Monitoring system based on Internet of Things equipment health monitoring platform

Abstract

The invention provides a monitoring system based on an Internet of Things equipment health monitoring platform, and belongs to the technical field of equipment monitoring management, and the system comprises a monitoring module which collects the real-time equipment operation data of the Internet of Things equipment health monitoring platform, and obtains the historical equipment operation data of the Internet of Things equipment health monitoring platform; the analysis module is used for determining a real-time equipment characteristic matrix and determining real-time equipment fault data; the alarm module is used for sending out real-time alarm information based on the real-time equipment fault data and maintaining the Internet of Things equipment health monitoring platform based on the real-time alarm information; and the reporting module generates a monitoring report. The potential fault risk of the equipment can be comprehensively and accurately identified, the accuracy and timeliness of fault prediction are enhanced, the probability of equipment fault occurrence is reduced, the reliability and operation efficiency of the equipment are improved, the monitoring precision and efficiency of an Internet of Things equipment health monitoring platform are improved, and the automation and intelligence of the platform are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring and management, and in particular to a monitoring system based on an Internet of Things equipment health monitoring platform. Background Art

[0002] The concept of IoT device health monitoring platform originates from the fields of industrial automation and intelligent manufacturing. With the rapid development of IoT technology, more and more devices and sensors are deployed in various industrial and commercial environments. Real-time monitoring of the operating status of equipment has become an important means to optimize maintenance, improve efficiency and reduce downtime. Traditional equipment management and maintenance methods mainly rely on manual inspection and regular maintenance, but this method is inefficient and cannot identify potential equipment failures in advance. With the advancement of big data analysis and artificial intelligence technology, more and more equipment health monitoring systems adopt data-based predictive maintenance methods, but they often lack intelligent analysis capabilities and the accuracy of fault prediction is low.

[0003] Therefore, the present invention provides a monitoring system based on an Internet of Things device health monitoring platform. Summary of the invention

[0004] The present invention provides a monitoring system based on an IoT device health monitoring platform, which analyzes the real-time device operation data collected from the IoT device health monitoring platform to determine the real-time device feature matrix, combines historical device operation data to determine real-time device fault data, issues real-time alarm information, maintains the IoT device health monitoring platform, and generates monitoring reports. It can comprehensively and accurately identify the potential failure risks of equipment, enhance the accuracy and timeliness of fault prediction, reduce the probability of equipment failure, improve the reliability and operational efficiency of equipment, enhance the accuracy and efficiency of IoT device health monitoring platform monitoring, and improve the automation and intelligence of the platform.

[0005] The present invention provides a monitoring system based on an IoT device health monitoring platform, comprising: Monitoring module: collects real-time equipment operation data of the IoT equipment health monitoring platform, and obtains historical equipment operation data of the IoT equipment health monitoring platform; Analysis module: Analyze the real-time equipment operation data to determine the real-time equipment feature matrix, and determine the real-time equipment fault data based on the real-time equipment feature matrix and historical equipment operation data; Alarm module: issues real-time alarm information based on real-time equipment failure data, and maintains the IoT equipment health monitoring platform based on real-time alarm information; Report module: Generates monitoring reports based on real-time equipment operation data, real-time equipment failure data, real-time alarm information and maintenance results.

[0006] The monitoring system based on the health monitoring platform of the Internet of Things device provided by the present invention includes: Data collection unit: obtains the monitoring requirements of the IoT device health monitoring platform and the device types of all devices, and determines multiple data collection points for each device and the sensor group for each data collection point based on the monitoring requirements and the device types of all devices; Real-time equipment operation sub-data unit: based on the sensor group of all data collection points of each equipment, collects the real-time equipment operation sub-data of each equipment within a specified time period; Real-time device operation data unit: determines the real-time device operation data of the IoT device health monitoring platform based on the real-time device operation sub-data of all devices; Sub-historical device operation data unit: obtains sub-historical device operation data of the IoT device health monitoring platform within multiple historical specified time periods, wherein the sub-historical device operation data includes a historical device feature matrix and historical device fault data, and the historical fault data includes device status values ​​and device status of all devices; Historical device operation data unit: determines the historical device operation data of the IoT device health monitoring platform based on all sub-historical device operation data within a historical specified time period.

[0007] The monitoring system based on the health monitoring platform of the Internet of Things device provided by the present invention and the analysis module include: Real-time device feature vector unit: pre-processes the real-time device operation data, extracts features from each real-time device operation sub-data in the pre-processed real-time device operation data, and determines the real-time device feature vector of each device in the IoT device health monitoring platform; Vector dimension processing unit: determines the dimension threshold based on the vector dimension of the real-time device feature vector of all devices, performs dimension increase processing on the devices whose vector dimension of the real-time device feature vector is less than the dimension threshold, and performs dimension reduction processing on the devices whose vector dimension of the real-time device feature vector is greater than the dimension threshold; Real-time device feature matrix unit: determines the real-time device feature matrix of the IoT device health monitoring platform based on the real-time device feature vectors of all devices after dimensionality increase or dimensionality reduction processing.

[0008] The monitoring system based on the IoT device health monitoring platform provided by the present invention, the analysis module, further includes: A first determining unit: determining first real-time device fault data and second real-time device fault data based on all historical device feature matrices, historical device fault data and real-time device feature matrices within a specified historical time period; A real-time device failure data unit is used to determine the real-time device failure data based on the first real-time device failure data and the second real-time device failure data, wherein the real-time device failure data includes the comprehensive prediction state value and the comprehensive prediction state of all devices.

[0009] According to the monitoring system based on the Internet of Things device health monitoring platform provided by the present invention, the first determination unit includes: Similarity matrix subunit: based on the historical device feature matrix and the real-time device feature matrix in each historical specified time period, determine the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods; ; ; in, Represents the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods, Represents the period similarity vector of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of the 1st historical specified time period, the tth historical specified time period, and the N2th historical specified time period, where N2 represents the number of historical specified time periods. Respectively represent the similarity values ​​between the real-time device feature vectors after the 1st device, the i-th device, and the N1-th device in the real-time device feature matrix undergoing dimensionality increase or dimensionality reduction processing and the historical device feature vector of the i-th device in the historical device feature matrix; Device similarity vector subunit: extracts the similarity value between the real-time device feature vector of each device in the similarity matrix after dimension increase or dimension reduction and the historical device feature vector of the corresponding device in all historical device feature matrices, and determines the device similarity vector of each device; The first real-time device fault data subunit determines the first real-time device fault data based on the device similarity vectors of all devices.

[0010] According to the monitoring system based on the IoT device health monitoring platform provided by the present invention, the first real-time device fault data subunit includes: A first predicted state value function block: determining a first predicted state value of each device based on a device similarity vector of each device; ; ; ; ; ; ; ; in, represents the first predicted state value of the ith device in the IoT device health monitoring platform, represents the first predicted sub-state value, the second predicted sub-state value, and the third predicted sub-state value of the ith device of the IoT device health monitoring platform, w1 represents the first weight, w2 represents the second weight, and w3 represents the first weight. Indicates the maximum similarity value between the device similarity vector of the ith device and the historical device feature vector of the ith device in all historical specified time periods. represents the first similarity threshold, 2 represents the second similarity threshold, represents the first state indication value, represents the second state indication value, represents the third state indication value, represents the device state value corresponding to the similarity value between the real-time device feature vector of the i-th device in the real-time device feature matrix after dimensionality increase or dimensionality reduction and the historical device feature vector of the i-th device in the historical device feature matrix, represents the initial period attenuation factor, represents the periodic decay growth rate; The first predicted state function block is used to determine the first predicted state of each device based on the first predicted state value of each device of the IoT device health monitoring platform; The first real-time device fault data functional block: determines the first real-time device fault data based on the first predicted state value and the first predicted state of all devices on the IoT device health monitoring platform.

[0011] According to the monitoring system based on the Internet of Things device health monitoring platform provided by the present invention, the first determination unit also includes: Fault prediction model subunit: taking the historical equipment feature matrix in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the input of the fault prediction model, and taking the historical equipment failure data in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the output of the fault prediction model, and training the fault prediction model; The second real-time device fault data subunit: inputs the real-time device feature matrix into the trained fault prediction model, and determines the second real-time device fault data of the Internet of Things device health monitoring platform based on the output result of the fault prediction model, wherein the second real-time device fault data includes the second predicted state value and the second predicted state of all devices of the Internet of Things device health monitoring platform.

[0012] The monitoring system based on the health monitoring platform of the Internet of Things device provided by the present invention and the alarm module include: Real-time alarm sub-information unit: determines the real-time alarm sub-information of each device based on the comprehensive predicted fault status of each device in the real-time device fault data; Fault handling or maintenance unit: Based on the real-time alarm sub-information issued by each device on the IoT device health monitoring platform, fault handling or maintenance is performed on each device.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The real-time equipment operation data collected from the IoT equipment health monitoring platform is analyzed to determine the real-time equipment feature matrix, and the real-time equipment failure data is determined in combination with the historical equipment operation data. Real-time alarm information is issued, and the IoT equipment health monitoring platform is maintained to generate monitoring reports. It can comprehensively and accurately identify the potential failure risks of equipment, enhance the accuracy and timeliness of failure prediction, reduce the probability of equipment failure, improve equipment reliability and operational efficiency, improve the accuracy and efficiency of IoT equipment health monitoring platform monitoring, and improve the automation and intelligence of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a structural diagram of a monitoring system based on an Internet of Things device health monitoring platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0017] Embodiment 1: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, such as Figure 1 As shown, including: Monitoring module: collects real-time equipment operation data of the IoT equipment health monitoring platform, and obtains historical equipment operation data of the IoT equipment health monitoring platform; Analysis module: Analyze the real-time equipment operation data to determine the real-time equipment feature matrix, and determine the real-time equipment fault data based on the real-time equipment feature matrix and historical equipment operation data; Alarm module: issues real-time alarm information based on real-time equipment failure data, and maintains the IoT equipment health monitoring platform based on real-time alarm information; Report module: Generates monitoring reports based on real-time equipment operation data, real-time equipment failure data, real-time alarm information and maintenance results.

[0018] In this embodiment, the monitoring module collects real-time device operation data and historical device operation data of the device. Real-time device operation data refers to the status information of the device in a real-time specified time period, such as operating parameters such as temperature, pressure, and current. Historical device operation data records the operation information of the device in multiple historical specified time periods in the past. Analysis module: In this embodiment, the real-time device feature matrix represents the operating characteristics of all devices in the IoT device health monitoring platform within a real-time specified time period.

[0019] In this embodiment, the real-time device fault data includes a comprehensive predicted state value determined based on the first predicted state value and the first predicted state value, and a comprehensive predicted state determined based on the comprehensive predicted state value, the first predicted state, and the second predicted state for all devices of the IoT device health monitoring platform.

[0020] In this embodiment, after the real-time device fault data analysis is completed, the alarm module will issue real-time alarm information based on these data. The real-time alarm information includes the real-time alarm sub-information of all devices in the real-time device fault data of the IoT device health monitoring platform.

[0021] The beneficial effects of the above technical solution are: analyzing the real-time equipment operation data collected from the IoT equipment health monitoring platform to determine the real-time equipment feature matrix, combining historical equipment operation data to determine the real-time equipment fault data, issuing real-time alarm information, maintaining the IoT equipment health monitoring platform, and generating monitoring reports. It can comprehensively and accurately identify the potential failure risks of equipment, enhance the accuracy and timeliness of fault prediction, reduce the probability of equipment failure, improve equipment reliability and operational efficiency, improve the accuracy and efficiency of IoT equipment health monitoring platform monitoring, and improve the automation and intelligence of the platform.

[0022] Embodiment 2: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, and a monitoring module, including: Data collection unit: obtains the monitoring requirements of the IoT device health monitoring platform and the device types of all devices, and determines multiple data collection points for each device and the sensor group for each data collection point based on the monitoring requirements and the device types of all devices; Real-time equipment operation sub-data unit: based on the sensor group of all data collection points of each equipment, collects the real-time equipment operation sub-data of each equipment within a specified time period; Real-time device operation data unit: determines the real-time device operation data of the IoT device health monitoring platform based on the real-time device operation sub-data of all devices; Sub-historical device operation data unit: obtains sub-historical device operation data of the IoT device health monitoring platform within multiple historical specified time periods, wherein the sub-historical device operation data includes a historical device feature matrix and historical device fault data, and the historical fault data includes device status values ​​and device status of all devices; Historical device operation data unit: determines the historical device operation data of the IoT device health monitoring platform based on all sub-historical device operation data within a historical specified time period.

[0023] In this embodiment, the monitoring requirements of the IoT device health monitoring platform and the device types of all devices are obtained. Based on these requirements and device types, the system determines multiple data collection points for each device and equips each data collection point with a corresponding sensor group. The data collection points may include multiple parameters such as temperature, humidity, vibration, etc., and the sensor group is responsible for collecting these data in real time.

[0024] In this embodiment, based on the sensor group of all data collection points of each device, the real-time device operation sub-data of each device is collected within a specified time period. The real-time operation data of each device may include real-time environment and device performance data collected by various sensors, such as current, voltage, temperature, etc.

[0025] In this embodiment, based on the real-time device operation sub-data of all devices, the system aggregates and generates real-time device operation data of the IoT device health monitoring platform.

[0026] In this embodiment, sub-historical equipment operation data within multiple historical specified time periods are obtained. The sub-historical equipment operation data includes a historical equipment characteristic matrix (performance data reflecting the historical operation of the equipment) and historical equipment failure data (including equipment status value and equipment status, showing the performance of the equipment in the past and whether it has ever failed).

[0027] In this embodiment, based on the sub-historical device operation data of multiple historical time periods, the system generates historical device operation data of the entire Internet of Things device health monitoring platform.

[0028] The beneficial effects of the above technical solution are: collecting real-time equipment operation data of the IoT equipment health monitoring platform and obtaining historical equipment operation data of the IoT equipment health monitoring platform, which can provide a data basis for determining the real-time equipment feature matrix and real-time equipment fault data.

[0029] Embodiment 3: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, an analysis module, including: Real-time device feature vector unit: pre-processes the real-time device operation data, extracts features from each real-time device operation sub-data in the pre-processed real-time device operation data, and determines the real-time device feature vector of each device in the IoT device health monitoring platform; Vector dimension processing unit: determines the dimension threshold based on the vector dimension of the real-time device feature vector of all devices, performs dimension increase processing on the devices whose vector dimension of the real-time device feature vector is less than the dimension threshold, and performs dimension reduction processing on the devices whose vector dimension of the real-time device feature vector is greater than the dimension threshold; Real-time device feature matrix unit: determines the real-time device feature matrix of the IoT device health monitoring platform based on the real-time device feature vectors of all devices after dimensionality increase or dimensionality reduction processing.

[0030] In this embodiment, the real-time device operation data is preprocessed, and the preprocessing step may include data cleaning, denoising, etc. In the processed real-time device operation data, the operation sub-data of each device (such as temperature, pressure, vibration, etc.) will be feature extracted to extract features that can represent the health status of the device. Through this process, each device is assigned a feature vector to describe the real-time status and behavior pattern of the device.

[0031] In this embodiment, a dimension threshold is set according to the dimension of the real-time device feature vectors of all devices. If the dimension of the feature vector of a device is smaller than the threshold, dimensionality increase processing will be performed (increasing the feature dimension and enhancing the data representation capability); if the dimension of the feature vector of a device is larger than the threshold, dimensionality reduction processing will be performed (reducing redundant feature information and improving computational efficiency and model stability).

[0032] In this embodiment, based on all device feature vectors after dimension increase and dimension reduction processing, the system summarizes and generates a real-time device feature matrix of the IoT device health monitoring platform.

[0033] The beneficial effects of the above technical solution are as follows: analyzing the real-time equipment operation data to determine the real-time equipment characteristic matrix can improve the accuracy and efficiency of equipment health monitoring and provide structured, multi-dimensional data support for determining real-time equipment fault data.

[0034] Embodiment 4: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, an analysis module, and further includes: A first determining unit: determining first real-time device fault data and second real-time device fault data based on all historical device feature matrices, historical device fault data and real-time device feature matrices within a specified historical time period; A real-time device failure data unit is used to determine the real-time device failure data based on the first real-time device failure data and the second real-time device failure data, wherein the real-time device failure data includes the comprehensive prediction state value and the comprehensive prediction state of all devices.

[0035] In this embodiment, the historical equipment feature matrix and historical equipment failure data within all historical specified time periods are used. These data reflect the past health status and failure conditions of the equipment. Combined with the current real-time equipment feature matrix, through comparison and analysis, the system can obtain two sets of real-time failure data: first real-time equipment failure data and second real-time equipment failure data.

[0036] In this embodiment, after obtaining the first and second groups of fault data, the real-time device fault data unit comprehensively analyzes and obtains the final real-time device fault data based on the two groups of data.

[0037] In this embodiment, the comprehensive prediction state value reflects the failure risk of the device. The comprehensive prediction state is the result of evaluating the health state of the device based on the comprehensive prediction state value of the device, the first prediction state and the second prediction state.

[0038] The beneficial effects of the above technical solution are as follows: by determining real-time equipment fault data based on the real-time equipment feature matrix and historical equipment operation data, the potential failure risks of equipment can be comprehensively and accurately identified, the probability of equipment failure can be reduced, and the reliability and operational efficiency of the IoT equipment health monitoring platform can be improved.

[0039] Embodiment 5: The embodiment of the present invention provides a monitoring system based on an Internet of Things device health monitoring platform, wherein a first determining unit includes: Similarity matrix subunit: based on the historical device feature matrix and the real-time device feature matrix in each historical specified time period, determine the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods; ; ; in, Represents the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods, Represents the period similarity vector of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of the 1st historical specified time period, the tth historical specified time period, and the N2th historical specified time period, where N2 represents the number of historical specified time periods. Respectively represent the similarity values ​​between the real-time device feature vectors after the 1st device, the i-th device, and the N1-th device in the real-time device feature matrix undergoing dimensionality increase or dimensionality reduction processing and the historical device feature vector of the i-th device in the historical device feature matrix; Device similarity vector subunit: extracts the similarity value between the real-time device feature vector of each device in the similarity matrix after dimension increase or dimension reduction and the historical device feature vector of the corresponding device in all historical device feature matrices, and determines the device similarity vector of each device; The first real-time device fault data subunit determines the first real-time device fault data based on the device similarity vectors of all devices.

[0040] In this embodiment, based on the historical device feature matrix and the real-time device feature matrix within each historical specified time period, the period similarity vectors of the real-time device feature matrix and the historical device feature matrix within each historical specified time period are calculated, and on this basis, a similarity matrix is ​​determined by integrating all the period similarity vectors.

[0041] In this embodiment, after the similarity matrix is ​​obtained, similarity values ​​between the processed real-time device feature vector of each device and the historical device feature vectors of the corresponding device in all historical device feature matrices are extracted, that is, each column of the similarity matrix.

[0042] In this embodiment, They respectively represent the real-time device feature vectors after dimensionality increase or dimensionality reduction processing of the 1st device, the i-th device, and the N1-th device in the real-time device feature matrix, and N1 represents the number of devices in the IoT device health monitoring platform.

[0043] In this embodiment, Respectively represent the historical device feature vectors of the 1st device, the ith device, and the N1th device in the historical device feature matrix of the tth historical specified time period.

[0044] The beneficial effects of the above technical solution are as follows: based on the historical equipment feature matrix, historical equipment failure data and real-time equipment feature matrix within all historical specified time periods, the first real-time equipment failure data is determined, which can efficiently identify the correlation between the real-time equipment operation data and the historical equipment operation data, enhance the accuracy of fault prediction, and improve the sensitivity of fault detection.

[0045] Embodiment 6: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, wherein a first real-time device fault data subunit includes: A first predicted state value function block: determining a first predicted state value of each device based on a device similarity vector of each device; ; ; ; ; ; ; ; in, represents the first predicted state value of the ith device in the IoT device health monitoring platform, represents the first predicted sub-state value, the second predicted sub-state value, and the third predicted sub-state value of the ith device of the IoT device health monitoring platform, w1 represents the first weight, w2 represents the second weight, and w3 represents the first weight. Indicates the maximum similarity value between the device similarity vector of the ith device and the historical device feature vector of the ith device in all historical specified time periods. represents the first similarity threshold, 2 represents the second similarity threshold, represents the first state indication value, represents the second state indication value, represents the third state indication value, represents the device state value corresponding to the similarity value between the real-time device feature vector of the i-th device in the real-time device feature matrix after dimensionality increase or dimensionality reduction and the historical device feature vector of the i-th device in the historical device feature matrix, represents the initial period attenuation factor, represents the periodic decay growth rate; The first predicted state function block is used to determine the first predicted state of each device based on the first predicted state value of each device of the IoT device health monitoring platform; The first real-time device fault data functional block: determines the first real-time device fault data based on the first predicted state value and the first predicted state of all devices on the IoT device health monitoring platform.

[0046] In this embodiment, the first weight w1 represents the weight when the similarity value is greater than the first similarity threshold, the second weight w2 represents the weight when the similarity value is greater than or equal to the second similarity threshold and less than or equal to the first similarity threshold, and the first weight w3 represents the weight when the similarity value is less than the second similarity threshold.

[0047] In this embodiment, express When it is greater than the first similarity threshold, the value is 1. express When it is greater than or equal to the second similarity threshold and less than or equal to the first similarity threshold, the value is 1. express When it is less than the second similarity threshold, the value is 1.

[0048] In this embodiment, express When it is greater than the first similarity threshold, the value is , express When it is greater than or equal to the second similarity threshold and less than or equal to the first similarity threshold, the value is , express When it is less than the second similarity threshold, the value is .

[0049] In this embodiment, by further analyzing the first predicted state value, a more accurate state prediction is provided for each device to determine whether the current health state of the device is in a risk range. The first predicted state may be a state such as "normal", "warning" or "fault".

[0050] In this embodiment, the first real-time device fault data is determined by combining the first predicted state values ​​and the first predicted state of all devices of the IoT device health monitoring platform.

[0051] The beneficial effects of the above technical solution are as follows: based on the equipment similarity vectors of all equipment, the first real-time equipment failure data is determined, which can improve data support for determining the real-time equipment failure data, timely identify the potential failure risks of equipment, dynamically predict the status of each device, improve the accuracy and timeliness of equipment failure warnings, and provide more intelligent decision support for equipment maintenance.

[0052] Embodiment 7: The embodiment of the present invention provides a monitoring system based on an Internet of Things device health monitoring platform, wherein the first determination unit further includes: Fault prediction model subunit: taking the historical equipment feature matrix in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the input of the fault prediction model, and taking the historical equipment failure data in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the output of the fault prediction model, and training the fault prediction model; The second real-time device fault data subunit: inputs the real-time device feature matrix into the trained fault prediction model, and determines the second real-time device fault data of the Internet of Things device health monitoring platform based on the output result of the fault prediction model, wherein the second real-time device fault data includes the second predicted state value and the second predicted state of all devices of the Internet of Things device health monitoring platform.

[0053] In this embodiment, the system uses the sub-historical equipment operation data of each time period in the historical equipment operation data, including the historical equipment feature matrix, as the input data of the model. These data reflect the past operating status of the equipment. The system also uses the historical equipment failure data of the corresponding time period as output data to record the actual failure conditions of the equipment. By using these historical data to train the model, the system can learn the laws and patterns of equipment failures, providing a basis for subsequent fault prediction.

[0054] In this embodiment, after the fault prediction model training is completed, the system inputs the real-time device feature matrix of the real-time device into the trained fault prediction model for prediction, and the result output by the model is used to generate the second real-time device fault data.

[0055] In this embodiment, the second real-time device fault data includes a second predicted state value and a second predicted state of each device.

[0056] The beneficial effects of the above technical solution are as follows: based on the historical equipment feature matrix, historical equipment failure data and real-time equipment feature matrix within all historical specified time periods, the second real-time equipment failure data is determined, which can improve data support for determining the real-time equipment failure data, timely identify the potential failure risks of the equipment, dynamically predict the status of each device, improve the accuracy and timeliness of equipment failure warnings, and provide more intelligent decision support for equipment maintenance.

[0057] Embodiment 8: The embodiment of the present invention provides a monitoring system based on an IoT device health monitoring platform, an alarm module, including: Real-time alarm sub-information unit: determines the real-time alarm sub-information of each device based on the comprehensive predicted fault status of each device in the real-time device fault data; Fault handling or maintenance unit: Based on the real-time alarm sub-information issued by each device on the IoT device health monitoring platform, fault handling or maintenance is performed on each device.

[0058] In this embodiment, the comprehensive fault prediction status in the real-time equipment fault data is analyzed, and real-time alarm sub-information is generated for each device. These alarm information may include warning levels (such as minor faults, serious faults, or impending faults), and can provide maintenance personnel with the current comprehensive fault prediction status and fault warning of the equipment.

[0059] In this embodiment, based on the generated real-time alarm sub-information, the system guides the fault handling or maintenance operation for each device, and determines the appropriate maintenance operation according to the level and content of the real-time alarm sub-information.

[0060] The beneficial effects of the above technical solution are: issuing real-time alarm information based on real-time equipment fault data, and maintaining the IoT equipment health monitoring platform based on the real-time alarm information, which can reduce manual intervention, improve the timeliness and accuracy of fault prevention and fault handling, optimize equipment maintenance processes, reduce downtime and reduce operating costs, and improve the intelligence of the IoT equipment health monitoring platform.

[0061] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0062] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The monitoring system based on the IoT device health monitoring platform is characterized by: include: Monitoring module: collects real-time equipment operation data of the IoT equipment health monitoring platform, and obtains historical equipment operation data of the IoT equipment health monitoring platform; Analysis module: Analyze the real-time equipment operation data to determine the real-time equipment feature matrix, and determine the real-time equipment fault data based on the real-time equipment feature matrix and historical equipment operation data; Alarm module: issues real-time alarm information based on real-time equipment failure data, and maintains the IoT equipment health monitoring platform based on real-time alarm information; Report module: Generates monitoring reports based on real-time equipment operation data, real-time equipment failure data, real-time alarm information and maintenance results.

2. The monitoring system based on the IoT device health monitoring platform according to claim 1 is characterized in that: Monitoring modules, including: Data collection unit: obtains the monitoring requirements of the IoT device health monitoring platform and the device types of all devices, and determines multiple data collection points for each device and the sensor group for each data collection point based on the monitoring requirements and the device types of all devices; Real-time equipment operation sub-data unit: based on the sensor group of all data collection points of each equipment, collects the real-time equipment operation sub-data of each equipment within a specified time period; Real-time device operation data unit: determines the real-time device operation data of the IoT device health monitoring platform based on the real-time device operation sub-data of all devices; Sub-historical device operation data unit: obtains sub-historical device operation data of the IoT device health monitoring platform within multiple historical specified time periods, wherein the sub-historical device operation data includes a historical device feature matrix and historical device fault data, and the historical fault data includes device status values ​​and device status of all devices; Historical device operation data unit: determines the historical device operation data of the IoT device health monitoring platform based on all sub-historical device operation data within a historical specified time period.

3. The monitoring system based on the health monitoring platform of Internet of Things devices according to claim 1 is characterized in that: Analysis modules, including: Real-time device feature vector unit: pre-processes the real-time device operation data, extracts features from each real-time device operation sub-data in the pre-processed real-time device operation data, and determines the real-time device feature vector of each device in the IoT device health monitoring platform; Vector dimension processing unit: determines the dimension threshold based on the vector dimension of the real-time device feature vector of all devices, performs dimension increase processing on the devices whose vector dimension of the real-time device feature vector is less than the dimension threshold, and performs dimension reduction processing on the devices whose vector dimension of the real-time device feature vector is greater than the dimension threshold; Real-time device feature matrix unit: determines the real-time device feature matrix of the IoT device health monitoring platform based on the real-time device feature vectors of all devices after dimensionality increase or dimensionality reduction processing.

4. The monitoring system based on the health monitoring platform of Internet of Things devices according to claim 2 is characterized in that: The analysis module also includes: A first determining unit: determining first real-time device fault data and second real-time device fault data based on all historical device feature matrices, historical device fault data and real-time device feature matrices within a specified historical time period; A real-time device failure data unit is used to determine the real-time device failure data based on the first real-time device failure data and the second real-time device failure data, wherein the real-time device failure data includes the comprehensive prediction state value and the comprehensive prediction state of all devices.

5. The monitoring system based on the health monitoring platform of Internet of Things devices according to claim 4 is characterized in that: The first determining unit includes: Similarity matrix subunit: based on the historical device feature matrix and the real-time device feature matrix in each historical specified time period, determine the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods; ； ； in, Represents the similarity matrix of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of all historical specified time periods, Represents the period similarity vector of the real-time device feature matrix of all devices in the IoT device health monitoring platform and the historical device feature matrix of the 1st historical specified time period, the tth historical specified time period, and the N2th historical specified time period, where N2 represents the number of historical specified time periods. Respectively represent the similarity values ​​between the real-time device feature vectors after the 1st device, the i-th device, and the N1-th device in the real-time device feature matrix undergoing dimensionality increase or dimensionality reduction processing and the historical device feature vector of the i-th device in the historical device feature matrix; Device similarity vector subunit: extracts the similarity value between the real-time device feature vector of each device in the similarity matrix after dimension increase or dimension reduction and the historical device feature vector of the corresponding device in all historical device feature matrices, and determines the device similarity vector of each device; The first real-time device fault data subunit determines the first real-time device fault data based on the device similarity vectors of all devices.

6. The monitoring system based on the Internet of Things device health monitoring platform according to claim 5 is characterized in that: The first real-time equipment fault data subunit includes: A first predicted state value function block: determining a first predicted state value of each device based on a device similarity vector of each device; ； ； ； ； ； ； ； in, represents the first predicted state value of the ith device in the IoT device health monitoring platform, represents the first predicted sub-state value, the second predicted sub-state value, and the third predicted sub-state value of the ith device of the IoT device health monitoring platform, w1 represents the first weight, w2 represents the second weight, and w3 represents the first weight. Indicates the maximum similarity value between the device similarity vector of the ith device and the historical device feature vector of the ith device in all historical specified time periods. represents the first similarity threshold, 2 represents the second similarity threshold, represents the first state indication value, represents the second state indication value, represents the third state indication value, represents the device state value corresponding to the similarity value between the real-time device feature vector of the i-th device in the real-time device feature matrix after dimensionality increase or dimensionality reduction and the historical device feature vector of the i-th device in the historical device feature matrix, represents the initial period attenuation factor, represents the periodic decay growth rate; A first predicted state function block: determining a first predicted state of each device based on a first predicted state value of each device on the IoT device health monitoring platform; The first real-time device fault data functional block: determines the first real-time device fault data based on the first predicted state value and the first predicted state of all devices on the IoT device health monitoring platform.

7. The monitoring system based on the Internet of Things device health monitoring platform according to claim 4 is characterized in that: The first determining unit further includes: Fault prediction model subunit: taking the historical equipment feature matrix in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the input of the fault prediction model, and taking the historical equipment failure data in all sub-historical equipment operation data within the historical specified time period in the historical equipment operation data as the output of the fault prediction model, and training the fault prediction model; The second real-time device fault data subunit: inputs the real-time device feature matrix into the trained fault prediction model, and determines the second real-time device fault data of the Internet of Things device health monitoring platform based on the output result of the fault prediction model, wherein the second real-time device fault data includes the second predicted state value and the second predicted state of all devices of the Internet of Things device health monitoring platform.

8. The monitoring system based on the Internet of Things device health monitoring platform according to claim 4 is characterized in that: Alarm module, including: Real-time alarm sub-information unit: determines the real-time alarm sub-information of each device based on the comprehensive predicted fault status of each device in the real-time device fault data; Fault handling or maintenance unit: Based on the real-time alarm sub-information issued by each device on the IoT device health monitoring platform, fault handling or maintenance is performed on each device.

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