Abnormity recovery method, device and equipment of Internet of Things acquisition system and medium

Abstract

The invention discloses an abnormity recovery method, device and equipment of an Internet of Things acquisition system and a medium, and relates to the technical field of Internet of Things, and the method comprises the steps: monitoring each to-be-monitored object in the Internet of Things acquisition system, so as to obtain a plurality of monitoring results; the to-be-monitored object comprises a first system log used for recording key parameters of a preset data acquisition point, a second system log used for recording a protocol interaction condition between the acquired device and the server, and a third system log used for recording a network connection state of the Internet of Things acquisition system; detecting each monitoring result by using an anomaly detection logic to obtain an anomaly detection result; and if any to-be-monitored object is abnormal, calling a target self-recovery rule corresponding to the abnormal to-be-monitored object, and executing an abnormal self-recovery program corresponding to the target self-recovery rule to complete an abnormal recovery operation for the abnormal to-be-monitored object. Therefore, the stability, the reliability and the real-time performance of data acquisition can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an abnormal recovery method, device, equipment and medium of an Internet of Things acquisition system. Background Art

[0002] With the widespread application of IoT (Internet of Things) technology, IoT data collection systems have been widely deployed in various complex industrial scenarios. These systems are responsible for real-time collection of the operating status, sensor data and production data of various equipment, and transmit them to the central system for processing and analysis to ensure the continuity and efficiency of production.

[0003] However, the current data acquisition system still has some significant deficiencies and defects in practical applications, which are mainly reflected in the following six aspects:

[0004] First, the data acquisition system’s anomaly detection capability is insufficient: Existing data acquisition systems are unable to identify and handle sudden problems in industrial environments in a timely manner, which can easily lead to interruption or distortion of data collection.

[0005] Second, the data acquisition system has weak disconnection recovery capabilities: the equipment and network connections in the industrial environment are complex, and the acquisition system can only restore the data acquisition process through manual intervention after a disconnection, which increases the system's maintenance costs and fault recovery time, affecting production stability.

[0006] Third, the protocol compatibility of the data acquisition system is poor: Due to the diversity of industrial field equipment, multiple communication protocols need to be used in the data acquisition process. However, when the existing IoT acquisition system encounters abnormal situations such as incompatible protocol versions or communication format errors, the system cannot automatically adjust or roll back to a compatible version, thus affecting the integrity and accuracy of the data.

[0007] Fourth, the data collection system’s ability to handle data anomalies is insufficient: Existing data collection systems usually cannot provide an effective repair mechanism when dealing with abnormal situations such as data loss, duplication, or data outside the expected range.

[0008] Fifth, the resource management of the data collection system is imperfect: the equipment and network resources in the Internet of Things collection system are limited, but when resources are tight, the existing data collection system cannot prioritize the stable operation of key data collection tasks, resulting in a decline in the overall performance of the system.

[0009] Sixth, the data collection system lacks an automated self-recovery mechanism: Exception handling in existing IoT collection systems mostly relies on manual intervention, which makes it impossible to repair the system in a timely manner when a failure occurs, affecting the continuity of production.

[0010] From the above, it can be seen that how to improve the stability, reliability and real-time performance of data collection during the abnormal recovery process of the Internet of Things collection system is an urgent problem to be solved. Summary of the invention

[0011] In view of this, the purpose of the present invention is to provide an abnormal recovery method, device, equipment and medium for an Internet of Things acquisition system, which can improve the stability, reliability and real-time performance of the Internet of Things acquisition system when performing data acquisition, thereby improving the user experience. The specific scheme is as follows:

[0012] In a first aspect, the present application provides an abnormal recovery method for an Internet of Things acquisition system, comprising:

[0013] Determine a number of objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points;

[0014] Monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system;

[0015] Using the abnormality detection logic pre-configured for each of the objects to be monitored, each of the monitoring results is subjected to abnormality detection to obtain an abnormality detection result corresponding to each of the objects to be monitored;

[0016] If any abnormality detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from the preset self-recovery rule library, and the abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormality recovery operation for the monitored object with the abnormality.

[0017] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with the abnormality, including:

[0018] If the abnormality detection result corresponding to the first system log generated by the Internet of Things collection system for recording the key parameters of the preset data collection point indicates that the first system log has an abnormality, calling the first self-recovery rule corresponding to the first system log from the preset self-recovery rule library;

[0019] The preset data collection point is reset using the first self-recovery program corresponding to the first self-recovery rule, and it is determined whether the reset data collection point meets the performance indicator corresponding to the first system log; if the reset data collection point does not meet the performance indicator corresponding to the first system log, switch to the preset backup data collection point.

[0020] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with the abnormality, including:

[0021] If the abnormality detection result corresponding to the second system log for recording the protocol interaction between the collected device and the data collection server indicates that the second system log has an abnormality, then the second self-recovery rule corresponding to the second system log is retrieved from the preset self-recovery rule library;

[0022] Utilize the second self-recovery program corresponding to the second self-recovery rule to execute the preset protocol rollback process, record the operation information corresponding to the preset protocol rollback process in the target system log corresponding to the Internet of Things acquisition system, and send corresponding alarm information;

[0023] Determine whether the preset protocol rollback process is successfully executed; if the preset protocol rollback process is not successfully executed, use the preset dynamic negotiation operation to re-determine the target protocol for protocol interaction between the collected device and the data collection server.

[0024] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with the abnormality, including:

[0025] If the abnormality detection result corresponding to the third system log for recording the network connection status of the Internet of Things acquisition system indicates that the third system log has an abnormality, then the third self-recovery rule corresponding to the third system log is retrieved from the preset self-recovery rule library;

[0026] re-establishing the network connection using a third self-recovery program corresponding to the third self-recovery rule, and determining whether the network connection is successfully established;

[0027] If the network connection is not successfully established, the currently collected data collected by the Internet of Things collection system for the collected device is cached in a preset local cache space, and attempts to establish a network connection are continued until the network connection is successfully established.

[0028] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with the abnormality, including:

[0029] If the abnormal detection result corresponding to the collected data collected by the IoT collection system on the collected device indicates that the collected data exceeds the preset threshold range, a fourth self-recovery rule corresponding to the collected data is retrieved from a preset self-recovery rule library;

[0030] The collected data is recovered using the fourth self-recovery program corresponding to the fourth self-recovery rule, and it is determined whether the recovered collected data is successfully recovered; if the recovered collected data is not successfully recovered, the operation information corresponding to the recovered collected data is recorded in the target system log corresponding to the Internet of Things collection system, and a corresponding alarm message is sent.

[0031] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormality recovery operation for the monitored object with the abnormality, the method further includes:

[0032] The resource usage of the collected equipment is monitored to obtain monitoring results, and the scheduling strategies corresponding to the data collection tasks issued by the Internet of Things collection system are adjusted based on the monitoring results, and each of the data collection tasks is scheduled based on the adjusted scheduling strategies.

[0033] Optionally, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormality recovery operation for the monitored object with the abnormality, the method further includes:

[0034] Determine whether the abnormal recovery operation performed by each of the abnormal self-recovery programs on the monitored object with abnormality is successfully executed;

[0035] If the abnormal recovery operation performed by each abnormal self-recovery program on the abnormal object to be monitored is not successfully executed, corresponding alarm information is generated based on the corresponding abnormal object to be monitored, and a gateway restart operation or a service restart operation is performed.

[0036] In a second aspect, the present application provides an abnormal recovery device for an Internet of Things acquisition system, comprising:

[0037] The module for determining objects to be monitored is used to determine a number of objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected devices to the data acquisition server through preset data collection points;

[0038] An object monitoring module is used to monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system;

[0039] A detection logic allocation module, used to perform anomaly detection on each of the monitoring results using anomaly detection logics pre-configured for each of the objects to be monitored, so as to obtain anomaly detection results corresponding to each of the objects to be monitored;

[0040] The self-recovery rule calling module is used to retrieve the target self-recovery rule corresponding to the object to be monitored with the abnormality from the preset self-recovery rule library if any abnormality detection result indicates that the corresponding monitored object has an abnormality, and complete the abnormal recovery operation for the monitored object with the abnormality by executing the abnormality self-recovery program corresponding to the target self-recovery rule.

[0041] In a third aspect, the present application provides an electronic device, including:

[0042] Memory, used to store computer programs;

[0043] The processor is used to execute the computer program to implement the aforementioned abnormal recovery method of the Internet of Things acquisition system.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned abnormal recovery method of the Internet of Things acquisition system.

[0045] As can be seen from the above, before performing abnormal recovery of the Internet of Things acquisition system, the present application needs to determine several monitored objects in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points; each of the objects to be monitored in the Internet of Things acquisition system is monitored to obtain corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data collection points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; using the abnormal detection logic pre-configured for each of the objects to be monitored, each of the monitoring results is detected for abnormalities to obtain the abnormal detection results corresponding to each of the objects to be monitored; if any abnormal detection result indicates that the corresponding object to be monitored has an abnormality, the target self-recovery rule corresponding to the object to be monitored with the abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the object to be monitored with the abnormality. It can be seen that the present application determines several objects to be monitored in the Internet of Things acquisition system, and monitors each of the objects to be monitored in the Internet of Things acquisition system, thereby using the abnormality detection logic pre-configured for each of the objects to be monitored to perform abnormality detection on each of the monitoring results to obtain the abnormality detection results corresponding to each of the objects to be monitored; if any abnormality detection result indicates that the corresponding object to be monitored is abnormal, the target self-recovery rule corresponding to the object to be monitored with the abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the object to be monitored with the abnormality. In this way, the stability, reliability and real-time performance of the Internet of Things acquisition system during data collection are improved, and the user experience is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0047] Figure 1 This is a flow chart of an abnormal recovery method for an Internet of Things acquisition system disclosed in this application;

[0048] Figure 2 A multi-layer anomaly detection operation flow chart disclosed in this application;

[0049] Figure 3 A schematic diagram of a process flow of an abnormal self-recovery mechanism disclosed in this application;

[0050] Figure 4 A schematic diagram of a multi-layer anomaly detection and self-recovery process disclosed in this application;

[0051] Figure 5 This is a structural flow chart of an abnormal recovery device of an Internet of Things acquisition system disclosed in this application;

[0052] Figure 6 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0053] 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.

[0054] With the widespread application of Internet of Things technology, Internet of Things data acquisition systems have been widely deployed in various complex industrial scenarios. These systems are responsible for real-time collection of the operating status, sensor data and production data of various types of equipment, and transmit them to the central system for processing and analysis to ensure the continuity and efficiency of production. However, the current data acquisition system still has some significant deficiencies and defects in practical applications. To this end, the present application provides an abnormal recovery method for an Internet of Things acquisition system, which can improve the stability, reliability and real-time performance of the Internet of Things acquisition system when performing data acquisition, and enhance the user experience.

[0055] See also Figure 1 As shown, an embodiment of the present invention discloses an abnormal recovery method for an Internet of Things acquisition system, comprising:

[0056] Step S11, determining a number of objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected devices to the data acquisition server through preset data collection points.

[0057] In this embodiment, before determining several objects to be monitored in the IoT acquisition system, it is necessary to generate system logs and performance indicators. That is, the data acquisition system needs to generate system logs and performance indicators by embedding points in different functional modules of the data acquisition system. Among them, the log covers detailed information of the data acquisition system during the operation of the system, including but not limited to the status changes of data acquisition points, protocol execution status and transmission status of data channels; performance indicators are formed by defining key parameters in the data acquisition process to form quantitative standards for system performance; key parameters include but are not limited to point response time, data transmission delay, etc., to form quantitative standards for system performance.

[0058] Subsequently, the generated system logs and performance indicators need to be collected and stored: the system uses a distributed data collection mechanism to write the generated system logs and performance indicators into the corresponding object database and time series database in real time through a distributed data collection mechanism. That is, the object database is used to store structured log data, while the time series database stores and manages time series data, that is, stores performance indicators, so that the system logs and performance indicators can be centrally processed and analyzed later.

[0059] It is worth mentioning that the various monitoring levels in the IoT collection system are as follows:

[0060] In the first specific implementation, the data collection points need to be monitored: the key parameters such as the operating status, response time and collection frequency of each data collection point in different functional modules are monitored in real time to ensure the continuity and accuracy of data collection. It is worth mentioning that the embodiment of the present application can detect potential problems in the data collection points in advance by performing trend analysis on the historical data in the data collection points.

[0061] In the second specific implementation, it is necessary to monitor the acquisition protocol used between the data acquisition device and the data acquisition server: monitor the protocol interaction between the data acquisition device and the data acquisition server to ensure that the protocol versions used by both parties are consistent and the data format is correct. In addition, the data acquisition system also needs to verify the compatibility of the protocol to prevent data anomalies caused by protocol errors or mismatches.

[0062] In the third specific implementation, the network connection status needs to be monitored: the network connection status is continuously monitored, and the collected data is cached in time when a disconnection occurs, and an attempt is made to reconnect, thereby preventing data transmission interruption. That is, the embodiment of the present application ensures that the collected data will not be lost during the disconnection through an automated disconnection detection and reconnection mechanism, and ensures the integrity of the data by caching the collected data.

[0063] In the fourth specific implementation, the data acquisition system needs to monitor the collected data after collecting the data output by the collection device: after the data acquisition system collects the collected data, the integrity and accuracy of the collected data need to be checked to identify abnormal phenomena, such as data loss, data duplication or data anomalies, so as to handle different abnormal phenomena. It is worth mentioning that the data acquisition system can interpolate or repair abnormal data to ensure the consistency and validity of the collected data.

[0064] In the fifth specific implementation, the data transmission channel needs to be monitored: the transmission channel of the collected data from the collection end to the upper system is monitored to ensure that the data can be transmitted stably and safely. When an abnormality occurs in the data transmission channel, the data collection system can automatically switch from the current data transmission channel to the backup channel to avoid data transmission interruption.

[0065] Step S12, monitor each of the monitored objects in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the monitored objects include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system.

[0066] In this embodiment, the multi-layer anomaly detection operation flow chart is as follows: Figure 2 As shown: First, the abnormal detection configuration needs to be performed, and then the monitoring of each monitoring level is performed, that is, the location of the data collection point is monitored, the network status is monitored for disconnection, the collected data is monitored for abnormalities, the data transmission channel is monitored, the computer resources occupied by the data collection device are monitored, and the protocol interaction between the data collection device and the upper system is monitored. Then, the preset engine template is queried, and the system logs and performance indicators are queried and analyzed using the preset syntax. Finally, the corresponding alarm information is sent based on the corresponding alarm rules.

[0067] It is worth mentioning that the data acquisition system has a self-recovery mechanism. When the monitoring module in the data acquisition system finds an abnormal situation during the data acquisition process, it can automatically perform recovery operations based on the self-recovery mechanism. The specific implementation of the self-recovery function is as follows:

[0068] In the first specific implementation, since each data collection point in the data collection system is monitored in real time by the monitoring module in the data collection system, that is, the data collection system will regularly monitor the response time, data flow and status information corresponding to the data collection point. When the parameters corresponding to a certain data collection point exceed the set normal operating range, the data collection system will automatically mark the data collection point as an abnormal collection point and immediately reset the parameters of the data collection point to try to restore the data collection point to normal. If the reset is invalid, the data collection system will automatically switch to the backup point for data collection to ensure the continuity and integrity of the data. Among them, the backup point is a redundant data collection point pre-established by the data collection system.

[0069] In the second specific implementation, the protocol interaction between the data acquisition device and the data acquisition server is monitored, and after an abnormal situation is monitored, a rollback implementation method is executed. That is, the data acquisition system will detect the protocol interaction between the data acquisition device and the data acquisition server, and determine the compatibility of the protocol. When it is found that the protocol version is incompatible or the data format is wrong, the data acquisition system will automatically trigger the protocol rollback process to switch the protocol to the last known and compatible version, while recording the above abnormal situation and sending an alarm. It is worth mentioning that if the protocol rollback process cannot be rolled back, the system will use a dynamic negotiation strategy to re-determine the protocol version to ensure that the data acquisition task can continue to be executed.

[0070] In a third specific implementation, after the data acquisition system monitors the network status through the network connection monitoring module and detects a disconnection, the data acquisition system will automatically attempt to re-establish the connection. If multiple reconnection attempts fail, the data acquisition system will cache the current data in the local cache for storage and continue to try to restore the connection until the network connection is successfully restored. The data cached in the local cache will be synchronized to the preset server to ensure data integrity, and relevant information corresponding to the above-mentioned disconnection will be recorded in the system log.

[0071] In a fourth specific implementation, after the data acquisition system acquires the data output by the acquisition device to obtain the acquired data, it is necessary to determine whether the acquired data is within a preset threshold range. If the acquired data is not within the preset threshold range, the acquired data is marked as abnormal data, and the abnormal data is automatically recovered by a preset data recovery method. Among them, the preset data recovery method is an interpolation algorithm or a data completion method, which is not specifically limited here. It is worth mentioning that for data that cannot be repaired, the data acquisition system will issue an alarm message based on the alarm rules, and record the information corresponding to the abnormal data in the system log for subsequent manual intervention and processing.

[0072] In the fifth specific implementation, the data acquisition system will monitor the resource usage of the device to be collected in real time, such as the CPU (Central Processing Unit) occupancy rate and memory usage, and determine the current resource status based on the monitoring results, so as to dynamically adjust the task scheduling strategy based on the current resource status. That is, when computer resources are tight, the data acquisition system will give priority to ensuring the normal operation of key data acquisition tasks, and automatically reduce the execution frequency of other tasks or suspend non-critical tasks, so as to avoid equipment overload.

[0073] It is worth mentioning that if the implementation methods described in the above embodiments cannot solve the abnormal situations that occur when the data acquisition system collects data, alarm information corresponding to the above abnormal situations will continue to appear, and gateway restart operations and service restart operations will be attempted for different abnormal situations based on the alarm information.

[0074] The flowchart of the abnormal self-recovery mechanism is as follows: Figure 3 As shown: when the data acquisition system detects one or more abnormal situations among abnormal points, protocol abnormalities, disconnection abnormalities, data abnormalities and resource abnormalities, the abnormal self-recovery module in the data acquisition system will perform corresponding recovery operations for several abnormal situations: if an abnormal point occurs, the acquisition point is reset or switched to a backup acquisition point; if a protocol abnormality occurs, the protocol version is switched; if a disconnection abnormality occurs, the currently collected data is cached, the network connection status is reset, and the cached data is resent after the network is successfully connected; if a data abnormality occurs, the data is restored using a preset data completion method or a preset type conversion method; if a resource abnormality occurs, non-critical tasks are suspended to allocate the computer resources occupied by non-critical tasks to critical tasks, so that critical tasks can be executed normally. If the above recovery operations do not restore the data acquisition process of the data acquisition system to normal, the gateway is restarted and the corresponding alarm information is sent.

[0075] It is worth mentioning that the data collection system can query the preset engine template and use syntax similar to SQL statements to query and analyze system logs and performance indicators. Through query and analysis, the data collection system can automatically detect abnormal situations in different monitoring levels to help quickly locate problems and achieve efficient exception handling.

[0076] Step S13: using the abnormality detection logic pre-configured for each of the objects to be monitored, perform abnormality detection on each of the monitoring results to obtain the abnormality detection results corresponding to each of the objects to be monitored.

[0077] In this embodiment, the following technical means can be flexibly introduced according to specific application scenarios and user needs to enhance the performance of the IoT collection system or meet user needs in specific scenarios.

[0078] In the first specific implementation, the embodiment of the present application can obtain a high availability solution by redundantly designing data collection points and data transmission channels. That is, in order to enhance the reliability and stability of the Internet of Things acquisition system, by introducing redundant data collection points and data transmission channels, it is ensured that when some devices or transmission links fail, the Internet of Things acquisition system can switch to a backup data collection point or data transmission channel, thereby ensuring that the data collection task can still proceed normally. In addition, the Internet of Things acquisition system can deploy multiple sets of acquisition services to achieve a high-availability architecture. When the acquisition equipment fails or the network is congested, the system can automatically switch to a backup node or backup channel, avoiding the interruption of the Internet of Things acquisition system in the process of collecting data output by the collected device, and further improving the reliability of the Internet of Things acquisition system.

[0079] In the second specific implementation, the embodiment of the present application can introduce a visual abnormal monitoring and alarm interface to facilitate system operation and maintenance personnel to view the operating status of the Internet of Things acquisition system, abnormal conditions of each data collection point, and data transmission status in real time. It is worth mentioning that by displaying equipment status, data flow, and abnormal records through charts and graphical interfaces, operation and maintenance personnel can intuitively understand the health of the system and quickly locate the fault point. At the same time, the visual abnormal monitoring and alarm interface can also combine historical data to provide trend analysis functions, thereby predicting possible abnormalities, so as to take protective measures in advance and improve the data collection efficiency of the Internet of Things acquisition system.

[0080] In the third specific implementation, the embodiment of the present application can introduce edge computing technology to achieve real-time processing, that is, in the scenario where the response speed of the IoT collection system needs to be improved, the embodiment of the present application can combine edge computing technology to achieve real-time processing and anomaly detection of some data on the collected device or edge node. Among them, the edge node can undertake part of the tasks of data preprocessing, filtering and preliminary analysis, thereby reducing the load pressure of the central server and improving the response speed of the IoT collection system, which is suitable for scenarios with unstable networks or high real-time requirements.

[0081] Step S14: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with the abnormality.

[0082] In this embodiment, it is necessary to perform abnormal detection on multiple monitoring levels in the system, and perform corresponding self-recovery operations when abnormal results occur. It is worth mentioning that the Internet of Things acquisition system can divide the data acquisition process of the data acquisition system into different monitoring levels, thereby achieving comprehensive and detailed abnormal detection and recovery of key links in the Internet of Things acquisition process.

[0083] In the first specific implementation, the data collection point needs to be monitored, and the corresponding self-recovery operation is triggered after an abnormal situation occurs. Specifically, if any abnormal detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with an abnormality, which may include: if the abnormal detection result corresponding to the first system log generated by the Internet of Things acquisition system for recording the key parameters of the preset data collection point indicates that the first system log has an abnormality, the first self-recovery rule corresponding to the first system log is retrieved from the preset self-recovery rule library; the preset data collection point is reset using the first self-recovery program corresponding to the first self-recovery rule, and it is determined whether the reset data collection point meets the performance index corresponding to the first system log, and if the reset data collection point does not meet the performance index corresponding to the first system log, it is switched to the preset standby data collection point.

[0084] In the second specific implementation, it is necessary to monitor the acquisition protocol applicable between the acquisition device and the data acquisition server, and trigger a corresponding self-recovery operation after an abnormal situation occurs. Specifically, if any abnormality detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from the preset self-recovery rule library, and the abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with the abnormality, which may include: if the abnormality detection result corresponding to the second system log used to record the protocol interaction between the collected device and the data acquisition server shows that the second system log has an abnormality, the second self-recovery rule corresponding to the second system log is retrieved from the preset self-recovery rule library; the preset protocol rollback process is executed by using the second self-recovery program corresponding to the second self-recovery rule, and the operation information corresponding to the preset protocol rollback process is recorded in the target system log corresponding to the Internet of Things acquisition system, and the corresponding alarm information is sent; it is determined whether the preset protocol rollback process is executed successfully; if the preset protocol rollback process is not executed successfully, the target protocol for protocol interaction between the collected device and the data acquisition server is re-determined by using the preset dynamic negotiation operation.

[0085] In the third specific implementation, it is necessary to monitor the network connection status and trigger the corresponding self-recovery operation after an abnormal situation occurs. Specifically, if any abnormal detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with an abnormality, which may include: if the abnormal detection result corresponding to the third system log used to record the network connection status of the Internet of Things acquisition system indicates that the third system log has an abnormality, the third self-recovery rule corresponding to the third system log is retrieved from the preset self-recovery rule library; the network connection is re-established using the third self-recovery program corresponding to the third self-recovery rule, and it is determined whether the network connection is successfully established; if the network connection is not successfully established, the currently collected data collected by the Internet of Things acquisition system for the collected device is cached to the preset local cache space, and the network connection is continuously attempted to be established until the network connection is successfully established.

[0086] In a fourth specific implementation, the data acquisition system needs to monitor the collected data after collecting the data output by the collection device, and trigger the corresponding self-recovery operation after an abnormal situation occurs. Specifically, if any abnormal detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with an abnormality, which may include: if the abnormal detection result corresponding to the collected data collected by the Internet of Things collection system for the collected device indicates that the collected data exceeds the preset threshold range, the fourth self-recovery rule corresponding to the collected data is retrieved from the preset self-recovery rule library; the collected data is restored using the fourth self-recovery program corresponding to the fourth self-recovery rule, and it is determined whether the restored collected data is successfully restored. If the restored collected data is not successfully restored, the operation information corresponding to the restored collected data is recorded in the target system log corresponding to the Internet of Things collection system, and the corresponding alarm information is sent.

[0087] In the fifth specific implementation, it is necessary to monitor the computer resource usage of the collected equipment and trigger the corresponding self-recovery operation after an abnormal situation occurs. Specifically, if any abnormal detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with an abnormality. It can also include: monitoring the resource usage of the collected equipment to obtain monitoring results, adjusting the scheduling strategy corresponding to several data collection tasks issued by the Internet of Things collection system based on the monitoring results, and scheduling each of the data collection tasks based on the adjusted scheduling strategy.

[0088] It is worth mentioning that after the above self-recovery operation is executed, it is necessary to judge whether the abnormal recovery operation performed by each abnormal self-recovery program on the monitored object with abnormality is successfully executed, so as to perform subsequent operations based on the judgment result. Specifically, if any abnormal detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with abnormality. It can also include: judging whether the abnormal recovery operation performed by each abnormal self-recovery program on the monitored object with abnormality is successfully executed; if the abnormal recovery operation performed by each abnormal self-recovery program on the monitored object with abnormality is not successfully executed, then the corresponding alarm information is generated based on the corresponding monitored object with abnormality, and a gateway restart operation or a service restart operation is performed.

[0089] It is understandable that when the data collection system collects data output by the collection equipment, if an abnormal situation occurs in one of the several monitoring levels, it is necessary to send the corresponding alarm information based on the preset alarm rules. Among them, the alarm rules of the Internet of Things collection system are formulated according to different business scenarios and data characteristics, and provide multi-level and intelligent anomaly detection capabilities. It is understandable that the alarm rule module adopts a polymorphic optimization design, implements a variety of alarm algorithms and strategies through standard interfaces, can dynamically adapt and expand alarms according to different scenarios, and implements threshold rules, pattern matching, statistical rules, logical rules, historical analysis, machine learning and other alarm rules through configuration.

[0090] The embodiment of the present application will provide message notifications from multiple channels. When the data acquisition system detects an abnormal situation, it can trigger multiple forms of message notifications based on different alarm levels. Among them, the message notification types are divided into SMS notifications, email notifications, API notifications, system notifications, push notifications, instant messaging tool notifications, voice notifications, and abnormal self-recovery push notifications.

[0091] As can be seen from the above, before performing abnormal recovery of the Internet of Things acquisition system, the embodiment of the present application needs to determine several monitored objects in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points; each of the monitored objects in the Internet of Things acquisition system is monitored to obtain corresponding monitoring results; wherein the monitored objects include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data collection points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; using the abnormal detection logic pre-configured for each of the monitored objects, each of the monitoring results is detected for abnormality to obtain the abnormal detection result corresponding to each of the monitored objects; if any abnormal detection result indicates that the corresponding monitored object is abnormal, the target self-recovery rule corresponding to the monitored object with abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the monitored object with abnormality. It can be seen that the present application determines several objects to be monitored in the Internet of Things acquisition system, and monitors each of the objects to be monitored in the Internet of Things acquisition system, thereby using the abnormality detection logic pre-configured for each of the objects to be monitored to perform abnormality detection on each of the monitoring results to obtain the abnormality detection results corresponding to each of the objects to be monitored; if any abnormality detection result indicates that the corresponding object to be monitored is abnormal, the target self-recovery rule corresponding to the object to be monitored with the abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the object to be monitored with the abnormality. In this way, the stability, reliability and real-time performance of the Internet of Things acquisition system during data collection are improved.

[0092] For further information, see Figure 4 As shown, an embodiment of the present invention discloses an abnormal recovery method for an Internet of Things acquisition system, comprising:

[0093] In this embodiment, the flowchart of multi-layer anomaly detection and self-recovery for the IoT collection system is as follows: Figure 4 As shown:

[0094] First, the Internet of Things monitoring and alarm module collects relevant information in the Internet of Things collection system through the log points in the Internet of Things collection system, establishes a system log file based on the collected relevant information, and stores the system log file in a preset object database; in addition, the embodiment of the present application needs to call a preset indicator interface and generate a number of performance indicators based on the preset indicator definition, and store the several performance indicators in a preset time series database.

[0095] Then, anomaly detection is performed on multiple monitoring levels in the IoT collection system, that is, the data collection points in the IoT collection system are monitored; it is worth mentioning that when monitoring the IoT collection system, the system log files and corresponding performance indicators need to be extracted from the preset object database and the time series database to determine whether an abnormal situation has occurred based on the monitoring results. Among them, the monitoring operations performed by the IoT monitoring and alarm module on the IoT collection system include but are not limited to: monitoring whether the network connection status is disconnected; monitoring whether an abnormal situation has occurred in the collected data; monitoring the data transmission channel; monitoring the computer resource usage in the collected device; and monitoring the protocol interaction between the collected device and the data collection server.

[0096] Subsequently, the alarm information is notified based on a number of alarm rules and corresponding abnormal situations, wherein the number of alarm rules include but are not limited to data threshold rules, pattern matching rules, statistical rules, logical rules, historical analysis rules and machine learning rules.

[0097] Finally, when the IoT monitoring alarm module monitors multiple monitoring levels in the IoT collection system and detects an abnormal situation, the abnormal self-recovery mechanism in the self-recovery module will trigger the corresponding self-recovery operation, among which the self-recovery operation includes but is not limited to restarting the gateway, switching to a backup data collection point and data transmission channel, type conversion, switching the protocol version for interaction between the collected device and the data collection server, completing abnormal data, resetting system parameters and restoring the system engine.

[0098] As can be seen from the above, the present application determines several objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points; each of the objects to be monitored in the Internet of Things acquisition system is monitored to obtain corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data collection points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; using the anomaly detection logic pre-configured for each of the objects to be monitored, each of the monitoring results is detected for anomalies to obtain an anomaly detection result corresponding to each of the objects to be monitored; if any anomaly detection result indicates that the corresponding object to be monitored has an anomaly, the target self-recovery rule corresponding to the object to be monitored with the anomaly is retrieved from the preset self-recovery rule library, and the anomaly self-recovery program corresponding to the target self-recovery rule is executed to complete the anomaly recovery operation for the object to be monitored with the anomaly. In this way, the problems existing in the IoT collection system, such as insufficient anomaly detection capability, difficulty in disconnection recovery, imperfect data anomaly processing, and inflexible resource management, are solved. That is, the IoT collection system realizes automatic detection and self-recovery of abnormal situations by building a multi-level monitoring and anomaly processing mechanism, ensuring the stability and reliability of the IoT collection system in data collection in complex industrial environments.

[0099] Accordingly, see Figure 5 As shown, the present application also provides an abnormal recovery device for an Internet of Things acquisition system, including:

[0100] The to-be-monitored object determination module 11 is used to determine a number of to-be-monitored objects in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through a preset data collection point;

[0101] The object monitoring module 12 is used to monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system;

[0102] The detection logic allocation module 13 is used to perform anomaly detection on each of the monitoring results using anomaly detection logics pre-configured for each of the objects to be monitored, so as to obtain anomaly detection results corresponding to each of the objects to be monitored;

[0103] The self-recovery rule calling module 14 is used to retrieve the target self-recovery rule corresponding to the object to be monitored with the abnormality from the preset self-recovery rule library if any abnormality detection result indicates that the corresponding object to be monitored has an abnormality, and complete the abnormal recovery operation for the object to be monitored with the abnormality by executing the abnormality self-recovery program corresponding to the target self-recovery rule.

[0104] As can be seen from the above, before the abnormal recovery of the Internet of Things acquisition system is performed in the embodiment of the present application, it is necessary

[0105] Determine several objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points; monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data collection points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; use the abnormality detection logic pre-configured for each of the objects to be monitored to perform abnormality detection on each of the monitoring results to obtain the abnormality detection results corresponding to each of the objects to be monitored; if any abnormality detection result indicates that the corresponding object to be monitored has an abnormality, then retrieve the target self-recovery rule corresponding to the object to be monitored with the abnormality from the preset self-recovery rule library, and complete the abnormal recovery operation for the object to be monitored with the abnormality by executing the abnormality self-recovery program corresponding to the target self-recovery rule. It can be seen that the present application determines several objects to be monitored in the Internet of Things acquisition system, and monitors each of the objects to be monitored in the Internet of Things acquisition system, thereby using the abnormality detection logic pre-configured for each of the objects to be monitored to perform abnormality detection on each of the monitoring results to obtain the abnormality detection results corresponding to each of the objects to be monitored; if any abnormality detection result indicates that the corresponding object to be monitored is abnormal, the target self-recovery rule corresponding to the object to be monitored with the abnormality is retrieved from the preset self-recovery rule library, and the abnormal self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormal recovery operation for the object to be monitored with the abnormality. In this way, the stability, reliability and real-time performance of the Internet of Things acquisition system during data collection are improved, and the user experience is enhanced.

[0106] In some specific implementations, the self-recovery rule calling module 14 may specifically include:

[0107] A first self-recovery rule calling unit is configured to call a first self-recovery rule corresponding to a first system log generated by the Internet of Things collection system and used to record the key parameters of the preset data collection point from a preset self-recovery rule library if an abnormality detection result corresponding to the first system log for recording the key parameters of the preset data collection point indicates that the first system log has an abnormality;

[0108] A collection point reset unit is used to use the first self-recovery program corresponding to the first self-recovery rule to reset the preset data collection point, and determine whether the reset data collection point meets the performance indicator corresponding to the first system log; if the reset data collection point does not meet the performance indicator corresponding to the first system log, switch to the preset backup data collection point.

[0109] In some specific implementations, the self-recovery rule calling module 14 may specifically include:

[0110] A second self-recovery rule calling unit is used to call a second self-recovery rule corresponding to the second system log from a preset self-recovery rule library if an abnormality detection result corresponding to the second system log for recording the protocol interaction between the collected device and the data collection server indicates that the second system log has an abnormality;

[0111] A protocol rollback unit, configured to execute a preset protocol rollback process using a second self-recovery program corresponding to the second self-recovery rule, and record operation information corresponding to the preset protocol rollback process in a target system log corresponding to the Internet of Things acquisition system, and send corresponding alarm information;

[0112] The rollback process judgment unit is used to judge whether the preset protocol rollback process is executed successfully; if the preset protocol rollback process is not executed successfully, the preset dynamic negotiation operation is used to re-determine the target protocol for protocol interaction between the collected device and the data collection server.

[0113] In some specific implementations, the self-recovery rule calling module 14 may specifically include:

[0114] A third self-recovery rule calling unit is configured to retrieve a third self-recovery rule corresponding to the third system log from a preset self-recovery rule library if an abnormality detection result corresponding to the third system log for recording the network connection status of the Internet of Things acquisition system indicates that the third system log has an abnormality;

[0115] a network connection establishing unit, configured to reestablish the network connection by using a third self-recovery program corresponding to the third self-recovery rule, and determine whether the network connection is successfully established;

[0116] The data cache unit is used to cache the currently collected data collected by the Internet of Things collection system for the collected device into a preset local cache space if the network connection is not successfully established, and continue to try to establish a network connection until the network connection is successfully established.

[0117] In some specific implementations, the self-recovery rule calling module 14 may specifically include:

[0118] A fourth self-recovery rule calling unit is used to call a fourth self-recovery rule corresponding to the collected data from a preset self-recovery rule library if an abnormality detection result corresponding to the collected data collected by the Internet of Things collection system on the collected device indicates that the collected data exceeds a preset threshold range;

[0119] A data recovery unit is used to recover the collected data using a fourth self-recovery program corresponding to the fourth self-recovery rule, and determine whether the recovered collected data is successfully recovered; if the recovered collected data is not successfully recovered, then record the operation information corresponding to the recovered collected data in the target system log corresponding to the Internet of Things collection system, and send corresponding alarm information.

[0120] In some specific implementations, the abnormality recovery device of the Internet of Things acquisition system may also include:

[0121] The resource monitoring unit is used to monitor the resource usage of the collected equipment to obtain monitoring results, adjust the scheduling strategies corresponding to the several data collection tasks issued by the Internet of Things collection system based on the monitoring results, and schedule each of the data collection tasks based on the adjusted scheduling strategies.

[0122] In some specific implementations, the abnormality recovery device of the Internet of Things acquisition system may also include:

[0123] A recovery operation judgment unit, used to judge whether the abnormal recovery operation performed by each abnormal self-recovery program on the monitored object with abnormality is successfully executed;

[0124] An alarm information generating unit is used to generate corresponding alarm information based on the corresponding object to be monitored with an abnormality, and perform a gateway restart operation or a service restart operation if the abnormal recovery operation performed by each abnormal self-recovery program on the object to be monitored with an abnormality is not successfully executed.

[0125] Furthermore, the present application also discloses an electronic device. Figure 6 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input and output interface 25 and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the abnormal recovery method of the Internet of Things acquisition system disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment can specifically be an electronic computer.

[0126] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0127] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0128] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the abnormal recovery method of the Internet of Things acquisition system executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0129] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the abnormal recovery method of the IoT acquisition system disclosed above is implemented. The specific steps of the method can be referred to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0130] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0131] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0132] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0133] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0134] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An abnormal recovery method for an Internet of Things acquisition system, characterized in that: include: Determine a number of objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected device to the data acquisition server through preset data collection points; Monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; Using the abnormality detection logic pre-configured for each of the objects to be monitored, each of the monitoring results is subjected to abnormality detection to obtain an abnormality detection result corresponding to each of the objects to be monitored; If any abnormality detection result indicates that the corresponding monitored object has an abnormality, the target self-recovery rule corresponding to the monitored object with the abnormality is retrieved from the preset self-recovery rule library, and the abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete the abnormality recovery operation for the monitored object with the abnormality.

2. The abnormal recovery method of the Internet of Things acquisition system according to claim 1 is characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, including: If the abnormality detection result corresponding to the first system log generated by the Internet of Things collection system for recording the key parameters of the preset data collection point indicates that the first system log has an abnormality, calling the first self-recovery rule corresponding to the first system log from the preset self-recovery rule library; The preset data collection point is reset using the first self-recovery program corresponding to the first self-recovery rule, and it is determined whether the reset data collection point meets the performance indicator corresponding to the first system log; if the reset data collection point does not meet the performance indicator corresponding to the first system log, switch to the preset backup data collection point.

3. The abnormal recovery method of the Internet of Things acquisition system according to claim 1 is characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, including: If the abnormality detection result corresponding to the second system log for recording the protocol interaction between the collected device and the data collection server indicates that the second system log has an abnormality, then the second self-recovery rule corresponding to the second system log is retrieved from the preset self-recovery rule library; Utilize the second self-recovery program corresponding to the second self-recovery rule to execute the preset protocol rollback process, record the operation information corresponding to the preset protocol rollback process in the target system log corresponding to the Internet of Things acquisition system, and send corresponding alarm information; Determine whether the preset protocol rollback process is successfully executed; if the preset protocol rollback process is not successfully executed, use the preset dynamic negotiation operation to re-determine the target protocol for protocol interaction between the collected device and the data collection server.

4. The abnormal recovery method of the Internet of Things acquisition system according to claim 1 is characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, including: If the abnormality detection result corresponding to the third system log for recording the network connection status of the Internet of Things acquisition system indicates that the third system log has an abnormality, then the third self-recovery rule corresponding to the third system log is retrieved from the preset self-recovery rule library; re-establishing the network connection using a third self-recovery program corresponding to the third self-recovery rule, and determining whether the network connection is successfully established; If the network connection is not successfully established, the currently collected data collected by the Internet of Things collection system for the collected device is cached in a preset local cache space, and attempts to establish a network connection are continued until the network connection is successfully established.

5. The abnormal recovery method of the Internet of Things acquisition system according to claim 1 is characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, including: If the abnormal detection result corresponding to the collected data collected by the IoT collection system on the collected device indicates that the collected data exceeds the preset threshold range, a fourth self-recovery rule corresponding to the collected data is retrieved from a preset self-recovery rule library; The collected data is recovered using the fourth self-recovery program corresponding to the fourth self-recovery rule, and it is determined whether the recovered collected data is successfully recovered; if the recovered collected data is not successfully recovered, the operation information corresponding to the recovered collected data is recorded in the target system log corresponding to the Internet of Things collection system, and a corresponding alarm message is sent.

6. The abnormal recovery method of the Internet of Things acquisition system according to claim 1 is characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, then a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, and further includes: The resource usage of the collected equipment is monitored to obtain monitoring results, and the scheduling strategies corresponding to the data collection tasks issued by the Internet of Things collection system are adjusted based on the monitoring results, and each of the data collection tasks is scheduled based on the adjusted scheduling strategies.

7. The abnormal recovery method of the Internet of Things acquisition system according to any one of claims 1 to 6, characterized in that: If any abnormality detection result indicates that the corresponding monitored object has an abnormality, then a target self-recovery rule corresponding to the monitored object with an abnormality is retrieved from a preset self-recovery rule library, and an abnormality self-recovery program corresponding to the target self-recovery rule is executed to complete an abnormality recovery operation for the monitored object with an abnormality, and further includes: Determine whether the abnormal recovery operation performed by each of the abnormal self-recovery programs on the monitored object with abnormality is successfully executed; If the abnormal recovery operation performed by each abnormal self-recovery program on the abnormal object to be monitored is not successfully executed, corresponding alarm information is generated based on the corresponding abnormal object to be monitored, and a gateway restart operation or a service restart operation is performed.

8. A system self-recovery device, characterized in that: include: The module for determining objects to be monitored is used to determine a number of objects to be monitored in the Internet of Things acquisition system; the Internet of Things acquisition system is used to collect device data output by the collected devices to the data acquisition server through preset data collection points; An object monitoring module is used to monitor each of the objects to be monitored in the Internet of Things acquisition system to obtain a number of corresponding monitoring results; wherein the objects to be monitored include a first system log generated by the Internet of Things acquisition system for recording key parameters of the preset data acquisition points, a second system log for recording the protocol interaction between the collected device and the data acquisition server, and a third system log for recording the network connection status of the Internet of Things acquisition system; A detection logic allocation module, used to perform anomaly detection on each of the monitoring results using anomaly detection logics pre-configured for each of the objects to be monitored, so as to obtain anomaly detection results corresponding to each of the objects to be monitored; The self-recovery rule calling module is used to retrieve the target self-recovery rule corresponding to the object to be monitored with the abnormality from the preset self-recovery rule library if any abnormality detection result indicates that the corresponding monitored object has an abnormality, and complete the abnormal recovery operation for the monitored object with the abnormality by executing the abnormality self-recovery program corresponding to the target self-recovery rule.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the abnormal recovery method of the Internet of Things acquisition system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the steps of the abnormal recovery method of the Internet of Things acquisition system according to any one of claims 1 to 7 are implemented.

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