Elevator health index calculation system and method based on multi-source big data fusion

Abstract

The invention discloses a multi-source big data fusion-based elevator health index calculation system and method.The calculation system comprises a data layer, a service layer and an application layer, the application layer comprises a data acquisition module and a data processing module, and the data processing module cleans, preprocesses and classifies elevator multi-source big data acquired by the data acquisition module; the data layer comprises a data storage module, the data storage module stores data processed by the data processing module into corresponding storage libraries according to categories, the service layer comprises a calculation model module and an elevator health code generation module, and the calculation model module obtains elevator health indexes based on weighted calculation of three-level index items. And the elevator health code generation module divides elevator health codes of different grades according to the score of the elevator health index, and generates the elevator health codes in an application layer. The running state of the elevator can be monitored in real time, elevator faults and potential safety hazards can be found and processed in time, the safety and reliability of the elevator are improved, and elevator accidents are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator health management, and more specifically, to an elevator health index calculation system and method based on multi-source big data fusion. Background Art

[0002] With the acceleration of urbanization, elevators, as an indispensable vertical transportation tool in high-rise buildings, are directly related to the safety of people's lives and property. Traditional elevator health monitoring methods rely on manual inspections and regular maintenance, which cannot fully and comprehensively grasp the operating status of elevators in real time, making it difficult to detect and deal with elevator failures and safety hazards in a timely manner. Therefore, it is necessary to develop a system that can monitor the health status of elevators in real time to improve the safety and reliability of elevators. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and to provide an elevator health index calculation system and method based on multi-source big data fusion.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] The invention discloses an elevator health index calculation system based on multi-source big data fusion, comprising a data layer for providing data support, a service layer for constructing a three-dimensional one-code, and an application layer for providing services to users. The data layer, the service layer and the application layer are connected in communication. The application layer comprises a data acquisition module and a data processing module. The data processing module cleans, pre-processes and classifies the multi-source big data of elevators collected by the data acquisition module. The data layer comprises a data storage module. The data storage module stores the data processed by the data processing module into a corresponding storage repository according to categories. The service layer comprises a calculation model module and an elevator health code generation module. The calculation model module obtains the elevator health index based on the weighted calculation of three-level index items. The elevator health code generation module divides the elevator health codes into different levels according to the scores of the elevator health index, and generates the elevator health codes at the application layer.

[0006] Furthermore, it also includes a data exchange module, which shares data with the enterprise maintenance system, maintenance APP, and third-party system through a data interface.

[0007] Furthermore, the data exchange module encrypts and protects the integrity of the data during the data transmission process to ensure that the data transmission is timely and effective.

[0008] Furthermore, the three-level indicators include basic information of elevators, operation monitoring, social supervision, daily maintenance, supervision and inspection and testing.

[0009] Furthermore, the total score of the elevator health index is 100 points, 0≤elevator health index<60 is marked as a red code, 60≤elevator health index<80 is marked as a yellow code, and 80≤elevator health index≤100 is marked as a green code.

[0010] Furthermore, the application layer includes a smart rescue unit, a smart supervision unit, an on-demand maintenance unit and an Internet of Things sensing unit. The smart rescue unit is used for rapid response and positioning, rescue process monitoring, and rescue data recording and analysis. The smart supervision unit is used for elevator health code production, supervision data collection, and supervision decision support. The on-demand maintenance unit is used for elevator maintenance data collection, maintenance unit data collection, and maintenance effect evaluation. The Internet of Things sensing unit collects elevator behavior data, elevator operation data, and data storage and processing based on the Internet of Things.

[0011] Furthermore, the service layer monitors the health of the elevator through the elevator operation unit, the fault handling unit and the equipment hidden danger management unit. The elevator operation unit performs real-time monitoring, operation data analysis, and operation scheduling based on the repository of the data layer. The fault handling unit performs fault warning, fault diagnosis, emergency response, and maintenance scheduling based on the repository of the data layer. The equipment hidden danger management unit performs hidden danger investigation, risk assessment, rectification tracking, and updates the hidden danger database based on the repository of the data layer.

[0012] Furthermore, the data layer includes an elevator equipment data repository, an elevator operation data repository, a maintenance unit data repository, an elevator maintenance data repository, a supervision data repository and an elevator riding behavior data repository.

[0013] The present invention also provides a calculation method of an elevator health index calculation system based on multi-source big data fusion, comprising the following steps:

[0014] Step S1, the data collection module collects basic information, operation data, maintenance data, maintenance unit data, supervision and management data and elevator behavior data of the elevator;

[0015] Step S2, the data processing module cleans, preprocesses and classifies the collected data;

[0016] Step S3, the calculation model module obtains the elevator health index based on the weighted calculation of the three-level index items;

[0017] Step S4, the elevator health code generation module divides the elevator health index into three levels: red code, yellow code, and green code according to the score of the elevator health index;

[0018] Step S5, the data storage module meets the requirements of data quality and data security, and stores the data into the equipment data repository, maintenance unit data repository, maintenance personnel data repository, maintenance record data repository, alarm data repository, and log data repository according to categories.

[0019] The beneficial effects of the present invention are:

[0020] 1. The present invention can monitor the operating status of the elevator in real time, promptly discover and handle elevator failures and potential safety hazards, improve the safety and reliability of the elevator, and reduce the occurrence of elevator accidents.

[0021] 2. The present invention optimizes the elevator maintenance plan and reduces maintenance costs through real-time analysis and processing of data.

[0022] 3. The present invention generates an elevator health code by cooperating with a calculation model module and an elevator health code generation module to realize the visualization of the health status of the elevator, making it convenient for users and managers to understand and monitor the health status of the elevator. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the structure of the elevator health index calculation system based on multi-source big data fusion in this embodiment;

[0024] Figure 2 The present invention is a flow chart of a calculation method of an elevator health index calculation system based on multi-source big data fusion in this embodiment.

[0025] Figure numerals: data layer 1, service layer 2, application layer 3. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiment: An elevator health index calculation system based on multi-source big data fusion, such as Figure 1 As shown, it includes a data layer 1 for providing data support, a service layer 2 for constructing a three-dimensional code, and an application layer 3 for providing services to users. The data layer 1, the service layer 2 and the application layer 3 are connected in communication.

[0028] Among them, application layer 3 includes a data acquisition module and a data processing module. The data acquisition module is used to collect multi-source big data of elevators, including basic information, operation data, maintenance data, maintenance unit data, supervision management data and elevator behavior data of elevators; the data processing module cleans, pre-processes and classifies the multi-source big data of elevators collected by the data acquisition module to provide efficient data query and analysis functions.

[0029] The data layer 1 includes a data storage module, which stores the data processed by the data processing module into corresponding storage libraries according to categories.

[0030] Service layer 2 includes a calculation model module and an elevator health code generation module. The calculation model module obtains the elevator health index based on the weighted calculation of the three-level indicator items. The elevator health code generation module divides the elevator health codes into different levels according to the scores of the elevator health index, and generates the elevator health codes at application layer 3.

[0031] Furthermore, the third-level indicators include basic information about elevators, operation monitoring, social supervision, daily maintenance, supervision and inspection and testing.

[0032] The total score of the elevator health index is 100 points. When 0≤the elevator health index<60, it is classified as a red code; when 60≤the elevator health index<80, it is classified as a yellow code; when 80≤the elevator health index≤100, it is classified as a green code.

[0033] By generating an elevator health code, the health status of the elevator can be visualized, making it easier for users and managers to understand and monitor the health status of the elevator.

[0034] Furthermore, application layer 3 includes a smart rescue unit, a smart supervision unit, an on-demand maintenance unit and an IoT sensing unit. The smart rescue unit is used for rapid response and positioning, rescue process monitoring, and rescue data record analysis. The smart supervision unit is used for elevator health code production, supervision data collection, and supervision decision support. The on-demand maintenance unit is used for elevator maintenance data collection, maintenance unit data collection, and maintenance effect evaluation. The IoT sensing unit collects elevator behavior data, elevator operation data, and data storage and processing based on the Internet of Things.

[0035] The data acquisition module is used to collect data acquired and recorded in the smart rescue unit, smart supervision unit, on-demand maintenance unit and Internet of Things sensing unit. The data processing module is used to process data acquired and recorded in the smart rescue unit, smart supervision unit, on-demand maintenance unit and Internet of Things sensing unit.

[0036] Furthermore, the service layer 2 monitors the health of the elevator through the elevator operation unit, the fault handling unit and the equipment hidden danger management unit. The elevator operation unit performs real-time monitoring, operation data analysis, and operation scheduling based on the repository of data layer 1. The fault handling unit performs fault warning, fault diagnosis, emergency response, and maintenance scheduling based on the repository of data layer 1. The equipment hidden danger management unit performs hidden danger investigation, risk assessment, rectification tracking, and updates the hidden danger database based on the repository of data layer 1.

[0037] Furthermore, data layer 1 includes repositories such as elevator equipment data, elevator operation data, maintenance unit data, elevator maintenance data, supervision data, and elevator behavior data. The data stored in data layer 1 must ensure its authenticity, integrity, reliability, and consistency, and at the same time, the security of the data must be guaranteed, that is, the data security protection dimension must comply with the requirements of 4.2 in GB / T37973-2019.

[0038] Furthermore, the elevator health index calculation system also includes a data exchange module, which has a data interface and can share data with the enterprise maintenance system, maintenance APP, and third-party system through the data interface. The data exchange module needs to encrypt and protect the integrity of the data during the data transmission process to ensure that the data transmission is timely and effective.

[0039] This embodiment also provides a calculation method of an elevator health index calculation system based on multi-source big data fusion, such as Figure 2 As shown, the following steps are included:

[0040] Step S1, the data acquisition module collects the basic information, operation data, maintenance data, maintenance unit data, supervision management data and elevator riding behavior data of the elevator; the data acquisition module can be a sensor and monitoring equipment installed in the elevator, which is used to collect the operation data and maintenance data of the elevator in real time.

[0041] Step S2, the data processing module cleans, preprocesses and classifies the collected data; the data processing module transmits the classified data to the data storage module of the data layer 1;

[0042] Step S3, the calculation model module obtains the elevator health index based on the weighted calculation of the three-level index items;

[0043] Step S4, the elevator health code generation module divides the elevator health index into three levels: red code, yellow code, and green code according to the score of the elevator health index;

[0044] Step S5, the data storage module meets the requirements of data quality and data security, and stores the data into storage repositories such as equipment data, maintenance unit data, maintenance personnel data, maintenance record data, alarm data, and log data according to categories to ensure data security and traceability.

[0045] Through the above method, the operating status of the elevator can be monitored in real time and comprehensively, elevator failures and safety hazards can be discovered and handled in a timely manner, and the safety and reliability of the elevator can be improved.

[0046] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An elevator health index calculation system based on multi-source big data fusion, characterized in that: The invention comprises a data layer (1) for providing data support, a service layer (2) for constructing a three-dimensional code, and an application layer (3) for providing services to users. The data layer (1), the service layer (2) and the application layer (3) are connected in communication. The application layer (3) comprises a data acquisition module and a data processing module. The data processing module cleans, pre-processes and classifies the multi-source elevator big data collected by the data acquisition module. The data layer (1) comprises a data storage module. The data storage module stores the data processed by the data processing module into a corresponding storage repository according to categories. The service layer (2) comprises a calculation model module and an elevator health code generation module. The calculation model module obtains an elevator health index based on weighted calculation of three-level index items. The elevator health code generation module divides the elevator health code into different levels according to the score of the elevator health index, and generates the elevator health code in the application layer (3).

2. According to claim 1, an elevator health index calculation system based on multi-source big data fusion is characterized in that: It also includes a data exchange module, which shares data with the enterprise maintenance system, maintenance APP, and third-party system through a data interface.

3. The elevator health index calculation system based on multi-source big data fusion according to claim 2 is characterized in that: The data exchange module encrypts and protects the integrity of data during data transmission to ensure that data transmission is timely and effective.

4. The elevator health index calculation system based on multi-source big data fusion according to claim 1 is characterized in that: The three-level indicators include basic information about elevators, operation monitoring, social supervision, daily maintenance, supervision and inspection and testing.

5. The elevator health index calculation system based on multi-source big data fusion according to claim 1 is characterized in that: The total score of the elevator health index is 100 points. 0≤elevator health index<60 is marked as red code, 60≤elevator health index<80 is marked as yellow code, and 80≤elevator health index≤100 is marked as green code.

6. The elevator health index calculation system based on multi-source big data fusion according to claim 1 is characterized in that: The application layer (3) includes a smart rescue unit, a smart supervision unit, an on-demand maintenance unit and an Internet of Things sensing unit. The smart rescue unit is used for rapid response and positioning, rescue process monitoring, and rescue data record analysis. The smart supervision unit is used for elevator health code production, supervision data collection, and supervision decision support. The on-demand maintenance unit is used for elevator maintenance data collection, maintenance unit data collection, and maintenance effect evaluation. The Internet of Things sensing unit collects elevator behavior data, elevator operation data, and data storage and processing based on the Internet of Things.

7. The elevator health index calculation system based on multi-source big data fusion according to claim 1 is characterized in that: The service layer (2) monitors the health of the elevator through an elevator operation unit, a fault handling unit and an equipment hidden danger management unit. The elevator operation unit performs real-time monitoring, operation data analysis and operation scheduling based on the repository of the data layer (1). The fault handling unit performs fault warning, fault diagnosis, emergency response and maintenance scheduling based on the repository of the data layer (1). The equipment hidden danger management unit performs hidden danger investigation, risk assessment, rectification tracking and updates the hidden danger database based on the repository of the data layer (1).

8. The elevator health index calculation system based on multi-source big data fusion according to claim 1 is characterized in that: The data layer (1) includes an elevator equipment data repository, an elevator operation data repository, a maintenance unit data repository, an elevator maintenance data repository, a supervision data repository and an elevator riding behavior data repository.

9. A calculation method of an elevator health index calculation system based on multi-source big data fusion according to any one of claims 1 to 8, characterized in that: The steps include: Step S1, the data collection module collects basic information, operation data, maintenance data, maintenance unit data, supervision and management data and elevator behavior data of the elevator; Step S2, the data processing module cleans, preprocesses and classifies the collected data; Step S3, the calculation model module obtains the elevator health index based on the weighted calculation of the three-level index items; Step S4, the elevator health code generation module divides the elevator health index into three levels: red code, yellow code, and green code according to the score of the elevator health index; Step S5, the data storage module meets the requirements of data quality and data security, and stores the data into the equipment data repository, maintenance unit data repository, maintenance personnel data repository, maintenance record data repository, alarm data repository, and log data repository according to categories.

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