An Internet of Things device activity monitoring and management method and system

Abstract

The present invention relates to the technical field of Internet of Things supervision, and discloses an Internet of Things device activity monitoring and management method and system. The management method includes the following steps: S1: Divide a natural day into several detection periods; S2: Obtain the traffic consumption data of each Internet of Things device; S3: Collect the status data of each Internet of Things device within each detection period; S4: Obtain the activity index of each Internet of Things device in each detection period within a preset past time period through an analysis module; S5: Obtain the network priority of each Internet of Things device in the detection period according to the activity index of each Internet of Things device in the same detection period; S6: Obtain a judgment index through the analysis module and judge whether each Internet of Things device is abnormal; enable Internet of Things devices with higher activity indexes to obtain higher network priorities, ensure the network quality of Internet of Things devices with high activity, and give an alarm in time and perform maintenance in time when the Internet of Things device is abnormal.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things supervision, and particularly relates to a method and system for monitoring and managing the activity of Internet of Things devices. Background Art

[0002] An Internet of Things device is a device that can be wirelessly connected to a network and has the ability to transmit data; with the rapid development of Internet of Things technology, the functions and application scenarios of Internet of Things devices are increasing, and now Internet of Things devices have been widely used in fields such as smart homes, industrial manufacturing, medical agriculture, and smart cities.

[0003] Generally, Internet of Things devices are always connected to the network; as more and more Internet of Things devices are connected to the same network, each Internet of Things device needs to constantly compete for resources in the network to complete its work.

[0004] However, the active time of each Internet of Things device is different, and different Internet of Things devices may have a weak ability to compete for resources due to reasons such as their placement or communication quality, which may cause some devices to be unable to obtain necessary resources, thus affecting the user experience; therefore, it is also difficult to determine whether each Internet of Things device is abnormal, resulting in the inability to maintain Internet of Things devices in a timely manner, increasing the risk of unexpected downtime. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for monitoring and managing the activity of Internet of Things devices to solve the following technical problems:

[0006] 1. How to determine the network priority of each Internet of Things device;

[0007] 2. How to more accurately determine whether each Internet of Things device is abnormal.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A method for monitoring and managing the activity of Internet of Things devices includes the following steps:

[0010] S1: Divide a natural day into several detection periods;

[0011] S2: Obtain the traffic consumption data of each Internet of Things device through a traffic monitoring module;

[0012] S3: Collect the status data of each Internet of Things device within each detection period through a device status data acquisition module;

[0013] S4: Analyze the traffic consumption data and status data of each Internet of Things device within the detection period in the past preset time period through an analysis module to obtain the activity index of each Internet of Things device within each detection period in the past preset time period;

[0014] S5: Obtain the network priority of each Internet of Things device in this detection period according to the activity index of each Internet of Things device in the same detection period;

[0015] S6: Analyze the traffic consumption data and status data of the detection period that has been completed and is closest to the current time, as well as the traffic consumption data and status data of each Internet of Things device in the detection period within a preset past time period, by the analysis module, to obtain a judgment index, and judge whether each Internet of Things device is abnormal according to the judgment index.

[0016] As a further solution of the present invention: Through the formula: Calculate the activity index of the th Internet of Things device in the th detection period within a preset past time period ;

[0017] Wherein, is the total number of days when the th Internet of Things device is in a normal working state within a preset past time period, ; is the traffic activity of the th Internet of Things device in the rd day and the th detection period when it is in a normal working state within a preset past time period; is the first preset constant of the activity index; is the online rate of the th Internet of Things device in the rd day and the th detection period when it is in a normal working state within a preset past time period; is the second preset constant of the activity index; is the third preset constant of the activity index; is the fourth preset constant of the activity index; is the de-unit coefficient.

[0018] As a further solution of the present invention: Through the formula: Calculate the traffic activity of the th Internet of Things device in the nd day and the th detection period when it is in a normal working state within a preset past time period ;

[0019] Wherein, is the start time of the th detection period; is the duration of the th detection period; The variation curve of the traffic consumption over time for the th day when the th Internet of Things device was in a normal working state within a preset past time period; The traffic value consumed per unit of activity.

[0020] As a further solution of the present invention: The process of obtaining the online rate for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle includes the following steps:

[0021] S10: Set the number of monitoring cycles for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle according to a preset rule;

[0022] S20: Through the device status data acquisition module, collect heartbeat data once in each monitoring cycle, and obtain the online status value according to whether the collection is successful;

[0023] S30: Analyze the online status values of each monitoring cycle for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle, and obtain the online rate for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle.

[0024] As a further solution of the present invention: In step S30, through the formula: Calculate the online rate for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle ;

[0025] Wherein, is the number of monitoring cycles for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle; is the cumulative value of the online status values for the th day when the th Internet of Things device was in a normal working state within a preset past time period for the th detection cycle.

[0026] As a further solution of the present invention: in step S10, the preset rule is:

[0027]

[0028] Calculate the number of monitoring cycles in the th detection cycle when the th Internet of Things device is in a normal working state ;

[0029] Wherein, is the basic number of monitoring cycles.

[0030] As a further solution of the present invention: through the formula:

[0031]

[0032] Calculate the judgment index of the th Internet of Things device;

[0033] Wherein, is the first judgment function. When , ; when , ; is the end time of the detection cycle that has been completed and is closest to the current time; is the th Internet of Things device's daily traffic consumption change curve over time; is the online rate of the detection cycle that has been completed and is closest to the current time; is the th Internet of Things device's th detection cycle's traffic activity error adjustment coefficient; is the th Internet of Things device's th detection cycle's online rate error adjustment coefficient; is the traffic activity allowable error value; is the online rate allowable error value.

[0034] As a further solution of the present invention: through the formula:

[0035]

[0036] Calculate the traffic activity error adjustment coefficient and the online rate error adjustment coefficient of the th Internet of Things device's th detection cycle;

[0037] Wherein, is the first preset adjustment coefficient; is the second preset adjustment coefficient.

[0038] As a further solution of the present invention: In step S6, the judgment process according to the judgment index is as follows:

[0039] When , it indicates that the th Internet of Things device has no abnormality;

[0040] When , it indicates that the th Internet of Things device is abnormal.

[0041] An Internet of Things device activity monitoring and management system, the system includes:

[0042] A traffic monitoring module for obtaining the traffic consumption data of each Internet of Things device;

[0043] A device status data acquisition module for collecting the status data of each Internet of Things device;

[0044] An analysis module for analyzing the traffic consumption data and status data within the detection period of each Internet of Things device in the past preset time period to obtain the activity index of each Internet of Things device in each detection period in the past preset time period; and obtaining the network priority of each Internet of Things device in this detection period according to the activity index of each Internet of Things device in the same detection period; it is also used to analyze the traffic consumption data, status data, traffic consumption data and status data within the detection period of each Internet of Things device in the past preset time period of the completed detection period closest to the current time to obtain a judgment index, and judge whether each Internet of Things device is abnormal according to the judgment index.

[0045] The beneficial effects of the present invention:

[0046] The present invention first divides a natural day into several detection periods; then obtains the traffic consumption data of each Internet of Things device through a traffic monitoring module; subsequently collects the status data of each Internet of Things device within each detection period through a device status data acquisition module; then analyzes the traffic consumption data and status data of each Internet of Things device within the detection period in a preset past time period through an analysis module to obtain the activity index of each Internet of Things device within each detection period in the preset past time period; obtains the network priority of each Internet of Things device in this detection period according to the activity index of each Internet of Things device in the same detection period; finally analyzes the traffic consumption data, status data, traffic consumption data and status data of each Internet of Things device within the detection period in the preset past time period of the completed detection period closest to the current time through the analysis module to obtain a judgment index, and judges whether each Internet of Things device is abnormal according to the judgment index; in the same detection period, enables the Internet of Things device with a higher activity index to obtain a higher network priority, ensures the quality of the network of the Internet of Things device with a high activity, and can more accurately judge whether the Internet of Things device is abnormal, and when the Internet of Things device is abnormal, gives an alarm in time and performs maintenance in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described below with reference to the accompanying drawings.

[0048] Figure 1 is a flowchart of a method according to an embodiment of the present invention;

[0049] Figure 2 is a system module framework diagram according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Please refer to Figure 1 As shown, in one embodiment, a method for monitoring and managing the activity of Internet of Things devices is provided, including the following steps:

[0052] S1: Divide a natural day into several detection periods;

[0053] S2: Obtain the traffic consumption data of each Internet of Things device through a traffic monitoring module;

[0054] S3: Collect the status data of each Internet of Things device within each detection period through a device status data acquisition module;

[0055] S4: Analyze the traffic consumption data and status data within the detection cycles of each IoT device in the past preset time period through the analysis module to obtain the activity index of each IoT device in each detection cycle within the past preset time period;

[0056] S5: Obtain the network priority of each IoT device in this detection cycle based on the activity index of each IoT device in the same detection cycle;

[0057] S6: Analyze the traffic consumption data, status data, the traffic consumption data and status data within the detection cycles of each IoT device in the past preset time period in the completed detection cycle closest to the current time through the analysis module to obtain a judgment index, and judge whether each IoT device is abnormal based on the judgment index;

[0058] Through the above technical solution, in this embodiment, the natural day is first divided into several detection cycles; then the traffic consumption data of each IoT device is obtained through the traffic monitoring module; subsequently, the status data of each IoT device in each detection cycle is collected through the device status data acquisition module; then the traffic consumption data and status data within the detection cycles of each IoT device in the past preset time period are analyzed through the analysis module to obtain the activity index of each IoT device in each detection cycle within the past preset time period; the network priority of each IoT device in this detection cycle is obtained based on the activity index of each IoT device in the same detection cycle; finally, the traffic consumption data, status data, the traffic consumption data and status data within the detection cycles of each IoT device in the past preset time period in the completed detection cycle closest to the current time are analyzed through the analysis module to obtain a judgment index, and judge whether each IoT device is abnormal based on the judgment index; enabling IoT devices with a higher activity index to obtain a higher network priority within the same detection cycle, ensuring the quality of the network for IoT devices with high activity, and being able to more accurately judge whether an IoT device is abnormal. When an IoT device is abnormal, an alarm is given in a timely manner and maintenance is carried out in a timely manner.

[0059] As an implementation manner of the present invention, through the formula:

[0060]

[0061] Calculate the activity index of the th IoT device in the th detection cycle within the past preset time period ;

[0062] wherein, is the total number of days when the th IoT device was in a normal working state within the past preset time period, ; is the The traffic activity of the th day and the th detection cycle when the Internet of Things device is in a normal working state; Is the preset constant for the first activity index; For the th Internet of Things device in a normal working state during the preset past time period, the th day and the th detection cycle's online rate; Is the preset constant for the second activity index; Is the preset constant for the third activity index; Is the preset constant for the fourth activity index; Is the coefficient for removing the unit;

[0063] Through the above technical solution, in this embodiment For the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle's average traffic activity; the average traffic activity of the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle The larger it is, the th Internet of Things device's th detection cycle's activity index The larger it is; For the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle's average online rate; the average online rate of the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle The larger it is, the th Internet of Things device's th detection cycle's activity index The larger it is; For the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle's standard deviation of traffic activity; the standard deviation of traffic activity of the th Internet of Things device in a normal working state during the preset past time period, the th detection cycle The smaller it is, it indicates that during the preset past time period, the th Internet of Things device in a normal working state, the The flow activity of each detection period is more stable. Therefore, the activity index of the th Internet of Things device in the past preset time period for the th detection period is larger; Let be the standard deviation of the online rate of the th day in the normal working state of the th detection period of the th Internet of Things device; the standard deviation of the online rate of the th day in the normal working state of the th detection period of the th Internet of Things device is smaller, indicating that the online state of the th detection period of the th Internet of Things device in the normal working state in the past preset time period is more stable. Therefore, the activity index of the th detection period of the th Internet of Things device

[0064] It should be noted that the first activity index preset constant , the second activity index preset constant , the third activity index preset constant , the fourth activity index preset constant and the unit removal coefficient are preset values obtained based on experience and will not be elaborated here.

[0065] As an implementation manner of the present invention, through the formula:

[0066]

[0067] Calculate the flow activity of the th Internet of Things device in the normal working state on the th day for the th detection period ;

[0068] Wherein, is the start time of the th detection period; is the duration of the th detection period; is the curve of the consumed traffic over time on the th day in the normal working state of the th Internet of Things device; is the traffic value consumed per unit activity;

[0069] Through the above technical solution, in this embodiment is the cumulative value of the traffic consumed on the th day when the th Internet of Things device is in a normal working state within a preset past time period; is the result of rounded up, representing the traffic activity on the th day when the th Internet of Things device is in a normal working state within a preset past time period; the cumulative value of the traffic consumed on the th day when the th Internet of Things device is in a normal working state The larger it is, the th day when the th Internet of Things device is in a normal working state, the th detection cycle has a higher traffic activity ;

[0070] It should be noted that the duration of the th detection cycle and the traffic value consumed per unit activity are preset values obtained based on experience and will not be elaborated here.

[0071] As an implementation manner of the present invention, the process of obtaining the online rate of the th Internet of Things device in a normal working state on the th day and the th detection cycle includes the following steps:

[0072] S10: Set the number of monitoring cycles of the th Internet of Things device in a normal working state on the th day and the th detection cycle according to a preset rule;

[0073] S20: Through the device status data acquisition module, collect heartbeat data once in each monitoring cycle, and obtain the online status value based on whether the collection is successful;

[0074] S30: Analyze the online status values of each monitoring cycle of the th Internet of Things device in a normal working state on the th day and the th detection cycle to obtain the online rate of the th Internet of Things device in a normal working state on the th day and the th detection cycle;

[0075] Through the above technical solution, first, set the number of monitoring cycles for the th IoT device in the normal working state on the th day of the th detection cycle according to the preset rules; then, through the device status data acquisition module, collect heartbeat data once in each monitoring cycle, and obtain the online status value according to whether the collection is successful; finally, analyze the online status values of each monitoring cycle of the th IoT device in the normal working state on the th day of the th detection cycle to obtain the online rate of the th IoT device in the normal working state on the th day of the th detection cycle within the past preset time period;

[0076] As an implementation manner of the present invention, in step S30, through the formula:

[0077]

[0078] Calculate the online rate of the th IoT device in the normal working state on the th day of the th detection cycle within the past preset time period ;

[0079] Wherein, is the number of monitoring cycles of the th IoT device in the normal working state on the th day of the th detection cycle; is the cumulative value of the online status values of the th IoT device in the normal working state on the th day of the th detection cycle within the past preset time period;

[0080] Through the above technical solution, set the number of monitoring cycles of the th IoT device in the normal working state on the th day of the th detection cycle according to the preset rules ; through the device status data acquisition module, collect heartbeat data once in each monitoring cycle, and obtain the online status value according to whether the collection is successful; if the collection is successful, the online status value is 1; if the collection fails, the online status value is 0; the sum of the online status values of one monitoring cycle is used to obtain the th IoT device in the normal working state within the past preset time period The cumulative value of the online status values for the nth detection cycle ; is the online rate for the nth Internet of Things device in a normal working state on the mth day and the nth detection cycle;

[0081] As an implementation manner of the present invention, in step S10, the preset rule is:

[0082]

[0083] Calculate the number of monitoring cycles for the nth Internet of Things device in a normal working state in the nth detection cycle ;

[0084] wherein, is the basic number of monitoring cycles;

[0085] Through the above technical solution, in this embodiment is the ceiling value of , representing the number of monitoring cycles for the nth Internet of Things device in a normal working state in the nth detection cycle; the standard deviation of the traffic activity of the nth Internet of Things device in a normal working state in the nth detection cycle in the past preset time period The larger it is, the more unstable the traffic activity of the nth Internet of Things device in a normal working state in the nth detection cycle is. Therefore, the number of monitoring cycles for the nth Internet of Things device in a normal working state in the nth detection cycle is larger; the standard deviation of the online rate of the nth Internet of Things device on the mth day in a normal working state in the nth detection cycle The larger it is, the more unstable the online status of the nth Internet of Things device in a normal working state in the nth detection cycle is. Therefore, the number of monitoring cycles for the nth Internet of Things device in a normal working state in the nth detection cycle

[0086] It should be noted that the basic quantity of the monitoring period is a preset value, obtained based on experience, and will not be elaborated here.

[0087] As an implementation manner of the present invention, through the formula:

[0088]

[0089] Calculate the judgment index of the th Internet of Things device ;

[0090] Among them, is the first judgment function. When , ; when , ; is the end time of the detection period that has been completed and is closest to the current time; is the change curve of the daily consumed traffic of the th Internet of Things device over time; is the online rate of the detection period that has been completed and is closest to the current time; is the traffic activity error adjustment coefficient of the th Internet of Things device in the th detection period; is the online rate error adjustment coefficient of the th Internet of Things device in the th detection period; is the allowable error value of traffic activity; is the allowable error value of online rate;

[0091] Through the above technical solution, the detection period that has been completed and is closest to the current time in this embodiment is the th detection period; is the traffic activity of the th Internet of Things device in the th detection period of the current day; is the difference between the traffic activity of the th Internet of Things device in the th detection period of the current day and the average traffic activity of the th Internet of Things device in the th detection period in the normal working state within the past preset time period; is the allowable error value of traffic activity of the th Internet of Things device in the th detection period; is the th Internet of Things device in the The difference between the traffic activity in a detection period and the average traffic activity in the th detection period when the th Internet of Things device was in a normal working state, and the difference between the difference and the allowable error value of the traffic activity in the th detection period of the th Internet of Things device. In the formula , the first judgment function refers to ; when , it indicates that the difference between the traffic activity in the th detection period of the th Internet of Things device today and the average traffic activity in the th detection period when the th Internet of Things device was in a normal working state within a past preset time period is within the allowable error value of the traffic activity in the th detection period of the th Internet of Things device, and the th Internet of Things device has no abnormality. ; when , it indicates that the difference between the traffic activity in the th detection period of the th Internet of Things device today and the average traffic activity in the th detection period when the th Internet of Things device was in a normal working state within a past preset time period exceeds the allowable error value of the traffic activity in the th detection period of the th Internet of Things device, and the th Internet of Things device may have an abnormality. ; ; is the difference between the online rate in the th detection period of the th Internet of Things device today and the average online rate in the th detection period when the th Internet of Things device was in a normal working state within a past preset time period; is the allowable error value of the online rate in the th detection period of the th Internet of Things device; is the difference between the online rate in the th detection period of the th Internet of Things device today and the average online rate in the th detection period when the th Internet of Things device was in a normal working state within a past preset time period and the th Internet of Things device. The difference in the allowable error value of the online rate for one detection cycle; in the formula , the first judgment function in refers to ; when , it indicates that the difference between the online rate of the th Internet of Things device for the th detection cycle of the current day and the average online rate of the th Internet of Things device in a normal working state for the th detection cycle within a past preset time period is within the allowable error value of the online rate of the th Internet of Things device for the th detection cycle, and the th Internet of Things device has no abnormality, ; when , it indicates that the difference between the online rate of the th Internet of Things device for the th detection cycle of the current day and the average online rate of the th Internet of Things device in a normal working state for the th detection cycle within a past preset time period exceeds the allowable error value of the online rate of the th Internet of Things device for the th detection cycle, and the th Internet of Things device may have an abnormality, ;

[0092] As an implementation manner of the present invention, through the formula:

[0093]

[0094] Calculate the traffic activity error adjustment coefficient and the online rate error adjustment coefficient for the th Internet of Things device for the th detection cycle;

[0095] Among them, is the first preset adjustment coefficient; is the second preset adjustment coefficient;

[0096] Through the above technical solution, the larger the standard deviation of the traffic activity of the th Internet of Things device in a normal working state for the th detection cycle within a past preset time period of this embodiment, the th Internet of Things device for the th detection cycle of the traffic activity error adjustment coefficient The greater the th Internet of Things device in the past preset time period that was in a normal working state on the th detection cycle, the greater the standard deviation of the online rate; The greater the th Internet of Things device's online rate error adjustment coefficient for the th detection cycle;

[0097] It should be noted that the first preset adjustment coefficient and the second preset adjustment coefficient are preset values obtained based on experience and will not be elaborated here.

[0098] As an implementation manner of the present invention, in step S6, the judgment process based on the judgment index is as follows:

[0099] When , it indicates that the th Internet of Things device has no abnormality;

[0100] When , it indicates that the th Internet of Things device is abnormal.

[0101] Please refer to Figure 2 shown, an Internet of Things device activity monitoring and management system includes:

[0102] A traffic monitoring module for obtaining the traffic consumption data of each Internet of Things device;

[0103] A device status data acquisition module for collecting the status data of each Internet of Things device;

[0104] An analysis module for analyzing the traffic consumption data and status data of each Internet of Things device within the detection cycle in the past preset time period to obtain the activity index of each Internet of Things device in each detection cycle in the past preset time period; and obtaining the network priority of each Internet of Things device in this detection cycle based on the activity index of each Internet of Things device in the same detection cycle; it is also used to analyze the traffic consumption data, status data, traffic consumption data and status data of each Internet of Things device within the detection cycle in the past preset time period for the detection cycle that has been completed and is closest to the current time to obtain a judgment index, and judge whether each Internet of Things device is abnormal based on the judgment index.

[0105] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for monitoring and managing the activity of IoT devices, characterized in that: The following steps are involved: S1: Divide the natural day into several detection cycles; S2: Obtain the traffic consumption data of each IoT device through the traffic monitoring module; S3: Collect the status data of each IoT device in each detection cycle through the device status data collection module; S4: Analyze the traffic consumption data and status data of each IoT device detection cycle in the past preset time period through the analysis module to obtain the activity index of each IoT device in each detection cycle in the past preset time period; S5: Obtain the network priority of each IoT device in the detection cycle according to the activity index of each IoT device in the same detection cycle; S6: Analyze the traffic consumption data and status data of the completed detection cycle closest to the current time, and the traffic consumption data and status data of the detection cycle of each IoT device in the past preset time period through the analysis module to obtain a judgment index, and judge whether each IoT device is abnormal according to the judgment index; By formula: ； Calculate the number of IoT devices Activity index of detection cycles ; in, The number of The total number of days that IoT devices are in normal working condition, ; The number of IoT devices are in normal working condition Tiandi Traffic activity during a detection cycle; Preset constant for first activity index; The number of IoT devices are in normal working condition Tiandi Online rate of each detection cycle; Preset constant for second activity index; Preset constant for third activity index; Preset constant for fourth activity index; is the unit factor.

2. The method for monitoring and managing the activity of IoT devices according to claim 1, characterized in that: By formula: ； Calculate the number of IoT devices are in normal working condition Tiandi Traffic activity in a detection cycle ; in, For the The start time of a detection cycle; For the The duration of a testing cycle; The number of IoT devices are in normal working condition The curve of daily consumption flow changing with time; The traffic value consumed per unit activity.

3. The method for monitoring and managing the activity of an Internet of Things device according to claim 2, characterized in that: The number of times in the past preset time period IoT devices are in normal working condition Tiandi The process of obtaining the online rate of a detection cycle includes the following steps: S10: Set the first IoT devices are in normal working condition Tiandi The number of monitoring cycles for each detection cycle; S20: Collect heartbeat data once in each monitoring cycle through the device status data collection module, and obtain the online status value based on whether the collection is successful; S30: By IoT devices are in normal working condition Tiandi The online status values ​​of each monitoring cycle of the detection cycle are analyzed to obtain the online status values ​​of the first monitoring cycle in the past preset time period. IoT devices are in normal working condition Tiandi The online rate of a detection cycle.

4. The method for monitoring and managing the activity of IoT devices according to claim 3, characterized in that: In step S30, by formula: ； Calculate the number of IoT devices are in normal working condition Tiandi Online rate of detection cycle ; in, For the IoT devices are in normal working condition Tiandi The number of monitoring cycles for each detection cycle; The number of IoT devices are in normal working condition Tiandi The accumulated value of the online status value in the detection cycle.

5. A method for monitoring and managing the activity of IoT devices according to claim 4, characterized in that: In step S10, the preset rule is: ； Calculate the IoT devices are in normal working condition The number of monitoring cycles for each detection cycle ; in, is the basic number of monitoring cycles.

6. A method for monitoring and managing the activity of IoT devices according to claim 5, characterized in that: By formula: ； Calculate the Judgment index of IoT devices ; in, is the first judgment function, when hour, ;when hour, ; The end time of the completed detection cycle that is closest to the current time; For the The daily traffic consumption curve of each IoT device changes over time; The online rate of the completed detection cycle closest to the current time; For the IoT devices Traffic activity error adjustment coefficient for each detection cycle; For the IoT devices Online rate error adjustment coefficient of a detection cycle; is the allowable error value of traffic activity; It is the allowable error value of online rate.

7. The method for monitoring and managing the activity of IoT devices according to claim 6, characterized in that: By formula: ； Calculate the IoT devices Traffic activity error adjustment coefficient for each detection cycle and online rate error adjustment coefficient ; in, is the first preset adjustment coefficient; is the second preset adjustment coefficient.

8. The method for monitoring and managing the activity of IoT devices according to claim 7, characterized in that: In step S6, the judgment process according to the judgment index is: when , explain There are no abnormalities in the IoT devices; when , explain An IoT device is abnormal.

9. An Internet of Things device activity monitoring and management system, applicable to an Internet of Things device activity monitoring and management method according to any one of claims 1 to 8, characterized in that: The system comprises: Traffic monitoring module, used to obtain traffic consumption data of each IoT device; Device status data collection module, used to collect status data of each IoT device; The analysis module is used to analyze the traffic consumption data and status data of each IoT device detection cycle in the past preset time period, and obtain the activity index of each IoT device in each detection cycle in the past preset time period; and obtain the network priority of each IoT device in the detection cycle according to the activity index of each IoT device in the same detection cycle; and is also used to analyze the traffic consumption data and status data of the detection cycle that has been completed and is closest to the current time, and the traffic consumption data and status data of each IoT device detection cycle in the past preset time period, to obtain a judgment index, and judge whether each IoT device is abnormal according to the judgment index.

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