A method and device for real-time updating of a status of a master internet of things device

Abstract

The application discloses a kind of main animal internet of things equipment state real-time updating method and device, specifically including the following steps: S1, continuously receive the log data reported by internet of things equipment;S2, determine the type of the internet of things equipment, the type includes: active device, passive device, calculate update online invalid time;S3, receive current query instruction, extract the time of current query instruction, according to the time of current query instruction and the online invalid time, determine the state of the active device.The latest reporting time of the log data is the reporting time of current log data.The application distinguishes the calculation internet of things equipment type, and periodically calculates average reporting frequency θ, historical maximum reporting frequency maxΔT and standard deviation σ of reporting frequency, real-time updating the algorithm of its state, the state of real device, facilitate the management and maintenance of internet of things equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and in particular to a method and device for real-time updating of the state of a master Internet of Things device. BACKGROUND

[0002] An Internet of Things device is a non-standard computing device that can be wirelessly connected to a network and has the ability to transmit data. It helps the region achieve the expected function (such as environmental monitoring, security monitoring, event detection, high-definition monitoring, etc.) through Internet of Things and network technology, and drives service upgrade. Whether the management and maintenance of the Internet of Things device can be done is a necessary condition for realizing the above functions, and the offline state of the device is the main influencing factor. According to the investigation, the traditional Internet of Things device state updating method is mostly based on a log of the Internet of Things device to directly determine whether the device is online within a period of time, that is, according to the log time received by the device plus a fixed time period. The device is in an online state within this fixed time period, and is in an offline state beyond this time period, that is, a fixed state updating frequency is set for the Internet of Things device according to the system configuration, and when inquiring, if no log data of the Internet of Things device is reported beyond the state updating frequency, the Internet of Things device is determined to be in an offline state. In the actual use process, the Internet of Things causes the state updating frequency to change due to the attenuation or fluctuation of the battery power of the Internet of Things itself and other environmental factors (high temperature, high pressure), thereby causing the latest event of the log data received by the server from the Internet of Things device to deviate from the state updating frequency, and thus causing the system to misjudge the state of the Internet of Things device, which is not conducive to the management and maintenance of the Internet of Things device. SUMMARY

[0003] The purpose of the present application is to provide a method and device for real-time updating of the state of a master Internet of Things device, which distinguishes the type of the computing Internet of Things device and periodically calculates the average reporting rate , the historical maximum reporting frequency and the standard deviation of the reporting frequency , and updates the state of the device in real time, so as to truly reflect the state of the device and facilitate the management and maintenance of the Internet of Things device.

[0004] A method for real-time updating of the state of a master Internet of Things device, specifically comprising the following steps:

[0005] S1, continuously receiving log data reported by the Internet of Things device;

[0006] S2, judging the type to which the Internet of Things device belongs according to the log data, the type including: active device, passive device;

[0007] S3, if the Internet of Things device belongs to an active device, extracting the latest reporting time of the log data and calculating the average reporting rate ;

[0008] S4、according to the latest reporting time and average reporting rate Real-time calculation updates online invalid time;

[0009] S5, receiving the current query instruction, extracting the time of the current query instruction, according to the time of the current query instruction and the online invalid time, judging the state of the active device.

[0010] Further, the step S2 specifically comprises the following steps:

[0011] According to the historical log data reported by the Internet of Things device, the historical reporting frequency is calculated;

[0012] According to the historical reporting frequency, the average reporting rate , standard deviation is calculated respectively , standard deviation According to the average reporting rate , standard deviation , the standard deviation coefficient is calculated ;

[0013] According to the average reporting rate or the standard deviation coefficient , the type to which the Internet of Things device belongs is judged;

[0014] When the average reporting rate is less than the threshold value or the standard deviation coefficient is less than the threshold value , it is judged that the device is an active device.

[0015] Further, the average reporting rate is specifically obtained by the following steps:

[0016] According to the moving average method, the historical reporting frequency is updated ;

[0017] According to the updated historical reporting frequency , the average reporting rate and the standard deviation of the Internet of Things device are calculated by mean processing.

[0018] Further, the historical reporting frequency is specifically obtained by the following steps:

[0019] The historical log data of the Internet of Things device is sorted in descending order of time;

[0020] After sorting, the time difference between the two adjacent log data of the IOT device is calculated, and the time difference is the historical reporting frequency , wherein the maximum value is the maximum historical reporting frequency of the IOT device .

[0021] Further, the moving average method specifically includes the following steps:

[0022] Traverse the sorted historical log data, take the current traversal historical log data and the previous log data, calculate the average of the historical reporting frequency of the current traversal historical log data .

[0023] Further, the judging the state of the master IOT device specifically includes the following steps:

[0024] If the online invalidation time is a default value, the active device is judged to be in an inactive state;

[0025] If the time of the current query instruction is less than the online invalidation time, the active device is judged to be in an online state;

[0026] If the time of the current query instruction is greater than the online invalidation time, the active device is judged to be in an offline state.

[0027] Further, the online invalidation time = latest reporting time + average reporting rate *3.

[0028] Further, the basic information of the IOT device is obtained through the message middleware Kafka and written into the device basic information table sensor_all_info, and the basic information includes the reporting frequency of the IOT device, the IOT device type information;

[0029] The log data reported by the IOT device is obtained through the message middleware Kafka

[0030] The reporting time of the log data is obtained through the message middleware Kafka, and written into the reporting time table sensor_status;

[0031] The reporting time is matched with the device basic information table (sensor_all_info) and the calculated frequency information through the unique product code data_code and the device code sensor_id of the IOT device, and the frequency information includes the historical maximum reporting frequency , the average reporting rate and the Internet of Things device type, and write the updated online invalid time into the device basic information table (sensor_all_info).

[0032] A device state real-time updating apparatus for a master Internet of Things device, comprising:

[0033] one or more processors;

[0034] a storage unit for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the device state real-time updating method for a master Internet of Things device.

[0035] The present application has the following beneficial effects:

[0036] The uploaded log data is received by the Internet of Things device, the Internet of Things device type is distinguished, and the average reporting rate of the corresponding Internet of Things device is calculated and determined ; a query request is sent to the target Internet of Things device, so that the target Internet of Things device feeds back its own device identity information; the latest reporting time of the log data is obtained according to the device identity information, so that the cloud can be based on the latest reporting time; if the time of the current query instruction is less than the online invalid time, it is judged that the master Internet of Things device is in an online state. The present application is compatible with the traditional Internet of Things device access mode, that is, on the basis of accessing the cloud through the external network, the Internet of Things device is directly connected with mobile phones, computers and other Internet of Things devices, and the Internet of Things device respectively interacts with the cloud through the message middleware, so that the state of the Internet of Things device can be updated in real time. The present application realizes the interaction between the Internet of Things device and the cloud and the device end, and on the basis of being compatible with the mainstream Internet of Things device access mode, it can also support the binding and a series of interactive services of the Internet of Things device, so that the construction of the Internet of Things is more flexible, not only can meet the needs of the Internet of Things control in different regions and industries, but also can save the construction and maintenance cost.

[0037] The device online invalid time is obtained by the method of the present application, which can flexibly and effectively calculate the real-time online invalid time of the device, and can avoid the influence of a series of problems such as instantaneous high power, full power floating charging time length on the online invalid time, and meet the requirements of the Internet of Things device for management and maintenance in various environments. The present application further realizes the remote real-time state updating and parameter deployment of the Internet of Things device, greatly reduces the time cost and labor cost, solves the timeliness problem caused by time and regional differences, and makes the operation and maintenance of the Internet of Things device more simple and convenient. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 It is a flowchart of the state real-time updating method of the present application;

[0039] Figure 2 It is a data diagram in the architecture of the present application;

[0040] Figure 3 The figure is a schematic diagram of the method for calculating the average reporting rate of the application;

[0041] Figure 4 The figure is a schematic diagram of the state real-time updating system of the application; DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. The description of the at least one example embodiment is actually only illustrative, but not as any limitation on the application of the application or use. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0043] Unless specifically stated otherwise, the relative arrangement of the components and steps, numerical expressions, and values set forth in the examples are not intended to limit the scope of the application.

[0044] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship.

[0045] In addition, for the sake of brevity and clarity, descriptions of well-known structures, functions and configurations are omitted. Those skilled in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the disclosure.

[0046] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices should be considered as falling within the scope of the present disclosure.

[0047] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of example embodiments can have different values.

[0048] Embodiment 1

[0049] The collection of the Internet of Things log data is automatically collected by the Internet of Things equipment and uploaded to the Internet of Things cloud platform without human intervention. The collected log data has the following main features:

[0050] 1. Data automatic and continuous collection,

[0051] The Internet of Things equipment applied in many industry monitoring fields has the characteristics of high frequency and uninterrupted continuous collection of index data. For example, environmental protection monitoring equipment collects a group of monitoring indexes every 30 seconds and uploads them to the cloud.

[0052] 2. The device collects data at a fast rate,

[0053] The high-frequency data collection of the Internet of Things device leads to a rapid increase in data volume. For example, if a single device collects and uploads a set of 6 monitoring indicators every 30 seconds, the daily data collection volume of a single device can reach 17280.

[0054] 3. The data has a time sequence,

[0055] The Internet of Things sensing automation collects data without manual reporting, and has a time sequence feature, i.e. each collected indicator data has a collection time point, and the sequence of data collection is clear.

[0056] I. Characteristics of Internet of Things devices

[0057] Internet of Things devices have the ability to transmit data. According to the time interval of data transmission, most Internet of Things devices can be classified into active devices and passive devices.

[0058] Active devices transmit data at regular intervals regardless of external conditions (e.g. temperature sensors), which are persistent and regular.

[0059] Passive devices transmit data only when they detect certain external events (e.g. fire alarms), which are accidental and one-time.

[0060] Based on the above characteristics, active and passive devices will be accurately defined in the algorithm construction, and then the real-time state of the device will be determined.

[0061] II. Advantages of real-time update algorithm for Internet of Things device state

[0062] According to the research, the traditional Internet of Things device state update method is mostly based on a single log of the Internet of Things device to directly determine whether the device is online within a certain period of time, i.e. according to the log time received from the device plus a fixed time period. The device is online within this fixed time period, and offline beyond this time period. The algorithm proposed in this document has the following advantages compared to the traditional method:

[0063] According to the requirements of Internet of Things scenarios, the device state is defined to accurately describe the state of the device.

[0064] The Internet of Things devices are divided into active and passive devices to improve the accuracy of real-time state judgment of device types.

[0065] Using message middleware Kafka can meet the real-time update.

[0066] Based on the characteristics of the scene, including the processing of special cases (when the system is just enabled, the data is not enough to easily misjudge the device type), improve the accuracy of real-time state judgment of device type. After the system runs for a period of time, it can automatically correct the device type and state.

[0067] III. Algorithm implementation

[0068] Before explaining the algorithm, the following needs to be defined:

[0069] Historical reporting frequency : The time interval between the adjacent two data transmissions of the Internet of Things device (unit: seconds).

[0070] Average reporting rate : Calculated by moving average method according to the reporting frequency (unit: seconds).

[0071] Inactive: The device has not reported any logs.

[0072] Active device: Fixed average reporting rate.

[0073] Passive device: No fixed average reporting rate (non-active device is a passive device).

[0074] Historical maximum reporting frequency : The maximum reporting frequency of the device .

[0075] Standard deviation coefficient : The quotient of the standard deviation and the mean of the average reporting rate.

[0076] Passive device online expiration time (expired_time): The latest log reporting time + historical maximum reporting frequency .

[0077] Active device online expiration time (expired_time): The latest log reporting time + average reporting rate *3.

[0078] Implementation steps of the real-time update algorithm of the Internet of Things device state:

[0079] 1. Periodically calculate the historical reporting frequency according to the device reported log data.

[0080] 2. Calculate the average reporting rate , the historical maximum reporting frequency and the standard deviation of the reporting frequency .

[0081] 3. Determine the device type according to the results of step 2.

[0082] 4. According to the latest reporting time of the log combined with the second and third steps, the online failure time of various types of devices is calculated in real time.

[0083] 5. According to the results of the third and fourth steps, the device state is judged in real time.

[0084] Four, algorithm details

[0085] The device state real-time updating algorithm is performed according to the above five steps, and the specific implementation of each step is described in turn.

[0086] 1. Calculate the historical reporting frequency

[0087] According to the log data reported by the device (including information for determining the uniqueness of the device, and historical reporting log time), the historical reporting frequency is calculated .

[0088] The data of each device is sorted in descending order according to the historical reporting log time.

[0089] In order to improve the calculation efficiency and exclude the influence of historical data, the nearest piece of data to the current time is selected, where can be adjusted according to the calculation environment and scene requirements, and the default .

[0090] The difference between the adjacent two log reporting times of each device is calculated, which is the historical reporting frequency .

[0091] 2. Calculate the average reporting rate and historical maximum reporting frequency

[0092] According to the historical reporting frequency of the device , the historical maximum reporting frequency of the device is calculated, and then the moving average method is used to update , and then the average reporting rate of the device is obtained by averaging , and the standard deviation of the updated . .

[0093] The historical maximum reporting frequency of each device is obtained .

[0094] Implementation of moving average method: for the sorted data, take the data from the current row to the front piece of data, calculate the average value of as the of the current row data. By default , the use of moving average method can eliminate the influence of periodic fluctuations and random fluctuations.

[0095] The updated , the avg function is used to calculate the average reporting rate of each device . Calculate the standard deviation of the reporting frequency .

[0096] 3. Determine the device type

[0097] According to the results calculated in 2, determine the type of device.

[0098] Calculate the standard deviation coefficient / .

[0099] Determine whether the device is an active device: if the average reporting rate of the device is less than the threshold value or the standard deviation coefficient is less than the threshold value , the device is determined to be an active device, marked as 0. The standard deviation coefficient can reflect the stability of the data, which can represent whether the device has a basic fixed reporting frequency in this scenario, where the smaller the better, the default threshold value . is less than the threshold value , it means that the device has a higher reporting frequency, and the default threshold value . The above two threshold values can be changed according to the scene requirements.

[0100] Devices that do not meet the active device condition are defined as passive devices, marked as 1.

[0101] and device type, update interval , and device type, update interval , default

[0102] 4. Calculate the online expiration time of the device

[0103] According to the results of 2 and 3, combined with the log information of the message middleware Kafka, the online expiration time (expired_time) of the device is updated in real time.

[0104] After the new device basic data is reported, the online expiration time is initialized to 1970-01-01 08:00:00.

[0105] The device reports the log, and the latest_time of the log information in Kafka is matched with the device basic information (sensor_all_info) in the clickhouse library and the calculated frequency information (sensor_ifonline), and the online invalid time of the online device is updated to write the device basic information table (sensor_all_info), so that the function of real-time updating can be realized.

[0106] The online invalid time of the passive device: the latest reporting time of the log + the maximum historical reporting frequency .

[0107] The online invalid time of the active device: the latest reporting time of the log + the average reporting rate *3.

[0108] 5. Determine the real-time state of the device

[0109] According to the results of 3 and 4, the state of the device is determined in real time when querying.

[0110] The device state with expired_time of 1970-01-01 00:00:00 is: not activated.

[0111] If the query time is less than or equal to the online invalid time of the device, the device is in an online state; if the query time is greater than the online invalid time of the device, the device is in an offline state.

[0112] Five, architecture and process

[0113] The device basic information is obtained from the topic (iot_device_info) of Kafka, and after parsing, it is written into the table sensor_all_info. During the writing process, some fields (reporting frequency of the device, device type information, etc.) are set to default values.

[0114] The device historical log reporting time is obtained from the table sensor_status, and after preprocessing, the historical maximum reporting frequency , the average reporting rate and the device type are written into the table sensor_ifonline.

[0115] sensor_ifonline is associated with sensor_all_info, and the reporting frequency of the device, the device type and other information are filled in, saved in the memory (sensor_all_info_tp), and the structure is consistent with sensor_all_info.

[0116] Get the device real-time reported log through the topic of Kafka (iot_device_log_*), and associate it with the unique information of the device (product code data_code and device code sensor_id) and sensor_all_info_tp. Use the reporting time of the log (latest_time) to update the online invalid time (expired_time) of the device in real time. The obtained data is saved in the memory (sensor_all_info_e), and the structure is consistent with sensor_all_info.

[0117] Write sensor_all_info_e into sensor_all_info.

[0118] Figure 2 In the above, the meaning of each node is:

[0119] sensor_status: The historical log reporting time of the Internet of Things device.

[0120] sensor_ifonline: Calculate the historical maximum reporting frequency of each device through a sql statement , the average reporting rate and the device type, and store it in Clickhouse.

[0121] iot_device_log_*: Log topic reported by Internet of Things device, json format, stored in kafka.

[0122] sensor_all_info: All basic information and state information of Internet of Things device

[0123] Six, process

[0124] The device state real-time updating algorithm process is as follows:

[0125] Algorithm Device state real-time updating algorithm

[0126] 1. Start timing execution: start execution every minutes, default value , , ;

[0127] 2. Exclude abnormal data;

[0128] 3. Sort and filter the nearest piece of data from the current time, default value ;

[0129] 4. Historical reporting frequency of the computing device ;

[0130] 5. Maximum historical reporting frequency max of the computing device , the moving average method updates the historical reporting frequency , the number of moving average periods is , the default value ;

[0131] 6. The mean of the historical reporting frequency of the computing device , the standard deviation and the standard deviation coefficient ;

[0132] 7. Determine the device type: If ;

[0133] The device type is active device, recorded as 0;

[0134] Else

[0135] The device type is passive device, recorded as 1;

[0136] 8. End timing execution;

[0137] 9. Initialize the online invalid time of the new device as 1970-01-01 00:00:00;

[0138] 10. Update the online invalid time of the device in real time using Kafka:

[0139] If the device type = 0;

[0140] The online invalid time = the latest reporting time of the log + the average reporting rate * 3;

[0141] Else

[0142] The online invalid time = the latest reporting time of the log + the maximum historical reporting frequency ;

[0143] 11. When querying, determine the real-time state of the device:

[0144] If the online invalid time of the device = 1970-01-01 00:00:00;

[0145] The device state = not activated;

[0146] Elseif the current query time < the online invalid time of the device;

[0147] The device state = online;

[0148] Else

[0149] Device status = Offline.

[0150] Embodiment 2

[0151] A state real-time updating device of a master Internet of Things device, comprising:

[0152] one or more processors;

[0153] a storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the state real-time updating method of the master Internet of Things device.

[0154] Embodiment 3

[0155] A state real-time updating system, comprising:

[0156] one or more Internet of Things devices, which respectively interact with a message middleware through a router, and a cloud server, which obtains log data uploaded by the Internet of Things devices by subscribing to the content of the message middleware.

[0157] Embodiment 4, a computer readable storage medium, which stores a computer program, the computer program, when executed by a processor, can implement the state real-time updating method of the master Internet of Things device.

[0158] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. According to the technical essence of the present application, any simple modification, equivalent replacement and improvement of the above embodiment, etc. still belongs to the protection scope of the technical solution of the present application within the spirit and principles of the present application.

Claims

1. A method for real-time status update of an active Internet of Things (IoT) device, characterized in that, Specifically, the following steps are included: S1. Continuously receive log data reported by IoT devices; S2. Based on the log data, determine the type of the IoT device, including: active device and passive device; The active device is the device that transmits data at a certain time interval; A passive device is a device that transmits data only after detecting an event. S3. If the IoT device is an active device, extract the latest reporting time of the log data and calculate the average reporting rate. ; S4. Based on the latest reporting time and average reporting rate The online failure time is calculated and updated in real time. The online failure time is calculated as follows: Online failure time = Latest reporting time + Average reporting rate *3; S5. Receive the current query command, extract the time of the current query command, and if the online expiration time is the default value, determine that the active device is in an inactive state. If the current query command time is less than the online failure time, then the active device is determined to be online. If the current query time is greater than the online failure time, then the active device is determined to be offline.

2. The method for real-time status update of an active Internet of Things (IoT) device according to claim 1, characterized in that, S2 specifically includes the following steps: Calculate the historical reporting frequency based on the historical log data reported by IoT devices; Calculate the average reporting rate based on historical reporting frequency. Standard deviation and based on the average reporting rate Standard deviation Calculate the coefficient of variation ,in = / ; Based on the average reporting rate or standard deviation coefficient Determine the type of the IoT device; When the average reporting rate Less than the threshold or standard deviation coefficient Less than the threshold If so, the device is determined to be an active device.

3. The method for real-time status update of an active Internet of Things (IoT) device according to claim 2, characterized in that, The average reporting rate Specifically, it is obtained through the following steps: Update historical reporting frequency based on moving average method ; Based on the updated historical reporting frequency Perform averaging to calculate the average reporting rate of IoT devices. and standard deviation .

4. The method for real-time status update of an active Internet of Things (IoT) device according to claim 3, characterized in that, The historical reporting frequency Specifically, it is obtained through the following steps: The historical log data of IoT devices are sorted from longest to shortest time. Calculate the time difference between two adjacent log data entries from an IoT device, where the time difference is the historical reporting frequency. The maximum value is the highest historical reporting frequency of IoT devices. .

5. The method for real-time status update of an active Internet of Things (IoT) device according to claim 4, characterized in that, The moving average method specifically includes the following steps: Iterate through the sorted historical log data, then retrieve the currently iterated historical log data and the previous data. Count historical log data, calculate Historical reporting frequency The average value is used to update the historical reporting frequency of the currently traversed historical log data. .

6. The method for real-time status update of an active Internet of Things (IoT) device according to claim 1, characterized in that, The basic information of IoT devices is obtained through the message middleware Kafka and written into the device basic information table sensor_all_info. The basic information includes the reporting frequency and IoT device type information of the IoT devices. Obtain log data reported by IoT devices through the message middleware Kafka. The report time of log data is obtained through the message middleware Kafka and written to the report time table sensor_status; By using the unique product code (data_code) and device code (sensor_id) of the IoT device, the reporting time is matched with the device basic information table (sensor_all_info) and the calculated frequency information, including the historical maximum reporting frequency. Average reporting rate The system also records the IoT device type and writes the updated online failure time to the device basic information table (sensor_all_info).

7. A real-time status update device for an active Internet of Things (IoT) device, characterized in that, include: One or more processors; A storage unit is used to store one or more programs, which, when executed by one or more processors, enable the one or more processors to implement a real-time status update method for an active Internet of Things device according to any one of claims 1 to 6.

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