CN117319179A - Equipment abnormality monitoring method and system based on mechanism model and industrial Internet of things - Google Patents
Equipment abnormality monitoring method and system based on mechanism model and industrial Internet of things Download PDFInfo
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Abstract
The utility model relates to a device abnormality monitoring method and system based on a mechanism model and an industrial Internet of things, which calculates the similarity between the acquired real-time device data and the corresponding historical fault data according to the acquired real-time device data and the corresponding historical fault data, judges whether the real-time device data is abnormal according to a preset fault threshold value and the similarity, judges that the fault type exists, carries out abnormality detection analysis according to the real-time device data by adopting the mechanism model corresponding to the fault type if the fault type exists, and sends an alarm instruction corresponding to the device if the abnormality detection analysis result is that the device is abnormal. The method introduces a mechanism model, and judges whether the equipment is likely to generate abnormality and predicts which fault type exists in advance by corresponding to historical data in a big data analysis mode while improving the accuracy of abnormality detection, so that the calculation frequency is reduced, the calculation performance is improved, and the maintenance time of the equipment is effectively shortened.
Description
Technical Field
The application relates to the technical field of industrial Internet, in particular to a method and a system for monitoring equipment abnormality based on a mechanism model and an industrial Internet of things.
Background
With the development of data science, the internet of things is increasingly and widely applied, and a sensor can be generally utilized to directly obtain some statistics and signal characteristics as indexes for state monitoring and anomaly detection. However, for many devices, different working conditions and different parameter settings are involved in the normal operation of the device, so that the collected sensor data always changes, and the change is just the performance of the physical mechanism of the device. Many situations therefore cannot predict health simply by statistics and signal characteristics of the sensors.
However, in the prior art, whether the device has a fault can only be judged by the device data received by the sensor. In practice, however, various faults occur in each device, and after a device fault alarm is received, the fault type can be further judged only on the site of the device, and the fault type cannot be predicted in advance. In this way, the maintenance time is also greatly prolonged.
Disclosure of Invention
Accordingly, in order to solve the above-mentioned problems, it is necessary to provide a method and a system for monitoring equipment abnormality based on a mechanism model and an industrial internet of things, which can improve the detection efficiency and accuracy at the same time.
A method for monitoring equipment anomalies based on a mechanism model and an industrial internet of things, the method comprising:
acquiring real-time equipment data, wherein the real-time equipment data comprises a plurality of operation parameters related to corresponding equipment;
extracting historical fault data of corresponding equipment according to the real-time equipment data, and calculating the similarity between the historical fault data and the real-time equipment data, wherein the equipment corresponds to a plurality of fault types, the historical fault data comprises historical data of each operation parameter when different fault types occur in the equipment, and when the similarity between the historical fault data and the real-time equipment data is calculated, the similarity between the historical data corresponding to the fault type and each operation parameter in the real-time equipment data is calculated according to each fault type, and the similarity is calculated by adopting the following formula:
；
in the above-mentioned description of the invention,historical data representing an ith operating parameter corresponding to a type of fault,/for example>Representing an ith operation parameter in the real-time equipment data, wherein n represents the number of the operation parameters in the real-time equipment data, and Pm represents the similarity when corresponding to one fault type;
judging whether the real-time equipment data is abnormal or not according to a preset fault threshold value and similarity, and if the real-time equipment data is abnormal of a certain fault type, adopting a mechanism model corresponding to the equipment to perform abnormality detection analysis according to operation parameters related to the fault type in the real-time equipment data;
and if the abnormality detection analysis result shows that the equipment is abnormal, sending an alarm instruction of the corresponding equipment.
In one embodiment, the determining whether the real-time device data has an abnormality according to a preset failure threshold and a similarity includes:
judging the similarity corresponding to various fault types by utilizing the preset fault threshold, and judging that the real-time equipment data has the abnormality of the fault type if the similarity corresponding to a certain fault type is larger than or equal to the preset fault threshold;
if the similarity corresponding to a certain fault type is smaller than the preset fault threshold, judging that the real-time equipment data does not have the abnormality of the fault type.
The application also provides a device abnormality monitoring system based on the mechanism model and the industrial Internet of things, which comprises a device data acquisition module, a data abnormality judging module, a mechanism model analysis module and a device abnormality alarming module;
the device data acquisition module is used for acquiring real-time device data, wherein the real-time device data comprises a plurality of operation parameters related to corresponding devices, and the real-time device data is sent to the data abnormality judgment module;
the data anomaly judging module is used for extracting historical fault data of corresponding equipment according to the real-time equipment data, calculating the similarity between the historical fault data and the real-time equipment data, judging whether the real-time equipment data is anomalous according to a preset fault threshold value and the similarity, wherein the equipment corresponds to a plurality of fault types, the historical fault data comprises historical data of each operation parameter when different fault types occur in the equipment, and when the similarity between the historical fault data and the real-time equipment data is calculated, the similarity between the historical data corresponding to the fault type and each operation parameter in the real-time equipment data is calculated according to each fault type, and the similarity is calculated by adopting the following formula:
；
in the above-mentioned description of the invention,historical data representing an ith operating parameter corresponding to a type of fault,/for example>Representing an ith operation parameter in the real-time equipment data, wherein n represents the number of the operation parameters in the real-time equipment data, and Pm represents the similarity when corresponding to one fault type;
if the real-time equipment data has abnormality of a certain fault type, the mechanism model analysis module adopts a mechanism model corresponding to the equipment to carry out abnormality detection analysis according to the operation parameters related to the fault type in the real-time equipment data, and if the abnormality detection analysis result is that the equipment has abnormality, the abnormality detection analysis result is sent to the equipment abnormality alarm module;
and the equipment abnormality alarm module is used for sending alarm instructions of corresponding equipment according to the abnormality detection analysis result.
In one embodiment, the industrial internet of things anomaly monitoring system further includes: the data processing module is connected between the equipment data acquisition module and the data abnormality judgment module;
the data processing module is used for decrypting and decompressing the real-time equipment data acquired by the equipment data acquisition module, converting the real-time equipment data into a format which can be identified by the industrial Internet of things anomaly monitoring system to obtain the identifiable real-time equipment data, and then sending the identifiable real-time equipment data to the data anomaly judging module.
In one embodiment, the industrial internet of things anomaly monitoring system further includes: the device management module and the device control module;
the device management module is used for receiving a device management instruction and sending the device management instruction to the device control module;
the device control module is used for converting the device management instruction into an operation instruction according to a device protocol and then issuing the operation instruction to the device through the data processing module.
In one embodiment, the device abnormality alarm module sends the alarm instruction by way of a micro-message notification, a short-message notification or system internal communication.
According to the equipment anomaly monitoring method and system based on the mechanism model and the industrial Internet of things, the similarity between the real-time equipment data and the corresponding historical fault data is calculated according to the obtained real-time equipment data and the corresponding historical fault data, whether the real-time equipment data is abnormal or not is judged according to the preset fault threshold value and the similarity, the fault type is judged, if the fault type is abnormal, the mechanism model corresponding to the fault type is adopted to conduct anomaly detection analysis according to the real-time equipment data, and if the anomaly detection analysis result is that the equipment is abnormal, an alarm instruction corresponding to the equipment is sent. The method introduces a mechanism model, and judges whether the equipment is likely to generate abnormality and predicts which fault type exists in advance by corresponding to historical data in a big data analysis mode while improving the accuracy of abnormality detection, and judges by adopting the mechanism model and adopting the mechanism model corresponding to the predicted fault type, thereby reducing the calculation frequency, improving the calculation performance and effectively shortening the maintenance time of the equipment.
Drawings
FIG. 1 is a schematic flow chart of a method for monitoring equipment anomalies based on a mechanism model and an industrial Internet of things in one embodiment;
FIG. 2 is a schematic structural diagram of an equipment anomaly monitoring system based on a mechanism model and an industrial Internet of things in one embodiment;
FIG. 3 is a flow chart illustrating a method for monitoring abnormal status of a device in the system shown in FIG. 2;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Aiming at the problem that in the prior art, the abnormality detection efficiency and the accuracy of the equipment monitoring method through the Internet of things system are limited, as shown in fig. 1, the equipment abnormality monitoring method based on a mechanism model and the industrial Internet of things is provided, and comprises the following steps:
step S100, acquiring real-time equipment data, wherein the real-time equipment data comprises a plurality of operation parameters related to corresponding equipment;
step S110, extracting historical fault data of corresponding equipment according to the real-time equipment data, and calculating the similarity between the historical fault data and the real-time equipment data;
step S120, judging whether the real-time equipment data is abnormal or not according to a preset fault threshold value and the similarity, and if so, adopting a corresponding mechanism model to perform abnormality detection analysis according to the real-time equipment data;
step S130, if the abnormality detection analysis result shows that the equipment is abnormal, an alarm instruction of the corresponding equipment is sent.
In this embodiment, when the mechanism model is introduced into the internet of things system to detect the state of the device, since the mechanism model parameters and calculation are complex and frequent calculation has a large loss on the performance of the computer when the mechanism model is subjected to anomaly analysis, in the method, when the mechanism model is introduced, the historical data is compared in a big data analysis mode to initially judge whether the device is likely to be abnormal, so that the calculation frequency of the mechanism model is reduced, the performance of the computer is improved, and the calculation efficiency is improved.
In step S100, the method may actually monitor the states of a plurality of devices at the same time, and a device is illustrated herein as an example. When monitoring a plurality of devices, the frequency of receiving the real-time data uploaded by each device can be set to prevent network congestion caused by overlarge data quantity, and meanwhile, the phenomenon that the monitoring result of the abnormal state of the device is influenced due to insufficient data collection quantity of the device is prevented.
In step S110, each device corresponds to multiple fault types, that is, there are multiple types of faults that can occur in each device, and it is possible that one fault type occurs in the device, and multiple fault types may also occur simultaneously. In the method, the current equipment is preliminarily judged whether to have abnormality and which fault type the current equipment belongs to by comparing the current equipment with the historical fault data.
In this embodiment, the historical fault data includes historical data for each operating parameter when the device was experiencing a different fault type. Assuming that the historical fault data has M fault types, the historical operation parameters corresponding to each fault type are as follows: sm= { D1, D2, D3, & gt, dn }. Whereas the device parameters received in real time are expressed as: sc= { C1, C2, C3,..cn }.
Further, when calculating the similarity between the historical fault data and the real-time equipment data, calculating the similarity between the corresponding historical fault data of each fault type and each operation parameter in the real-time equipment data.
In this embodiment, the following formula is used for calculating the similarity:
（1）
in the case of the formula (1),historical data representing an ith operating parameter corresponding to a type of fault,/for example>The i-th operation parameter in the real-time equipment data is represented, n represents the number of the operation parameters in the real-time equipment data, and Pm represents the similarity corresponding to one fault type.
In step S120, determining whether the real-time device data has an abnormality according to the preset failure threshold and the similarity includes: and respectively judging the similarity corresponding to various fault types by utilizing a preset fault threshold, judging that the real-time equipment data has the abnormality of the fault type if the similarity corresponding to a certain fault type is larger than or equal to the preset fault threshold, and judging that the real-time equipment data has no abnormality of the fault type if the similarity corresponding to a certain fault type is smaller than the preset fault threshold.
For further explanation, the process of preliminary judging that the device data is abnormal using the similarity between the historical fault data and the real-time device data is illustrated as follows:
setting real-time equipment data corresponding to equipment for carrying out state monitoring to comprise 5 operation parameters, wherein the operation parameters respectively comprise: inlet and outlet pressure differential P, rotational speed W, motor torque M, pump torque T, pump outlet flow Q, and historical fault data are shown in table 1:
TABLE 1 historical failure data
And the real-time device data is set as shown in table 2:
table 2 real time device data
The similarity between the historical data and the real-time data is calculated for each fault type according to formula (1), and then the following can be obtained:
；
；
；
in the above formulas, P1, P2, P3, P4 are the similarities of the faults 1 to 4, respectively.
Then, assuming that the preset fault threshold is equal to zero, it can be seen that P1 is greater than zero, which indicates that the currently received real-time device data is stored in the abnormality of fault 1, and then the fault is further confirmed by using a mechanism model.
When the fault threshold is preset, the fault threshold can be set according to specific conditions, if the fault threshold is set lower, the probability of judging the fault is higher, the computer performance is more consumed, and if the fault threshold is set higher, the probability of judging the fault is smaller, more computer performance is not consumed, but false detection is possible. When the setting is performed, the setting can be performed according to the importance of the corresponding industrial equipment or the weight of a certain fault.
In step S130, if there is an abnormality, performing abnormality detection analysis according to the real-time device data using the corresponding mechanism model includes: if the real-time equipment data has an abnormality of a certain fault type, adopting a mechanism model corresponding to the equipment to perform abnormality detection analysis according to the operation parameters related to the fault type in the real-time equipment data.
In this embodiment, a plurality of mechanism models corresponding to a plurality of failure categories, respectively, are built for each of the industrial devices being monitored. When the current industrial equipment is judged to be abnormal according to the historical data and the real-time data, a corresponding mechanism model is called according to the judged fault type to carry out final judgment according to the real-time data. Thus, the accuracy and the efficiency of equipment monitoring are further improved. In addition, for maintenance personnel, the fault type of the equipment is predicted in advance, a maintenance scheme can be set in advance according to the predicted fault type, and required maintenance accessories are prepared in advance, so that the maintenance time can be effectively shortened.
In this embodiment, if it is determined that the current device data is not abnormal through the processing of step S120, a subsequent step is performed, and the process returns to step S100 to monitor the device data acquired at the next time.
Finally, in step S140, a corresponding alarm command is sent according to the analysis result.
The system comprises a device data acquisition module, a data abnormality judgment module, a mechanism model analysis module and a device abnormality alarm module. The device data acquisition module is used for acquiring real-time device data, wherein the real-time device data comprises a plurality of operation parameters related to corresponding devices, and the real-time device data is sent to the data abnormality judgment module. The data anomaly judging module is used for extracting historical fault data of corresponding equipment according to the real-time equipment data, calculating the similarity between the historical fault data and the real-time equipment data, judging whether the real-time equipment data is abnormal according to a preset fault threshold value and the similarity, if so, carrying out anomaly detection analysis according to the real-time equipment data by adopting a corresponding mechanism model by the mechanism model analyzing module, if the anomaly detection analysis result is that the equipment is abnormal, sending the anomaly detection analysis result to the equipment anomaly alarming module, and finally, sending an alarming instruction of the corresponding equipment by the equipment anomaly alarming module according to the anomaly detection analysis result.
In this embodiment, the system further includes a device connection module for connecting with a plurality of terminal devices and exchanging data, and the terminal devices upload device data to the device acquisition module through the module. Meanwhile, when the equipment control module needs to control equipment, a control instruction is issued to the equipment through the equipment connection module to realize control.
In this embodiment, the system further includes: and the data processing module is connected between the equipment data acquisition module and the data abnormality judgment module. The data processing module decrypts and decompresses the real-time equipment data acquired by the equipment data acquisition module, converts the real-time equipment data into a format which can be identified by the industrial Internet of things abnormality monitoring system to obtain identifiable real-time equipment data, and then sends the identifiable real-time equipment data to the data abnormality judging module.
Specifically, the data processing module is responsible for transforming the format required by the device data into the system, ensuring the safety and transmissibility of the data, encrypting and compressing the data issued by the device control module, and decrypting and decompressing the data uploaded by the data acquisition module.
In this embodiment, the system further includes: the device management module and the device control module. The device management module is used for receiving the device management instruction and sending the device management instruction to the device control module. The device control module converts the device management instruction into an operation instruction according to the device protocol and then transmits the operation instruction to the device through the data processing module.
Specifically, the device management module is further configured to manage all devices of the platform, and can view, add, delete, and edit device information including device names, mechanism model matching, and device SN codes, and display information such as device positions, device connection states, device motion states, and device switches. The equipment can be controlled through the management module page, the control command is sent to the equipment control module, and the equipment control module converts the control command into the equipment operation command and issues the equipment operation command.
Specifically, the device control module is used for managing a device protocol, analyzing a device operation command, analyzing data reported by the device according to the protocol, uploading the analyzed data to the device management module, and updating the device data. Meanwhile, the equipment operation issued by the equipment management module is converted into an operation instruction according to an equipment protocol, and the operation instruction is issued to each equipment terminal through the data processing module and the equipment connection module, so that the remote operation of equipment is realized.
In this embodiment, the system further includes: and a mechanism model management module. The mechanism model management module is used for managing the platform mechanism model, and can query, add, delete and edit mechanism model information, including mechanism model names, mechanism model parameter settings, mechanism model parameter templates and the like. And meanwhile, the binding relation between the mechanism model and the equipment is managed, and a mechanism model anomaly monitoring algorithm is set. When the equipment needs to detect abnormality, the binding relation between the equipment and the mechanism model is obtained through the equipment management module, a calculation formula is obtained through the corresponding mechanism model, and whether faults occur or not is calculated according to the formula.
In this embodiment, the data anomaly determination module is configured to monitor the collected and reported implementation device data, calculate the similarity between the current data and the historical fault parameters through the similarity, estimate possible faults according to the similarity, and then call the mechanism model analysis module interface to perform mechanism model anomaly analysis on the device, thereby improving the device fault detection efficiency and the computer utilization rate. In this module, the above method is implemented in step S110 and step S120, and the specific limitation thereof is referred to the limitation of the method for monitoring abnormal status of equipment hereinabove, and will not be repeated herein.
In this embodiment, the system further includes: and a mechanism model display module. The mechanism model display module uses a 3D model, and based on mechanism model parameters and equipment data, the 3D model is used for displaying equipment, so that a user can more intuitively view the equipment.
In this embodiment, the mechanism model analysis module implements step S130 in the above method, and is configured to provide an equipment anomaly detection interface, and when the data anomaly determination module analyzes that an equipment may fail, invoke the module interface to trigger detection, the mechanism model analysis module searches a bound mechanism model from the equipment management module according to the equipment ID, obtains a parameter type required for anomaly detection of the mechanism model, obtains corresponding equipment data and an anomaly detection formula, performs calculation, determines whether an anomaly is generated according to a calculation result, and invokes an equipment anomaly alarm module interface to alarm if the anomaly occurs. The abnormality detection formula is a detection formula corresponding to the fault type obtained by the data abnormality judgment module.
In this embodiment, the device abnormality alarm module is responsible for providing an alarm interface, calling the interface when it is determined that abnormality occurs in the device, and then notifying a system manager in a manner of WeChat notification, SMS notification, and system internal information.
As shown in fig. 2, a schematic structural diagram of an equipment anomaly monitoring system based on a mechanism model and an industrial internet of things is provided, and each module in the schematic diagram is displayed according to a module hierarchical relationship.
As shown in fig. 3, a flow diagram of the device anomaly monitoring method herein is implemented in the system shown in fig. 2.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store fault history data and mechanism model data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a device anomaly monitoring method based on a mechanism model and an industrial Internet of things.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring real-time equipment data, wherein the real-time equipment data comprises a plurality of operation parameters related to corresponding equipment;
extracting historical fault data of corresponding equipment according to the real-time equipment data, and calculating the similarity between the historical fault data and the real-time equipment data;
judging whether the real-time equipment data is abnormal or not according to a preset fault threshold value and similarity, and if so, adopting a corresponding mechanism model to perform abnormality detection analysis according to the real-time equipment data;
and if the abnormality detection analysis result shows that the equipment is abnormal, sending an alarm instruction of the corresponding equipment.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring real-time equipment data, wherein the real-time equipment data comprises a plurality of operation parameters related to corresponding equipment;
extracting historical fault data of corresponding equipment according to the real-time equipment data, and calculating the similarity between the historical fault data and the real-time equipment data;
judging whether the real-time equipment data is abnormal or not according to a preset fault threshold value and similarity, and if so, adopting a corresponding mechanism model to perform abnormality detection analysis according to the real-time equipment data;
and if the abnormality detection analysis result shows that the equipment is abnormal, sending an alarm instruction of the corresponding equipment.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (6)
1. The method for monitoring the equipment abnormality based on the mechanism model and the industrial Internet of things is characterized by comprising the following steps:
acquiring real-time equipment data, wherein the real-time equipment data comprises a plurality of operation parameters related to corresponding equipment;
extracting historical fault data of corresponding equipment according to the real-time equipment data, and calculating the similarity between the historical fault data and the real-time equipment data, wherein the equipment corresponds to a plurality of fault types, the historical fault data comprises historical data of each operation parameter when different fault types occur in the equipment, and when the similarity between the historical fault data and the real-time equipment data is calculated, the similarity between the historical data corresponding to the fault type and each operation parameter in the real-time equipment data is calculated according to each fault type, and the similarity is calculated by adopting the following formula:
；
in the above-mentioned description of the invention,historical data representing an ith operating parameter corresponding to a type of fault,/for example>Representing an ith operation parameter in the real-time equipment data, wherein n represents the number of the operation parameters in the real-time equipment data, and Pm represents the similarity when corresponding to one fault type;
judging whether the real-time equipment data is abnormal or not according to a preset fault threshold value and similarity, and if the real-time equipment data is abnormal of a certain fault type, adopting a mechanism model corresponding to the equipment to perform abnormality detection analysis according to operation parameters related to the fault type in the real-time equipment data;
and if the abnormality detection analysis result shows that the equipment is abnormal, sending an alarm instruction of the corresponding equipment.
2. The device anomaly monitoring method according to claim 1, wherein the determining whether the real-time device data has anomalies according to the preset fault threshold and the similarity comprises:
judging the similarity corresponding to various fault types by utilizing the preset fault threshold, and judging that the real-time equipment data has the abnormality of the fault type if the similarity corresponding to a certain fault type is larger than or equal to the preset fault threshold;
if the similarity corresponding to a certain fault type is smaller than the preset fault threshold, judging that the real-time equipment data does not have the abnormality of the fault type.
3. The equipment abnormality monitoring system based on the mechanism model and the industrial Internet of things is characterized by comprising an equipment data acquisition module, a data abnormality judging module, a mechanism model analysis module and an equipment abnormality alarming module;
the device data acquisition module is used for acquiring real-time device data, wherein the real-time device data comprises a plurality of operation parameters related to corresponding devices, and the real-time device data is sent to the data abnormality judgment module;
the data anomaly judging module is used for extracting historical fault data of corresponding equipment according to the real-time equipment data, calculating the similarity between the historical fault data and the real-time equipment data, judging whether the real-time equipment data is anomalous according to a preset fault threshold value and the similarity, wherein the equipment corresponds to a plurality of fault types, the historical fault data comprises historical data of each operation parameter when different fault types occur in the equipment, and when the similarity between the historical fault data and the real-time equipment data is calculated, the similarity between the historical data corresponding to the fault type and each operation parameter in the real-time equipment data is calculated according to each fault type, and the similarity is calculated by adopting the following formula:
；
in the above-mentioned description of the invention,historical data representing an ith operating parameter corresponding to a type of fault,/for example>Representing the real objectWhen the ith operation parameter in the equipment data is used, n represents the number of the operation parameters in the real-time equipment data, and Pm represents the similarity corresponding to one fault type;
if the real-time equipment data has abnormality of a certain fault type, the mechanism model analysis module adopts a mechanism model corresponding to the equipment to carry out abnormality detection analysis according to the operation parameters related to the fault type in the real-time equipment data, and if the abnormality detection analysis result is that the equipment has abnormality, the abnormality detection analysis result is sent to the equipment abnormality alarm module;
and the equipment abnormality alarm module is used for sending alarm instructions of corresponding equipment according to the abnormality detection analysis result.
4. The equipment anomaly monitoring system of claim 3, further comprising: the data processing module is connected between the equipment data acquisition module and the data abnormality judgment module;
the data processing module is used for decrypting and decompressing the real-time equipment data acquired by the equipment data acquisition module, converting the real-time equipment data into a format which can be identified by the industrial Internet of things anomaly monitoring system to obtain the identifiable real-time equipment data, and then sending the identifiable real-time equipment data to the data anomaly judging module.
5. The equipment anomaly monitoring system of claim 4, further comprising: the device management module and the device control module;
the device management module is used for receiving a device management instruction and sending the device management instruction to the device control module;
the device control module is used for converting the device management instruction into an operation instruction according to a device protocol and then issuing the operation instruction to the device through the data processing module.
6. The equipment anomaly monitoring system of claim 5, wherein the equipment anomaly alarm module sends the alarm instruction by way of a WeChat notification, a SMS notification, or a system internal communication.
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