Internet of Things equipment activeness monitoring management method and system

Abstract

The invention relates to the technical field of Internet of Things supervision, and discloses an Internet of Things equipment activeness monitoring management method and system, and the management method comprises the following steps: S1, dividing a natural day into a plurality of detection periods; s2, acquiring traffic consumption data of each piece of Internet of Things equipment; s3, collecting state data of each piece of Internet of Things equipment in each detection period; s4, obtaining an active index of each detection period of each piece of Internet of Things equipment in a past preset time period through an analysis module; s5, according to the active index of each piece of Internet of Things equipment in the same detection period, obtaining the network priority of each piece of Internet of Things equipment in the detection period; s6, obtaining a judgment index through an analysis module, and judging whether each piece of Internet of Things equipment is abnormal or not; according to the invention, the Internet of Things equipment with a higher activity index can obtain a higher network priority, the network quality of the Internet of Things equipment with a high activity degree is ensured, and when the Internet of Things equipment is abnormal, an alarm is given in time and maintenance is carried out in time.

Description

Technical Field

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

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

[0003] Usually, IoT devices are always connected to the network; as more and more IoT devices are connected to the same network, each IoT device needs to constantly seize resources in the network to complete its work.

[0004] However, each IoT device has a different active time. Different IoT devices have weaker ability to seize resources due to their placement or communication quality, which may make some devices unable to obtain necessary resources, thus affecting the user experience. Therefore, it is also difficult to determine whether each IoT device is abnormal, resulting in the inability to maintain the IoT 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 IoT devices to solve the following technical problems:

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

[0007] 2. How to more accurately determine whether each IoT 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 an Internet of Things device comprises the following steps:

[0010] S1: Divide the natural day into several detection cycles;

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

[0012] S3: Collect the status data of each IoT device in each detection cycle through the device status data collection module;

[0013] 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;

[0014] 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;

[0015] 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 each IoT device detection cycle in the past preset time period through the analysis module to obtain a judgment index, and judge whether each IoT device is abnormal based on the judgment index.

[0016] As a further solution of the present invention: by formula: Calculate the number of IoT devices Activity index of detection cycles ;

[0017] 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.

[0018] As a further solution of the present invention: by formula: Calculate the number of IoT devices are in normal working condition Tiandi Traffic activity in a detection cycle ;

[0019] 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.

[0020] As a further solution of the present invention: IoT devices are in normal working condition Tiandi The process of obtaining the online rate of a detection cycle includes the following steps:

[0021] S10: Set the first IoT devices are in normal working condition Tiandi The number of monitoring cycles for each detection cycle;

[0022] 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;

[0023] 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.

[0024] As a further solution of the present invention: in step S30, by formula: Calculate the number of IoT devices are in normal working condition Tiandi Online rate of detection cycle ;

[0025] 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.

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

[0027]

[0028] Calculate the IoT devices are in normal working condition The number of monitoring cycles for each detection cycle ;

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

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

[0031]

[0032] Calculate the Judgment index of IoT devices ;

[0033] 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.

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

[0035]

[0036] Calculate the IoT devices Traffic activity error adjustment coefficient for each detection cycle and online rate error adjustment coefficient ;

[0037] in, 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:

[0039] when , explain There are no abnormalities in the IoT devices;

[0040] when , explain An IoT device is abnormal.

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

[0042] Traffic monitoring module, used to obtain traffic consumption data of each IoT device;

[0043] Device status data collection module, used to collect status data of each IoT device;

[0044] 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.

[0045] Beneficial effects of the present invention:

[0046] The present invention first divides a natural day into a number of detection cycles; then obtains the flow consumption data of each Internet of Things device through a flow monitoring module; then collects the status data of each Internet of Things device in each detection cycle through a device status data acquisition module; then analyzes the flow consumption data and status data of each Internet of Things device in the detection cycle in the past preset time period through an analysis module to obtain the activity index of each Internet of Things device in each detection cycle in the past preset time period; obtains the network priority of each Internet of Things device in the detection cycle according to the activity index of each Internet of Things device in the same detection cycle; finally, analyzes the flow consumption data and status data of the detection cycle that has been completed and is closest to the current time, and the flow consumption data and status data of each Internet of Things device in the detection cycle in the past preset time period through an analysis module to obtain a judgment index, and judges whether each Internet of Things device is abnormal according to the judgment index; so that in the same detection cycle, an Internet of Things device with a higher activity index can obtain a higher network priority, ensure the quality of the Internet of Things device network with high activity, and can more accurately judge whether the Internet of Things device is abnormal, and when the Internet of Things device is abnormal, an alarm is issued in time and maintenance is carried out in time. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 1 A method flow chart of an embodiment of the present invention;

[0049] Figure 2 A system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] See also Figure 1 As shown, in one embodiment, a method for monitoring and managing activity of an IoT device is provided, comprising the following steps:

[0052] S1: Divide the natural day into several detection cycles;

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

[0054] S3: Collect the status data of each IoT device in each detection cycle through the device status data collection module;

[0055] 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;

[0056] 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;

[0057] 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;

[0058] Through the above technical solution, this embodiment first divides a natural day into several detection cycles; then obtains the traffic consumption data of each IoT device through the traffic monitoring module; then collects the status data of each IoT device in each detection cycle through the device status data acquisition module; then analyzes the traffic consumption data and status data of each IoT device in the 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; obtains 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; finally, 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 in the detection cycle in the past preset time period are analyzed by the analysis module to obtain the judgment index, and judge whether each IoT device is abnormal according to the judgment index; so that in the same detection cycle, the IoT device with a higher activity index can obtain a higher network priority, ensure the quality of the IoT device network with high activity, and can more accurately judge whether the IoT device is abnormal, and when the IoT device is abnormal, timely alarm and timely maintenance are carried out.

[0059] As an implementation mode of the present invention, by formula:

[0060]

[0061] Calculate the number of IoT devices Activity index of detection cycles ;

[0062] 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-removing coefficient;

[0063] Through the above technical solution, this embodiment The number of IoT devices are in normal working condition The average traffic activity of the detection cycle; the average traffic activity of the IoT devices are in normal working condition Average traffic activity in detection cycles The larger the value, the more IoT devices Activity index of detection cycles The bigger; The number of IoT devices are in normal working condition The average online rate of the detection cycle; the average online rate of the detection cycle in the past preset time period; IoT devices are in normal working condition Average online rate of detection cycles The larger the value, the IoT devices Activity index of detection cycles The bigger; The number of IoT devices are in normal working condition The standard deviation of traffic activity in the past detection cycle; IoT devices are in normal working condition The standard deviation of traffic activity in each detection cycle The smaller the value, the more IoT devices are in normal working condition The more stable the traffic activity is in each detection cycle, the more stable the traffic activity is in each detection cycle. IoT devices Activity index of detection cycles The bigger; The number of IoT devices Day in normal working order The standard deviation of the online rate of the detection cycle; IoT devices Day in normal working order The standard deviation of the online rate of the detection cycle The smaller the value, the more IoT devices are in normal working condition The more stable the online status of each detection cycle, the more stable the online status of each detection cycle. IoT devices Activity index of detection cycles The bigger;

[0064] It should be noted that the first activity index preset constant , Second active index preset constant , the third active index preset constant , the fourth activity index preset constant and remove the unit coefficient It is a preset value obtained based on experience and will not be described in detail here.

[0065] As an implementation mode of the present invention, by formula:

[0066]

[0067] Calculate the number of IoT devices are in normal working condition Tiandi Traffic activity in a detection cycle ;

[0068] 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;

[0069] Through the above technical solution, this embodiment The number of IoT devices are in normal working condition The accumulated value of daily traffic consumption; For The value of is rounded up to indicate the number of IoT devices are in normal working condition The traffic activity of the day; the traffic activity of the IoT devices are in normal working condition The accumulated flow rate of the day The larger the value, the IoT devices are in normal working condition Tiandi Traffic activity in a detection cycle The bigger;

[0070] It should be noted that The duration of a detection cycle And the traffic value consumed by unit activity It is a preset value obtained based on experience and will not be described in detail here.

[0071] As an embodiment of the present invention, the first IoT devices are in normal working condition Tiandi The process of obtaining the online rate of a detection cycle includes the following steps:

[0072] S10: Set the first IoT devices are in normal working condition Tiandi The number of monitoring cycles for each detection cycle;

[0073] 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;

[0074] 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 Online rate of each detection cycle;

[0075] Through the above technical solution, first set the first IoT devices are in normal working condition Tiandi The number of monitoring cycles of a detection cycle; then, 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 acquisition is successful; finally, 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 Online rate of each detection cycle;

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

[0077]

[0078] Calculate the number of IoT devices are in normal working condition Tiandi Online rate of detection cycle ;

[0079] 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 cumulative value of the online status value of the detection cycle;

[0080] Through the above technical solution, the first IoT devices are in normal working condition Tiandi The number of monitoring cycles for each detection cycle ; Through the device status data acquisition module, heartbeat data is collected once in each monitoring cycle, and the online status value is obtained based on 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 online status values ​​of the monitoring cycles are added to obtain the online status values ​​of the preset time period in the past. IoT devices are in normal working condition Tiandi The cumulative value of the online status value of the detection cycle ; The number of IoT devices are in normal working condition Tiandi Online rate of each detection cycle;

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

[0082]

[0083] Calculate the IoT devices are in normal working condition The number of monitoring cycles for each detection cycle ;

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

[0085] Through the above technical solution, this embodiment For The value of is rounded up to the integer value. IoT devices are in normal working condition The number of monitoring cycles in the past preset time period. IoT devices are in normal working condition The standard deviation of traffic activity in each detection cycle The larger the value, the more IoT devices are in normal working condition The more unstable the traffic activity is in the first detection cycle, the IoT devices are in normal working condition The number of monitoring cycles for each detection cycle The larger the number, the IoT devices Day in normal working order The standard deviation of the online rate of the detection cycle The larger the value, the more IoT devices are in normal working condition The more unstable the online state is, the more IoT devices are in normal working condition The number of monitoring cycles for each detection cycle The bigger;

[0086] It should be noted that the basic number of monitoring cycles It is a preset value obtained based on experience and will not be described in detail here.

[0087] As an implementation mode of the present invention, by formula:

[0088]

[0089] Calculate the Judgment index of IoT devices ;

[0090] 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; is the allowable error value of the online rate;

[0091] Through the above technical solution, the detection cycle that has been completed in this embodiment and is closest to the current time is A detection cycle; For the IoT devices today Traffic activity during a detection cycle; For the IoT devices today The traffic activity of the detection cycle is compared with the traffic activity of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference in average traffic activity between detection cycles; For the IoT devices The permissible error value of traffic activity in a detection cycle; For the IoT devices today The traffic activity of the detection cycle is compared with the traffic activity of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference between the average traffic activity of the first detection period and the IoT devices The difference in the allowable error value of the traffic activity of the detection cycle is given by the formula In the first judgment function In Reference ;when , explain IoT devices today The traffic activity of the detection cycle is compared with the traffic activity of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference in the average traffic activity of the detection cycles is IoT devices The traffic activity of the detection cycle is within the allowable error value. There is no abnormality in the IoT devices. ;when , explain IoT devices today The traffic activity of the detection cycle is compared with the traffic activity of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference in the average traffic activity of the detection cycles exceeds IoT devices The permissible error value of the traffic activity in the detection cycle, IoT devices may have anomalies ; For the IoT devices today The online rate of the detection cycle is compared with the online rate of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference of the average online rate of the detection cycles; For the IoT devices The online rate allowable error value of a detection cycle; For the IoT devices today The online rate of the detection cycle is compared with the online rate of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference between the average online rate of the first detection cycle and the IoT devices The difference in the online rate allowable error value of the detection cycle; in the formula In the first judgment function In Reference ;when , explain IoT devices today The online rate of the detection cycle is compared with the online rate of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference in the average online rate of the detection cycles is IoT devices The online rate of the detection cycle is within the allowable error value. There is no abnormality in the IoT devices. ;when , explain IoT devices today The online rate of the detection cycle is compared with the online rate of the first detection cycle in the past preset time period. IoT devices are in normal working condition The difference in the average online rate of the detection cycles exceeds IoT devices The online rate allowable error value of the detection cycle, There may be anomalies in IoT devices. ;

[0092] As an implementation mode of the present invention, by formula:

[0093]

[0094] Calculate the IoT devices Traffic activity error adjustment coefficient for each detection cycle and online rate error adjustment coefficient ;

[0095] in, is the first preset adjustment coefficient; is a second preset adjustment coefficient;

[0096] Through the above technical solution, in this embodiment, the first IoT devices are in normal working condition The standard deviation of traffic activity in each detection cycle The bigger, the IoT devices Traffic activity error adjustment coefficient for each detection cycle The larger the value, the IoT devices Day in normal working order The standard deviation of the online rate of the detection cycle The bigger, the IoT devices Online rate error adjustment coefficient of detection cycle The bigger;

[0097] It should be noted that the first preset adjustment coefficient and the second preset adjustment factor It is a preset value obtained based on experience and will not be described in detail here.

[0098] As an implementation mode of the present invention, in step S6, the judgment process according to the judgment index is:

[0099] when , explain There are no abnormalities in the IoT devices;

[0100] when , explain An IoT device is abnormal.

[0101] See also Figure 2 As shown, an IoT device activity monitoring and management system includes:

[0102] Traffic monitoring module, used to obtain traffic consumption data of each IoT device;

[0103] Device status data collection module, used to collect status data of each IoT device;

[0104] 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.

[0105] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage 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 each IoT device detection cycle in the past preset time period through the analysis module to obtain a judgment index, and judge whether each IoT device is abnormal based on the judgment index.

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 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.

3. The method for monitoring and managing the activity of an Internet of Things device according to claim 2, 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.

4. The method for monitoring and managing the activity of IoT devices according to claim 3, 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.

5. A method for monitoring and managing the activity of IoT devices according to claim 4, 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.

6. A method for monitoring and managing the activity of IoT devices according to claim 5, 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.

7. The method for monitoring and managing the activity of IoT devices according to claim 6, 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.

8. The method for monitoring and managing the activity of IoT devices according to claim 7, 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.

9. The method for monitoring and managing the activity of IoT devices according to claim 8, 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.

10. 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 9, 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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