A big data-based elevator maintenance method, intelligent terminal device, and computer-readable storage medium

Abstract

This invention relates to an elevator maintenance method, intelligent terminal device, and computer-readable storage medium based on big data. The method includes the following steps: S1 data acquisition; S2 feature event extraction and analysis; S3 elevator safety risk monitoring model construction, assigning corresponding weight coefficients to different types of feature events, and calculating the comprehensive risk value of a single elevator; S4 risk value standardization processing; S5 risk classification establishment; S6 dynamic identification and early warning of high-risk elevators; S7 closed-loop monitoring. This invention solves the problem of difficulty in predicting potential elevator hazards in advance and effectively reduces the probability of elevator malfunctions and safety accidents.

Description

Technical Field

[0001] This invention relates to the technical field of elevators, and in particular to an elevator maintenance method based on big data, an intelligent terminal device, and a computer-readable storage medium. Background Technology

[0002] With the acceleration of urbanization and the widespread adoption of high-rise buildings, elevators have become an indispensable vertical transportation tool in modern society. However, the rapid increase in the number of elevators has also brought increasingly severe challenges to safety supervision. Traditional elevator safety supervision mainly relies on periodic inspections and manual patrols. However, supervisors face a massive number of elevators, and manual inspections are insufficient to cover all of them. Furthermore, the inspection cycle is long, making it difficult to detect safety hazards in a timely manner. Consequently, it is difficult to identify potential hazards in elevators in advance, hindering the provision of targeted maintenance for potentially dangerous locations and ultimately failing to reduce the probability of elevator malfunctions and safety accidents. Summary of the Invention

[0003] The purpose of this invention is to provide an elevator maintenance method based on big data, which has the characteristic of reducing the probability of elevator malfunctions and safety accidents.

[0004] The above-mentioned objective of this invention is achieved through the following technical solution: A big data-based elevator maintenance method, characterized by the following steps: S1 Data Acquisition: Collects multi-source elevator data within a preset monitoring time period through the elevator intelligent monitoring platform; S2 Feature Event Extraction and Analysis: Extract elevator safety-related feature events from the S1 dataset; S3 Elevator Safety Risk Monitoring Model Construction: An elevator safety risk monitoring model is established by combining the probability of occurrence of characteristic events with the severity of their consequences; corresponding weight coefficients are assigned to different types of characteristic events, and the comprehensive risk value of a single elevator is calculated; S4 Risk Value Standardization Processing: Obtain the overall risk value distribution of all monitored elevators, and use the standard score method to convert the original risk value of each elevator into a standard score; S5 Risk Classification Establishment: Based on the standardized risk value distribution characteristics, four risk thresholds are set to classify elevator safety risks into four levels: low risk, medium risk, high risk, and extremely high risk. S6 High-Risk Elevator Dynamic Identification and Early Warning: Establish a graded alarm mechanism corresponding to each risk level. When the standardized risk value of an elevator exceeds the preset high-risk threshold, an early warning message is automatically generated and pushed to the receiving terminal. S7 Closed-Loop Monitoring: When an early warning message is received, maintenance orders are automatically dispatched. After maintenance is completed, feedback information on maintenance completion is received, and the maintenance results are verified online to form a closed loop.

[0005] By adopting the above technical solution, the elevator safety risk monitoring model calculates the comprehensive risk value of each elevator based on the collected data and performs standardized processing. When the standardized risk value of a certain elevator exceeds the preset high-risk threshold, the system automatically generates an early warning message and pushes it to the maintenance unit. The maintenance unit then dispatches a repair order, thereby reducing the probability of elevator malfunctions and safety accidents.

[0006] In a preferred embodiment, the present invention can be further configured such that the multi-source elevator data in step S1 includes elevator Internet of Things monitoring data, including elevator running speed, acceleration, vibration amplitude, car position, door opening and closing status, load, number of runs, running time, and fault codes.

[0007] In a preferred embodiment, the present invention can be further configured such that the characteristic events described in step S2 include elevator entrapment, overshooting, bottoming out, door malfunction, abnormal operation, overdue maintenance, unqualified maintenance, unqualified inspection, emergency response timeout, and user complaints.

[0008] In a preferred embodiment, the present invention can be further configured such that the comprehensive risk value is calculated using the following formula: R = Σ(Wi × Pi × Si); In the formula, R is the comprehensive risk value of the elevator, Wi is the weight coefficient of the i-th type of characteristic event, Pi is the probability of the occurrence of the i-th type of characteristic event, and Si is the severity of the consequences of the i-th type of characteristic event.

[0009] In a preferred embodiment, the present invention can be further configured such that the standard score is calculated using the following formula: Z = (R - μ) / σ; In the formula, Z is the standardized risk value, R is the original comprehensive risk value of the elevator, μ is the average of the original risk values ​​of all monitored elevators, and σ is the standard deviation of the original risk values ​​of all monitored elevators.

[0010] The second objective of this invention is to provide an intelligent terminal device that reduces the probability of elevator malfunctions and safety accidents.

[0011] The second objective of this invention is achieved through the following technical solution: A smart terminal device includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to perform the aforementioned elevator maintenance method based on big data.

[0012] The third objective of this invention is to provide a computer storage medium capable of storing corresponding programs, which facilitates the reduction of the probability of elevator malfunctions and safety accidents.

[0013] The above-mentioned third objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by the aforementioned big data-based elevator maintenance method.

[0014] In summary, this application includes at least one of the following beneficial technical effects: This application calculates the comprehensive risk value of each elevator through an elevator safety risk monitoring model and performs standardized processing. When the standardized risk value of a certain elevator exceeds the preset high-risk threshold, the system automatically generates an early warning message and pushes it to the maintenance unit. The maintenance unit then dispatches a repair order, thereby reducing the probability of elevator malfunctions and safety accidents. This application constructs an elevator safety risk monitoring model by combining the probability of occurrence of characteristic events with the severity of their consequences. It uses a standard scoring method to standardize the risk values, making the risk values ​​of different elevators comparable and improving the scientificity and accuracy of risk assessment. This application establishes a graded risk alarm mechanism that can dynamically identify high-risk elevators and push early warning information in real time, thereby realizing the prevention of elevator safety risks and effectively reducing the incidence of elevator safety accidents. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the elevator maintenance method based on big data according to one embodiment of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the accompanying drawings.

[0017] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

[0018] This invention provides an elevator maintenance method based on big data, referring to... Figure 1 The specific steps are as follows: S1: Data Acquisition and Preprocessing.

[0019] The elevator intelligent monitoring platform collects multi-source data on all elevators in a city over a certain period of time. This example uses 10,000 elevators in a city over the past 30 days as an example. The collected multi-source data includes the following: Elevator IoT monitoring data: Real-time data such as elevator running speed, acceleration, vibration amplitude, car position, door opening and closing status, load, number of runs, running time, and fault codes are collected from sensors installed on the elevator. The data collection frequency can be set to 1 time / second, or the collection frequency can be increased as needed. Maintenance data: Maintenance records are collected from the elevator maintenance company's management system, including maintenance time, maintenance content, and maintenance results; Inspection and testing data: Collect elevator periodic inspection and supervision inspection reports from the special equipment inspection agency's system, including inspection time, inspection items, inspection results, non-conformities, etc. Emergency response data: Records of emergency events such as elevator entrapment are collected from the elevator emergency response platform, including the time, location, cause, rescue time, and rescue results of the event; User complaint data: User complaint records regarding elevator safety are collected from the 12345 government service hotline and the elevator smart supervision APP, including the complaint time, complaint content, and handling results.

[0020] The collected raw data is preprocessed. First, data cleaning is performed to remove duplicate data and obviously erroneous data, such as abnormal data where the elevator running speed exceeds twice the rated speed. User complaint data is compared with elevator IoT monitoring data to eliminate false alarms or obviously unreasonable data. Then, format conversion is performed: data from different sources is converted into a unified format, such as unifying the date and time format to "YYYY-MM-DDHH:MM:SS".

[0021] S2: Feature event extraction and analysis.

[0022] The following elevator safety-related feature events were extracted from the preprocessed dataset: 1. Abnormal operation events: including elevator entrapment, overshooting, bottoming out, overspeeding, overloading, door malfunction, abnormal operating noises, etc.; 2. Maintenance anomalies: These include overdue maintenance, missing maintenance items, and substandard maintenance. 3. Inspection anomalies: These include inspection failures and the existence of significant safety hazards; 4. Emergency anomalies: including emergency response timeouts, excessively long rescue times, etc. 5. User complaints: These include complaints about safety issues such as unstable elevator operation, abnormal door opening and closing, and unusual noises.

[0023] Statistics are compiled on the number of occurrences, average duration, and number of people affected for each elevator in the past 30 days. These characteristic events can be added or removed based on the actual situation.

[0024] S3: Construction of elevator safety risk monitoring model.

[0025] In this embodiment, the weight coefficients Wi of various feature events are determined based on the analytic hierarchy process (AHP): Operational anomaly event W1=0.5, maintenance anomaly event W2=0.2, inspection anomaly event W3=0.2, emergency anomaly event W4=0.05, user complaint event W5=0.05.

[0026] The above coefficients are for reference only. The weighting coefficients can be re-determined based on the risk situation of elevators in other cities, as well as the relevance and importance of the coefficients.

[0027] For each type of characteristic event, calculate its probability of occurrence Pi, which is calculated based on the frequency of occurrence of the event in the past 30 days and the historical probability of occurrence of the same event in the same type of elevator in the city. The value ranges from 0 to 1.

[0028] For each type of characteristic event, it is divided into 5 levels according to the degree of casualties, property damage and social impact that the event may cause, with values ​​of S1=1, S2=2, S3=3, S4=4 and S5=5 respectively.

[0029] Calculate the overall risk value of a single elevator: R=0.5×P1×S1+0.2×P2×S2+0.2×P3×S3+0.05×P4×S4+0.05×P5×S5; S4: Risk value standardization.

[0030] In this embodiment, the average value μ of the original comprehensive risk value of 10,000 elevators is calculated to be 2.35, and the standard deviation σ is 1.28. It can be recalculated according to the actual situation.

[0031] The original risk value of each elevator is converted into a standard score using the standard score method: Z=(R-2.35) / 1.28.

[0032] S5: Risk classification is established.

[0033] Based on the distribution characteristics of the standardized risk values, the following risk thresholds are set: Low risk: Z < -0.5 (corresponding to R < 1.71); Medium risk: -0.5≤Z<0.5 (corresponding to 1.71≤R<2.99); High risk: 0.5≤Z<1.5 (corresponding to 2.99≤R<4.27); Extremely high risk: Z≥1.5 (corresponding to R≥4.27).

[0034] S6: Dynamic identification and early warning of high-risk elevators.

[0035] If the monitored elevator is in a low-risk state, the system will automatically record it without special handling. If the monitored elevator is in a medium-risk state, a reminder message will be sent to the maintenance unit, requiring them to strengthen daily inspections and maintenance. If the monitored elevator is in a high-risk state, a yellow warning will be sent to the maintenance unit, requiring a comprehensive inspection and rectification. If the monitored elevator is in an extremely high-risk state, a red warning will be sent to both the elevator user and the maintenance unit, requiring them to immediately stop using the elevator and complete rectification and re-inspection within 24 hours.

[0036] The system automatically updates elevator data every 4 hours, recalculating the comprehensive risk value and standardized risk value of each elevator. When the standardized risk value of an elevator exceeds 0.5, the system automatically generates a yellow warning message; when the standardized risk value exceeds 1.5, the system automatically generates a red warning message.

[0037] Simultaneously, elevators with different risk levels can be marked with different colors on the electronic map, allowing regulators to intuitively view the overall risk distribution of elevators throughout the city and the specific locations of high-risk elevators. For high-risk elevators, the system automatically generates monitoring tasks.

[0038] S7: Closed-loop monitoring. Upon receiving an alert, the system automatically dispatches maintenance tasks, sending them to maintenance personnel via app, SMS, fax, etc. For yellow alerts, maintenance personnel must complete the task within 48 hours; for red alerts, they must complete it within 24 hours. After completing the maintenance, personnel upload feedback information. The system receives this feedback and verifies the results online, forming a closed loop.

[0039] Closed-loop supervision enables automatic dispatch of maintenance orders based on risk warnings. Different time limits are set according to different risk levels. After rectification is completed, maintenance personnel verify the rectification data online through maintenance terminals, conduct source analysis of hidden dangers and risks, and archive and close qualified work orders, forming a closed-loop management system covering the entire process of risk monitoring, early warning, handling, review and archiving.

[0040] In this embodiment of the invention, multiple sources of data, including elevator IoT monitoring, maintenance, inspection and testing, emergency response, and user complaints, are integrated. This allows for a comprehensive and objective reflection of the actual safety status of elevators, avoiding risk assessment bias caused by a single data source. Furthermore, an elevator safety risk monitoring model is constructed by combining the probability of characteristic events and the severity of their consequences. A standard scoring method is used to standardize risk values, making risk values ​​comparable between different elevators and improving the scientific rigor and accuracy of risk assessment. A graded risk alarm mechanism is established, which can dynamically identify high-risk elevators and push early warning information in real time. After receiving the early warning information, maintenance units can promptly and effectively reduce the incidence of elevator safety accidents.

[0041] The intelligent terminal includes a memory and a processor that are coupled to each other. The memory stores program data, and the processor executes the program data stored in the memory to implement any of the above-mentioned big data-based elevator maintenance methods.

[0042] Optionally, the smart terminal can be any reasonable smart electronic device such as a microcomputer, server, mobile phone, tablet computer, or smartwatch, and this application does not limit it.

[0043] Specifically, the processor controls itself and the memory to implement the steps of any of the above-described embodiments of the elevator maintenance method based on big data. The processor can also be called a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor. Furthermore, the processor can be implemented using integrated circuit chips.

[0044] The computer-readable storage medium stores program data that can be executed by a processor to implement any of the above-mentioned big data-based elevator maintenance methods.

[0045] In the embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0047] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0048] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0050] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

Claims

1. A big data-based elevator maintenance method, characterized in that, Includes the following steps: S1 Data Acquisition: Collects multi-source elevator data within a preset monitoring time period through the elevator intelligent monitoring platform; S2 Feature Event Extraction and Analysis: Extract feature events related to elevator safety from the S1 dataset; S3 Elevator Safety Risk Monitoring Model Construction: An elevator safety risk monitoring model is established by combining the probability of occurrence of characteristic events with the severity of their consequences; corresponding weight coefficients are assigned to different types of characteristic events, and the comprehensive risk value of a single elevator is calculated; S4 Risk Value Standardization Processing: Obtain the overall risk value distribution of all monitored elevators, and use the standard score method to convert the original risk value of each elevator into a standard score; S5 Risk Classification Establishment: Based on the standardized risk value distribution characteristics, four risk thresholds are set to classify elevator safety risks into four levels: low risk, medium risk, high risk, and extremely high risk. S6 High-Risk Elevator Dynamic Identification and Early Warning: Establish a graded alarm mechanism corresponding to each risk level. When the standardized risk value of an elevator exceeds the preset high-risk threshold, an early warning message will be automatically generated and pushed. S7 Closed-Loop Monitoring: When an early warning message is received, maintenance orders are automatically dispatched. After maintenance is completed, feedback information on maintenance completion is received, and the maintenance results are verified online to form a closed loop.

2. The elevator maintenance method based on big data according to claim 1, characterized in that, The multi-source elevator data in step S1 includes elevator IoT monitoring data, which includes elevator running speed, acceleration, vibration amplitude, car position, door opening and closing status, load, number of runs, running time, and fault codes.

3. The elevator maintenance method based on big data according to claim 1, characterized in that, The characteristic events mentioned in step S2 include elevator entrapment, overshooting, bottoming out, door malfunction, abnormal operation, overdue maintenance, substandard maintenance, substandard inspection, emergency response timeout, and user complaints.

4. The elevator maintenance method based on big data according to claim 1, characterized in that, The overall risk value is calculated using the following formula: R = Σ(Wi × Pi × Si); In the formula, R is the comprehensive risk value of the elevator, Wi is the weight coefficient of the i-th type of characteristic event, Pi is the probability of the occurrence of the i-th type of characteristic event, and Si is the severity of the consequences of the i-th type of characteristic event.

5. The elevator maintenance method based on big data according to claim 4, characterized in that, The standard score is calculated using the following formula: Z = (R - μ) / σ; In the formula, Z is the standardized risk value, R is the original comprehensive risk value of the elevator, μ is the average of the original risk values ​​of all monitored elevators, and σ is the standard deviation of the original risk values ​​of all monitored elevators.

6. A smart terminal device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed according to any one of claims 1 to 5.

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