Elevator Internet of Things data statistical method and system

Abstract

The invention discloses an elevator Internet of Things data statistical method and system, and relates to the technical field of Internet of Things, and the method comprises the steps: collecting the Internet of Things data of each elevator in a target building in real time through Internet of Things equipment deployed in the elevator in the target building; the Internet of Things data comprises operation state data and monitoring video data; the cloud server is connected with the cloud platform, encrypts the Internet of Things data and transmits the encrypted Internet of Things data to the cloud platform; reading the Internet of Things data stored in the cloud platform, counting the operation index data of each elevator based on the preprocessed Internet of Things data, and generating an elevator operation index report based on the operation index data; and the elevator operation index report is displayed through an information publication interface. On the basis of the data statistics result, the abrasion condition and the fault frequency of each part of the elevator can be known, so that a more accurate maintenance plan is formulated, unnecessary maintenance cost can be reduced, maintenance efficiency can be improved, and the service life of the elevator can be prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet of Things, and particularly to a method and system for statistical analysis of elevator Internet of Things data. Background Art

[0002] With the acceleration of the urbanization process, the height and quantity of high-rise buildings are increasing continuously. Elevators are important vertical transportation devices in high-rise buildings, providing many conveniences for people. With the increase in the number and usage frequency of elevators in high-rise buildings, problems such as the safety management, maintenance, and operation efficiency of elevators have become increasingly prominent. In order to ensure the safe operation of elevators, improve the efficiency and effect of maintenance, and optimize the elevator dispatching strategy, it is necessary to comprehensively and accurately collect and analyze elevator Internet of Things data.

[0003] Traditional methods for statistical analysis of elevator operation data have limited data collection scope and single data type for elevator operation data, making it difficult to accurately reflect the actual operation status and fault conditions of elevators. Moreover, the statistical method uses simple manual judgment, making it difficult to judge possible faults in elevators. Summary of the Invention

[0004] To solve the above problems, the present application proposes a method for statistical analysis of elevator Internet of Things data, including: Real-time collecting, by Internet of Things devices deployed in elevators in a target building, the Internet of Things data of each elevator in the target building; the Internet of Things data includes operation status data and monitoring video data; Connecting to a cloud platform, encrypting and transmitting the Internet of Things data to the cloud platform, and performing distributed storage of the Internet of Things data through the cloud platform; Reading the Internet of Things data stored in the cloud platform, preprocessing the Internet of Things data, statistically analyzing operation index data of each elevator based on the preprocessed Internet of Things data, and generating an elevator operation index report based on the operation index data; Displaying the elevator operation index report through an information publicity interface.

[0005] In a feasible implementation manner, real-time collecting, by Internet of Things devices deployed in elevators in a target building, the Internet of Things data of each elevator in the target building specifically includes: The Internet of Things devices include sensor devices and camera devices; Real-time collecting, by the sensor devices and camera devices in each elevator in the target building, the corresponding operation status data and monitoring video data of the elevator respectively; Adding corresponding identification information to the operation status data and the monitoring video data respectively based on the coding of the elevator in the target building.

[0006] In a feasible implementation, based on the preprocessed Internet of Things data, the operation index data of each elevator is statistically analyzed, specifically including: The operation index data includes the passenger cycle flow and the operation cycle status; Based on the preprocessed monitoring video data corresponding to each elevator, the passenger cycle flow of each elevator within a preset period is statistically analyzed; Based on the preprocessed operation status data corresponding to each elevator, the operation cycle status of each elevator within the preset period is statistically analyzed, and the operation cycle status includes the door opening frequency, the door opening duration, the operation time, and the fault information.

[0007] In a feasible implementation, after statistically analyzing the operation index data of each elevator based on the preprocessed Internet of Things data, the method further includes: According to the passenger cycle flow, the load weight of the elevator is determined, and the number of overweight times when the load weight exceeds the preset load value is statistically analyzed; According to the door opening frequency, the door opening duration, and the operation time, the daily operation times of the elevator are determined, and based on the daily operation times, the monthly usage frequency of the elevator is statistically analyzed; According to the fault information, the fault type of the elevator is determined, and the monthly fault frequency of each fault type is statistically analyzed respectively.

[0008] In a feasible implementation, after statistically analyzing the operation index data of each elevator based on the preprocessed Internet of Things data, the method further includes: According to the number of overweight times, the elevator is predicted for overloading faults through a pre-trained machine learning model, and the overloading fault prediction result output by the machine learning model is obtained; According to the monthly fault frequency corresponding to different fault types, through a linear regression model, the fault occurrence type and probability of the elevator are predicted, and a fault occurrence prediction result is obtained.

[0009] In a feasible implementation, based on the operation index data, an elevator operation index report is generated, specifically including: Based on the behavior frequencies of different uncivilized types, an elevator riding behavior report is generated; Based on the monthly fault frequency, an elevator fault report is generated; Based on the monthly usage frequency, an elevator usage report is generated; Based on the overloading fault prediction result and the fault occurrence prediction result, a fault prediction report is generated.

[0010] In a feasible implementation, after reading the Internet of Things data stored in the cloud platform and preprocessing the Internet of Things data, the method further includes: Analyze the human patterns in the surveillance video data through a behavior recognition algorithm to determine whether the human behavior belongs to uncivilized elevator riding behavior; If so, determine the uncivilized type of the human behavior, record the human behavior, and respectively count the behavior frequencies of the uncivilized types.

[0011] In a feasible implementation, display the elevator operation index report through an information publicity interface, specifically including: Obtain user information, determine the user type based on the user information, and generate a data interface for the content in the elevator operation index report that conforms to the user permissions according to the user permissions corresponding to the user type; Call the data interface and display the content in the elevator operation index report that conforms to the user permissions through the information publicity interface.

[0012] In a feasible implementation, after displaying the elevator operation index report through the information publicity interface, the method further includes: Obtain the feedback information input by the user through the information publicity interface, perform data analysis on the feedback information, and determine the feedback problem type; Based on the feedback problem type, send the feedback information to the corresponding elevator control department to optimize the operation of the elevator.

[0013] On the other hand, an embodiment of the present invention further provides a large building energy management system, which is characterized in that the system includes: An elevator data acquisition module, which is used to collect the Internet of Things data of all elevators in the target building in real time through Internet of Things sensors; An elevator data storage module, which is used to connect to the cloud platform, encrypt the Internet of Things data, upload it to the cloud platform, and perform distributed storage of the Internet of Things data through the cloud platform; An elevator data statistics module, which is used to read the Internet of Things data stored in the cloud platform, preprocess the Internet of Things data, and statistically calculate the operation index data of the elevator based on the preprocessed Internet of Things data, and is used to generate an elevator operation index report based on the operation index data; A statistical data display module, which is used to display the elevator operation index report through an information publicity interface.

[0014] The elevator Internet of Things data statistics method proposed in this application can bring the following beneficial effects: Based on the data statistics results, it is possible to understand the wear conditions and failure frequencies of various elevator components, thereby formulating a more accurate maintenance plan, which can not only reduce unnecessary maintenance costs, but also improve maintenance efficiency and extend the service life of the elevator.

[0015] Through the Internet of Things technology, the operating status of the elevator is monitored in real time. When the elevator has an abnormality or potential failure, it can be detected in time. By regularly analyzing the elevator operation data, the future operating status and possible failures of the elevator can be predicted, and preventive maintenance can be carried out before the failure occurs, which can effectively avoid the impact of the failure on the elevator operation and improve the reliability and stability of the elevator. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 is a schematic flowchart of an elevator Internet of Things data statistics method in an embodiment of the present application; Figure 2 is a schematic diagram of an elevator Internet of Things data statistics system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The following will describe in detail the technical solutions provided by each embodiment of the present application in conjunction with the drawings.

[0019] As Figure 1 shown, an embodiment of the present application provides an elevator Internet of Things data statistics method, including: S101: Real-time collect the Internet of Things data of each elevator in the target building through the Internet of Things devices deployed in the elevator in the target building; the Internet of Things data includes operating status data and monitoring video data.

[0020] Specifically, connect the sensor devices and camera devices installed in all elevators of the target building. Through the camera devices, record the interior of the elevator to obtain video data. Real-time monitor the operating status of the elevator through the sensor devices to obtain monitoring data, where the monitoring data includes basic elevator operation information, elevator environment information, elevator fault information, and elevator behavior information.

[0021] Among them, the basic elevator operation information includes the running speed, running direction, door opening and closing status, and load condition of the elevator; the elevator environment information includes the temperature, humidity, and air pressure inside the elevator car; the elevator fault information includes drive system faults (such as motor faults, frequency converter faults, etc.), control system faults (such as main board faults, button faults, etc.), door system faults (such as door machine faults, door lock faults, etc.), and safety system faults (such as overspeed protection, overload protection and other safety device faults); the elevator behavior information includes the elevator running track, the number of elevator switchings, the number of elevator operations, and elevator abnormal behaviors.

[0022] S102: Connect to the cloud platform, encrypt the IoT data and transmit it to the cloud platform, and perform distributed storage of the IoT data through the cloud platform.

[0023] Specifically, establish a connection between the IoT device and the cloud platform through a network communication protocol. Before uploading the IoT data to the cloud platform, encrypt the data to protect the privacy and security of the data. In the embodiments of the present application, data encryption can be implemented using various encryption algorithms (such as AES, RSA, etc.).

[0024] Furthermore, upload the encrypted IoT data to the cloud platform through network transmission and ensure the reliability and integrity of the data transmission to avoid data loss or damage. The cloud platform will perform distributed storage on the received IoT data. In a distributed storage system, the data is usually divided into multiple parts (or called shards) and then stored on multiple nodes respectively. These nodes can be physical servers, virtual machines, or containers, etc. Through distributed storage, the cloud platform can ensure the high availability and scalability of the IoT data while reducing the risk of data loss.

[0025] S103: Read the IoT data stored in the cloud platform, preprocess the IoT data, statistically calculate the operation index data of each elevator based on the preprocessed IoT data, and generate an elevator operation index report based on the operation index data.

[0026] Specifically, read the previous Internet of Things data from the cloud platform. The read Internet of Things data may contain problems such as noise, duplicate values, missing values, or inconsistent formats. It is necessary to preprocess the Internet of Things data, where the preprocessing includes data cleaning (such as removing duplicate values and filling in missing values), data conversion (such as converting string-type data to numerical type), data normalization or standardization, etc. Through preprocessing, the quality and consistency of the data can be improved, providing reliable input for subsequent data analysis.

[0027] Furthermore, based on the preprocessed Internet of Things data, count the operation index data of the elevator. The operation index data includes the frequency of uncivilized behaviors, the monthly failure frequency, the monthly usage frequency, etc.

[0028] In an embodiment of the present application, read the stored video data from the cloud platform, perform frame splitting on the read video data, split the continuous video stream into individual image frames to obtain video frame data, and perform data cleaning on the video frame data to remove possible noise, blurred frames, or irrelevant information to improve the accuracy of subsequent behavior recognition.

[0029] Furthermore, use a behavior recognition algorithm to analyze the cleaned video frame data. By analyzing the human patterns (such as actions, postures, etc.) in the video frames, determine whether these behaviors belong to uncivil elevator-riding behaviors. Among them, uncivil elevator-riding behaviors include randomly pressing buttons, playing in the elevator, deliberately damaging elevator facilities, etc. If an uncivil elevator-riding behavior is recognized, the system will determine the specific type of this behavior (such as randomly pressing buttons, playing, etc.).

[0030] Record the uncivil behaviors, including recording information such as the time, location, and involved personnel of the behavior occurrence, count the behavior frequency of each uncivil type separately, determine the number of occurrences of each behavior within a period of time, and generate an elevator-riding behavior report based on the behavior frequencies of different uncivil types.

[0031] In addition, for the elevator monitoring data, determine the historical load weight of the elevator according to the basic operation information of the elevator. Set the preset load value within a range lower than the upper limit according to the upper limit of the elevator's safe load to count the number of times the elevator is worn due to load. During the operation of the elevator, if the load weight exceeds the preset load value, an overweight event will be recorded.

[0032] According to the fault information of the elevator, identify the fault type of the elevator. Count the number of occurrences of each fault type on a monthly basis, that is, the monthly fault frequency. Generate an elevator fault report based on the monthly fault frequency. By counting the monthly fault frequency, the high-incidence types of elevator faults can be discovered in a timely manner, providing a basis for the preventive maintenance and fault troubleshooting of the elevator.

[0033] Based on the behavior information of the elevator, determine the daily operation times of the elevator. Taking a month as a unit, count the sum of the daily operation times of the elevator, that is, the monthly usage frequency. Based on the monthly usage frequency, generate an elevator usage report. By statistically analyzing the monthly usage frequency, it can provide data support for elevator maintenance, energy consumption management, etc.

[0034] According to the number of overweight occurrences, use a pre-trained machine learning model to predict the overload fault of the elevator, and obtain the overload fault prediction result output by the machine learning model. According to the monthly fault frequency corresponding to different fault types, use a linear regression model to predict the fault occurrence type and probability of the elevator, and obtain the fault occurrence prediction result. Based on the overload fault prediction result and the fault occurrence prediction result, generate a fault prediction report.

[0035] S104: Display the elevator operation index report through the information publicity interface.

[0036] Specifically, obtain user information, determine the user type based on the user information, and according to the user permissions corresponding to the user type, generate a data interface for the content in the elevator operation index report that conforms to the user permissions. Call the data interface and display the content in the elevator operation index report that conforms to the user permissions through the information publicity interface.

[0037] Furthermore, through the information publicity interface, obtain the feedback information input by the user, perform data analysis on the feedback information, determine the type of feedback problem, and based on the type of feedback problem, send the feedback information to the corresponding elevator control department to optimize the operation of the elevator.

[0038] Based on the data statistical results, it is possible to understand the wear condition and fault frequency of each component of the elevator, thereby formulating a more precise maintenance plan, which can not only reduce unnecessary maintenance costs, but also improve maintenance efficiency and extend the service life of the elevator.

[0039] Through the Internet of Things technology, the operation status of the elevator is monitored in real time, and abnormal or potential faults of the elevator can be detected in a timely manner. By regularly analyzing the elevator operation data, the future operation status and possible faults of the elevator can be predicted, and preventive maintenance can be carried out before the occurrence of faults, which can effectively avoid the impact of faults on the elevator operation and improve the reliability and stability of the elevator.

[0040] As Figure 2 shown, on the other hand, the embodiment of the present invention also provides an elevator Internet of Things data statistical system, which is characterized in that the system includes: An elevator data collection module, which is used to collect the Internet of Things data of all elevators in the target building in real time through Internet of Things sensors; An elevator data storage module, which is used to connect to a cloud platform, encrypt the Internet of Things data, upload it to the cloud platform, and perform distributed storage of the Internet of Things data through the cloud platform; An elevator data statistics module, which is used to read the Internet of Things data stored in the cloud platform, preprocess the Internet of Things data, and based on the preprocessed Internet of Things data, statistically calculate the operation index data of the elevator, and is used to generate an elevator operation index report based on the operation index data; A statistical data display module, which displays the elevator operation index report through an information publicity interface.

[0041] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0042] The devices and media provided in the embodiments of this application correspond one-to-one with the methods. Therefore, the devices and media also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0043] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0045] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in one or more of the blocks or blocks.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in one or more of the blocks or blocks.

[0047] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0048] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0049] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0050] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0051] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An elevator Internet of Things data statistics method, characterized in that, include: Through the IoT devices deployed in the elevators in the target building, IoT data of each elevator in the target building is collected in real time; the IoT data includes operation status data and monitoring video data; Connecting to a cloud platform, encrypting the IoT data and transmitting it to the cloud platform, and distributing and storing the IoT data through the cloud platform; Reading the IoT data stored in the cloud platform, preprocessing the IoT data, collecting statistics on the operation index data of each elevator based on the preprocessed IoT data, and generating an elevator operation index report based on the operation index data; The elevator operation index report is displayed through the information disclosure interface.

2. The elevator Internet of Things data statistics method according to claim 1, characterized in that, The IoT devices deployed in the elevators in the target building are used to collect IoT data of each elevator in the target building in real time, specifically including: The IoT devices include sensor devices and camera devices; Through the sensor equipment and camera equipment in each elevator in the target building, the operating status data and monitoring video data corresponding to the elevator are collected in real time; Based on the coding of the elevator in the target building, corresponding identification information is added to the operation status data and the monitoring video data respectively.

3. A method for elevator Internet of Things data statistics according to claim 1, characterized in that, The operation index data of each elevator is counted based on the pre-processed IoT data, specifically including: The operation index data includes passenger cycle flow and operation cycle status; Based on the pre-processed surveillance video data corresponding to each of the elevators, counting the passenger cycle flow of each of the elevators within a preset period; Based on the pre-processed running status data corresponding to each of the elevators, the running cycle status of each of the elevators within the preset period is counted, and the running cycle status includes door opening frequency, door opening time, running time, and fault information.

4. A method for elevator Internet of Things data statistics according to claim 3, characterized in that After counting the operation index data of each elevator based on the pre-processed IoT data, the method further includes: Determine the load weight of the elevator according to the passenger cycle flow, and count the number of times the load weight exceeds a preset load value; Determine the daily operation frequency of the elevator according to the door opening frequency, the door opening duration and the operation time, and calculate the monthly use frequency of the elevator based on the daily operation frequency; The fault type of the elevator is determined according to the fault information, and the monthly fault frequencies of the fault types are counted respectively.

5. A method for elevator Internet of Things data statistics according to claim 4, characterized in that, After counting the operation index data of each elevator based on the pre-processed IoT data, the method further includes: According to the number of overloads, an overload fault prediction is performed on the elevator using a pre-trained machine learning model to obtain an overload fault prediction result output by the machine learning model; According to the monthly fault frequencies corresponding to different fault types, the fault type and probability of the elevator are predicted through a linear regression model to obtain a fault occurrence prediction result.

6. A method for elevator Internet of Things data statistics according to any one of claims 5, characterized in that, The generating of the elevator operation index report based on the operation index data specifically includes: Generate elevator behavior reports based on the frequency of different types of uncivilized behaviors; generating an elevator fault report based on the monthly fault frequency; generating an elevator usage report based on the monthly usage frequency; Generate a fault prediction report based on the overloading fault prediction result and the fault occurrence prediction result.

7. A method for elevator Internet of Things data statistics according to claim 3, characterized in that After reading the Internet of Things data stored in the cloud platform and preprocessing the Internet of Things data, the method further includes: Analyze the human patterns in the monitored video data through a behavior recognition algorithm to determine whether the human behavior belongs to uncivil elevator-riding behavior; If so, determine the uncivil type of the human behavior, record the human behavior, and separately count the behavior frequencies of the uncivil types.

8. A method for elevator Internet of Things data statistics according to claim 1, characterized in that The display of the elevator operation index report through the information publicity interface specifically includes: Obtain user information, determine the user type based on the user information, and generate a data interface for the content in the elevator operation index report that conforms to the user permissions according to the user permissions corresponding to the user type; Call the data interface and display the content in the elevator operation index report that conforms to the user permissions through the information publicity interface.

9. The elevator Internet of Things data statistics method according to claim 1, characterized in that After the elevator operation index report is displayed through the information publicity interface, the method further includes: Obtain the feedback information input by the user through the information publicity interface, perform data analysis on the feedback information, and determine the type of feedback problem; Based on the type of feedback problem, send the feedback information to the corresponding elevator control department to optimize the operation of the elevator.

10. An elevator Internet of Things data statistics system, characterized in that, The system includes: An elevator data acquisition module for real-time collecting the Internet of Things data of all elevators in a target building through Internet of Things sensors; An elevator data storage module for connecting to the cloud platform, encrypting the Internet of Things data, uploading it to the cloud platform, and performing distributed storage of the Internet of Things data through the cloud platform; An elevator data statistics module for reading the Internet of Things data stored in the cloud platform, preprocessing the Internet of Things data, statistically calculating the operation index data of the elevator based on the preprocessed Internet of Things data, and generating an elevator operation index report based on the operation index data; A statistical data display module for displaying the elevator operation index report through an information publicity interface.

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