An equipment management platform based on big data and a processing method thereof

Abstract

The application discloses an equipment management platform based on big data and a processing method thereof, which comprises a data analysis module, a data calling module, a data fusion module, a cloud service module, a data storage module, a data display module, an information collection module and an input terminal module, wherein the data analysis module is connected with the data fusion module in control mode, the operation data of equipment is collected through the information collection module, meanwhile, the storage, use and maintenance information of the equipment is imported through the input terminal module, the service record of the equipment is convenient to query in the later period, the practicability of the management platform is improved, the visual display module is utilized in the equipment management process to realize real-time visual display of the equipment operation state, the convenience of equipment management is improved, the big data information is combined with the fusion processing data through the preparation analysis module, the operation state of the equipment is analyzed based on the big data, and the maintenance plan of the equipment is formulated, the safe operation of the equipment is ensured, and the safety hidden danger is eliminated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent management systems, in particular to an equipment management platform based on big data and a processing method thereof. BACKGROUND

[0002] Equipment is a general term for production and material means composed of certain circuits, air circuits or mechanical components, used to provide working conditions, improve work efficiency, complete the intended task and basically maintain the original physical form and function in long-term and repeated use. In the process of task management, it is usually necessary to use an equipment management platform to register and manage the equipment. However, the existing equipment management platform can only store the information of the equipment independently, cannot query the service record of the equipment, affects the practicability of the management platform, and at the same time, the existing management platform cannot analyze the running state of the equipment according to the actual running parameters of the equipment, it is difficult to plan the maintenance and remanufacturing scheme of the equipment, there is a certain safety hidden danger, and the existing equipment management platform is difficult to visually display the real-time state of the equipment, thereby affecting the convenience of equipment management. Therefore, it is necessary to design an equipment management platform based on big data and a processing method thereof. SUMMARY

[0003] The purpose of the present application is to provide an equipment management platform based on big data and a processing method thereof to solve the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an equipment management platform based on big data, comprising a data analysis module, a data calling module, an equipment library module, a data fusion module, a cloud service module, a data storage module, a data display module, a data classification module, an information collection module and an input terminal module, the data analysis module is connected to the data fusion module, and the data fusion module is connected to the cloud service module and the data storage module, and the cloud service module and the data storage module are connected to the data display module.

[0005] Preferably, the data analysis module is connected to the data calling module and the data classification module, and the data classification module is connected to the information collection module and the input terminal module, and the data calling module is connected to the equipment library module.

[0006] Preferably, the equipment library module is composed of an equipment library management module, an entity import module, a prototype input module, a storage library module, a library parameter calling module, an instruction receiving module and a parameter export module, and the equipment library management module is connected to the entity import module and the prototype input module.

[0007] Preferably, the entity import module and the prototype input module control the connection storage module respectively, and the storage module controls the connection library parameter calling module, and the library parameter calling module controls the connection instruction receiving module and the parameter export module respectively.

[0008] Preferably, the data classification module is composed of an information receiving module, an information identification module, a classification processing module and a data output module, the information receiving module controls the connection information identification module, and the information identification module controls the connection classification processing module, and the classification processing module controls the connection data output module.

[0009] Preferably, the cloud service module is composed of a fusion receiving module, a preparation analysis module, an analysis calling module, a cloud data module and an analysis output module, the fusion receiving module controls the connection preparation analysis module, and the preparation analysis module controls the connection analysis calling module, the cloud data module and the analysis output module respectively, and the analysis calling module controls the connection cloud data module.

[0010] Preferably, the data display module is composed of an analysis receiving module, a visual display module, an equipment management module and a storage calling module, and the analysis receiving module controls the connection visual display module, the visual display module controls the connection equipment management module, and the equipment management module controls the connection storage calling module.

[0011] A large data-based equipment management processing method, comprising the following steps: step one, equipment data acquisition; step two, data analysis calling; step three, equipment state analysis; step four, platform display management;

[0012] In the above step one, the information acquisition module acquires the running data of the equipment in real time, and the input terminal module imports the storage, use and maintenance information of the equipment, then the information acquisition module and the input terminal module respectively transmit the running data of the equipment and the storage, use and maintenance information of the equipment to the information receiving module in the data classification module, then the information identification module identifies the received information, and the classification processing module divides the received information into equipment running data and equipment environment data according to the identification result, and then the data output module transmits the equipment running data and the equipment environment data to the data analysis module;

[0013] In the above step two, the data analysis module sends calling instructions to the data calling module in combination with task requirements and data characteristics, then the data calling module transmits the calling instructions to the instruction receiving module in the equipment library module, then the library parameter calling module calls the entity import parameters and the prototype input parameters of the corresponding equipment from the storage module according to the calling instructions, then the called entity import parameters and prototype input parameters are transmitted to the data calling module through the parameter export module and sent to the data analysis module;

[0014] Among them, in the above step three, the entity import parameter, the prototype input parameter, the equipment running data and the equipment environment data are sent to the data fusion module for fusion processing by the data analysis module, and then the fusion processed data are transmitted to the data storage module and the cloud service module respectively, the use record, the storage record, the maintenance record, the actual running parameter and the storage parameter of the equipment are stored by the data storage module, at the same time, the fusion processed data are received by the fusion receiving module, and the equipment analysis instruction is sent to the analysis calling module by the whole equipment analysis module combined with the fusion processing result, then the equipment analysis instruction is sent to the cloud data module by the analysis calling module, then the big data information in the cloud data is scanned by the cloud data module, and the corresponding big data information is sent to the whole equipment analysis module, the big data information and the fusion processed data of the equipment are combined by the whole equipment analysis module, the running state of the equipment is analyzed, the maintenance plan of the equipment is made, and the running state of the equipment and the maintenance plan of the equipment are sent to the data display module through the analysis output module;

[0015] Among them, in the above step four, the equipment running state and the equipment maintenance plan sent by the analysis receiving module are received by the analysis receiving module, and the real-time visual display of the equipment running state is realized by the visual display module, at the same time, the storage calling instruction is sent from the equipment management module to the storage calling module according to the need, then the use record, the storage record, the maintenance record, the actual running parameter and the storage parameter of the corresponding equipment in the data storage module are called to the visual display module for the visual display of the equipment storage record by the storage calling module combined with the storage calling instruction.

[0016] Compared with the prior art, the equipment management platform based on big data and the processing method have the advantages that the running data of the equipment is collected in real time by the information collection module, the storage, use and maintenance information of the equipment is imported by the input terminal module, and the information is stored in the data storage module after data processing, so that the service record of the equipment can be inquired in the later period, the practicability of the management platform is improved, the equipment analysis instruction is sent to the analysis calling module by the overhaul analysis module combined with the fusion processing result, then the analysis calling module sends the equipment analysis instruction to the cloud data module, the cloud data module scans the big data information in the cloud data, and sends the corresponding big data information to the overhaul analysis module, the big data information and the fusion processing data of the equipment are combined by the overhaul analysis module, the running state of the equipment is analyzed, and the maintenance plan of the equipment is made, so that the safe operation of the equipment is ensured, and the safety hidden danger is eliminated, the equipment running state and the equipment maintenance plan sent by the analysis output module are received by the analysis receiving module, the visual display module is used for visual display of the equipment running state in real time, and the storage calling module is used for sending the storage calling instruction from the equipment management module to the storage calling module, then the storage calling module combines the storage calling instruction to call the use record, storage record, maintenance record, actual running parameter and storage parameter of the corresponding equipment in the data storage module to the visual display module for visual display of the equipment storage record, and the convenience of equipment management is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The system flowchart of the application;

[0018] Figure 2 The system framework diagram of the application;

[0019] Figure 3 The method flowchart of the application;

[0020] In the figure: 1, data analysis module; 2, data calling module; 3, equipment library module; 4, data fusion module; 5, cloud service module; 6, data storage module; 7, data display module; 8, data classification module; 9, information collection module; 10, input terminal module; 301, equipment library management module; 302, entity import module; 303, prototype input module; 304, storage library module; 305, library parameter calling module; 306, instruction receiving module; 307, parameter export module; 501, fusion receiving module; 502, overhaul analysis module; 503, analysis calling module; 504, cloud data module; 505, analysis output module; 701, analysis receiving module; 702, visual display module; 703, equipment management module; 704, storage calling module; 801, information receiving module; 802, information identification module; 803, classification processing module; 804, data output module. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] Please refer to Figures 1-2The application provides an equipment management platform based on big data, which comprises a data analysis module 1, a data calling module 2, an equipment library module 3, a data fusion module 4, a cloud service module 5, a data storage module 6, a data display module 7, a data classification module 8, an information collection module 9 and an input terminal module 10, the data analysis module 1 is connected with the data fusion module 4 in control mode, the data fusion module 4 is connected with the cloud service module 5 and the data storage module 6 in control mode respectively, the cloud service module 5 and the data storage module 6 are connected with the data display module 7 in control mode, the data analysis module 1 is connected with the data calling module 2 and the data classification module 8 in control mode respectively, the data classification module 8 is connected with the information collection module 9 and the input terminal module 10 in control mode respectively, the data calling module 2 is connected with the equipment library module 3 in control mode, the equipment library module 3 comprises an equipment library management module 301, a physical entity import module 302, a prototype input module 303, a storage library module 304, a library parameter calling module 305, an instruction receiving module 306 and a parameter export module 307, the equipment library management module 301 is connected with the physical entity import module 302 and the prototype input module 303 in control mode respectively, the physical entity import module 302 and the prototype input module 303 are connected with the storage library module 304 in control mode respectively, the storage library module 304 is connected with the library parameter calling module 305 in control mode, the library parameter calling module 305 is connected with the instruction receiving module 306 and the parameter export module 307 in control mode respectively, the data classification module 8 comprises an information receiving module 801, an information identification module 802, a classification processing module 803 and a data output module 804, the information receiving module 801 is connected with the information identification module 802 in control mode, the information identification module 802 is connected with the classification processing module 803 in control mode, the classification processing module 803 is connected with the data output module 804 in control mode, the cloud service module 5 comprises a fusion receiving module 501, a preparation analysis module 502, an analysis calling module 503, a cloud data module 504 and an analysis output module 505, the fusion receiving module 501 is connected with the preparation analysis module 502 in control mode, the preparation analysis module 502 is connected with the analysis calling module 503, the cloud data module 504 and the analysis output module 505 in control mode respectively, the analysis calling module 503 is connected with the cloud data module 504 in control mode, the data display module 7 comprises an analysis receiving module 701, a visual display module 702, an equipment management module 703 and a storage calling module 704, the analysis receiving module 701 is connected with the visual display module 702 in control mode, the visual display module 702 is connected with the equipment management module 703 in control mode, the equipment management module 703 is connected with the storage calling module 704 in control mode, the equipment management module 703 is used for calling the use record, the storage record, the maintenance record, the actual operation parameter and the library storage parameter of equipment in the data storage module 6, and then the equipment storage record is transmitted to the visual display module 702 for visual display, thereby improving the convenience of device management and the use experience of management personnel.

[0023] Please refer to Figure 3 The application provides an embodiment of a large data-based equipment management processing method, comprising the following steps: step one, equipment data acquisition; step two, data analysis calling; step three, equipment state analysis; and step four, platform display management.

[0024] In the above step one, the running data of the equipment is collected in real time by the information acquisition module 9, and the storage, use and maintenance information of the equipment is imported by the input terminal module 10, then the running data of the equipment and the storage, use and maintenance information of the equipment are transmitted to the information receiving module 801 in the data classification module 8 by the information acquisition module 9 and the input terminal module 10 respectively, then the received information is identified by the information identification module 802, and the received information is divided into equipment running data and equipment environment data according to the identification result by the classification processing module 803, then the equipment running data and the equipment environment data are transmitted to the data analysis module 1 by the data output module 804;

[0025] In the above step two, the calling instruction is sent to the data calling module 2 by the data analysis module 1 in combination with the task demand and the data characteristics, then the calling instruction is transmitted to the instruction receiving module 306 in the equipment library module 3 by the data calling module 2, then the entity import parameters and the prototype input parameters of the corresponding equipment are called from the storage library module 304 by the library parameter calling module 305 according to the calling instruction, then the called entity import parameters and prototype input parameters are transmitted to the data calling module 2 by the parameter export module 307, and are sent to the data analysis module 1;

[0026] In the above step three, the entity import parameters, the prototype input parameters, the equipment running data and the equipment environment data are sent to the data fusion module 4 for fusion processing by the data analysis module 1 respectively, then the fusion-processed data are transmitted to the data storage module 6 and the cloud service module 5 respectively, the use record, the storage record, the maintenance record, the actual running parameter and the library storage parameter of the equipment are stored by the data storage module 6, the fusion-processed data are received by the fusion receiving module 501, the equipment analysis instruction is sent to the analysis calling module 503 by the whole analysis module 502 in combination with the fusion processing result, then the equipment analysis instruction is sent to the cloud data module 504 by the analysis calling module 503, then the large data information in the cloud data is scanned by the cloud data module 504, and the corresponding large data information is sent to the whole analysis module 502, the large data information and the fusion-processed data of the equipment are combined by the whole analysis module 502, the running state of the equipment is analyzed, the maintenance plan of the equipment is made, and the running state of the equipment and the maintenance plan of the equipment are sent to the data display module 7 by the analysis output module 505;

[0027] The equipment running state and the equipment maintenance scheme sent by the analysis output module 505 are received by the analysis receiving module 701, and the equipment running state is visually displayed in real time by the visual display module 702, and the storage calling instruction is sent from the equipment management module 703 to the storage calling module 704 as required, and then the storage calling module 704 calls the use record, the storage record, the maintenance record, the actual running parameter and the storage parameter of the corresponding equipment in the data storage module 6 to the visual display module 702 for visual display of the equipment storage record in combination with the storage calling instruction.

[0028] Based on the above, the advantages of the present application are that when the present application is used, the running data of the equipment is collected in real time by the information collection module 9, the storage, use and maintenance information of the equipment is imported by the input terminal module 10, and after data processing, the information is stored in the data storage module 6, which is convenient for inquiring the service record of the equipment in the later period, improves the practicability of the management platform, the equipment analysis instruction is sent to the analysis calling module 503 by the overhaul analysis module 502 in combination with the fusion processing result, then the analysis calling module 503 sends the equipment analysis instruction to the cloud data module 504, then the cloud data module 504 scans the big data information in the cloud data, and sends the corresponding big data information to the overhaul analysis module 502, the big data information and the fusion processing data of the equipment are combined by the overhaul analysis module 502, the running state of the equipment is analyzed, and the maintenance plan of the equipment is made, so that the safe operation of the equipment is ensured, and the safety hidden danger is eliminated, the equipment running state and the equipment maintenance scheme sent by the analysis output module 505 are received by the analysis receiving module 701, and the equipment running state is visually displayed in real time by the visual display module 702, and the storage calling instruction is sent from the equipment management module 703 to the storage calling module 704 as required, and then the storage calling module 704 calls the use record, the storage record, the maintenance record, the actual running parameter and the storage parameter of the corresponding equipment in the data storage module 6 to the visual display module 702 for visual display of the equipment storage record in combination with the storage calling instruction, which improves the convenience of equipment management.

[0029] It is apparent for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and range of the equivalent elements of the claims are intended to be embraced in the present application. Any reference signs in the claims should not be considered as limiting the claims to which they relate.

Claims

1. A big data-based equipment management processing method, wherein the method is applied to a big data-based equipment management platform, the big data-based equipment management platform comprising a data analysis module (1), a data retrieval module (2), an equipment library module (3), a data fusion module (4), a cloud service module (5), a data storage module (6), a data display module (7), a data classification module (8), an information collection module (9), and an input terminal module (10), comprising the following steps: Step 1: Equipment data acquisition; Step 2: Data analysis and retrieval; Step 3: Equipment status analysis; Step 4: Platform display and management; Its features include: The data analysis module (1) controls the connection to the data fusion module (4), and the data fusion module (4) controls the connection to the cloud service module (5) and the data storage module (6) respectively. The cloud service module (5) and the data storage module (6) both control the connection to the data display module (7). In step one above, the information acquisition module (9) collects the equipment's operation data in real time, and the input terminal module (10) imports the equipment's storage, usage and maintenance information. Then, the information acquisition module (9) and the input terminal module (10) transmit the equipment's operation data and the equipment's storage, usage and maintenance information to the information receiving module (801) in the data classification module (8), respectively. Then, the information identification module (802) identifies the received information, and the classification processing module (803) divides the received information into equipment operation data and equipment environment data according to the identification results. Then, the data output module (804) transmits the equipment operation data and equipment environment data to the data analysis module (1). In step two above, the data analysis module (1) sends a call instruction to the data call module (2) in combination with the task requirements and data characteristics. Then, the data call module (2) transmits the call instruction to the instruction receiving module (306) in the equipment library module (3). After that, the library parameter call module (305) calls the entity import parameters and prototype input parameters of the corresponding equipment from the storage library module (304) according to the call instruction. Then, the parameter export module (307) transmits the called entity import parameters and prototype input parameters to the data call module (2) and sends them to the data analysis module (1). In step three above, the data analysis module (1) sends the entity import parameters, prototype input parameters, equipment operation data, and equipment environment data to the data fusion module (4) for fusion processing. After fusion processing, the data is transmitted to the data storage module (6) and the cloud service module (5). The data storage module (6) stores the equipment's usage records, storage records, maintenance records, actual operating parameters, and library storage parameters. At the same time, the fusion receiving module (501) receives the fusion-processed data, and the preparation analysis module (502) combines the fusion processing results to call the analysis module. The module (503) sends the equipment analysis command, and then the analysis call module (503) sends the equipment analysis command to the cloud data module (504). Subsequently, the cloud data module (504) scans the big data information in the cloud data and sends the corresponding big data information to the maintenance analysis module (502). The maintenance analysis module (502) combines the big data information and the equipment fusion processing data to analyze the equipment's operating status and formulate the equipment's maintenance plan. The analysis output module (505) then sends the equipment's operating status and maintenance plan to the data display module (7). In step four above, the analysis receiving module (701) receives the equipment operating status and equipment maintenance plan sent by the analysis output module (505), and the visualization display module (702) displays the equipment operating status in real time. At the same time, as needed, the equipment management module (703) sends a storage call instruction to the storage call module (704). Then, the storage call module (704) combines the storage call instruction to call the corresponding equipment usage records, storage records, maintenance records, actual operating parameters and library storage parameters in the data storage module (6) to the visualization display module (702) for the visualization display of equipment storage records.

2. The equipment management and processing method based on big data according to claim 1, characterized in that: The data analysis module (1) controls the data call module (2) and the data classification module (8) respectively, and the data classification module (8) controls the information acquisition module (9) and the input terminal module (10) respectively, and the data call module (2) controls the equipment library module (3).

3. The equipment management and processing method based on big data according to claim 2, characterized in that: The equipment library module (3) consists of an equipment library management module (301), an entity import module (302), a prototype input module (303), a storage module (304), a library parameter call module (305), an instruction receiving module (306), and a parameter export module (307). The equipment library management module (301) controls and connects the entity import module (302) and the prototype input module (303) respectively.

4. The equipment management and processing method based on big data according to claim 3, characterized in that: The entity import module (302) and the prototype input module (303) respectively control the connection repository module (304), and the repository module (304) controls the connection library parameter call module (305). The library parameter call module (305) respectively controls the connection instruction receiving module (306) and the parameter export module (307).

5. The equipment management and processing method based on big data according to claim 4, characterized in that: The data classification module (8) consists of an information receiving module (801), an information identification module (802), a classification processing module (803), and a data output module (804). The information receiving module (801) controls and connects to the information identification module (802), and the information identification module (802) controls and connects to the classification processing module (803). The classification processing module (803) controls and connects to the data output module (804).

6. The equipment management and processing method based on big data according to claim 1, characterized in that: The cloud service module (5) consists of a fusion receiving module (501), a preparation and analysis module (502), an analysis and invocation module (503), a cloud data module (504), and an analysis and output module (505). The fusion receiving module (501) controls the connection to the preparation and analysis module (502), and the preparation and analysis module (502) controls the connection to the analysis and invocation module (503), the cloud data module (504), and the analysis and output module (505), respectively. The analysis and invocation module (503) controls the connection to the cloud data module (504).

7. The equipment management and processing method based on big data according to claim 1, characterized in that: The data display module (7) consists of an analysis receiving module (701), a visualization display module (702), an equipment management module (703), and a storage and retrieval module (704). The analysis receiving module (701) controls the connection to the visualization display module (702), the visualization display module (702) controls the connection to the equipment management module (703), and the equipment management module (703) controls the connection to the storage and retrieval module (704).

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