CN115028036A - Elevator management method based on big data - Google Patents
Elevator management method based on big data Download PDFInfo
- Publication number
- CN115028036A CN115028036A CN202210485947.0A CN202210485947A CN115028036A CN 115028036 A CN115028036 A CN 115028036A CN 202210485947 A CN202210485947 A CN 202210485947A CN 115028036 A CN115028036 A CN 115028036A
- Authority
- CN
- China
- Prior art keywords
- data
- elevator
- target
- management
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 188
- 238000004891 communication Methods 0.000 claims abstract description 51
- 230000002159 abnormal effect Effects 0.000 claims abstract description 22
- 238000012544 monitoring process Methods 0.000 claims abstract description 20
- 238000005516 engineering process Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 20
- 210000002569 neuron Anatomy 0.000 claims description 20
- 238000006073 displacement reaction Methods 0.000 claims description 12
- 238000013507 mapping Methods 0.000 claims description 7
- 238000004806 packaging method and process Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000009193 crawling Effects 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 description 14
- 230000009286 beneficial effect Effects 0.000 description 11
- 238000013210 evaluation model Methods 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 2
- 238000005303 weighing Methods 0.000 description 2
- YZCKVEUIGOORGS-OUBTZVSYSA-N Deuterium Chemical compound [2H] YZCKVEUIGOORGS-OUBTZVSYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 210000004709 eyebrow Anatomy 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0012—Devices monitoring the users of the elevator system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3423—Control system configuration, i.e. lay-out
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3446—Data transmission or communication within the control system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
Abstract
The invention provides an elevator management method based on big data, which comprises the following steps: acquiring elevator operation dynamic data of a target management building based on a management terminal, and determining a target cloud data platform; the target cloud data platform is in communication link with the management terminal, and the elevator operation dynamic data are transmitted to the target cloud data platform based on the management terminal; and analyzing the elevator operation dynamic data based on the target cloud data platform, and performing alarm operation when abnormal dynamic data exists. Through monitoring the elevator of target building to carry out analysis to elevator operation dynamic data based on big data technology, ensure to carry out real-time effectual control to the elevator of target building, in time know each elevator operation situation, improved the unusual speed of discovery elevator, realize the accurate management to elevator operation safety and improved the security of elevator operation.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an elevator management method based on big data.
Background
At present, with the higher living standard, the elevator becomes a common tool in daily life;
however, as the larger the number of the elevators is, the more difficult the elevator management is, the conventional elevator management can only perform regular maintenance or check whether the elevator normally runs through a camera installed in the elevator, the real-time running state of the elevator cannot be monitored, and the maintenance time is often inaccurate, so that the elevator management difficulty is large;
therefore, the invention provides an elevator management method based on big data, which is used for monitoring the elevator of a target building and analyzing the dynamic data of the operation of the elevator based on big data technology, so that the elevator of the target building is effectively monitored in real time, the operation conditions of the elevators are known in time, the rate of finding the abnormity of the elevator is improved, the accurate management of the operation safety of the elevator is realized, and the operation safety of the elevator is improved.
Disclosure of Invention
The invention provides an elevator management method based on big data, which is used for monitoring an elevator of a target building and analyzing the dynamic running data of the elevator based on big data technology, so that the elevator of the target building is effectively monitored in real time, the running condition of each elevator is known in time, the abnormal speed of the elevator is found, the accurate management of the running safety of the elevator is realized, and the running safety of the elevator is improved.
The invention provides an elevator management method based on big data, which comprises the following steps:
step 1: acquiring elevator operation dynamic data of a target management building based on a management terminal, and determining a target cloud data platform;
step 2: the target cloud data platform is in communication link with the management terminal, and the elevator operation dynamic data are transmitted to the target cloud data platform based on the management terminal;
and step 3: and analyzing the elevator operation dynamic data based on the target cloud data platform, and performing alarm operation when abnormal dynamic data exists.
Preferably, in step 2, the elevator management method based on big data further includes:
the target cloud data platform and the management terminal are both connected with a mobile phone end through a wireless communication technology, and meanwhile, the target cloud data platform and the management terminal are monitored in real time based on the mobile phone end.
Preferably, in step 1, the process of acquiring elevator operation dynamic data of a target management building based on a management terminal includes:
monitoring the elevator running condition of the target management building according to a preset monitoring device, and generating elevator running dynamic data about the target management building according to the elevator running condition;
generating an elevator operation model based on the operation mode of the target management building, and meanwhile, inputting the elevator operation dynamic data into the elevator operation model for data processing;
outputting a display file of elevator operation dynamic data of the target management building based on the processing result, and simultaneously acquiring a number of the target management building;
and taking the digital number as a file identifier of the display file, and uploading the display file to the management terminal.
Preferably, after the display file is uploaded to the management terminal, the elevator management method based on big data further includes:
reading a file identifier of the display file, generating a storage layer corresponding to the display file based on the file identifier, and storing the display file in the storage layer;
and setting a dynamic update window in the storage layer, receiving update data in the display file based on the dynamic update window when the display file is updated, and loading the update data into the storage layer to complete the receiving and storing of the display file.
Preferably, in step 2, the process of performing communication link between the target cloud data platform and the management terminal includes:
reading a first data communication feature code of the target cloud data platform, and meanwhile, determining a second data communication feature code of the management terminal;
determining a link request for establishing a communication link between the target cloud data platform and the management terminal based on the first data communication feature code and the second data communication feature code;
respectively transmitting the link request to the target cloud data platform and the management terminal for request authentication;
and when the target cloud data platform and the management terminal pass the request authentication, establishing a communication link between the target cloud data platform and the management terminal based on the link request.
Preferably, in step 2, the elevator operation dynamic data is transmitted to the target cloud data platform based on the management terminal, and the elevator operation dynamic data includes:
determining a target data communication link for transmitting elevator operation dynamic data of the management terminal to the target cloud data platform based on the communication link;
setting a data relay node according to the link characteristics of the target communication link;
determining elevator operation dynamic data to be uploaded in the management terminal, performing first packaging on the elevator data to be uploaded, and determining a first target data packet;
the first target data packet is uploaded to the data relay node through the communication link, the first target data packet is unpacked in the data relay node, meanwhile, the dynamic data of the elevator to be uploaded are screened in the relay node according to preset conditions based on the unpacking result, and useless data in the dynamic data of the elevator to be uploaded are determined based on the screening result;
filtering the useless data to obtain target elevator operation dynamic data, performing second packaging on the target elevator operation dynamic data based on the relay node, and determining a second target data packet;
transmitting the second target data packet in the relay node to the target cloud data platform based on the communication link.
Preferably, in step 3, analyzing the elevator operation dynamic data based on the target cloud data platform further includes determining an elevator operation rule of the target management building, and the specific working process includes:
reading the elevator running dynamic data based on the target cloud data platform, and determining running displacement data, time stay data and elevator bearing capacity of the elevator at a target moment, wherein the running displacement data, the time stay data and the elevator bearing capacity of the elevator at the target moment are comprehensive data of the elevator running in a preset target monitoring time period;
determining a first inertia index of the elevator according to the running displacement data of the elevator, a second inertia index of the elevator according to the time staying data of the elevator and a third inertia index according to the elevator bearing capacity of the elevator at the target moment in the target cloud data platform based on a big data technology;
determining an elevator operation rule of the target management building according to the first inertia index, the second inertia index and the third inertia index;
generating an operation scheme of the elevator based on the elevator operation rule of the target management building, and simultaneously generating an elevator regulation and control instruction based on the operation scheme of the elevator;
and controlling the elevator to run on the target management building based on the elevator regulation and control instruction.
Preferably, in step 3, the process of analyzing the elevator operation dynamic data according to the target cloud data platform includes:
crawling elevator operation data in the internet based on the target cloud data platform;
extracting target data from the elevator running data according to a preset data selection strategy, and constructing a clustering node by using the target data as a clustering label;
dividing the elevator operation data based on the clustering nodes, determining sub-elevator operation data, and storing the sub-elevator fault data to the corresponding clustering nodes;
determining the node number of the clustering nodes, and determining neurons for constructing a network based on the node number of the clustering nodes;
reading node identifications of the clustering nodes, determining node connections among the clustering nodes according to the node identifications, and simultaneously determining branch combinations of the neurons according to the node connections of the clustering nodes;
constructing an elevator operation big data network based on the neuron and the branch combination of the target neuron;
inputting the elevator operation dynamic data into the elevator operation big data network for correlation mapping, and matching each elevator operation dynamic data with a corresponding target neuron in the elevator operation big data network based on a mapping result;
determining node branches of the elevator operation dynamic data in the elevator operation big data network according to the target neurons;
reading branch elevator operation data of the node branch, and setting a balance interval based on the branch elevator operation data;
monitoring whether the elevator operation dynamic data are in the balance interval in real time, and generating a data exception report when the elevator operation dynamic data are not in the balance interval;
and transmitting the data exception report to the management terminal based on the target cloud data platform, and carrying out alarm reminding based on the management terminal.
Preferably, the elevator management method based on big data transmits the data exception report to the management terminal based on the target cloud data platform, and performs an alarm reminding process based on the management terminal, and includes:
reading the data exception report based on the management terminal, and determining target exception data in the data exception report;
matching the target abnormal data with a target fault section of the elevator, determining a set numerical range of the target fault section, and simultaneously comparing the target abnormal data with the set numerical range to judge whether the elevator is a real fault;
when the target abnormal data is in the set numerical value range, judging that the elevator is not a real fault, and not performing alarm operation;
and when the target abnormal data is not in the set numerical range, judging that the elevator is a real fault, and simultaneously, stopping the elevator when the elevator is in an unmanned state, maintaining a target fault section of the elevator, and performing alarm operation.
Preferably, in step 1, the elevator operation dynamic data is analyzed based on the target cloud data platform, and the elevator management method based on big data further includes:
extracting face image data in an elevator car from the elevator running dynamic data, and analyzing the face image data in the target cloud data platform;
determining face contour data and face feature data of a target person in the face image data based on an analysis result;
determining fixed passenger data of the stairs in the target management building according to the face contour data and the face feature data of the target person;
generating an elevator personnel management data file based on the fixed passenger data, and recording the number of times of taking of a new passenger when the new passenger appears in the elevator;
when the number of times of taking the new passenger is equal to or greater than a preset threshold value, updating the human face image data of the new passenger to the elevator personnel management data file;
and transmitting the elevator personnel management data file to the management terminal through the target cloud platform to realize the management of elevator personnel taking.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a big data based elevator management method in an embodiment of the present invention;
fig. 2 is a flow chart of step 1 in a big data based elevator management method in an embodiment of the present invention;
fig. 3 is a flow chart of step 2 of a big data based elevator management method in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Example 1:
the embodiment provides an elevator management method based on big data, as shown in fig. 1, including:
step 1: acquiring elevator operation dynamic data of a target management building based on a management terminal, and determining a target cloud data platform;
step 2: the target cloud data platform is in communication link with the management terminal, and the elevator operation dynamic data are transmitted to the target cloud data platform based on the management terminal;
and step 3: and analyzing the elevator operation dynamic data based on the target cloud data platform, and performing alarm operation when abnormal dynamic data exists.
In this embodiment, the elevator operation dynamic data may be the current load capacity of the elevator, the speed at which the elevator is operated, the total number of elevators on the floor where the elevator is currently parked or the target management floor, the operation state of each elevator, and the like.
In this embodiment, target cloud data platform can be the data platform that integrates all data of elevator operation environment and form, and elevator operation dynamic data is through under the big data environment in target cloud data platform, can accurately analyze out whether have unusual dynamic data in the elevator operation dynamic data, and then improve the efficiency and the accuracy of analysis.
In the embodiment, the management terminal can monitor and acquire the elevator operation dynamic data of the target management building in real time.
In this embodiment, the abnormal dynamic data may be data inconsistent with the normal elevator operation dynamic, and when the abnormal dynamic data occurs, there are two situations, the first situation is that the elevator fails, and the second situation is that the monitoring end fails.
The beneficial effects of the above technical scheme are: through monitoring the elevator of target building to carry out the analysis to elevator operation dynamic data based on big data technology, ensure to carry out real-time effectual control to the elevator of target building, in time know each elevator operation conditions, improved the unusual speed of discovery elevator, realize the accurate management to elevator operation safety and improved the security of elevator operation.
Example 2:
on the basis of embodiment 1, this embodiment provides an elevator management method based on big data, and in step 2, the method further includes:
the target cloud data platform and the management terminal are both connected with a mobile phone end through a wireless communication technology, and meanwhile, the target cloud data platform and the management terminal are monitored in real time based on the mobile phone end.
The beneficial effects of the above technical scheme are: the elevator is monitored through the mobile phone by respectively connecting the target cloud data platform and the management terminal with the mobile phone end, and the real-time performance and effectiveness of monitoring are improved.
Example 3:
on the basis of embodiment 1, this embodiment provides a method for managing elevators based on big data, as shown in fig. 2, in step 1, a process for acquiring elevator operation dynamic data of a target management building based on a management terminal includes:
step 101: monitoring the elevator running condition of the target management building according to a preset monitoring device, and generating elevator running dynamic data about the target management building according to the elevator running condition;
step 102: generating an elevator operation model based on the operation mode of the target management building, and meanwhile, inputting the elevator operation dynamic data into the elevator operation model for data processing;
step 103: outputting a display file of elevator operation dynamic data of the target management building based on the processing result, and simultaneously acquiring a number of the target management building;
step 104: and taking the digital number as a file identifier of the display file, and uploading the display file to the management terminal.
In this embodiment, the preset monitoring device is set in advance, and may be a camera or the like installed in the elevator in advance.
In this embodiment, the display file may be used to display the elevator operation state data in text form.
In this embodiment, the number may be a location tag for distinguishing different target management buildings, and the corresponding building can be quickly and accurately determined by the number.
In this embodiment, the file identifier may be a chinese label used to mark different display files, and the file identifier corresponds to the number one to one.
The beneficial effects of the above technical scheme are: the running condition of the elevator is obtained through the monitoring device, the running dynamic data of the elevator is determined according to the running condition of the elevator, and meanwhile, the running dynamic data of the elevator is generated into a corresponding display file to be matched with the building number, so that the running state data of the elevator can be accurately and effectively obtained, and the elevator can be accurately and effectively managed.
Example 4:
on the basis of embodiment 3, this embodiment provides an elevator management method based on big data, and after uploading the presentation file to the management terminal, the method further includes:
reading a file identifier of the display file, generating a storage layer corresponding to the display file based on the file identifier, and storing the display file in the storage layer;
and setting a dynamic update window in the storage layer, receiving update data in the display file based on the dynamic update window when the display file is updated, and loading the update data into the storage layer to complete the receiving and storing of the display file.
In this embodiment, the storage layer may be a storage result or a storage area for storing the presentation file.
In this embodiment, the dynamic update window may be used to receive the latest elevator operation dynamic data in real time, so as to implement timely data update on the display file.
In this embodiment, the update data may be different data than in the presentation file.
The beneficial effects of the above technical scheme are: the display file is stored in the corresponding storage layer, whether the data in the display file is updated or not is detected in real time, so that the data in the display file can be accurately and effectively updated in time when the data is updated, the elevator is strictly monitored, and the accuracy of elevator management according to the elevator running state data is improved.
Example 5:
on the basis of embodiment 1, this embodiment provides a big data-based elevator management method, as shown in fig. 3, in step 2, a process of performing communication link between the target cloud data platform and the management terminal includes:
step 201: reading a first data communication feature code of the target cloud data platform, and meanwhile, determining a second data communication feature code of the management terminal;
step 202: determining a link request for establishing a communication link between the target cloud data platform and the management terminal based on the first data communication feature code and the second data communication feature code;
step 203: respectively transmitting the link request to the target cloud data platform and the management terminal for request authentication;
step 204: and when the target cloud data platform and the management terminal pass the request authentication, establishing a communication link between the target cloud data platform and the management terminal based on the link request.
In this embodiment, the first data communication feature code may be a communication address for characterizing the target cloud data platform or data for characterizing the identity of the target cloud data platform.
In this embodiment, the second data communication feature code may be a communication address for characterizing the management terminal or data for characterizing the identity of the management terminal.
In this embodiment, the request for authentication may be to check a condition for establishing a communication link between the target cloud data platform and the management terminal.
The beneficial effects of the above technical scheme are: the link requests of the target cloud data platform and the management terminal are accurately confirmed by determining the data communication feature codes of the target cloud data platform and the management terminal, the link requests are verified, and a communication link between the target cloud data platform and the management terminal is effectively established, so that convenience is provided for the target cloud data platform to receive elevator operation dynamic data and manage the elevator according to the elevator operation dynamic data and the Xining elevator.
Example 6:
on the basis of embodiment 1, this embodiment provides an elevator management method based on big data, and in step 2, transmitting the elevator operation dynamic data to the target cloud data platform based on the management terminal includes:
determining a target data communication link for transmitting elevator operation dynamic data of the management terminal to the target cloud data platform based on the communication link;
setting a data relay node according to the link characteristics of the target communication link;
determining elevator operation dynamic data to be uploaded in the management terminal, performing first packaging on the elevator data to be uploaded, and determining a first target data packet;
the first target data packet is uploaded to the data relay node through the communication link, the first target data packet is unpacked in the data relay node, meanwhile, the dynamic data of the elevator to be uploaded are screened in the relay node according to preset conditions based on the unpacking result, and useless data in the dynamic data of the elevator to be uploaded are determined based on the screening result;
filtering the useless data to obtain target elevator operation dynamic data, performing second packaging on the target elevator operation dynamic data based on the relay node, and determining a second target data packet;
transmitting the second target data packet in the relay node to the target cloud data platform based on the communication link.
In this embodiment, the destination data communication link may be a link adapted to transmit elevator operation dynamic data.
In this embodiment, the link characteristics may be data interpretation characteristics of the target data communication link, specifically, data transmission rate, bandwidth, and the like.
In this embodiment, the data relay node may be configured to temporarily buffer the elevator operation dynamic data, so as to process the elevator operation dynamic data and remove useless data therein.
In this embodiment, the elevator operation dynamic data to be uploaded may be elevator monitoring data to be transmitted to the target cloud data platform.
In this embodiment, the first target data packet may be a data packet obtained by encapsulating the dynamic data of the elevator operation to be uploaded.
In the embodiment, the preset conditions are set in advance and are used for screening the running dynamic data of the elevator to be uploaded and eliminating useless data in the running dynamic data.
In this embodiment, the useless data can be data irrelevant to the operation dynamic state of the elevator, data fragments with data missing and the like.
In this embodiment, the target elevator operation dynamic data may be data obtained by screening the elevator operation dynamic data to be uploaded and removing useless data therein.
In this embodiment, the second destination data packet may be a data packet obtained by encapsulating the dynamic data of the destination elevator operation, and the inside of the data packet is all useful elevator operation state data.
The beneficial effects of the above technical scheme are: the elevator running state data to be uploaded are uploaded to the data relay node, and are strictly and effectively screened, so that the accuracy of the elevator running dynamic data is ensured, the screened data are packaged and transmitted to the target cloud data platform, and the elevator running state is accurately analyzed.
Example 7:
on the basis of embodiment 1, this embodiment provides an elevator management method based on big data, and in step 3, analyzing the elevator operation dynamic data based on the target cloud data platform further includes determining an elevator operation rule of the target management building, and a specific working process includes:
reading the elevator running dynamic data based on the target cloud data platform, and determining running displacement data, time stay data and elevator bearing capacity of the elevator at a target moment, wherein the running displacement data, the time stay data and the elevator bearing capacity of the elevator at the target moment are comprehensive data of the elevator running in a preset target monitoring time period;
determining a first inertia index of the elevator according to the running displacement data of the elevator, a second inertia index of the elevator according to the time stay data of the elevator and a third inertia index according to the elevator bearing capacity of the elevator at a target moment in the target cloud data platform based on a big data technology;
determining an elevator operation rule of the target management building according to the first inertia index, the second inertia index and the third inertia index;
generating an operation scheme of the elevator based on the elevator operation rule of the target management building, and simultaneously generating an elevator regulation and control instruction based on the operation scheme of the elevator;
and controlling the elevator to run on the target management building based on the elevator regulation and control instruction.
In this embodiment, the travel displacement data may be data that is used to characterize the up and down going condition of the elevator.
In this embodiment the time dwell data can be used to characterize the time situation of the elevator dwell at each floor.
In this embodiment, the target time may be any time.
In this embodiment, the preset target monitoring time period is set in advance, and may be set according to an actual situation.
In this embodiment, the first inertia index is an index parameter used for representing the displacement condition of the elevator.
In this embodiment, the second inertia index is an index parameter used to characterize the elevator stopping situation.
In this embodiment no third inertia index is an index parameter that is used to characterize the load situation of the elevator at a certain moment in time.
In this embodiment, the elevator operation rule may be the floor at which the elevator frequently stops in a certain time period or the change of weighing in a certain time period.
The beneficial effects of the above technical scheme are: the operation rule of the elevator is effectively analyzed by determining the movement displacement data, the stop data and the weighing data at a certain moment of the elevator, so that a corresponding operation scheme is generated conveniently according to the operation rule of the elevator, the elevator is effectively managed, and the operation efficiency of the elevator is improved.
Example 8:
on the basis of embodiment 1, this embodiment provides an elevator management method based on big data, and in step 3, a process of analyzing the elevator operation dynamic data according to the target cloud data platform includes:
crawling elevator operation data in the internet based on the target cloud data platform;
extracting target data from the elevator running data according to a preset data selection strategy, and constructing a clustering node by taking the target data as a clustering label;
dividing the elevator operation data based on the clustering nodes, determining sub-elevator operation data, and storing the sub-elevator fault data to the corresponding clustering nodes;
determining the node number of the clustering nodes, and determining neurons for constructing a network based on the node number of the clustering nodes;
reading node identifications of the clustering nodes, determining node connections among the clustering nodes according to the node identifications, and simultaneously determining branch combinations of the neurons according to the node connections of the clustering nodes;
constructing an elevator operation big data network based on the neuron and the branch combination of the target neuron;
inputting the elevator operation dynamic data into the elevator operation big data network for correlation mapping, and matching each elevator operation dynamic data with a corresponding target neuron in the elevator operation big data network based on a mapping result;
determining node branches of the elevator operation dynamic data in the elevator operation big data network according to the target neurons;
reading branch elevator operation data of the node branch, and setting a balance interval based on the branch elevator operation data;
monitoring whether the elevator operation dynamic data are in the balance interval in real time, and generating a data exception report when the elevator operation dynamic data are not in the balance interval;
and transmitting the data exception report to the management terminal based on the target cloud data platform, and carrying out alarm reminding based on the management terminal.
In this embodiment, the crawling of the elevator operation data in the internet may be the checking of the general operation rules or the operation conditions of the elevator on the internet by a big data technology.
In the embodiment, the preset data selection strategy is set in advance and is used for extracting a certain segment of data or critical data from the elevator running data.
In this embodiment the target data may be critical data extracted from elevator operation data, etc.
In this embodiment, the cluster labels can be category labels used to distinguish elevator operation data.
In this embodiment, the cluster nodes may be data nodes for storing heterogeneous operational data.
In this embodiment, the sub-elevator operation data may be elevator operation data of each category obtained by dividing the elevator operation data by category.
In this embodiment, the number of nodes may be what is used to characterize the number of clustered nodes.
In this embodiment, the node identifier may be a kind of label tag used to label different kinds of clustered nodes.
In this embodiment, the association mapping may be binding or bundling data having an association relationship in the elevator operation dynamic data.
In this embodiment, the target neuron may be a neuron corresponding to the type of elevator operation state data.
In this embodiment, the branch elevator operation data may be corresponding elevator operation state data on each node branch.
In this embodiment, the balance interval may be a normal value range used to represent each functional parameter when the elevator is in normal operation.
The beneficial effects of the above technical scheme are: the normal range of the elevator inspection items corresponding to different elevator operation data types is confirmed by classifying the elevator operation data, so that the current operation condition of the elevator is analyzed according to the normal range, the abnormal type of the elevator is timely confirmed and corresponding alarm operation is carried out when the elevator is found to be abnormal, the monitoring efficiency and the monitoring effect of the elevator are improved, and the elevator can normally operate.
Example 9:
on the basis of embodiment 8, this embodiment provides an elevator management method based on big data, where the data exception report is transmitted to the management terminal based on the target cloud data platform, and an alarm reminding process is performed based on the management terminal, including:
reading the data exception report based on the management terminal, and determining target exception data in the data exception report;
matching the target abnormal data with a target fault section of the elevator, determining a set numerical range of the target fault section, and simultaneously comparing the target abnormal data with the set numerical range to judge whether the elevator is a real fault;
when the target abnormal data is in the set numerical value range, judging that the elevator is not a real fault, and not performing alarm operation;
and when the target abnormal data is not in the set numerical range, judging that the elevator is a real fault, and simultaneously, stopping the elevator when the elevator is in an unmanned state, maintaining a target fault section of the elevator, and performing alarm operation.
In this embodiment, the target abnormality data may be data corresponding to the occurrence of an abnormality in the elevator recorded in the data abnormality report.
In this embodiment, the target faulted segment may be the specific location or configuration of the elevator where the fault occurred.
In this embodiment, the set value range is set in advance, and is used to represent the value taking condition of the part or structure during normal operation.
The beneficial effects of the above technical scheme are: the fault position of the elevator is locked according to the target abnormal data, and meanwhile, the fault is judged according to the data corresponding to the position, so that the authenticity of the elevator in fault is ensured, the monitoring strictness of the elevator is improved, and the management effect of the elevator is guaranteed.
Example 10:
on the basis of embodiment 1, this embodiment provides an elevator management method based on big data, and in step 1, the analysis is performed on the elevator operation dynamic data based on the target cloud data platform, which further includes:
extracting face image data in an elevator car from the elevator running dynamic data, and analyzing the face image data in the target cloud data platform;
determining face contour data and face feature data of a target person in the face image data based on an analysis result;
determining fixed passenger data of the stairs in the target management building according to the face contour data and the face feature data of the target person;
generating an elevator personnel management data file based on the fixed passenger data, and recording the number of times of taking of a new passenger when the new passenger appears in the elevator;
when the number of times of taking the new passenger is equal to or greater than a preset threshold value, updating the human face image data of the new passenger to the elevator personnel management data file;
and transmitting the elevator personnel management data file to the management terminal through the target cloud platform to realize the management of elevator personnel taking.
In this embodiment, the target person may be a person in the elevator car.
In this embodiment, the face contour data may be the face shape of the target face, or the like.
In this embodiment, the face feature data may be distinctive features on the face of the target person, such as the shapes of the eyes, nose, and eyebrows.
In this embodiment, the fixed ride personnel data may be elevator ride permission personnel information.
In this embodiment, the elevator management material file may be a file obtained by recording information on the persons permitted to take the elevator.
In this embodiment, the new occupant may be occupant information that is not in the fixed occupant data.
In this embodiment, the preset threshold is set in advance.
The beneficial effects of the above technical scheme are: through managing elevator passenger's information, realize taking personnel and taking personnel newly and carry out accurate effectual judgement to fixed passenger, and take personnel's information newly and add in elevator personnel management data file when the number of times of taking personnel newly reaches certain numerical value, improved the management effect to elevator passenger's information, also realized the accurate management to the elevator simultaneously.
Example 11:
on the basis of embodiment 1, step 3 further includes:
after analyzing the elevator operation dynamic data based on the target cloud data platform, the method also comprises the following steps of evaluating the elevator operation safety of the current target management building, wherein the specific working process is as follows:
obtaining evaluation indexes for evaluating the elevator operation safety of the target management building, and determining an index characteristic value of each evaluation index;
establishing an evaluation model for evaluating the elevator operation safety of the target management building according to the evaluation indexes and the index characteristic value of each evaluation index;
wherein R represents an evaluation model for evaluating the elevator operation safety of the target management building; n is a radical of j Represents the jth evaluation index; c. C j An index characteristic value indicating a jth evaluation index; p is a radical of ij An index entropy value representing the j index under i elevator operation dynamic data; m represents the total number of the evaluation indexes; ln (·) represents a logarithmic function based on e; j represents a current evaluation index;
inputting elevator operation dynamic data corresponding to the evaluation index into the evaluation model for evaluation, and meanwhile, acquiring a standard threshold;
determining an evaluation grade for the elevator based on a comparison of the output value of the evaluation model with the standard threshold;
wherein N represents the level of output; n is a radical of 1 Indicating a first level, i.e. determining the safety of the elevator; n is a radical of hydrogen 2 Indicating a second level, i.e. determining that the elevator is substantially safe; n is a radical of 3 Indicating a third level, i.e. determining the elevator hazard; Φ represents the standard threshold;
and determining the operation safety condition of the elevator of the target management building according to the elevator evaluation grade, and meanwhile, performing alarm operation based on the management terminal when the grade of the elevator is a third grade.
In this embodiment, the evaluation index for evaluating the elevator operation safety of the target management building may be, for example, an elevator hoist temperature index, a machine room noise index, a door system index, or the like.
In the embodiment, the standard threshold can be set in advance and belongs to the value output by the evaluation model, and the safety level of the elevator can be determined more intuitively through the setting of the standard threshold.
In this embodiment, the alarm operation may be one or more of sound, vibration, or light.
The beneficial effects of the above technical scheme are: a rating model is established by determining the evaluation index, and the safe operation condition of the current elevator is determined by determining the grade of the elevator, so that the accurate management of the elevator operation safety of the target management building by the management terminal is more reasonably realized.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A big data based elevator management method, comprising:
step 1: acquiring elevator operation dynamic data of a target management building based on a management terminal, and determining a target cloud data platform;
step 2: the target cloud data platform is in communication link with the management terminal, and the elevator operation dynamic data are transmitted to the target cloud data platform based on the management terminal;
and step 3: and analyzing the elevator operation dynamic data based on the target cloud data platform, and performing alarm operation when abnormal dynamic data exists.
2. The big data-based elevator management method according to claim 1, wherein step 2 further comprises:
the target cloud data platform and the management terminal are both connected with a mobile phone end through a wireless communication technology, and meanwhile, the target cloud data platform and the management terminal are monitored in real time based on the mobile phone end.
3. The big data-based elevator management method according to claim 1, wherein the step 1 of obtaining the elevator operation dynamic data of the target management building based on the management terminal comprises:
monitoring the elevator running condition of the target management building according to a preset monitoring device, and generating elevator running dynamic data about the target management building according to the elevator running condition;
generating an elevator operation model based on the operation mode of the target management building, and simultaneously inputting the elevator operation dynamic data into the elevator operation model for data processing;
outputting a display file of elevator operation dynamic data of the target management building based on the processing result, and simultaneously acquiring a number of the target management building;
and taking the digital number as a file identifier of the display file, and uploading the display file to the management terminal.
4. The elevator management method based on big data according to claim 3, wherein after the display file is uploaded to the management terminal, the method further comprises:
reading a file identifier of the display file, generating a storage layer corresponding to the display file based on the file identifier, and storing the display file in the storage layer;
and setting a dynamic update window in the storage layer, receiving update data in the display file based on the dynamic update window when the display file is updated, and loading the update data into the storage layer to complete the receiving and storing of the display file.
5. The elevator management method based on big data as claimed in claim 1, wherein the step 2 of communicatively linking the target cloud data platform with the management terminal comprises:
reading a first data communication feature code of the target cloud data platform, and meanwhile, determining a second data communication feature code of the management terminal;
determining a link request for establishing a communication link between the target cloud data platform and the management terminal based on the first data communication feature code and the second data communication feature code;
respectively transmitting the link request to the target cloud data platform and the management terminal for request authentication;
and when the target cloud data platform and the management terminal pass the request authentication, establishing a communication link between the target cloud data platform and the management terminal based on the link request.
6. The elevator management method based on big data according to claim 1, wherein in step 2, transmitting the elevator operation dynamic data to the target cloud data platform based on the management terminal comprises:
determining a target data communication link for transmitting elevator operation dynamic data of the management terminal to the target cloud data platform based on the communication link;
setting a data relay node according to the link characteristics of the target communication link;
determining elevator operation dynamic data to be uploaded in the management terminal, performing first packaging on the elevator data to be uploaded, and determining a first target data packet;
the first target data packet is uploaded to the data relay node through the communication link, the first target data packet is unpacked in the data relay node, meanwhile, the dynamic data of the elevator to be uploaded are screened in the relay node according to preset conditions based on the unpacking result, and useless data in the dynamic data of the elevator to be uploaded are determined based on the screening result;
filtering the useless data to obtain target elevator operation dynamic data, performing second packaging on the target elevator operation dynamic data based on the relay node, and determining a second target data packet;
transmitting the second target data packet in the relay node to the target cloud data platform based on the communication link.
7. The elevator management method based on big data according to claim 1, wherein in step 3, analyzing the elevator operation dynamic data based on the target cloud data platform further comprises determining an elevator operation rule of the target management building, and the specific working process comprises:
reading the elevator running dynamic data based on the target cloud data platform, and determining running displacement data, time stay data and the elevator bearing capacity of the elevator at a target moment, wherein the running displacement data, the time stay data and the elevator bearing capacity of the elevator at the target moment are comprehensive data of the elevator running in a preset target monitoring time period;
determining a first inertia index of the elevator according to the running displacement data of the elevator, a second inertia index of the elevator according to the time staying data of the elevator and a third inertia index according to the elevator bearing capacity of the elevator at the target moment in the target cloud data platform based on a big data technology;
determining an elevator operation rule of the target management building according to the first inertia index, the second inertia index and the third inertia index;
generating an operation scheme of the elevator based on the elevator operation rule of the target management building, and simultaneously generating an elevator regulation and control instruction based on the operation scheme of the elevator;
and controlling the elevator to run on the target management building based on the elevator regulation and control instruction.
8. The elevator management method based on big data according to claim 1, wherein in step 3, the process of analyzing the elevator operation dynamic data according to the target cloud data platform comprises:
crawling elevator operation data in the internet based on the target cloud data platform;
extracting target data from the elevator running data according to a preset data selection strategy, and constructing a clustering node by using the target data as a clustering label;
dividing the elevator operation data based on the clustering nodes, determining sub-elevator operation data, and storing the sub-elevator fault data to the corresponding clustering nodes;
determining the number of the nodes of the cluster nodes, and determining neurons for constructing a network based on the number of the nodes of the cluster nodes;
reading node identifications of the clustering nodes, determining node connections among the clustering nodes according to the node identifications, and simultaneously determining branch combinations of the neurons according to the node connections of the clustering nodes;
constructing an elevator operation big data network based on the neuron and the branch combination of the target neuron;
inputting the elevator operation dynamic data into the elevator operation big data network for correlation mapping, and matching each elevator operation dynamic data with a corresponding target neuron in the elevator operation big data network based on a mapping result;
determining node branches of the elevator operation dynamic data in the elevator operation big data network according to the target neurons;
reading branch elevator operation data of the node branch, and setting a balance interval based on the branch elevator operation data;
monitoring whether the elevator operation dynamic data are in the balance interval in real time, and generating a data exception report when the elevator operation dynamic data are not in the balance interval;
and transmitting the data exception report to the management terminal based on the target cloud data platform, and carrying out alarm reminding based on the management terminal.
9. The elevator management method based on big data according to claim 8, wherein the process of transmitting the data exception report to the management terminal based on the target cloud data platform and performing alarm reminding based on the management terminal comprises:
reading the data exception report based on the management terminal, and determining target exception data in the data exception report;
matching the target abnormal data with a target fault section of the elevator, determining a set numerical range of the target fault section, and simultaneously comparing the target abnormal data with the set numerical range to judge whether the elevator is a real fault;
when the target abnormal data is in the set numerical value range, judging that the elevator is not a real fault, and not performing alarm operation;
and when the target abnormal data is not in the set numerical range, judging that the elevator is a real fault, and simultaneously, stopping the elevator when the elevator is in an unmanned state, maintaining a target fault section of the elevator, and performing alarm operation.
10. The elevator management method based on big data according to claim 1, wherein in step 1, the elevator operation dynamic data is analyzed based on the target cloud data platform, and further comprising:
extracting face image data in an elevator car from the elevator running dynamic data, and analyzing the face image data in the target cloud data platform;
determining face contour data and face feature data of a target person in the face image data based on an analysis result;
determining fixed passenger data of stairs in the target management building according to the face contour data and the face feature data of the target person;
generating an elevator personnel management data file based on the fixed passenger data, and recording the number of times of taking of a new passenger when the new passenger appears in the elevator;
when the number of times of taking the new passenger is equal to or greater than a preset threshold value, updating the human face image data of the new passenger to the elevator personnel management data file;
and transmitting the elevator personnel management data file to the management terminal through the target cloud platform to realize the management of elevator personnel taking.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210485947.0A CN115028036A (en) | 2022-05-06 | 2022-05-06 | Elevator management method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210485947.0A CN115028036A (en) | 2022-05-06 | 2022-05-06 | Elevator management method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115028036A true CN115028036A (en) | 2022-09-09 |
Family
ID=83119647
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210485947.0A Pending CN115028036A (en) | 2022-05-06 | 2022-05-06 | Elevator management method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115028036A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140022997A1 (en) * | 2011-06-07 | 2014-01-23 | Zte Corporation | Reporting Method, Detection Method, Device And System For Network State |
| CN105404252A (en) * | 2015-10-20 | 2016-03-16 | 同济大学 | Remote crane monitoring data transmission method |
| KR20170010253A (en) * | 2015-07-17 | 2017-01-26 | (주) 에스엘앤씨 | Elevator car control system and method performing thereof |
| CN205990117U (en) * | 2016-07-22 | 2017-03-01 | 广州广日电梯工业有限公司 | Intelligent security guard apparatus for controlling elevator |
| CN108861917A (en) * | 2018-06-22 | 2018-11-23 | 广西烽火信息技术有限公司 | A method of checking that elevator is run by wechat platform |
| CN110342355A (en) * | 2019-07-12 | 2019-10-18 | 苏州健雄职业技术学院 | A kind of intelligent building elevator dispatching plateform system and its working method |
| CN110577126A (en) * | 2019-09-23 | 2019-12-17 | 上海大学 | intelligent elevator safety management system |
| CN110861987A (en) * | 2019-10-23 | 2020-03-06 | 日立楼宇技术（广州）有限公司 | Elevator fault judgment logic verification method, system and storage medium |
| CN111268522A (en) * | 2020-02-03 | 2020-06-12 | 重庆特斯联智慧科技股份有限公司 | Elevator dispatching method and system based on big data analysis |
| CN113562562A (en) * | 2021-06-23 | 2021-10-29 | 合肥工业大学 | Elevator safety early warning monitoring system and judgment method thereof |
-
2022
- 2022-05-06 CN CN202210485947.0A patent/CN115028036A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140022997A1 (en) * | 2011-06-07 | 2014-01-23 | Zte Corporation | Reporting Method, Detection Method, Device And System For Network State |
| KR20170010253A (en) * | 2015-07-17 | 2017-01-26 | (주) 에스엘앤씨 | Elevator car control system and method performing thereof |
| CN105404252A (en) * | 2015-10-20 | 2016-03-16 | 同济大学 | Remote crane monitoring data transmission method |
| CN205990117U (en) * | 2016-07-22 | 2017-03-01 | 广州广日电梯工业有限公司 | Intelligent security guard apparatus for controlling elevator |
| CN108861917A (en) * | 2018-06-22 | 2018-11-23 | 广西烽火信息技术有限公司 | A method of checking that elevator is run by wechat platform |
| CN110342355A (en) * | 2019-07-12 | 2019-10-18 | 苏州健雄职业技术学院 | A kind of intelligent building elevator dispatching plateform system and its working method |
| CN110577126A (en) * | 2019-09-23 | 2019-12-17 | 上海大学 | intelligent elevator safety management system |
| CN110861987A (en) * | 2019-10-23 | 2020-03-06 | 日立楼宇技术（广州）有限公司 | Elevator fault judgment logic verification method, system and storage medium |
| CN111268522A (en) * | 2020-02-03 | 2020-06-12 | 重庆特斯联智慧科技股份有限公司 | Elevator dispatching method and system based on big data analysis |
| CN113562562A (en) * | 2021-06-23 | 2021-10-29 | 合肥工业大学 | Elevator safety early warning monitoring system and judgment method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113965404B (en) | Network security situation self-adaptive active defense system and method | |
| WO2021098634A1 (en) | Non-intrusive data analytics system for adaptive intelligent condition monitoring of lifts | |
| KR102005188B1 (en) | Industrial site safety management system based on artificial intelligence using real-time location tracking and Geographic Information System, and method thereof | |
| CN111445106B (en) | Safety control method and system for fault processing operation site of electricity acquisition equipment | |
| CN108529380B (en) | Elevator safety prediction method and system | |
| KR102035557B1 (en) | IoT based elevator pervasive autonomous system and method | |
| CN114585983B (en) | Method, device and system for detecting abnormal operation state of equipment | |
| CN113190421A (en) | Detection and analysis method for equipment health state of data center | |
| CN111650917A (en) | Multi-dimensional state online monitoring method and system for equipment | |
| CN114330138A (en) | Fault diagnosis method and device and electronic equipment | |
| CN106276459A (en) | A kind of elevator rescue system based on Internet of Things and method | |
| CN114894411A (en) | Bridge health monitoring method and system based on wireless sensor network | |
| CN113760670A (en) | Cable joint abnormity early warning method and device, electronic equipment and storage medium | |
| CN110143502A (en) | A kind of elevator monitoring based on Internet of Things, rescue method of disposal and system | |
| CN109685389A (en) | Elevator faults work dispatching method, device, server, storage medium and system | |
| KR102185737B1 (en) | Estimating system of road condition and estimating method thereof | |
| CN112668873A (en) | Mine safety situation analysis and prediction early warning method | |
| CN109896384B (en) | Traction elevator health state characteristic parameter extraction method based on big data analysis | |
| CN113554364A (en) | Disaster emergency management method, device, equipment and computer storage medium | |
| CN115744521A (en) | Elevator Internet of things maintenance remote supervision and management method and system | |
| CN104876082B (en) | Elevator monitoring system and method | |
| CN109263650A (en) | Identify the method, apparatus and the vehicles of manpower intervention | |
| CN115028036A (en) | Elevator management method based on big data | |
| CN109052087A (en) | Elevator safety monitoring method, device, smart machine and storage medium | |
| CN107140495A (en) | A kind of elevator safety management system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |