CN117172746A - Building management system capable of intelligently solving faults - Google Patents
Building management system capable of intelligently solving faults Download PDFInfo
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- CN117172746A CN117172746A CN202311132651.1A CN202311132651A CN117172746A CN 117172746 A CN117172746 A CN 117172746A CN 202311132651 A CN202311132651 A CN 202311132651A CN 117172746 A CN117172746 A CN 117172746A
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- 238000000034 method Methods 0.000 claims description 13
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- 238000007726 management method Methods 0.000 claims 11
- 238000013024 troubleshooting Methods 0.000 claims 2
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B50/00—Energy efficient technologies in elevators, escalators and moving walkways, e.g. energy saving or recuperation technologies
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Abstract
The invention relates to a building management system for intelligently solving faults, which comprises: the content storage device is arranged in the building management control room and is used for storing various elevator fault codes corresponding to various elevator faults respectively; the intelligent analysis mechanism is used for intelligently analyzing each occurrence probability of each elevator fault code corresponding to each elevator fault code of the target elevator at the next moment based on each basic data screened in a targeted manner by adopting a convolutional neural network model; and the prediction processing mechanism is connected with the intelligent analysis mechanism and is used for determining the predicted elevator fault type at the next moment based on the occurrence probability of each part. The building management system for intelligently solving the faults is intelligent, safe and reliable in operation. The probability values corresponding to faults at the future moment of the target elevator in the building can be intelligently predicted, so that the fault types of the target elevator at the future moment can be analyzed, and the probability of building safety accidents is effectively reduced.
Description
Technical Field
The invention relates to the field of building management, in particular to a building management system capable of intelligently solving faults.
Background
Building management refers to the management, maintenance, and increase of usage of buildings and public facilities to ensure normal operation of the building and comfort of occupants. Among them, an elevator inside a building is one of important management targets.
With the widespread use of elevators, faults are also unavoidable, as are several common elevator faults: elevator no response, elevator shake, false stop floors, inter-floor stops, and emergency stop faults. For example, an inter-floor stop may be due to a brake system failure or cable wear, requiring inspection of the brake system and cable, and replacement of any worn components; emergency stop faults may be caused by an emergency stop button or wiring fault, requiring inspection of the emergency stop system and wiring, and replacement of the associated faulty components.
However, in building management in the prior art, only the elevator which has failed can be maintained, but the future failure time of the elevator cannot be predicted, not to mention the future failure type of the elevator, so that the building management lacks foresight, and the failure processing and the elevator maintenance are too late, so that the building safety accident cannot be effectively avoided.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides a building management system capable of intelligently solving faults, which can intelligently predict probability values corresponding to faults of a target elevator in a building at future time based on screened basic data and a customized intelligent analysis mode, so as to analyze the fault types of the target elevator at the future time, thereby providing valuable reference information for subsequent elevator fault maintenance and building safety management.
According to the present invention, there is provided a building management system for intelligently resolving a fault, the system comprising:
the content storage device is arranged in the building management control room, a database is used for storing various elevator fault codes respectively corresponding to various elevator faults, and the various elevator faults comprise elevator non-response, elevator shake, mistaken stopping floors, inter-floor stopping and emergency stopping faults;
the time-sharing acquisition equipment is arranged in the building management control room and is used for acquiring each elevator operation data corresponding to each historical moment uniformly spaced before the next moment of the current moment of a target elevator, wherein the target elevator is positioned in a building;
the information extraction equipment is arranged near the time-sharing acquisition equipment and is used for acquiring each piece of configuration information of the target elevator, wherein each piece of configuration information of the target elevator comprises the weight of the target elevator, the number of floors of a building, the internal volume of the elevator and the maximum number of load-bearing people of the elevator;
the intelligent analysis mechanism is respectively connected with the content storage device, the time-sharing acquisition device and the information extraction device and is used for intelligently analyzing the occurrence probability of each part of the target elevator corresponding to each elevator fault code at the next moment based on each part of elevator operation data respectively corresponding to each historical moment of the target elevator which is uniformly spaced before the next moment of the current moment and each part of configuration information of the target elevator by adopting a convolutional neural network model;
the repeated learning mechanism is connected with the intelligent analysis mechanism and is used for performing repeated learning on the convolutional neural network to obtain a convolutional neural network model, and the number of times of repeated learning is proportional to the maximum number of load bearing persons of the elevator of the target elevator;
the prediction processing mechanism is connected with the intelligent analysis mechanism and is used for comparing the occurrence probabilities of the elevator fault codes of the target elevator at the next moment to obtain the maximum value of the occurrence probabilities, and outputting the associated elevator fault type of the elevator fault code corresponding to the maximum value as the predicted elevator fault type at the next moment.
Therefore, the invention has at least the following beneficial technical effects:
first,: each item of basic data screened in a targeted manner is used for intelligent analysis of each item of probability value corresponding to each fault occurrence at the future moment of a target elevator in a building, wherein each item of basic data is each item of elevator operation data corresponding to each historical moment of the target elevator at uniform intervals before the future moment and each item of configuration information of the target elevator;
secondly: the convolution neural network model with customized structure is used for executing intelligent analysis of probability values corresponding to faults of a target elevator in a building at future time, and in order to ensure the reliability and stability of intelligent analysis results of the model, the following construction measures are adopted: performing multiple learning on the convolutional neural network to obtain a convolutional neural network model, wherein the learning times are in direct proportion to the maximum number of load bearing persons of the elevator of the target elevator, and taking the occurrence probability of each elevator fault code of the target elevator at a certain past moment as the output content of the convolutional neural network in each learning to finish the learning operation;
again: and comparing the occurrence probabilities of the elevator fault codes of the target elevator at the future time to obtain the maximum value of the occurrence probabilities, and outputting the associated elevator fault type of the elevator fault code corresponding to the maximum value as the predicted elevator fault type at the future time, thereby completing the intelligent prediction of the fault type of the target elevator at the future time.
The building management system for intelligently solving the faults is intelligent, safe and reliable in operation. The probability values corresponding to faults at the future moment of the target elevator in the building can be intelligently predicted, so that the fault types of the target elevator at the future moment can be analyzed, and the probability of building safety accidents is effectively reduced.
Brief description of the drawings
Numerous advantages of the present invention may be better understood by those skilled in the art by reference to the accompanying drawings in which:
fig. 1 is a schematic diagram of an internal structure of a building management system for intelligently resolving faults according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of an internal structure of a building management system for intelligently resolving faults according to a second embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating an internal structure of a building management system for intelligently resolving faults according to a third embodiment of the present invention.
Detailed Description
Embodiments of the intelligent fault-solving building management system of the present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic internal structure of a building management system for intelligently resolving faults according to a first embodiment of the present invention, the system comprising:
the content storage device is arranged in the building management control room, a database is used for storing various elevator fault codes respectively corresponding to various elevator faults, and the various elevator faults comprise elevator non-response, elevator shake, mistaken stopping floors, inter-floor stopping and emergency stopping faults;
the content storage device can be a FLASH FLASH memory, an MMC memory chip or a TF memory chip, and is used for storing various elevator fault codes respectively corresponding to various elevator faults by adopting a database, wherein the various elevator faults comprise elevator non-response, elevator shake, mistaken stopping floors, inter-floor stopping and emergency stopping faults;
and illustratively, storing, with the memory, individual elevator fault codes that correspond to each of the various elevator faults, the various elevator faults including elevator no-response, elevator shake, false stop floor, inter-floor stop and emergency stop faults including: the database is an Oracle database, an ACCESS database, an SQL database, a DB2 database, a Sybase database, a MySQL database, a MariaDB database, a VF database, an SqlServer database or an SQLite database;
the time-sharing acquisition equipment is arranged in the building management control room and is used for acquiring each elevator operation data corresponding to each historical moment uniformly spaced before the next moment of the current moment of a target elevator, wherein the target elevator is positioned in a building;
the information extraction equipment is arranged near the time-sharing acquisition equipment and is used for acquiring each piece of configuration information of the target elevator, wherein each piece of configuration information of the target elevator comprises the weight of the target elevator, the number of floors of a building, the internal volume of the elevator and the maximum number of load-bearing people of the elevator;
the intelligent analysis mechanism is respectively connected with the content storage device, the time-sharing acquisition device and the information extraction device and is used for intelligently analyzing the occurrence probability of each part of the target elevator corresponding to each elevator fault code at the next moment based on each part of elevator operation data respectively corresponding to each historical moment of the target elevator which is uniformly spaced before the next moment of the current moment and each part of configuration information of the target elevator by adopting a convolutional neural network model;
for example, a simulation processing process of intelligently analyzing each occurrence probability of each elevator fault code of the target elevator at the next moment based on each elevator operation data and each configuration information of the target elevator, which correspond to each historical moment of the target elevator at even intervals before the next moment of the current moment, by adopting a convolutional neural network model can be realized by selecting a numerical simulation mode;
the repeated learning mechanism is connected with the intelligent analysis mechanism and is used for performing repeated learning on the convolutional neural network to obtain a convolutional neural network model, and the number of times of repeated learning is proportional to the maximum number of load bearing persons of the elevator of the target elevator;
the prediction processing mechanism is connected with the intelligent analysis mechanism and is used for comparing the occurrence probabilities of the elevator fault codes of the target elevator at the next moment to obtain the maximum value of the occurrence probabilities, and outputting the associated elevator fault type of the elevator fault code corresponding to the maximum value as the predicted elevator fault type at the next moment;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and comprises the following steps: the single elevator operation data corresponding to each moment of the target elevator comprises the nearest floor value, the number of carrying persons, the carrying weight, the operation speed and the operation acceleration of the target elevator at the moment;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and further comprises the following steps: each of the history times that are evenly spaced before the next time to the current time includes the current time.
Fig. 2 is a schematic diagram of an internal structure of a building management system for intelligently resolving faults according to a second embodiment of the present invention.
In comparison to fig. 1, the building management system for intelligently resolving faults according to the second embodiment of the present invention in fig. 2 may further include:
the data communication mechanism is connected with the prediction processing mechanism and is used for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to a remote big data server;
the data communication mechanism may be, for example, a wireless communication mechanism based on a time division duplex communication link or a frequency division duplex communication link;
the method for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to the remote big data server comprises the following steps: packing the received character data corresponding to the predicted elevator fault type at the next moment and the serial number of the target elevator into a network data packet;
the method for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to the remote big data server further comprises the following steps: and transmitting the network data packet to a remote big data server through a wireless communication network.
Fig. 3 is a schematic diagram illustrating an internal structure of a building management system for intelligently resolving faults according to a third embodiment of the present invention.
In comparison to fig. 1, the building management system for intelligently resolving faults according to the third embodiment of the present invention in fig. 3 may further include:
and the real-time display mechanism is connected with the prediction processing mechanism and is used for receiving and displaying character data corresponding to the predicted elevator fault type at the next moment.
Next, a further explanation of the specific structure of the building management system for intelligently solving the failure according to the present invention will be continued.
In a building management system that intelligently resolves faults according to various embodiments of the present invention:
performing a plurality of learnings on the convolutional neural network to obtain a convolutional neural network model, the number of learnings being proportional to the maximum number of bearers of the elevator of the target elevator comprising: in each learning, the occurrence probability of each elevator fault code of the target elevator at a certain past moment is used as the output content of the convolutional neural network, and each elevator operation data and each configuration information of the target elevator, which are respectively corresponding to each historical moment of the target elevator at even intervals before the certain past moment, are used as the input content of the convolutional neural network, so that the learning operation is completed.
In a building management system that intelligently resolves faults according to various embodiments of the present invention:
the intelligent analysis of each occurrence probability of each elevator fault code of the target elevator at the next moment based on each elevator operation data and each configuration information of the target elevator, which correspond to each historical moment of the target elevator at uniform intervals before the next moment of the current moment, by adopting a convolutional neural network model comprises the following steps: the method comprises the steps of inputting each elevator running data and each configuration information of a target elevator, which correspond to each historical moment uniformly spaced before the next moment of the current moment, into a convolutional neural network model;
the intelligent analysis of the occurrence probability of each elevator fault code of the target elevator at the next moment based on each elevator operation data and each configuration information of the target elevator, which correspond to each historical moment of the target elevator at uniform intervals before the next moment of the current moment, by adopting the convolutional neural network model further comprises the following steps: and executing the convolutional neural network model to obtain each occurrence probability of each elevator fault code of the target elevator output by the convolutional neural network model at the next moment.
In a building management system that intelligently resolves faults according to various embodiments of the present invention:
each elevator operation data corresponding to each historical time uniformly spaced before the next time of the current time of the target elevator is collected, and the target elevator is located in a building and further comprises: the next time of the current time and each history time uniformly spaced before the next time of the current time form a complete time interval together;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and further comprises the following steps: the interval duration between the next time of the current time and the current time is equal to the interval duration between any two adjacent historical times;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and further comprises the following steps: the more the number of floors of the building in which the target elevator is located, the shorter the interval duration of every two adjacent historical moments.
In addition, in the building management system for intelligently solving the faults, a database is used for storing various elevator fault codes respectively corresponding to various elevator faults, the various elevator faults comprise elevator non-response, elevator shake, misoperation floor, inter-floor stop and emergency stop faults, and the method comprises the following steps: and different binary values are adopted to represent the elevator fault codes respectively corresponding to the various elevator faults.
While the invention has been described with considerable specificity, it should be appreciated that those skilled in the art may change the elements thereof without departing from the spirit and scope of the invention. It is believed that the system of the present invention and the attendant advantages thereof will be understood by the foregoing description and it will be apparent that various changes may be made in the form, construction and arrangement of the components thereof without departing from the scope and spirit of the invention or without sacrificing all of its material advantages, the form herein before described being merely an explanatory embodiment thereof, and further without providing additional material change. The claims are intended to cover and include such modifications.
Claims (9)
1. A building management system for intelligently resolving a fault, the system comprising:
the content storage device is arranged in the building management control room, a database is used for storing various elevator fault codes respectively corresponding to various elevator faults, and the various elevator faults comprise elevator non-response, elevator shake, mistaken stopping floors, inter-floor stopping and emergency stopping faults;
the time-sharing acquisition equipment is arranged in the building management control room and is used for acquiring each elevator operation data corresponding to each historical moment uniformly spaced before the next moment of the current moment of a target elevator, wherein the target elevator is positioned in a building;
the information extraction equipment is arranged near the time-sharing acquisition equipment and is used for acquiring each piece of configuration information of the target elevator, wherein each piece of configuration information of the target elevator comprises the weight of the target elevator, the number of floors of a building, the internal volume of the elevator and the maximum number of load-bearing people of the elevator;
the intelligent analysis mechanism is respectively connected with the content storage device, the time-sharing acquisition device and the information extraction device and is used for intelligently analyzing the occurrence probability of each part of the target elevator corresponding to each elevator fault code at the next moment based on each part of elevator operation data respectively corresponding to each historical moment of the target elevator which is uniformly spaced before the next moment of the current moment and each part of configuration information of the target elevator by adopting a convolutional neural network model;
the repeated learning mechanism is connected with the intelligent analysis mechanism and is used for performing repeated learning on the convolutional neural network to obtain a convolutional neural network model, and the number of times of repeated learning is proportional to the maximum number of load bearing persons of the elevator of the target elevator;
the prediction processing mechanism is connected with the intelligent analysis mechanism and is used for comparing the occurrence probabilities of the elevator fault codes of the target elevator at the next moment to obtain the maximum value of the occurrence probabilities, and outputting the associated elevator fault type of the elevator fault code corresponding to the maximum value as the predicted elevator fault type at the next moment.
2. The intelligent fault-solving building management system of claim 1, wherein:
each elevator operation data corresponding to each historical time uniformly spaced before the next time of the current time of the target elevator is collected, and the target elevator is located in a building and comprises: the single elevator operation data corresponding to each moment of the target elevator comprises the nearest floor value, the number of carrying persons, the carrying weight, the operation speed and the operation acceleration of the target elevator at the moment;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and further comprises the following steps: each of the history times that are evenly spaced before the next time to the current time includes the current time.
3. The intelligent troubleshooting building management system of claim 2, wherein said system further comprises:
the data communication mechanism is connected with the prediction processing mechanism and is used for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to a remote big data server;
the method for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to the remote big data server comprises the following steps: packing the received character data corresponding to the predicted elevator fault type at the next moment and the serial number of the target elevator into a network data packet;
the method for transmitting the received character data corresponding to the predicted elevator fault type at the next moment and the serial number I of the target elevator to the remote big data server further comprises the following steps: and transmitting the network data packet to a remote big data server through a wireless communication network.
4. The intelligent troubleshooting building management system of claim 2, wherein said system further comprises:
and the real-time display mechanism is connected with the prediction processing mechanism and is used for receiving and displaying character data corresponding to the predicted elevator fault type at the next moment.
5. A building management system for intelligently resolving faults as claimed in any of claims 2 to 4 in which:
performing a plurality of learnings on the convolutional neural network to obtain a convolutional neural network model, the number of learnings being proportional to the maximum number of bearers of the elevator of the target elevator comprising: in each learning, the occurrence probability of each elevator fault code of the target elevator at a certain past moment is used as the output content of the convolutional neural network, and each elevator operation data and each configuration information of the target elevator, which are respectively corresponding to each historical moment of the target elevator at even intervals before the certain past moment, are used as the input content of the convolutional neural network, so that the learning operation is completed.
6. A building management system for intelligently resolving faults as claimed in any of claims 2 to 4 in which:
the intelligent analysis of each occurrence probability of each elevator fault code of the target elevator at the next moment based on each elevator operation data and each configuration information of the target elevator, which correspond to each historical moment of the target elevator at uniform intervals before the next moment of the current moment, by adopting a convolutional neural network model comprises the following steps: and respectively inputting each elevator operation data and each configuration information of the target elevator corresponding to each historical time uniformly spaced before the next time of the current time into the convolutional neural network model.
7. The intelligent fault-solving building management system of claim 6, wherein:
the intelligent analysis of the occurrence probability of each elevator fault code of the target elevator at the next moment based on each elevator operation data and each configuration information of the target elevator, which correspond to each historical moment of the target elevator at uniform intervals before the next moment of the current moment, by adopting the convolutional neural network model further comprises: and executing the convolutional neural network model to obtain each occurrence probability of each elevator fault code of the target elevator output by the convolutional neural network model at the next moment.
8. A building management system for intelligently resolving faults as claimed in any of claims 2 to 4 in which:
each elevator operation data corresponding to each historical time uniformly spaced before the next time of the current time of the target elevator is collected, and the target elevator is located in a building and further comprises: the next time of the current time and each history time uniformly spaced before the next time of the current time form a complete time interval together;
the method comprises the steps of collecting each elevator operation data corresponding to each historical moment of a target elevator at uniform intervals before the next moment of the current moment, wherein the target elevator is located in a building and further comprises the following steps: the interval duration between the next time of the current time and the current time is equal to the interval duration between any two adjacent historical times.
9. The intelligent fault-solving building management system of claim 8, wherein:
each elevator operation data corresponding to each historical time uniformly spaced before the next time of the current time of the target elevator is collected, and the target elevator is located in a building and further comprises: the more the number of floors of the building in which the target elevator is located, the shorter the interval duration of every two adjacent historical moments.
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| CN202311132651.1A CN117172746A (en) | 2023-09-05 | 2023-09-05 | Building management system capable of intelligently solving faults |
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| CN110040594A (en) * | 2019-01-08 | 2019-07-23 | 浙江新再灵科技股份有限公司 | A kind of elevator operation detection system and method based on convolutional neural networks |
| CN112365066A (en) * | 2020-11-17 | 2021-02-12 | 日立楼宇技术（广州）有限公司 | Elevator fault prediction method, system, device, computer equipment and storage medium |
| CN116258054A (en) * | 2021-12-09 | 2023-06-13 | 中国石油天然气股份有限公司 | Intelligent diagnosis model construction method for constant potential instrument faults of negative protection system |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110040594A (en) * | 2019-01-08 | 2019-07-23 | 浙江新再灵科技股份有限公司 | A kind of elevator operation detection system and method based on convolutional neural networks |
| CN112365066A (en) * | 2020-11-17 | 2021-02-12 | 日立楼宇技术（广州）有限公司 | Elevator fault prediction method, system, device, computer equipment and storage medium |
| CN116258054A (en) * | 2021-12-09 | 2023-06-13 | 中国石油天然气股份有限公司 | Intelligent diagnosis model construction method for constant potential instrument faults of negative protection system |
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