A method and device for real-time updating of the status of an animal internet-of-things device

Abstract

The application discloses a kind of state real-time updating method and device of animal internet-of-things equipment, specifically includes the following steps: S1, the log data reported by thing connection equipment is received uninterruptedly;S2, the type of the thing connection equipment is judged, 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, the state of the active device is judged.The latest reporting time of the log data is the reporting time of current log data.The application distinguishes the type of thing connection equipment by calculation, and periodically calculates average reporting frequency θ, maximum historical reporting frequency maxΔT and standard deviation σ of historical reporting frequency, real-time updates the algorithm of its state, truly reflects the state of equipment, facilitates 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 particularly relates to a state real-time updating method and device of passive Internet of Things equipment. BACKGROUND

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

[0003] The purpose of the present application is to provide a state real-time updating method and device of passive Internet of Things equipment, which distinguishes the types of the Internet of Things equipment and periodically calculates the average reporting rate , the maximum historical reporting frequency and the standard deviation of the historical reporting frequency , to update the state of the equipment in real time, so as to facilitate the management and maintenance of the Internet of Things equipment.

[0004] A state real-time updating method of passive Internet of Things equipment, specifically comprising the following steps:

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

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

[0007] S3, if the Internet of Things equipment belongs to passive equipment, extracting the latest reporting time of the log data and obtaining the maximum historical reporting frequency;

[0008] S4, calculating an online invalidation time in real time according to the latest reporting time and the maximum historical reporting frequency;

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

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

[0011] calculating a historical reporting frequency according to historical log data reported by the Internet-of-Things device;

[0012] calculating an average reporting rate respectively according to the historical reporting frequency , and calculating a standard deviation according to the average reporting rate ;

[0013] judging a type to which the Internet-of-Things device belongs according to the average reporting rate

[0014] When the average reporting rate is greater than a threshold value and the standard deviation coefficient is greater than a threshold value , the device is judged as a passive device.

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

[0016] updating the historical reporting frequency according to a moving average method ;

[0017] performing mean value processing according to the updated historical reporting frequency , and calculating an average reporting rate and a standard deviation of the Internet-of-Things device.

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

[0019] sorting historical log data of the Internet-of-Things device according to time from large to small;

[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 historical 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 passive device specifically includes the following steps:

[0024] If the online invalidation time is a default value, the passive device is judged as an inactive state;

[0025] If the time of the current query instruction is less than the online invalidation time, the passive device is judged as an online state;

[0026] If the time of the current query instruction is greater than the online invalidation time, the passive device is judged as an offline state.

[0027] Further, the online invalidation time = latest reporting time + maximum historical reporting frequency.

[0028] Further, the basic information of the IOT device is obtained through the message middleware Kafka, and is written into the device basic information table sensor_all_info, and the basic information includes the historical 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 is 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 maximum historical reporting frequency , the average reporting rate and the Internet of Things device type, and write the updated online invalid time into a device basic information table (sensor_all_info).

[0032] A state real-time updating device for an animal 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, enable the one or more processors to implement the state real-time updating method for an animal Internet of Things device.

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

[0036] The Internet of Things device receives uploaded log data, distinguishes the Internet of Things device type, and calculates and determines the maximum historical reporting frequency of the corresponding Internet of Things device ; queries a target Internet of Things device to make the target Internet of Things device feed back its own device identity information; acquires the latest reporting time of the log data 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 animal Internet of Things device is in an online state. The present application not only is compatible with the traditional Internet of Things device access mode, i.e., accessing the cloud through the external network, but also directly connects the Internet of Things device with mobile phones, computers and other Internet of Things devices. The Internet of Things device respectively interacts with the cloud through a message middleware, and the state real-time updating of the Internet of Things device can be realized. The present application realizes the interaction between the Internet of Things device and the cloud and the device end. On the basis of being compatible with the mainstream Internet of Things device access mode, the present application can also support the binding and a series of interactive services of the Internet of Things device, so that the Internet of Things construction is more flexible. Not only can the control needs of the Internet of Things in different regions and industries be met, but also the construction and maintenance costs can be saved.

[0037] The device online invalid time acquired by the method of the present application can not only flexibly and effectively calculate the real-time online invalid time of the device, but also can avoid the influence of a series of problems such as instantaneous high power, full-electricity floating charging time length, etc. on the online invalid time, so as to meet the management and maintenance requirements of the Internet of Things device 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 and on-site debugging more simple and convenient. BRIEF DESCRIPTION OF DRAWINGS

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

[0039] Figure 2 It is a data schematic 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 clearly and completely described with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only 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 in nature and by no means as any limitation to the application and its application or use. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the application.

[0043] Unless otherwise specified, the relative arrangement, numerical expressions and values of the components and steps set forth in the embodiments do not limit the scope of the application.

[0044] It should be understood that the sizes of the parts shown in the drawings are not drawn in accordance with the actual proportional relationship for the convenience of description.

[0045] In addition, the description of well-known structures, functions and configurations can be omitted for the sake of clarity and brevity. Those of ordinary skill 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 equipment known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the authorized description where appropriate.

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

[0048] Embodiment 1

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

[0050] 1. Data automatic and continuous collection,

[0051] The Internet of Things devices applied in many industry monitoring fields have the characteristics of high frequency and uninterrupted continuous collection of index data. For example, environmental protection monitoring devices collect a set of monitoring indicators every 30 seconds and upload them to the cloud;

[0052] 2. The amount of data collected by the device grows rapidly,

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

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

[0055] Internet of Things sensing and automatic data collection is not manually reported and has a time sequence feature, that is, 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 maintain a certain time interval to transmit data regardless of external conditions (such as temperature sensors), which have continuity and regularity.

[0059] Passive devices transmit data only when they detect certain external events (such as fire alarms), which have contingency and one-time nature.

[0060] Based on the above characteristics, active devices 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 log of Internet of Things devices 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 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 needs of Internet of Things scenarios, the device state is defined to accurately describe the state of the device.

[0064] Divide Internet of Things devices into active devices 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 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 combination of the latest reporting time of the log in Step 2 and Step 3, the online failure time of various types of devices is calculated in real time.

[0083] 5. According to the results of Step 3 and Step 4, the device state is judged in real time.

[0084] IV. Specific details of the algorithm

[0085] The device state real-time updating algorithm is performed according to the above 5 steps, and the specific implementation of each step is described as follows.

[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 of the device 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 pieces of data to the current time are selected, wherein can be adjusted according to the calculation environment and scene requirements, and the default value is .

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

[0091] 2. Calculate the average reporting rate and the 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 the average value of the device is obtained again to obtain the average reporting rate and the standard deviation after the update .

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

[0094] Implementation of the moving average method: for the sorted data, the data from the current row to the previous pieces of data are taken, and the average value of the is calculated as the of the current row data. By default , the use of the moving average method can eliminate the influence of periodic fluctuations and random fluctuations.

[0095] The updated The average reporting rate for each device is calculated using the avg function. Calculate the standard deviation of the reporting frequency. .

[0096] 3. Determine the equipment type

[0097] Based on the results calculated in step 2, determine the type of equipment.

[0098] Calculate the coefficient of variation / .

[0099] Determine if a device is an active device: If the device's average reporting rate is... Less than the threshold or standard deviation coefficient Less than the threshold If the value is zero, the device is considered an active device and is recorded as 0. The standard deviation coefficient reflects the stability of the data; in this scenario, it indicates whether the device has a relatively fixed reporting frequency. Smaller values ​​result in greater stability; this is the default threshold. . Less than the threshold This indicates that the device has a high reporting frequency, and the default threshold is [not specified]. The two thresholds above can be changed according to the needs of the scenario.

[0100] Equipment that does not meet the conditions for an active device is defined as a passive device and is denoted as 1.

[0101] And update regularly based on historical logs. , And device type, update interval is ,default .

[0102] 4. Calculate the online failure time of the equipment.

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

[0104] After the basic data of the new equipment is reported, the online failure 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_*: The log topic reported by the Internet of Things device, in json format, stored in Kafka.

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

[0123] Six, process

[0124] The device state real-time updating algorithm process is given below.

[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 pieces 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 value 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 an active device, recorded as 0;

[0134] Else

[0135] The device type is a 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] Online invalid time = latest log reporting time + average reporting rate * 3;

[0141] Else

[0142] Online invalid time = latest log reporting time + historical maximum reporting frequency ;

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

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

[0145] Device status = not activated;

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

[0147] Device status = online;

[0148] Else

[0149] Device state = Offline.

[0150] Embodiment 2

[0151] A state real-time updating device for an internet-of-things device for animals, 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 for the internet-of-things device for animals.

[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 contents 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 for the internet-of-things device for animals.

[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 belong 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 a passive 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 a passive device, extract the latest reporting time of the log data and obtain the maximum historical reporting frequency; S4. Calculate and update the online failure time in real time based on the latest reporting time and the maximum historical reporting frequency, where the online failure time = latest reporting time + maximum historical reporting frequency; 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 passive device is in an inactive state. If the current query time is less than the online failure time, then the passive device is determined to be online. If the current query time is greater than the online failure time, then the passive device is determined to be offline.

2. The method for real-time status update of a passive 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 Greater than the threshold and standard deviation coefficient Greater than the threshold If so, the device is determined to be a passive device.

3. The method for real-time status update of a passive 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 a passive 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 a passive 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 a passive 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 historical 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 maximum historical 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 a passive 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 method for real-time status update of a passive Internet of Things device according to any one of claims 1 to 6.

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