Fault prediction method based on BMC health management module

Abstract

The invention relates to a fault prediction method based on a BMC health management module, and belongs to the technical field of equipment detection. A data processing module comprises a data acquisition module, a data analysis module, a data analysis module and a data transmission module. The processing system comprises a central control system, a fault diagnosis module, a fault prediction module, an alarm event processing module, an equipment early warning module, an early warning notification module, a health management module, a health evaluation module, a fault alarm module, an information management module, a fault repair module and a display module. According to the method, the operation correctness, timeliness and part accuracy of maintenance activities are guaranteed, the whole fault identification process is high in efficiency and low in cost, automatic fault detection can be achieved instead of workers, missing detection and false detection caused by manual detection are effectively reduced, and the automation degree of fault detection is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment detection, and in particular relates to a fault prediction method based on a BMC health management module. Background Art

[0002] Fault detection refers to how the product determines which link has a problem when a situation occurs that the product cannot work normally, and informs the user of the fault through indicator lights or other methods. Generally speaking, these faults are actually some soft problems, and the main tasks are: fault detection, fault type judgment, fault location and fault recovery. Among them: Fault detection means that after establishing a connection with the system, the detection signal is periodically sent to the lower computer, and the system is judged whether a fault occurs through the received response data frame; Fault type judgment means that after the system detects a fault, it analyzes the cause and determines the type of system fault; Fault location is based on the first two steps, and refines the fault type, diagnoses the specific fault location and cause of the system, and prepares for fault recovery; Fault recovery is the last and most important link in the entire fault diagnosis process. Different measures need to be taken according to the cause of the fault to recover the system fault.

[0003] The existing equipment fault diagnosis system generally divides the equipment status into two types: normal and faulty, but lacks analysis and research on the intermediate status. The process from normal to faulty is a long-term accumulation and gradual development process. If it cannot be identified and warned in advance, it may cause significant losses. Equipment fault analysis usually relies on the traditional experience of production personnel to make fuzzy judgments. The prediction results are low in accuracy and highly dependent on manual experience. The universality is poor. They are all handled on-site after the fault occurs. The on-site equipment is out of service for a long time, which in turn affects the normal operation of related places. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The technical problem to be solved by the present invention is how to provide a fault prediction method based on the BMC health management module to solve the problem that the existing equipment fault diagnosis system lacks analysis and research on intermediate states.

[0006] (II) Technical solution

[0007] In order to solve the above technical problems, the present invention proposes a fault prediction method based on a BMC health management module, the method comprising:

[0008] S1, data processing module includes: data acquisition module, data parsing module, data analysis module and data transmission module;

[0009] The data acquisition module collects equipment data, the data analysis module analyzes the collected data, and the analyzed data is stored in a large database. The data analysis module is used to analyze the collected data based on the preset fault prediction and health management rules to obtain the corresponding equipment management information. After the analysis is completed, the data transmission module is used to transmit the data;

[0010] S2. The processing system includes: a central control system, a fault diagnosis module, a fault prediction module, an alarm event processing module, an equipment early warning module, an early warning notification module, a health management module, a health assessment module, a fault alarm module, an information management module, a fault repair module and a display module;

[0011] The fault diagnosis module is based on big data and artificial intelligence technology to establish a production data fitting analysis and fault diagnosis prediction model, automatically analyze the source and cause of equipment failure, and send an equipment early warning module;

[0012] The fault prediction module analyzes and diagnoses the equipment operation status by sorting and mining the production line big data, predicts the probability of fault occurrence, and realizes early fault prediction and automatic generation of maintenance plans;

[0013] The alarm event processing module is preset with normal status information of the device, compares the status information with the normal status information, extracts the status information with inconsistent comparison results, and generates an alarm event;

[0014] The equipment early warning module formulates a floating range according to the number of alarm events of abnormal equipment, obtains the number of alarms of other equipment in the same batch as the abnormal equipment within the statistical period according to the equipment number, and determines whether it is within the floating range. If it is, it is defined as the same abnormal equipment and notifies the central control system through the early warning notification module;

[0015] The health management module evaluates and manages the health status of the equipment and divides the health status of the equipment into multiple levels. When the equipment is operating normally and there are no abnormal points, it is in a normal state. When the fault diagnosis module detects abnormal points in the equipment, the equipment status is marked as sub-healthy. When the equipment status is close to failure or failure, the equipment information is sent to the fault alarm module;

[0016] The information management module is used to view fault prediction and diagnosis information, and automatically generate alarm information based on relevant fault information;

[0017] The fault repair module determines the fault type corresponding to the field device according to the device management information, and sends a fault repair instruction corresponding to the fault type to the field device to repair the field device;

[0018] The display module displays the collected key data in the form of charts on the interface, and also displays the prediction results and prompt information;

[0019] The health management module monitors and manages the health status of all equipment based on the evaluation results of the health assessment module. When the remaining life of the equipment is less than the set time, the equipment is marked as near failure, and the equipment maintenance personnel at the control terminal are notified to perform equipment maintenance. The equipment data and alarm information in the real-time database are regularly transferred to the big database for long-term storage.

[0020] (III) Beneficial effects

[0021] The present invention proposes a fault prediction method based on a BMC health management module. The present invention provides a fault prediction method based on a BMC health management module. Compared with the prior art, the fault prediction method based on the BMC health management module has the following beneficial effects: the fault prediction method based on the BMC health management module realizes the transformation of the maintenance concept from post-maintenance to state maintenance, transforms the passive response to the fault into active defense against the fault, and transforms the traditional post-fault diagnosis into fault prediction based on the intelligent system, thereby ensuring the correctness of the operation, timeliness and accuracy of the location of the maintenance activities. The entire fault identification process is highly efficient and low-cost, and can replace the workers to realize automated fault detection, effectively reduce missed detection and false detection caused by manual detection, and improve the automation of fault detection. The operation data of the field equipment is obtained through the fault prediction and health management equipment, and the corresponding equipment management information is further analyzed to realize the fault prediction and health management of the field equipment. The maintenance personnel can intervene before the field equipment fails, timely eliminate the fault risk or repair and replace it, avoid the long-term downtime of the field equipment affecting the normal operation of the relevant places, and improve the operation safety of the field equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural principle block diagram of the system of the present invention;

[0023] Figure 2 It is a structural principle block diagram of the data processing module of the present invention;

[0024] Figure 3 It is a structural principle block diagram of the processing system of the present invention.

[0025] In the figure: 1-data processing module, 11-data acquisition module, 12-data parsing module, 13-data analysis module, 14-data transmission module, 2-processing system, 21-central control system, 22-health assessment module, 23-health management module, 24-information management module, 25-alarm event processing module, 26-fault diagnosis module, 27-equipment early warning module, 28-early warning notification module, 29-fault repair module, 210-display module, 211-fault prediction module, 212-fault alarm module, 3-control terminal, 4-large database. DETAILED DESCRIPTION

[0026] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the drawings and examples.

[0027] In view of the deficiencies in the prior art, the present invention provides a fault prediction method based on a BMC health management module, which solves the problems mentioned in the above background technology.

[0028] To achieve the above objectives, the present invention is implemented through the following technical solutions: a fault prediction method based on a BMC health management module, specifically comprising the following steps:

[0029] S1, data processing module includes: data acquisition module, data parsing module, data analysis module and data transmission module;

[0030] The data acquisition module collects equipment data, the data analysis module analyzes the collected data, and the analyzed data is stored in a large database. The data analysis module is used to analyze the collected data based on the preset fault prediction and health management rules to obtain the corresponding equipment management information. After the analysis is completed, the data transmission module is used to transmit the data;

[0031] S2. The processing system includes: a central control system, a fault diagnosis module, a fault prediction module, an alarm event processing module, an equipment early warning module, an early warning notification module, a health management module, a health assessment module, a fault alarm module, an information management module, a fault repair module and a display module;

[0032] The fault diagnosis module is based on big data and artificial intelligence technology to establish a production data fitting analysis and fault diagnosis prediction model, automatically analyze the source and cause of equipment failure, and send an equipment early warning module;

[0033] The fault prediction module analyzes and diagnoses the equipment operation status by sorting and mining the production line big data, predicts the probability of fault occurrence, and realizes early fault prediction and automatic generation of maintenance plans;

[0034] The alarm event processing module is preset with normal status information of the device, compares the status information with the normal status information, extracts the status information with inconsistent comparison results, and generates an alarm event;

[0035] The equipment early warning module formulates a floating range according to the number of alarm events of abnormal equipment, obtains the number of alarms of other equipment in the same batch as the abnormal equipment within the statistical period according to the equipment number, and determines whether it is within the floating range. If it is, it is defined as the same abnormal equipment and notifies the central control system through the early warning notification module;

[0036] The health management module evaluates and manages the health status of the equipment and divides the health status of the equipment into multiple levels, including normal, sub-healthy, near failure, and fault. When the equipment is operating normally and there is no abnormal point, it is in a normal state. When the fault diagnosis module detects an abnormal point in the equipment, the equipment state is marked as sub-healthy. When the equipment state is near failure or fault, the equipment information is sent to the fault alarm module.

[0037] The information management module is used to view fault prediction and diagnosis information, and automatically generate alarm information based on relevant fault information;

[0038] The fault repair module determines the fault type corresponding to the field device according to the device management information, and sends a fault repair instruction corresponding to the fault type to the field device to repair the field device;

[0039] The display module displays the collected key data in the form of charts and other forms on the interface, and also displays the prediction results and prompt information;

[0040] The health management module monitors and manages the health status of all equipment based on the evaluation results of the health assessment module. When the remaining life of the equipment is less than the set time, the equipment is marked as near failure, and the equipment maintenance personnel at the control terminal are notified to perform equipment maintenance. The equipment data and alarm information in the real-time database are regularly transferred to the big database for long-term storage.

[0041] Preferably, the data acquisition module is used to collect equipment data, and the equipment data includes the status label of the PLC bottom layer, equipment temperature, motor current, CST power and operation and maintenance information database.

[0042] Preferably, the large database is used for data transmission between the real-time database and the historical database. When a data migration request is received from the historical database, an SQL statement is written according to the request information, the responsive device data is read from the real-time database, and then sent to the historical database, and the migration record is saved for query; the real-time database is used to store the device data of the day, and upload the data to the historical database for long-term storage during the network idle period of each day; MySQL is used to store and manage real-time data, and is used to store device data, fault data and maintenance data.

[0043] Preferably, the data acquisition module is used to acquire operating data of on-site equipment.

[0044] Preferably, the output end of the data acquisition module is connected to the input end of the data analysis module, and the output end of the data analysis module is connected to the input end of the data analysis module, and the output end of the data analysis module is connected to the input end of the data transmission module.

[0045] Preferably, the central control system is bidirectionally connected to the health assessment module, and the health assessment module is bidirectionally connected to the health management module, the output end of the central control system is connected to the input end of the information management module, and the output end of the central control system is connected to the input end of the alarm event processing module.

[0046] Preferably, the output end of the central control system is connected to the input end of the fault diagnosis module, and the output end of the fault diagnosis module is connected to the input end of the equipment early warning module, the output end of the equipment early warning module is connected to the input end of the early warning notification module, and the output end of the early warning notification module is connected to the input end of the central control system.

[0047] Preferably, the output end of the central control system is connected to the input end of the fault repair module, and the output end of the central control system is connected to the input end of the display module, the output end of the central control system is connected to the input end of the fault prediction module, and the output end of the central control system is connected to the input end of the fault alarm module.

[0048] Embodiment 1:

[0049] See also Figure 1-3 The present invention provides a technical solution: a fault prediction method based on a BMC health management module, which specifically includes the following steps:

[0050] S1, the data acquisition module 11 in the data processing module 1 collects equipment data, the data analysis module 12 analyzes the collected data, and the analyzed data is stored in the big database 4, the data analysis module 13 is used to analyze the operation data based on the preset fault prediction and health management rules to obtain the corresponding equipment management information, and transmit the data after the analysis is completed;

[0051] S2. The fault diagnosis module 26 in the processing system 2 is based on big data and artificial intelligence technology to establish a production data fitting analysis and fault diagnosis prediction model, automatically analyze the source and cause of equipment failure, and the fault prediction module 211 analyzes and diagnoses the equipment operation status by sorting and mining the production line big data, predicts the probability of failure, realizes early prediction of failures and automatically generates maintenance plans. The alarm event processing module 25 is preset with the normal status information of the equipment, compares the status information with the normal status information, and extracts the status information with inconsistent comparison results to generate alarm events. The equipment early warning module 27 formulates a floating interval according to the number of alarm events of abnormal equipment, obtains the number of alarms of other equipment in the same batch as the abnormal equipment within the statistical period according to the equipment number, and determines whether it is within the floating interval. If it is, it is defined as the same abnormal equipment, and the central control system is notified through the early warning notification module 28;

[0052] S3, the health management module 23 evaluates and manages the health status of the equipment, and divides the health status of the equipment into multiple levels, including normal, sub-healthy, near failure, and fault; when the equipment is operating normally and no abnormal point occurs, the state is normal; when the fault diagnosis module 26 detects that the equipment has an abnormal point, the equipment state is marked as sub-healthy; when the equipment state is near failure or fault, the equipment information is sent to the fault alarm module 212;

[0053] S4, the information management module 24 is used to view the fault prediction and diagnosis information, and automatically generate alarm information according to the relevant fault information. The fault repair module 29 determines the fault type corresponding to the field equipment according to the equipment management information, and sends the fault repair instruction corresponding to the fault type to the field equipment to repair the field equipment. The display module 210 displays the collected key data on the interface in the form of charts, etc., and displays the prediction results and prompt information at the same time;

[0054] S5. The health management module 23 monitors and manages the health status of all equipment based on the results of the health assessment. When the remaining life of the equipment is less than the set time, the equipment is marked as near failure, and the equipment maintenance personnel of the control terminal 3 are notified to perform equipment maintenance. The equipment data and alarm information in the real-time database are regularly transferred to the big database 4 for long-term storage.

[0055] In the present invention, the data acquisition module 11 is used to collect equipment data, and the equipment data includes the status label of the bottom layer of the PLC, equipment temperature, motor current, CST power and operation and maintenance information database.

[0056] In the present invention, the big database 4 is used for data transmission between the real-time database and the historical database. When a data migration request is received from the historical database, an SQL statement is written according to the request information, the responsive device data is read from the real-time database, and then sent to the historical database, and the migration record is saved for query; the real-time database is used to store the device data of the day, and upload the data to the historical database for long-term storage during the network idle period of each day; MySQL is used to store and manage real-time data, and is used to store device data, fault data and maintenance data.

[0057] In the present invention, the data acquisition module 11 is used to obtain the operating data of the field equipment.

[0058] In the present invention, the output end of the data acquisition module 11 is connected to the input end of the data analysis module 12 , and the output end of the data analysis module 12 is connected to the input end of the data analysis module 13 , and the output end of the data analysis module 13 is connected to the input end of the data transmission module 14 .

[0059] In the present invention, the central control system 21 is bidirectionally connected with the health assessment module 22, and the health assessment module 22 is bidirectionally connected with the health management module 23, the output end of the central control system 21 is connected with the input end of the information management module 24, and the output end of the central control system 21 is connected with the input end of the alarm event processing module 25.

[0060] In the present invention, the output end of the central control system 21 is connected to the input end of the fault diagnosis module 26, and the output end of the fault diagnosis module 26 is connected to the input end of the equipment early warning module 27, the output end of the equipment early warning module 27 is connected to the input end of the early warning notification module 28, and the output end of the early warning notification module 28 is connected to the input end of the central control system 21.

[0061] In the present invention, the output end of the central control system 21 is connected to the input end of the fault repair module 29, and the output end of the central control system 21 is connected to the input end of the display module 210, the output end of the central control system 21 is connected to the input end of the fault prediction module 211, and the output end of the central control system 21 is connected to the input end of the fault alarm module 212.

[0062] Embodiment 2:

[0063] A fault prediction method based on a BMC health management module specifically includes the following steps:

[0064] S1, a data collection module (11) in a data processing module (1) collects equipment data, a data analysis module (12) analyzes the collected data, and stores the analyzed data in a large database (4); a data analysis module (13) is used to analyze the operation data based on preset fault prediction and health management rules to obtain corresponding equipment management information, and transmit the data after the analysis is completed;

[0065] S2. The fault diagnosis module (26) in the processing system (2) is based on big data and artificial intelligence technology to establish a production data fitting analysis and fault diagnosis prediction model, automatically analyze the source and cause of equipment faults, and the fault prediction module (211) analyzes and diagnoses the equipment operation status by sorting and mining the production line big data, predicts the probability of fault occurrence, realizes early fault prediction and automatically generates maintenance plans, and the alarm event processing module (25) is preset with normal status information of the equipment, compares the status information with the normal status information, and extracts the status information with inconsistent comparison results to generate an alarm event; the equipment early warning module (27) formulates a floating interval according to the number of alarm events of the abnormal equipment, obtains the number of alarms of other equipment in the same batch as the abnormal equipment within the statistical period according to the equipment number, and determines whether it is within the floating interval. If it is, it is defined as the same abnormal equipment and notified to the central control system through the early warning notification module (28);

[0066] S3, the health management module (23) evaluates and manages the health status of the equipment, and divides the health status of the equipment into multiple levels, including normal, sub-healthy, near failure, and fault; when the equipment is operating normally and no abnormal point occurs, the state is normal; when the fault diagnosis module (26) detects that the equipment has an abnormal point, the equipment state is marked as sub-healthy; when the equipment state is near failure or fault, the equipment information is sent to the fault alarm module (212);

[0067] S4, the information management module (24) is used to view the information of fault prediction and diagnosis, and automatically generate alarm information according to the relevant fault information, the fault repair module (29) determines the fault type corresponding to the field equipment according to the equipment management information, and sends a fault repair instruction corresponding to the fault type to the field equipment to repair the field equipment, and the display module (210) displays the collected key data on the interface in the form of charts, etc., and displays the prediction results and prompt information at the same time;

[0068] S5. The health management module (23) monitors and manages the health status of all equipment based on the results of the health assessment. When the remaining life of the equipment is less than the set time, the equipment is marked as near failure, and the equipment maintenance personnel at the control terminal (3) are notified to perform equipment maintenance. The equipment data and alarm information in the real-time database are regularly transferred to the big database (4) for long-term storage.

[0069] The data acquisition module (11) is used to collect equipment data, and the equipment data includes the status label of the bottom layer of the PLC, equipment temperature, motor current, CST power and operation and maintenance information database.

[0070] The large database (4) is used for data transmission between the real-time database and the historical database. When a data migration request is received from the historical database, an SQL statement is written according to the request information to read the corresponding device data from the real-time database, and then send it to the historical database, and save the migration record for query; the real-time database is used to store the device data of the day, and upload the data to the historical database for long-term storage during the network idle period every day; MySQL is used to store and manage real-time data, and is used to store device data, fault data and maintenance data.

[0071] The data acquisition module (11) is used to acquire the operating data of the on-site equipment.

[0072] The output end of the data acquisition module (11) is connected to the input end of the data analysis module (12), and the output end of the data analysis module (12) is connected to the input end of the data analysis module (13), and the output end of the data analysis module (13) is connected to the input end of the data transmission module (14).

[0073] The central control system (21) is bidirectionally connected to the health assessment module (22), and the health assessment module (22) is bidirectionally connected to the health management module (23). The output end of the central control system (21) is connected to the input end of the information management module (24), and the output end of the central control system (21) is connected to the input end of the alarm event processing module (25).

[0074] The output end of the central control system (21) is connected to the input end of the fault diagnosis module (26), and the output end of the fault diagnosis module (26) is connected to the input end of the equipment early warning module (27), the output end of the equipment early warning module (27) is connected to the input end of the early warning notification module (28), and the output end of the early warning notification module (28) is connected to the input end of the central control system (21).

[0075] The output end of the central control system (21) is connected to the input end of the fault repair module (29), and the output end of the central control system (21) is connected to the input end of the display module (210), the output end of the central control system (21) is connected to the input end of the fault prediction module (211), and the output end of the central control system (21) is connected to the input end of the fault alarm module (212).

[0076] The present invention provides a fault prediction method based on a BMC health management module. Compared with the prior art, the fault prediction method based on the BMC health management module has the following beneficial effects: the fault prediction method based on the BMC health management module realizes the transformation of the maintenance concept from post-maintenance to status maintenance, transforms the passive response to faults into active defense against faults, and transforms the traditional post-fault diagnosis into fault prediction based on an intelligent system, thereby ensuring the correctness of the operation, timeliness and accuracy of the location of the maintenance activities. The entire fault identification process is highly efficient and low-cost, and can replace the workers to realize automated fault detection, effectively reduce missed detection and false detection caused by manual detection, and improve the degree of automation of fault detection. The operation data of the field equipment is obtained through the fault prediction and health management equipment, and the corresponding equipment management information is further analyzed to realize the fault prediction and health management of the field equipment. The maintenance personnel can intervene before the field equipment fails, timely eliminate the fault risk or repair and replace it, avoid the long-term downtime of the field equipment affecting the normal operation of related places, and improve the operation safety of the field equipment.

[0077] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fault prediction method based on a BMC health management module, characterized in that: The method includes: S1, data processing module includes: data acquisition module, data parsing module, data analysis module and data transmission module; The data acquisition module collects equipment data, the data analysis module analyzes the collected data, and the analyzed data is stored in a large database. The data analysis module is used to analyze the collected data based on the preset fault prediction and health management rules to obtain the corresponding equipment management information. After the analysis is completed, the data transmission module is used to transmit the data; S2. The processing system includes: a central control system, a fault diagnosis module, a fault prediction module, an alarm event processing module, an equipment early warning module, an early warning notification module, a health management module, a health assessment module, a fault alarm module, an information management module, a fault repair module and a display module; The fault diagnosis module is based on big data and artificial intelligence technology to establish a production data fitting analysis and fault diagnosis prediction model, automatically analyze the source and cause of equipment failure, and send an equipment early warning module; The fault prediction module analyzes and diagnoses the equipment operation status by sorting and mining the production line big data, predicts the probability of fault occurrence, and realizes early fault prediction and automatic generation of maintenance plans; The alarm event processing module is preset with normal status information of the device, compares the status information with the normal status information, extracts the status information with inconsistent comparison results, and generates an alarm event; The equipment early warning module formulates a floating range according to the number of alarm events of abnormal equipment, obtains the number of alarms of other equipment in the same batch as the abnormal equipment within the statistical period according to the equipment number, and determines whether it is within the floating range. If it is, it is defined as the same abnormal equipment and notifies the central control system through the early warning notification module; The health management module evaluates and manages the health status of the equipment and divides the health status of the equipment into multiple levels. When the equipment is operating normally and there are no abnormal points, it is in a normal state. When the fault diagnosis module detects abnormal points in the equipment, the equipment status is marked as sub-healthy. When the equipment status is close to failure or failure, the equipment information is sent to the fault alarm module; The information management module is used to view fault prediction and diagnosis information, and automatically generate alarm information based on relevant fault information; The fault repair module determines the fault type corresponding to the field device according to the device management information, and sends a fault repair instruction corresponding to the fault type to the field device to repair the field device; The display module displays the collected key data in the form of charts on the interface, and also displays the prediction results and prompt information; The health management module monitors and manages the health status of all equipment based on the evaluation results of the health assessment module. When the remaining life of the equipment is less than the set time, the equipment is marked as near failure, and the equipment maintenance personnel at the control terminal are notified to perform equipment maintenance. The equipment data and alarm information in the real-time database are regularly transferred to the big database for long-term storage.

2. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: Multiple levels include normal, sub-healthy, near failure and faulty.

3. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The data acquisition module is used to collect equipment data, and the equipment data includes the status tag of the PLC bottom layer, equipment temperature, motor current, CST power and operation and maintenance information database.

4. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The large database is used for data transmission between the real-time database and the historical database. When a data migration request is received from the historical database, an SQL statement is written according to the request information, the corresponding device data is read from the real-time database, and then sent to the historical database, and the migration record is saved for query; The real-time database is used to store the device data of the day and upload the data to the historical database for long-term storage during the network idle period every day.

5. The fault prediction method based on the BMC health management module according to claim 4, characterized in that: The real-time database uses MySQL to store and manage real-time data, and is used to store equipment data, fault data, and maintenance data.

6. The fault prediction method based on the BMC health management module according to claim 4, characterized in that: The data acquisition module is used to obtain the operating data of the on-site equipment.

7. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The output end of the data acquisition module is connected to the input end of the data analysis module, and the output end of the data analysis module is connected to the input end of the data analysis module, and the output end of the data analysis module is connected to the input end of the data transmission module.

8. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The central control system is bidirectionally connected to the health assessment module, and the health assessment module is bidirectionally connected to the health management module. The output end of the central control system is connected to the input end of the information management module, and the output end of the central control system is connected to the input end of the alarm event processing module.

9. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The output end of the central control system is connected to the input end of the fault diagnosis module, and the output end of the fault diagnosis module is connected to the input end of the equipment early warning module, the output end of the equipment early warning module is connected to the input end of the early warning notification module, and the output end of the early warning notification module is connected to the input end of the central control system.

10. The fault prediction method based on the BMC health management module according to claim 1, characterized in that: The output end of the central control system is connected to the input end of the fault repair module, and the output end of the central control system is connected to the input end of the display module, the output end of the central control system is connected to the input end of the fault prediction module, and the output end of the central control system is connected to the input end of the fault alarm module.

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