power is the temperature control evaluation power, and the determined temperature control evaluation power, charging time data, heat dissipation performance evaluation index and battery material conductivity data are input into the preset charging temperature prediction model for processing to obtain the predicted temperature corresponding to the charging time data;

[0155] The calculation formula for the predicted temperature in the charging temperature prediction model is:

[0156] ;

[0157] in, To predict the temperature, , , , They are temperature control evaluation power, charging time data, heat dissipation performance evaluation index and battery material conductivity data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0158] According to an embodiment of the present invention, the real-time ambient temperature of the magnetic power bank is obtained, processing is performed according to the real-time ambient temperature to obtain a predicted temperature influence factor, and the predicted temperature is corrected according to the predicted temperature influence factor to obtain a corrected predicted temperature, including:

[0159] Get the real-time ambient temperature of the magnetic charging treasure;

[0160] Comparing the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature deviation rate;

[0161] Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to the range to which the threshold comparison result belongs;

[0162] According to the ambient temperature deviation level, a mapping table of the relationship between the preset ambient temperature deviation level and the predicted temperature impact factor is searched to obtain the corresponding predicted temperature impact factor;

[0163] The predicted temperature is corrected according to the predicted temperature influence factor to obtain a corrected predicted temperature.

[0164] It should be noted that different charging environment temperatures have a great impact on the temperature change of the magnetic power bank. In order to further accurately evaluate the predicted temperature, the real-time ambient temperature is obtained based on the preset temperature sensor and compared with the preset standard ambient temperature to obtain the ambient temperature deviation rate. For example, if the real-time ambient temperature is 10°C and the preset standard ambient temperature is 25°C, then That is, the ambient temperature deviation rate, the ambient temperature deviation rate is compared with the preset ambient temperature deviation rate threshold, the ambient temperature deviation level is determined according to the range to which the threshold comparison result belongs, and the preset ambient temperature deviation level and the predicted temperature influence factor relationship mapping table is queried according to the obtained ambient temperature deviation level to obtain the corresponding predicted temperature influence factor, wherein the preset ambient temperature deviation level and the predicted temperature influence factor relationship mapping table is obtained by obtaining the ambient temperature deviation level of a large number of historical samples and the corresponding predicted temperature influence factor analysis, and finally the predicted temperature is corrected according to the obtained predicted temperature influence factor to obtain the corrected predicted temperature;

[0165] The modified predicted temperature calculation formula is:

[0166] ;

[0167] in, To correct the predicted temperature, , are the predicted temperature influencing factor and the predicted temperature, respectively. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0168] According to an embodiment of the present invention, performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to a range to which the threshold comparison result belongs, includes:

[0169] Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold;

[0170] If the ambient temperature deviation rate is less than or equal to the preset ambient temperature deviation rate threshold, it corresponds to the normal ambient deviation level;

[0171] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is lower than the preset standard ambient temperature, then it corresponds to the low temperature ambient deviation level;

[0172] If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is greater than the preset standard ambient temperature, a high temperature ambient deviation level is corresponding.

[0173] It should be noted that, in order to determine the ambient temperature deviation level, the obtained ambient temperature deviation rate is compared with the preset ambient temperature deviation rate threshold. In this embodiment, the preset ambient temperature deviation rate threshold is set to (0, 0.4], [0.4, 1], corresponding to the normal ambient deviation level, the low temperature ambient deviation level or the high temperature ambient deviation level; for example, if the obtained ambient temperature deviation rate is 0.3, it corresponds to the normal ambient deviation level; if the obtained ambient temperature deviation rate is 0.5, and the real-time ambient temperature is less than the preset standard ambient temperature, it corresponds to the low temperature ambient deviation level; if the obtained ambient temperature deviation rate is 0.5, and the real-time ambient temperature is greater than the preset standard ambient temperature, it corresponds to the high temperature ambient deviation level.

[0174] According to an embodiment of the present invention, the real-time temperature corresponding to the charging time data of the magnetic power bank is obtained, and the charging temperature control level is obtained by processing the real-time temperature and the corrected predicted temperature, including:

[0175] Get the real-time temperature corresponding to the charging time data of the magnetic power bank;

[0176] Comparing the corrected predicted temperature with the real-time temperature to obtain a predicted temperature deviation rate corresponding to the charging duration data;

[0177] Comparing the predicted temperature deviation rate with a preset predicted temperature deviation rate threshold, and obtaining a charging temperature control level according to a range to which the threshold comparison result belongs;

[0178] The charging temperature control level includes primary temperature control, secondary temperature control or tertiary temperature control.

[0179] It should be noted that in order to more accurately monitor the temperature of the magnetic power bank, the real-time temperature corresponding to the charging time data is obtained based on the preset sensor, and the corrected predicted temperature is compared with the real-time temperature to obtain the predicted temperature deviation rate corresponding to the charging time data. For example, if the corrected predicted temperature is 25 degrees Celsius and the real-time temperature is 28 degrees Celsius, then That is, the predicted temperature deviation rate is compared with the preset predicted temperature deviation rate threshold. In this embodiment, the preset predicted temperature deviation rate threshold is set to (0, 0.2], [0.2, 0.5], (0.5, 1], corresponding to the first level temperature control, the second level temperature control or the third level temperature control, respectively. For example, the obtained predicted temperature deviation rate is 0.12, which corresponds to the first level temperature control.

[0180] According to an embodiment of the present invention, the charging control scheme of the magnetic power bank obtained according to the temperature control level includes:

[0181] If the charging temperature control level is level one, a first charging control scheme is generated without adjusting the charging current or charging power;

[0182] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is lower than the real-time temperature, a second charging control scheme is generated, including reducing the charging current or reducing the charging power;

[0183] If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is greater than the real-time temperature, a third charging control scheme is generated, including increasing the charging current to level 1 or increasing the charging power to level 1;

[0184] If the charging temperature control level is level three, and the corrected predicted temperature is greater than the real-time temperature, a fourth charging control scheme is generated, including increasing the charging current to level two or increasing the charging power to level two;

[0185] If the charging temperature control level is level three temperature control, and the corrected predicted temperature is lower than the real-time temperature, a fifth charging control scheme is generated to stop charging.

[0186] It should be noted that in order to realize intelligent control after intelligent monitoring of the temperature of the magnetic power bank, the temperature control level of the predicted temperature deviation rate is first determined, and then the relationship between the corrected predicted temperature and the real-time temperature is determined, and finally the charging control plan is determined.

[0187] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0188] The method of obtaining the real-time temperature corresponding to the charging time data of the magnetic power bank also includes:

[0189] Get the real-time temperature of the area corresponding to the charging time data of the magnetic power bank, including battery temperature, circuit board temperature, magnetic component temperature and shell temperature;

[0190] The battery temperature, circuit board temperature, magnetic component temperature and housing temperature are input into a preset real-time temperature evaluation model for processing to obtain the real-time temperature.

[0191] It should be noted that the magnetic power bank has many components, and the heating conditions of each part are different during charging. Monitoring the temperature of a certain position alone cannot indicate the real temperature. Based on multiple preset sensors, the battery temperature, circuit board temperature, magnetic component temperature and shell temperature are obtained, and the battery temperature, circuit board temperature, magnetic component temperature and shell temperature are input into the preset real-time temperature evaluation model for processing to obtain the real-time temperature;

[0192] The real-time temperature calculation formula is:

[0193] ;

[0194] in, Real-time temperature, , , , They are battery temperature, circuit board temperature, magnetic component temperature and shell temperature. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0195] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0196] Obtain design parameter data of magnetic components, including coil turns, wire diameter, and electromagnetic conversion efficiency;

[0197] Get the battery internal resistance and discharge time data of the magnetic power bank;

[0198] Input the coil turns, wire diameter, electromagnetic conversion efficiency, battery internal resistance and discharge duration data into a preset discharge heat generation evaluation model for processing to obtain a discharge heat generation evaluation index;

[0199] Comparing the discharge heating evaluation index with a preset discharge heating evaluation threshold;

[0200] If it is less than or equal to the preset discharge heating assessment threshold, no adjustment is made;

[0201] If it is greater than the preset discharge heating assessment threshold, an early warning is output and the magnetic power bank is controlled to stop discharging.

[0202] It should be noted that when the magnetic power bank is charging a mobile phone, heating may occur due to electromagnetic conversion, so temperature monitoring should be carried out in a timely manner. First, the design parameter data of the magnetic component including the number of coil turns, wire diameter and electromagnetic conversion efficiency should be obtained, and then the battery internal resistance is obtained according to the model information of the power bank. The discharge time data is obtained based on the preset sensor, and the number of coil turns, wire diameter, electromagnetic conversion efficiency, battery internal resistance and discharge time data are input into the preset discharge heating evaluation model for processing to obtain the discharge heating evaluation index.

[0203] The calculation formula of the discharge heat evaluation index in the discharge heat evaluation model is:

[0204] ;

[0205] in, is the discharge heating evaluation index, , , Coil turns, wire diameter and electromagnetic conversion efficiency, , They are the battery internal resistance and discharge time data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset magnetic power bank intelligent monitoring platform).

[0206] A third aspect of the present invention provides a readable storage medium, which stores a program for a method for intelligently monitoring the temperature of a magnetic power bank. When the program for the method for intelligently monitoring the temperature of a magnetic power bank is executed by a processor, the steps of the method for intelligently monitoring the temperature of a magnetic power bank as described in any one of the above items are implemented.

[0207] The present invention discloses a method, system and medium for intelligently monitoring the temperature of a magnetic power bank, which realize intelligent monitoring of the temperature of the magnetic power bank by calculating a heat dissipation performance evaluation index, a corrected predicted temperature and a real-time temperature and comparing threshold values.

[0208] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0209] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0210] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0211] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0212] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for intelligently monitoring the temperature of a magnetic power bank, characterized in that: The following steps are involved: Acquire and process the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank; Obtain the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extract the maximum allowable charging power based on the charging protocol information; Processing is performed according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing is performed according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data; Obtain the real-time ambient temperature of the magnetic power bank, process according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correct the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature; Obtain the real-time temperature corresponding to the charging time data of the magnetic power bank, and process the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level; Obtaining a charging control scheme for the magnetic power bank according to the temperature control level; The step of obtaining the real-time ambient temperature of the magnetic power bank, performing processing according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correcting the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature includes: Get the real-time ambient temperature of the magnetic charging treasure; Comparing the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature deviation rate; Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to the range to which the threshold comparison result belongs; According to the ambient temperature deviation level, a mapping table of the relationship between the preset ambient temperature deviation level and the predicted temperature impact factor is searched to obtain the corresponding predicted temperature impact factor; Correcting the predicted temperature according to the predicted temperature influencing factor to obtain a corrected predicted temperature; The obtaining of the real-time temperature corresponding to the charging time data of the magnetic power bank, and processing according to the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level includes: Get the real-time temperature corresponding to the charging time data of the magnetic power bank; Comparing the corrected predicted temperature with the real-time temperature to obtain a predicted temperature deviation rate corresponding to the charging duration data; Comparing the predicted temperature deviation rate with a preset predicted temperature deviation rate threshold, and obtaining a charging temperature control level according to a range to which the threshold comparison result belongs; The charging temperature control level includes primary temperature control, secondary temperature control or tertiary temperature control.

2. The magnetic power bank temperature intelligent monitoring method according to claim 1 is characterized in that: The step of obtaining and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank includes: Obtain the heat dissipation performance characteristic data of the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material, and the heat dissipation efficiency of the heat sink; The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank.

3. The method for intelligent temperature monitoring of a magnetic power bank according to claim 2, characterized in that: The processing according to the maximum allowable charging power and the real-time charging power to obtain the temperature control evaluation power, and the processing according to the temperature control evaluation power combined with the charging time data, the battery material conductivity data and the heat dissipation performance evaluation index to obtain the predicted temperature corresponding to the charging time data, including: comparing the real-time charging power with the maximum allowed charging power; If the real-time charging power is less than or equal to the maximum allowed charging power, the real-time charging power is the temperature control evaluation power; If the real-time charging power is greater than the maximum allowable charging power, the maximum allowable charging power is the temperature control evaluation power; The temperature control evaluation power and charging time data as well as the heat dissipation performance evaluation index and battery material conductivity data are input into a preset charging temperature prediction model for processing to obtain a predicted temperature corresponding to the charging time data.

4. The method for intelligent temperature monitoring of a magnetic power bank according to claim 1, characterized in that: The step of comparing the ambient temperature deviation rate with a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to a range to which the threshold comparison result belongs, includes: Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold; If the ambient temperature deviation rate is less than or equal to the preset ambient temperature deviation rate threshold, it corresponds to the normal ambient deviation level; If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is lower than the preset standard ambient temperature, then it corresponds to the low temperature ambient deviation level; If the ambient temperature deviation rate is greater than the preset ambient temperature deviation rate threshold, and the real-time ambient temperature is greater than the preset standard ambient temperature, a high temperature ambient deviation level is corresponding.

5. The method for intelligent temperature monitoring of a magnetic power bank according to claim 1, characterized in that: The charging control scheme of the magnetic power bank obtained according to the temperature control level includes: If the charging temperature control level is level one, a first charging control scheme is generated without adjusting the charging current or charging power; If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is lower than the real-time temperature, a second charging control scheme is generated, including reducing the charging current or reducing the charging power; If the charging temperature control level is level 2 temperature control, and the corrected predicted temperature is greater than the real-time temperature, a third charging control scheme is generated, including increasing the charging current to level 1 or increasing the charging power to level 1; If the charging temperature control level is level three, and the corrected predicted temperature is greater than the real-time temperature, a fourth charging control scheme is generated, including increasing the charging current to level two or increasing the charging power to level two; If the charging temperature control level is level three temperature control, and the corrected predicted temperature is lower than the real-time temperature, a fifth charging control scheme is generated to stop charging.

6. A magnetic charging treasure temperature intelligent monitoring system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of a method for intelligently monitoring the temperature of a magnetic power bank, and when the program of the method for intelligently monitoring the temperature of a magnetic power bank is executed by the processor, the following steps are implemented: Acquire and process the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank; Obtain the charging protocol information, real-time charging power, charging time data and battery material conductivity data of the magnetic power bank, and extract the maximum allowable charging power based on the charging protocol information; Processing is performed according to the maximum allowable charging power and the real-time charging power to obtain a temperature control evaluation power, and processing is performed according to the temperature control evaluation power in combination with the charging duration data, the battery material conductivity data, and the heat dissipation performance evaluation index to obtain a predicted temperature corresponding to the charging duration data; Obtain the real-time ambient temperature of the magnetic power bank, process according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correct the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature; Obtain the real-time temperature corresponding to the charging time data of the magnetic power bank, and process the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level; Obtaining a charging control scheme for the magnetic power bank according to the temperature control level; The step of obtaining the real-time ambient temperature of the magnetic power bank, performing processing according to the real-time ambient temperature to obtain a predicted temperature influence factor, and correcting the predicted temperature according to the predicted temperature influence factor to obtain a corrected predicted temperature includes: Get the real-time ambient temperature of the magnetic charging treasure; Comparing the real-time ambient temperature with a preset standard ambient temperature to obtain an ambient temperature deviation rate; Performing a threshold comparison between the ambient temperature deviation rate and a preset ambient temperature deviation rate threshold, and determining the ambient temperature deviation level according to the range to which the threshold comparison result belongs; According to the ambient temperature deviation level, a mapping table of the relationship between the preset ambient temperature deviation level and the predicted temperature impact factor is searched to obtain the corresponding predicted temperature impact factor; Correcting the predicted temperature according to the predicted temperature influencing factor to obtain a corrected predicted temperature; The obtaining of the real-time temperature corresponding to the charging time data of the magnetic power bank, and processing according to the real-time temperature and the corrected predicted temperature to obtain the charging temperature control level includes: Get the real-time temperature corresponding to the charging time data of the magnetic power bank; Comparing the corrected predicted temperature with the real-time temperature to obtain a predicted temperature deviation rate corresponding to the charging duration data; Comparing the predicted temperature deviation rate with a preset predicted temperature deviation rate threshold, and obtaining a charging temperature control level according to a range to which the threshold comparison result belongs; The charging temperature control level includes primary temperature control, secondary temperature control or tertiary temperature control.

7. The magnetic power bank temperature intelligent monitoring system according to claim 6, characterized in that: The step of obtaining and processing the heat dissipation performance characteristic data of the magnetic power bank to obtain the heat dissipation performance evaluation index of the magnetic power bank includes: Obtain the heat dissipation performance characteristic data of the magnetic power bank, including the thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material, and the heat dissipation efficiency of the heat sink; The thermal conductivity data of the magnetic material, the thermal conductivity data of the magnetic isolation sheet material, the thermal conductivity data of the shell material and the heat dissipation efficiency of the heat sink are input into a preset heat dissipation performance evaluation model for processing to obtain the heat dissipation performance evaluation index of the magnetic power bank.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for the method for intelligently monitoring the temperature of a magnetic power bank. When the program for the method for intelligently monitoring the temperature of a magnetic power bank is executed by a processor, the steps of a method for intelligently monitoring the temperature of a magnetic power bank as described in any one of claims 1 to 5 are implemented.

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