Equipment charging management system and method based on Internet of Things

Abstract

The invention discloses an equipment charging management system and method based on the Internet of Things, and belongs to the technical field of equipment charging management. The method comprises the following steps: 1, predicting a charging index of each associated device; 2, predicting a real-time charging congestion coefficient of the charging loop; 3, analyzing the optimal charging time of each associated device based on the predicted charging index of each associated device and the real-time charging congestion coefficient of the charging loop; and 4, managing the charging condition of each associated device. According to the method, when the optimal power supply time of the associated equipment is analyzed, the distribution condition of the charging peak-valley-flat time period of the charging loop in each day is considered, peak-shifting charging of the charging loop to the associated equipment is realized, and the optimal power supply time of the associated equipment is analyzed according to the condition of the associated equipment which can be accessed by the charging loop at each moment. And charging management is performed on the associated equipment, so that the charging efficiency of the charging loop on the associated equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of device charging management, and in particular to a device charging management system and method based on the Internet of Things. Background Art

[0002] The charging status of smart devices determines how they are used. With the widespread use of smart devices, smart devices need to be charged frequently to meet the needs of users.

[0003] However, when the existing technology is used to manage the charging of smart devices, it is impossible to meet the charging needs of multiple smart devices at the same time. The charging order of multiple smart devices is determined only by judging the power levels of multiple smart devices, that is, no optimal power supply analysis is performed on the smart devices. At the same time, the existing technology is unable to analyze the charging timing of the associated smart devices based on the power levels of the associated smart devices and the charging conditions of the associated smart devices, resulting in charging congestion when the associated smart devices are charging, thereby reducing the charging management effect of the smart devices. In severe cases, it will cause charging circuit failure. Summary of the invention

[0004] The purpose of the present invention is to provide a device charging management system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a device charging management method based on the Internet of Things, the method comprising: Step 1: Obtain the historical charging amount set of the charging circuit, determine the charging peak, valley and flat time period of the charging circuit, and predict the charging index of each associated device through the charging status information and usage status information of each associated device collected by the information processing terminal, as well as the time when the charging status information of each associated device is entered into the smart terminal; Step 2: Collect the real-time power supply of the charging circuit, and predict the real-time charging congestion coefficient of the charging circuit based on the average power supply of the charging circuit during the charging peak, valley and flat time periods; Step 3: Analyze the optimal charging time for each associated device based on the predicted charging index of each associated device and the real-time charging congestion coefficient of the charging circuit; Step 4: Manage the charging status of each associated device.

[0006] Furthermore, the step 1 includes: S101: collecting the historical charging amount of the charging circuit at time intervals T to obtain a historical charging amount set A of the charging circuit. , where i=1,2,…,n, represents the number corresponding to each historical charging amount collection time point in the charging circuit, n represents the total number, x n Indicates the historical charging value collected at the historical charging collection time point numbered n. i The maximum power supply Y of the charging circuit within T time T For comparison, if x i >0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the peak charging time period. If 0.5Y T ≤x i ≤0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the charging level time period, x i <0.5Y T , then the time period [(i-1)*T+R,i*T+R] is the charging valley time period, where x i represents the historical charging value collected at the historical charging collection time point numbered i, and R represents the time value corresponding to the historical charging collection time point numbered 1; S102: When the device needs to be charged, the device user enters the charging status and usage status of the device to be charged on the smart terminal. The charging status includes the remaining power value of the device and the power value of the device to be charged. The usage status includes the continued use time of the device and the power consumption rate of the device. When the power value of the device to be charged is greater than the remaining power value of the device*5%, the information processing terminal collects the charging status information and usage status information entered into the smart terminal, and marks the device to be charged as an associated device. S103: Predicting the charging index of the associated device based on the time when the charging status information of the associated device is entered into the smart terminal. The specific prediction formula is: ; Among them, S represents the remaining power value of the associated device, F represents the power value of the associated device to be charged, H represents the power consumption rate of the associated device, and U represents the continued use time of the associated device. hour, ,when hour, , μ represents the charging index of the associated device.

[0007] Furthermore, the specific method of predicting the real-time charging congestion coefficient of the charging circuit in step 2 is: According to the acquired historical charging amount set of the charging circuit, the charging peak time period, the charging average time period and the charging valley time period of the charging circuit in each day are determined; based on the determination result, the charging peak intersection time period, the charging average intersection time period and the charging valley intersection time period of the charging circuit in each day are respectively acquired; based on the acquisition result, the average power supply of the charging circuit in each intersection time period is calculated; The power supply G of the charging circuit at time t t Collect and t The collection time is obtained, the intersection time period to which the collection time belongs is determined, and based on the determination result, the j value is determined to predict the real-time charging congestion coefficient of the charging circuit. The specific prediction formula is: ; ; Among them, j=1,2,3, when j=1, V j =V1 represents the average power supply of the charging circuit during the charging valley intersection time period, Indicates the starting time point of the charging circuit within the charging valley intersection time period, Indicates the termination time point of the charging circuit within the charging valley intersection time period. When j=2, V j =V2 represents the average power supply of the charging circuit during the charging level intersection time period, P j =P2 represents the starting time point of the charging circuit within the charging level intersection time period, Indicates the end time point of the charging circuit within the charging level intersection time period. When j=3, V j =V3 represents the average power supply of the charging circuit during the charging peak intersection period, P j =P3 represents the starting time point of the charging circuit within the charging peak intersection time period, Indicates the termination time point of the charging circuit within the charging peak intersection time period. signQ represents the sign extraction function. When Q≥0, signQ=1; when Q<0, signQ=-1. Represents the charging congestion coefficient of the charging circuit at time t.

[0008] Furthermore, the step three includes: S301: According to The value determines whether the charging circuit can charge the associated device at time t. , then the charging circuit cannot charge the associated device at time t. , then the charging circuit can charge the associated device at time t; S302: Put the charging index of each associated device into a set M, , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m, and the number value corresponding to maxM To confirm, , for the number value The real-time charging status of the corresponding associated device To obtain, at the same time, the number is The charging index of the associated device is deleted from the set M, where max represents the maximum value symbol; S303: If , then repeat the operation in S302 to determine the number value corresponding to the maximum value in the set M that has been deleted, until Stop operation when S304: The optimal charging time of the associated device corresponding to the number value determined in S302 and S303 is time t.

[0009] Furthermore, the specific method for managing the charging status of each associated device in step 4 is: when the optimal charging time of each associated device is reached, the information processing terminal feeds back the charging information to the smart terminal that records the charging status and usage status of the corresponding associated device, and the user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

[0010] An Internet of Things-based device charging management system, the system comprising an associated device charging index prediction module, a charging circuit congestion coefficient prediction module, an optimal charging time analysis module and a device charging management module; The associated device charging index prediction module is used to predict the charging index of each associated device; The charging circuit congestion coefficient prediction module is used to predict the real-time congestion coefficient of the charging circuit; The optimal charging time analysis module is used to analyze the optimal charging time of each associated device; The device charging management module is used to manage the charging status of each associated device.

[0011] Further, the associated device charging index prediction module includes a charging peak, valley and flat time period determination unit, an associated device determination unit and an associated device charging index prediction unit; The charging peak-valley-average time period determination unit determines the charging peak time period, the charging average time period and the charging valley time period of the charging circuit according to the historical charging amount set of the charging circuit and the maximum power supply of the charging circuit within the time T; When the device needs to be charged, the associated device determination unit inputs the charging status and usage status of the device to be charged into the smart terminal, and the information processing terminal collects the status information input into the smart terminal and marks the device to be charged as an associated device; The associated device charging index prediction unit predicts the charging index of the associated device based on the charging status information and usage status information of the associated device collected by the information processing terminal and the time when the charging status information of the associated device is entered into the smart terminal.

[0012] Further, the charging circuit congestion coefficient prediction module includes an average power supply calculation unit and a charging circuit congestion coefficient prediction unit; The average power supply calculation unit determines the charging peak time period, the charging average time period and the charging valley time period of the charging circuit in each day according to the historical charging amount set of the charging circuit, obtains the charging peak intersection time period, the charging average intersection time period and the charging valley intersection time period of the charging circuit in each day based on the determination result, and calculates the average power supply of the charging circuit in each intersection time period based on the acquisition result; The charging circuit congestion coefficient prediction unit predicts the real-time charging congestion coefficient of the charging circuit based on the real-time power supply of the charging circuit.

[0013] Further, the optimal charging time analysis module includes a charging judgment unit, a to-be-charged associated device search unit and an optimal charging time analysis unit; The charging judgment unit judges whether the charging circuit can charge the associated device at the current moment according to the prediction result of the charging circuit congestion coefficient prediction unit; The associated device to be charged searching unit puts the prediction result of the associated device charging index prediction unit into the set M, searches for the associated device to be charged according to the number value corresponding to the maximum value in the set M, and repeats the operation until the charging judgment unit determines that the charging circuit cannot charge the associated device at the current moment, wherein: , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m; The optimal charging time analysis unit analyzes and obtains the optimal charging time of the associated device to be charged found by the associated device to be charged search unit according to the time value corresponding to the current moment.

[0014] Furthermore, when the device charging management module reaches the optimal charging time for each associated device to be charged, the information processing terminal feeds back the charging information to the smart terminal which records the charging status and usage status of the corresponding associated device. The user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: 1. The present invention predicts the charging index of each associated device based on the charging status information and usage status information of each associated device collected by the information processing terminal, as well as the time when the charging status information of each associated device is entered into the intelligent terminal. In the prediction process, the continued use of the associated device by the device user is taken into consideration. Under the premise of not affecting the use of the associated device by the user, the charging time of the associated device is planned, thereby further improving the use effect of the system.

[0016] 2. The present invention analyzes the charging timing of associated equipment by predicting the real-time charging congestion coefficient of the charging circuit. Based on the analysis results, the charging circuit is guaranteed to work normally, which is beneficial to reducing the occurrence of charging circuit failures and improving the system's charging management effect on the equipment.

[0017] 3. When analyzing the optimal power supply time for associated devices, the present invention takes into account the distribution of the peak, valley and flat time periods of the charging circuit every day, and realizes staggered charging of the associated devices by the charging circuit. The associated devices that can be connected to the charging circuit at each moment are analyzed by predicting the charging index of each associated device and the real-time charging congestion coefficient of the charging circuit, that is, the charging sequence of the associated devices by the charging circuit is analyzed, which is beneficial to improving the charging efficiency of the charging circuit for the associated devices. Compared with the method of determining the charging sequence of multiple smart devices by judging the power values ​​of multiple smart devices, the charging sequence analyzed by the present invention is more reasonable and saves more electricity costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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: Figure 1 It is a schematic diagram of the working process of a device charging management system and method based on the Internet of Things of the present invention; Figure 2 It is a schematic structural diagram of the working principle of an Internet of Things-based device charging management system and method of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] See also Figure 1 and Figure 2 The present invention provides a technical solution: a device charging management method based on the Internet of Things, the method comprising: Step 1: Obtain a historical charging amount set of the charging circuit, determine the charging peak, valley and flat time periods of the charging circuit, where the charging peak, valley and flat time periods represent the peak time period, flat time period and valley time period of charging respectively, and predict the charging index of each associated device through the charging status information and usage status information of each associated device collected by the information processing terminal, as well as the time when the charging status information of each associated device is entered into the smart terminal; Step one includes: S101: collecting the historical charging amount of the charging circuit at time intervals T to obtain a historical charging amount set A of the charging circuit. , where i=1,2,…,n, represents the number corresponding to each historical charging amount collection time point in the charging circuit, n represents the total number, x n Indicates the historical charging value collected at the historical charging collection time point numbered n. i The maximum power supply Y of the charging circuit within T time T For comparison, if x i >0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the peak charging time period. If 0.5Y T ≤x i ≤0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the charging level time period, x i <0.5Y T , then the time period [(i-1)*T+R,i*T+R] is the charging valley time period, where x i represents the historical charging value collected at the historical charging collection time point numbered i, and R represents the time value corresponding to the historical charging collection time point numbered 1; S102: When the device needs to be charged, the device user enters the charging status and usage status of the device to be charged on the smart terminal. The charging status includes the remaining power value of the device and the power value of the device to be charged. The usage status includes the continued use time of the device and the power consumption rate of the device. When the power value of the device to be charged is greater than the remaining power value of the device*5%, the smart terminal includes a mobile phone, a tablet, etc., and the information processing terminal collects the charging status information and usage status information entered into the smart terminal, and marks the device to be charged as an associated device; S103: Predicting the charging index of the associated device based on the time when the charging status information of the associated device is entered into the smart terminal. The specific prediction formula is: ; Among them, S represents the remaining power value of the associated device, F represents the power value of the associated device to be charged, H represents the power consumption rate of the associated device, and U represents the continued use time of the associated device. hour, ,when hour, , μ represents the charging index of the associated device, and the charging index of the associated device refers to the degree to which the associated device urgently needs to be charged; Step 2: Collect the real-time power supply of the charging circuit, and predict the real-time charging congestion coefficient of the charging circuit based on the average power supply of the charging circuit during the charging peak, valley and flat time periods; The specific method of step 2 for predicting the real-time charging congestion coefficient of the charging circuit is: According to the acquired historical charging amount set of the charging circuit, the charging peak time period, charging average time period and charging valley time period of the charging circuit are determined every day. Based on the determination result, the charging peak intersection time period, charging average intersection time period and charging valley intersection time period of the charging circuit every day are acquired respectively. Based on the acquisition result, the average power supply of the charging circuit in each intersection time period is calculated. The average power supply = the total power supply of the charging circuit in each intersection time period / the total number of each intersection time period. The charging peak intersection time period refers to the intersection of the acquired charging peak time periods of the charging circuit every day. Similarly, the charging average intersection time period refers to the intersection of the acquired charging average time periods of the charging circuit every day. The charging valley intersection time period refers to the intersection of the acquired charging valley time periods of the charging circuit every day. For example: suppose the current time is December 13, the charging valley time period of the charging circuit on December 10 is [21:00, 24:00], the total power supply of the charging circuit during the charging valley time period on December 10 is 100kWh, the charging valley time period of the charging circuit on December 11 is [20:00, 24:00], the total power supply of the charging circuit during the charging valley time period on December 11 is 120kWh, the charging valley time period of the charging circuit on December 12 is [21:30, 24:00], the total power supply of the charging circuit during the charging valley time period on December 12 is 140kWh, then the charging valley intersection time period of the charging circuit every day is [21:30, 24:00]; The average power supply of the charging circuit during the charging valley intersection period = (140 + 120 + 100) / 3 = 120 kWh; The power supply G of the charging circuit at time t t Collect and t The collection time is obtained, the intersection time period to which the collection time belongs is determined, and based on the determination result, the j value is determined to predict the real-time charging congestion coefficient of the charging circuit. The specific prediction formula is: ; ; Among them, j=1,2,3, when j=1, V j =V1 represents the average power supply of the charging circuit during the charging valley intersection time period, P j =P1 represents the starting time point of the charging circuit within the charging valley intersection time period, Indicates the termination time point of the charging circuit within the charging valley intersection time period. When j=2, V j =V2 represents the average power supply of the charging circuit during the charging level intersection time period, P j =P2 represents the starting time point of the charging circuit within the charging level intersection time period, Indicates the end time point of the charging circuit within the charging level intersection time period. When j=3, V j =V3 represents the average power supply of the charging circuit during the charging peak intersection period, P j =P3 represents the starting time point of the charging circuit within the charging peak intersection time period, Indicates the termination time point of the charging circuit within the charging peak intersection time period. signQ represents the sign extraction function. When Q≥0, signQ=1; when Q<0, signQ=-1. Indicates the charging congestion coefficient of the charging circuit at time t. The charging congestion coefficient refers to the power consumption value of the charging circuit when the charging circuit is charging the associated equipment; Step 3: Analyze the optimal charging time for each associated device based on the predicted charging index of each associated device and the real-time charging congestion coefficient of the charging circuit; Step three includes: S301: According to The value determines whether the charging circuit can charge the associated device at time t. , then the charging circuit cannot charge the associated device at time t. , then the charging circuit can charge the associated device at time t; S302: Put the charging index of each associated device into a set M, , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m, and the number value corresponding to maxM To confirm, , for the number value The real-time charging status of the corresponding associated device To obtain, at the same time, the number is The charging index of the associated device is deleted from the set M, where max represents the maximum value symbol; S303: If , then repeat the operation in S302 to determine the number value corresponding to the maximum value in the set M that has been deleted, until Stop operation when S304: The optimal charging time of the associated device corresponding to the number value determined in S302 and S303 is time t; Step 4: Manage the charging status of each associated device; Step 4: The specific method for managing the charging status of each associated device is as follows: when the optimal charging time of each associated device is reached, the information processing terminal feeds back the charging information to the smart terminal that records the charging status and usage status of the corresponding associated device. The user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

[0021] An IoT-based device charging management system, the system includes an associated device charging index prediction module, a charging circuit congestion coefficient prediction module, an optimal charging time analysis module and a device charging management module; The associated device charging index prediction module is used to predict the charging index of each associated device; The associated device charging index prediction module includes a charging peak, valley and flat time period determination unit, an associated device determination unit and an associated device charging index prediction unit; The charging peak, valley and flat time period determination unit determines the charging peak time period, the charging flat time period and the charging valley time period of the charging circuit according to the historical charging amount set of the charging circuit and the maximum power supply of the charging circuit within the time T; When the device needs to be charged, the device user enters the charging status and usage status of the device to be charged in the smart terminal, and the information processing terminal collects the status information entered in the smart terminal and marks the device to be charged as an associated device; The associated device charging index prediction unit predicts the charging index of the associated device based on the charging status information and usage status information of the associated device collected by the information processing terminal and the time when the charging status information of the associated device is entered into the intelligent terminal; The charging circuit congestion coefficient prediction module is used to predict the real-time congestion coefficient of the charging circuit; The charging circuit congestion coefficient prediction module includes an average power supply calculation unit and a charging circuit congestion coefficient prediction unit; The average power supply calculation unit determines the charging peak time period, the charging average time period and the charging valley time period of the charging circuit in each day according to the historical charging amount set of the charging circuit, obtains the charging peak intersection time period, the charging average intersection time period and the charging valley intersection time period of the charging circuit in each day based on the determination result, and calculates the average power supply of the charging circuit in each intersection time period based on the acquisition result; The charging circuit congestion coefficient prediction unit predicts the real-time charging congestion coefficient of the charging circuit based on the real-time power supply of the charging circuit; The optimal charging time analysis module is used to analyze the optimal charging time of each associated device; The optimal charging time analysis module includes a charging judgment unit, a to-be-charged associated device search unit, and an optimal charging time analysis unit; The charging judgment unit judges whether the charging circuit can charge the associated device at the current moment according to the prediction result of the charging circuit congestion coefficient prediction unit; The associated device to be charged searching unit puts the prediction result of the associated device charging index prediction unit into the set M, searches for the associated device to be charged according to the number value corresponding to the maximum value in the set M, and repeats the operation until the charging judgment unit determines that the charging circuit cannot charge the associated device at the current moment, wherein: , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m; The optimal charging time analysis unit analyzes and obtains the optimal charging time of the associated device to be charged found by the associated device to be charged search unit according to the time value corresponding to the current moment; The device charging management module is used to manage the charging status of each associated device; When the device charging management module reaches the optimal charging time for each associated device to be charged, the information processing terminal feeds back the charging information to the smart terminal which records the charging status and usage status of the corresponding associated device. The user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

[0022] Example 1: Assume that the power supply of the charging circuit at 22:00 , since 22:00 is in the charging valley intersection time period [21:30, 24:00], j=1; set up , , T=1min, then the charging congestion coefficient of the charging circuit at 22:00 is: ; The charging congestion coefficient of the charging circuit at 22:00 is 0.299, where: Hour.

[0023] 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.

[0024] 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 device charging management method based on the Internet of Things, characterized in that: The method comprises: Step 1: Obtain the historical charging amount set of the charging circuit, determine the charging peak, valley and flat time period of the charging circuit, and predict the charging index of each associated device through the charging status information and usage status information of each associated device collected by the information processing terminal, as well as the time when the charging status information of each associated device is entered into the smart terminal; Step 2: Collect the real-time power supply of the charging circuit, and predict the real-time charging congestion coefficient of the charging circuit based on the average power supply of the charging circuit during the charging peak, valley and flat time periods; Step 3: Analyze the optimal charging time for each associated device based on the predicted charging index of each associated device and the real-time charging congestion coefficient of the charging circuit; Step 4: Manage the charging status of each associated device.

2. According to the device charging management method based on the Internet of Things according to claim 1, it is characterized by: The step one comprises: S101: collecting the historical charging amount of the charging circuit at time intervals T to obtain a historical charging amount set A of the charging circuit. , where i=1,2,…,n, represents the number corresponding to each historical charging amount collection time point in the charging circuit, n represents the total number, x n Indicates the historical charging value collected at the historical charging collection time point numbered n. i The maximum power supply Y of the charging circuit within T time T For comparison, if x i >0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the peak charging time period. If 0.5Y T ≤x i ≤0.9Y T , then the time period [(i-1)*T+R,i*T+R] is the charging level time period, x i <0.5Y T , then the time period [(i-1)*T+R,i*T+R] is the charging valley time period, where x i represents the historical charging value collected at the historical charging collection time point numbered i, and R represents the time value corresponding to the historical charging collection time point numbered 1; S102: When the device needs to be charged, the device user enters the charging status and usage status of the device to be charged on the smart terminal. The charging status includes the remaining power value of the device and the power value of the device to be charged. The usage status includes the continued use time of the device and the power consumption rate of the device. When the power value of the device to be charged is greater than the remaining power value of the device*5%, the information processing terminal collects the charging status information and usage status information entered into the smart terminal, and marks the device to be charged as an associated device. S103: Predicting the charging index of the associated device based on the time when the charging status information of the associated device is entered into the smart terminal. The specific prediction formula is: ； Among them, S represents the remaining power value of the associated device, F represents the power value of the associated device to be charged, H represents the power consumption rate of the associated device, and U represents the continued use time of the associated device. hour, ,when hour, , μ represents the charging index of the associated device.

3. According to the device charging management method based on the Internet of Things according to claim 2, it is characterized in that: The specific method of predicting the real-time charging congestion coefficient of the charging circuit in step 2 is: According to the acquired historical charging amount set of the charging circuit, the charging peak time period, the charging average time period and the charging valley time period of the charging circuit in each day are determined; based on the determination result, the charging peak intersection time period, the charging average intersection time period and the charging valley intersection time period of the charging circuit in each day are respectively acquired; based on the acquisition result, the average power supply of the charging circuit in each intersection time period is calculated; The power supply G of the charging circuit at time t t Collect and t The collection time is obtained, the intersection time period to which the collection time belongs is determined, and based on the determination result, the j value is determined to predict the real-time charging congestion coefficient of the charging circuit. The specific prediction formula is: ； ； Among them, j=1,2,3, when j=1, V j =V1 represents the average power supply of the charging circuit during the charging valley intersection time period, P j =P1 represents the starting time point of the charging circuit within the charging valley intersection time period, Indicates the termination time point of the charging circuit within the charging valley intersection time period. When j=2, V j =V2 represents the average power supply of the charging circuit during the charging level intersection time period, P j =P2 represents the starting time point of the charging circuit within the charging level intersection time period, Indicates the end time point of the charging circuit within the charging level intersection time period. When j=3, V j =V3 represents the average power supply of the charging circuit during the charging peak intersection period, P j =P3 represents the starting time point of the charging circuit within the charging peak intersection time period, Indicates the termination time point of the charging circuit within the charging peak intersection time period. signQ represents the sign extraction function. When Q≥0, signQ=1; when Q<0, signQ=-1. Represents the charging congestion coefficient of the charging circuit at time t.

4. According to the device charging management method based on the Internet of Things according to claim 3, it is characterized in that: The step three comprises: S301: According to The value determines whether the charging circuit can charge the associated device at time t. , then the charging circuit cannot charge the associated device at time t. , then the charging circuit can charge the associated device at time t; S302: Put the charging index of each associated device into a set M, , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m, and the number value corresponding to maxM To confirm, , for the number value The real-time charging status of the corresponding associated device To obtain, at the same time, the number is The charging index of the associated device is deleted from the set M, where max represents the maximum value symbol; S303: If , then repeat the operation in S302 to determine the number value corresponding to the maximum value in the set M that has been deleted, until Stop operation when S304: The optimal charging time of the associated device corresponding to the number value determined in S302 and S303 is time t.

5. According to the device charging management method based on the Internet of Things according to claim 4, it is characterized in that: The specific method of managing the charging status of each associated device in step 4 is: when the optimal charging time of each associated device is reached, the information processing terminal feeds back the charging information to the smart terminal that records the charging status and usage status of the corresponding associated device, and the user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

6. A device charging management system based on the Internet of Things applied to the device charging management method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes an associated device charging index prediction module, a charging circuit congestion coefficient prediction module, an optimal charging time analysis module and a device charging management module; The associated device charging index prediction module is used to predict the charging index of each associated device; The charging circuit congestion coefficient prediction module is used to predict the real-time congestion coefficient of the charging circuit; The optimal charging time analysis module is used to analyze the optimal charging time of each associated device; The device charging management module is used to manage the charging status of each associated device.

7. The device charging management system based on the Internet of Things according to claim 6 is characterized by: The associated device charging index prediction module includes a charging peak, valley and flat time period determination unit, an associated device determination unit and an associated device charging index prediction unit; The charging peak-valley-average time period determination unit determines the charging peak time period, the charging average time period and the charging valley time period of the charging circuit according to the historical charging amount set of the charging circuit and the maximum power supply of the charging circuit within the time T; When the device needs to be charged, the associated device determination unit inputs the charging status and usage status of the device to be charged into the smart terminal, and the information processing terminal collects the status information input into the smart terminal and marks the device to be charged as an associated device; The associated device charging index prediction unit predicts the charging index of the associated device based on the charging status information and usage status information of the associated device collected by the information processing terminal and the time when the charging status information of the associated device is entered into the smart terminal.

8. The device charging management system based on the Internet of Things according to claim 7 is characterized in that: The charging circuit congestion coefficient prediction module includes an average power supply calculation unit and a charging circuit congestion coefficient prediction unit; The average power supply calculation unit determines the charging peak time period, the charging average time period and the charging valley time period of the charging circuit in each day according to the historical charging amount set of the charging circuit, obtains the charging peak intersection time period, the charging average intersection time period and the charging valley intersection time period of the charging circuit in each day based on the determination result, and calculates the average power supply of the charging circuit in each intersection time period based on the acquisition result; The charging circuit congestion coefficient prediction unit predicts the real-time charging congestion coefficient of the charging circuit based on the real-time power supply of the charging circuit.

9. The device charging management system based on the Internet of Things according to claim 8, characterized in that: The optimal charging time analysis module includes a charging judgment unit, a to-be-charged associated device search unit and an optimal charging time analysis unit; The charging judgment unit judges whether the charging circuit can charge the associated device at the current moment according to the prediction result of the charging circuit congestion coefficient prediction unit; The associated device to be charged searching unit puts the prediction result of the associated device charging index prediction unit into the set M, searches for the associated device to be charged according to the number value corresponding to the maximum value in the set M, and repeats the operation until the charging judgment unit determines that the charging circuit cannot charge the associated device at the current moment, wherein: , j = 1, 2, ..., m, represents the number of each associated device, m represents the total number of associated devices, Indicates the charging index of the associated device numbered m; The optimal charging time analysis unit analyzes and obtains the optimal charging time of the associated device to be charged found by the associated device to be charged search unit according to the time value corresponding to the current moment.

10. The device charging management system based on the Internet of Things according to claim 9, characterized in that: When the device charging management module reaches the optimal charging time for each associated device to be charged, the information processing terminal feeds back the charging information to the smart terminal which records the charging status and usage status of the corresponding associated device. The user of the smart terminal connects the associated device to the charging circuit according to the feedback information received by the smart terminal.

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