Method, device, equipment and system for quickly diagnosing elevator faults based on artificial intelligence

Abstract

The invention discloses an elevator fault rapid diagnosis method, device, equipment and system based on artificial intelligence, the elevator fault rapid diagnosis device comprises a mobile phone A1, a remote monitoring client C1, an intelligent remote monitoring system Y1 arranged at a cloud end, an elevator E1, a DTU terminal T1, a maintainer P1 and an expert P2, and the intelligent remote monitoring system comprises a remote monitoring server S1, a communication server Z1, a database B1 and an elevator large model X1. The remote monitoring client C1 is installed on the mobile phone A1; according to the scheme, the elevator large model system is constructed, the causal relationship between behaviors and faults is set, the behaviors and the associated faults in the elevator before the faults occur are automatically and dynamically analyzed, the accurate reasons of the faults are quickly diagnosed, the elevator faults are automatically researched, judged and accurately positioned, the optimal fault solution is recommended, and the working efficiency is improved. Maintenance personnel are guided to carry out elevator fault processing in an intelligent and processized mode, the fault processing efficiency is greatly improved, the fault processing time is shortened, use of the elevator is rapidly recovered, and the method is far better than a traditional fault processing method and has innovativeness.

Description

Technical Field

[0001] The present invention belongs to the technical field of elevator maintenance and repair, and specifically relates to a method, device, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence. Background Art

[0002] As an indispensable vertical transportation tool in modern buildings, the safety and reliability of elevators are directly related to the life safety of passengers. Therefore, regular maintenance and upkeep of elevators can not only extend the service life of elevators, but also effectively reduce the occurrence of failures and accidents, and ensure the safety of passengers' travel. With the continuous increase in the number of elevator maintenance stations, the mobility of maintenance personnel is large and their technical levels are uneven. It is often time-consuming and laborious to troubleshoot based on their personal experience or consult fault manuals and request remote assistance. It takes a long time to analyze the cause of the fault and find a solution, which is inefficient, resulting in a long time to handle the fault, affecting customers' use of elevators.

[0003] Chinese invention patent CN106586753B discloses an intelligent processing system and method for elevator fault repair, which reduces elevator operating costs and ensures the safety of life and property. The method steps are: one-click repair or scan code repair; one-click repair is to press the one-click repair button set in the elevator car, and the fault information including the elevator name will be sent to the cloud platform through the network; scan code repair is to scan the repair QR code with a mobile phone, link to the cloud platform, and send the fault information to the cloud platform through the network; after the cloud platform receives the fault information, it sends the elevator location and fault information to the property terminal and the maintenance personnel terminal through the network; after the maintenance personnel see the fault information on the maintenance personnel terminal, they accept or reject the maintenance task and feedback to the cloud platform; the maintenance personnel feedback the progress and processing of the maintenance task through the maintenance personnel terminal; the cloud platform will send the progress and processing of the maintenance task to the property terminal and the maintenance personnel terminal through the network. The above scheme is just a basic way to assign maintenance tasks to maintenance personnel, and lacks the formulation of the optimal fault solution.

[0004] Therefore, the present invention provides a method, device, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence, which can quickly diagnose the exact cause of the fault and recommend the optimal fault solution. Summary of the invention

[0005] In view of the problems raised by the above background technology, the purpose of the present invention is to provide a method, device, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence. By constructing a large elevator model system, setting the causal relationship between behavior and faults, automatically and dynamically analyzing the behavior and related faults in the elevator before the fault occurs, quickly diagnosing the exact cause of the fault, realizing automatic analysis and precise positioning of elevator faults, and recommending the best fault solution, intelligently and procedurally guiding maintenance personnel to handle elevator faults, thereby greatly improving fault handling efficiency, shortening fault handling time, and quickly restoring the use of elevators. It is far superior to traditional fault handling methods, is innovative, and enhances the company's brand image.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A system for rapid diagnosis of elevator faults based on artificial intelligence includes a mobile phone A1, a remote monitoring client C1, an intelligent remote monitoring system Y1 arranged in the cloud, an elevator E1, a DTU terminal T1, a maintenance personnel P1 and an expert P2. The intelligent remote monitoring system includes a remote monitoring server S1, a communication server Z1, a database B1 and an elevator large model X1. The remote monitoring client C1 is installed on the mobile phone A1, and the DTU terminal T1 is connected to the elevator E1 for uploading faults.

[0008] It is further defined that the elevator large model includes a state and fault model M1, a video behavior analysis model M2, a fault diagnosis model M3 and a fault case model M4;

[0009] The video behavior analysis model M2 is used to accurately identify the behavior in the elevator;

[0010] The elevator E1 is used to upload the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally push it to the status and fault model M1 on the elevator large model X1;

[0011] The DTU terminal T1 is used to collect the status, faults and video inside the elevator E1, and upload the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1;

[0012] After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

[0013] It is further defined that after the elevator large model X1 receives the fault and video information of the elevator, the fault diagnosis model M3 is started to perform reverse analysis through the timeline.

[0014] It is further defined that the reverse analysis is used to analyze whether the fault meets the fault rules, and start the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnose the cause of the fault and recommend solutions.

[0015] A method for rapid diagnosis of elevator faults based on artificial intelligence comprises the following steps:

[0016] S1: pre-set the relationship rules between the behavior and faults and the recommended solutions of the fault diagnosis model M3;

[0017] S2: Train the video behavior analysis model M2 through video samples to accurately identify the behavior in the elevator;

[0018] S3: The elevator E1 uploads the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally pushes it to the status and fault model M1 on the elevator large model X1;

[0019] S4: After receiving the fault and video, the elevator model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault meets the fault rules, and then starts the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnoses the cause of the fault and recommends a solution;

[0020] S5: The elevator model X1 pushes the fault information, fault cause and recommended solution to the remote monitoring client C1;

[0021] S6: After receiving the information, maintenance personnel P1 selects the recommended solution for quick processing and rates the recommended solution;

[0022] S7: The remote monitoring client C1 uploads the fault information, solution and score to the elevator fault case M4 for long-term storage.

[0023] S8: The solution scores are used as the order of recommended solutions for the next time;

[0024] S9: The elevator fault has been resolved and the elevator has returned to normal.

[0025] It is further defined that S1 includes the rules that cleaning behavior causes the elevator to report a trapped person fault, the rules that pouring water on the elevator buttons causes the elevator to report a trapped person fault, and the rules that an electric car enters the elevator causes the elevator to report a trapped person fault, and the behaviors in the elevator in S2 include cleaning and blocking the elevator door with an obstacle, pouring water on the elevator buttons, and an electric car entering the elevator;

[0026] The specific rules for the elevator to report a trapped person fault due to cleaning behavior are:

[0027] S10: Cleaning staff blocked the elevator door with an obstacle;

[0028] S11: Elevator sends an alarm for door opening and closing failure;

[0029] S12: The elevator sends an alarm to stop running fault;

[0030] S13: The elevator reports a trapped person fault;

[0031] S14: The diagnosis result is that the cleaning behavior caused the elevator to send out an alarm of trapped person failure;

[0032] S15: The recommended solution is to explain the situation through the remote monitoring client and file the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site;

[0033] The specific rules for causing the elevator to report a trapped person fault due to pouring water on the elevator buttons are:

[0034] S16: Throwing water on the elevator buttons;

[0035] S17: Report button failure to the elevator;

[0036] S18: Elevator alarm mainboard self-protection fault;

[0037] S19: The elevator sends an alarm to stop running fault;

[0038] S20: The elevator reports a trapped person fault;

[0039] S21: Pouring water on the elevator buttons causes the elevator to report a trapped person fault;

[0040] S22: The recommended solution is to replace the elevator mainboard, replace the damaged parts of the elevator mainboard, or replace the elevator buttons;

[0041] The specific rules for the elevator to report a trapped person fault caused by the electric vehicle entering the elevator are:

[0042] S23: Elevator entering electric vehicle behavior;

[0043] S24: The elevator reports an electric vehicle failure;

[0044] S25 elevator sends an alarm to stop running fault;

[0045] S26: Elevator reports a trapped person fault;

[0046] S27: The electric vehicle enters the elevator, causing the elevator to report a trapped person fault;

[0047] S28: ① The recommended solution is to explain the situation through the remote monitoring client and archive the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site.

[0048] It is further defined that in S3, the DTU terminal T1 specifically collects the status, faults and video inside the elevator E1, and uploads the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1;

[0049] After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

[0050] It is further defined that the scoring system in S6 is a percentage system, with 100 points being the best and 0 points being the worst.

[0051] A device for rapid diagnosis of elevator faults based on artificial intelligence, comprising any mobile phone, computer, communication device and storage device capable of running the software model in claims 1 to 7.

[0052] A device for rapid diagnosis of elevator faults based on artificial intelligence, comprising any mobile phone, computer, communication device and storage device capable of running the software model in claims 1 to 7.

[0053] Beneficial effects of the present invention:

[0054] 1. This solution diagnoses the exact cause of the fault through behavioral analysis and fault information, reduces the time spent on troubleshooting fault information, effectively improves the efficiency of troubleshooting, shortens the fault handling cycle, thereby reducing costs and improving customer satisfaction.

[0055] 2. This solution reduces the time spent by on-site personnel seeking experts and searching for information by dynamically recommending solutions, greatly improving the speed of troubleshooting.

[0056] 3. This solution realizes knowledge sharing and automatically uploads fault cases to the elevator model X1 for use by maintenance personnel across the country.

[0057] 4. This solution solves the problem of fault handling quality by improving the quality of fault handling, and can improve the quality of fault handling through the optimal recommended solution.

[0058] 5. In summary, this solution builds a large elevator model system, sets the cause-and-effect relationship between behavior and faults, automatically and dynamically analyzes the behavior and related faults in the elevator before the fault occurs, quickly diagnoses the exact cause of the fault, realizes automatic analysis and precise positioning of elevator faults, and recommends the best fault solution. It guides maintenance personnel to handle elevator faults in an intelligent and process-based manner, thereby greatly improving fault handling efficiency, shortening fault handling time, and quickly restoring the use of elevators. It is far superior to traditional fault handling methods, is innovative, and enhances the company's brand image. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention can be further illustrated by the non-limiting examples given in the accompanying drawings;

[0060] Figure 1 The present invention is a structural schematic diagram of an embodiment of a method, device, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

[0062] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0063] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] Embodiment 1: Figure 1As shown, a system for rapid diagnosis of elevator faults based on artificial intelligence of the present invention includes a mobile phone A1, a remote monitoring client C1, an intelligent remote monitoring system Y1 arranged in the cloud, an elevator E1, a DTU terminal T1, a maintenance personnel P1 and an expert P2, the intelligent remote monitoring system includes a remote monitoring server S1, a communication server Z1, a database B1 and an elevator large model X1, the remote monitoring client C1 is installed on the mobile phone A1, and the DTU terminal T1 is connected to the elevator E1 for uploading faults.

[0065] Preferably, the elevator large model includes a state and fault model M1, a video behavior analysis model M2, a fault diagnosis model M3 and a fault case model M4;

[0066] The video behavior analysis model M2 is used to accurately identify the behavior in the elevator;

[0067] The elevator E1 is used to upload the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally push it to the status and fault model M1 on the elevator large model X1;

[0068] The DTU terminal T1 is used to collect the status, faults and video inside the elevator E1, and upload the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1;

[0069] After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

[0070] Preferably, after receiving the fault and video information of the elevator, the large elevator model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline.

[0071] Preferably, the reverse analysis is used to analyze whether the fault meets the fault rules, and start the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnose the cause of the fault and recommend a solution.

[0072] A method for rapid diagnosis of elevator faults based on artificial intelligence comprises the following steps:

[0073] S1: pre-set the relationship rules between the behavior and faults and the recommended solutions of the fault diagnosis model M3;

[0074] S2: Train the video behavior analysis model M2 through video samples to accurately identify the behavior in the elevator;

[0075] S3: The elevator E1 uploads the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally pushes it to the status and fault model M1 on the elevator large model X1;

[0076] S4: After receiving the fault and video, the elevator model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault meets the fault rules, and then starts the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnoses the cause of the fault and recommends a solution;

[0077] A specific example is: Elevator A had a trapped person fault at 10:00. Through reverse analysis of the diagnostic timeline of the fault diagnosis model M3, it was found that a stop failure occurred at 9:58 and a door opening and closing failure occurred at 9:56. The video behavior analysis model M2 analyzed that cleaning was performed at 9:54 and the elevator door was blocked with an obstacle, which met the rule that cleaning behavior causes the elevator to report a trapped person fault. The cause of the fault was diagnosed as: the cleaning behavior caused the elevator to report a trapped person fault.

[0078] S5: The elevator model X1 pushes the fault information, fault cause and recommended solution to the remote monitoring client C1;

[0079] S6: After receiving the information, maintenance personnel P1 selects the recommended solution for quick processing and rates the recommended solution;

[0080] S7: The remote monitoring client C1 uploads the fault information, solution and score to the elevator fault case M4 for long-term storage.

[0081] S8: The solution scores are used as the order of recommended solutions for the next time;

[0082] Specifically, for example, there are four solutions to the rule that the elevator reports a trapped person fault due to cleaning behavior. The default order is ① Explain the situation through the remote monitoring client and archive the trapped person fault; ② Report the situation to the property management; ③ Report the situation to the remote monitoring customer service phone; ④ Handle the situation on site. However, after scoring, the solution scores are ①89 points, ②95 points, ③92 points, and ④90 points. The recommended solution next time will automatically become: ① Report the situation to the property management; ② Report the situation to the remote monitoring customer service phone; ③ Handle the situation on site; ④ Explain the situation through the remote monitoring client and archive the trapped person fault.

[0083] S9: The elevator fault has been resolved and the elevator has returned to normal.

[0084] Preferably, S1 includes the rules that cleaning behavior causes the elevator to report a trapped person fault, pouring water on the elevator buttons causes the elevator to report a trapped person fault, and electric vehicles enter the elevator to report a trapped person fault, and the behaviors in the elevator in S2 include cleaning and blocking the elevator door with an obstacle, pouring water on the elevator buttons, and electric vehicles enter the elevator;

[0085] The specific rules for the elevator to report a trapped person fault due to cleaning behavior are:

[0086] S10: Cleaning staff blocked the elevator door with an obstacle;

[0087] S11: Elevator sends an alarm for door opening and closing failure;

[0088] S12: The elevator sends an alarm to stop running fault;

[0089] S13: The elevator reports a trapped person fault;

[0090] S14: The diagnosis result is that the cleaning behavior caused the elevator to send out an alarm of trapped person failure;

[0091] S15: The recommended solution is to explain the situation through the remote monitoring client and file the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site;

[0092] The specific rules for causing the elevator to report a trapped person fault due to pouring water on the elevator buttons are:

[0093] S16: Throwing water on the elevator buttons;

[0094] S17: Report button failure to the elevator;

[0095] S18: Elevator alarm mainboard self-protection fault;

[0096] S19: The elevator sends an alarm to stop running fault;

[0097] S20: The elevator reports a trapped person fault;

[0098] S21: Pouring water on the elevator buttons causes the elevator to report a trapped person fault;

[0099] S22: The recommended solution is to replace the elevator mainboard, replace the damaged parts of the elevator mainboard, or replace the elevator buttons;

[0100] The specific rules for the elevator to report a trapped person fault caused by the electric vehicle entering the elevator are:

[0101] S23: Elevator entering electric vehicle behavior;

[0102] S24: The elevator reports an electric vehicle failure;

[0103] S25 elevator sends an alarm to stop running fault;

[0104] S26: Elevator reports a trapped person fault;

[0105] S27: The electric vehicle enters the elevator, causing the elevator to report a trapped person fault;

[0106] S28: ① The recommended solution is to explain the situation through the remote monitoring client and archive the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site.

[0107] Preferably, in S3, the DTU terminal T1 collects the status, faults and video in the elevator E1, and uploads the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1;

[0108] After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

[0109] Preferably, the scoring system in S6 adopts a percentage system, with 100 points being the best and 0 points being the worst.

[0110] A device for rapid diagnosis of elevator faults based on artificial intelligence, comprising any mobile phone, computer, communication device and storage device capable of running the software model of the solution.

[0111] A device for rapid diagnosis of elevator faults based on artificial intelligence, comprising any mobile phone, computer, communication device and storage device capable of running the software model in claims 1 to 7.

[0112] Embodiment 2:

[0113] The difference between the second embodiment and the first embodiment lies in the reverse analysis. The example of the second embodiment is:

[0114] Elevator B had a trapped person fault at 12:00. Through reverse analysis of the diagnostic timeline of the fault diagnosis model M3, it was found that a stop failure occurred at 11:58, a mainboard self-protection failure occurred at 11:56, and a button failure occurred at 11:54. The video behavior analysis model M2 analyzed that there was an act of pouring water on the elevator button at 11:52, which met the rule that pouring water on the elevator button causes the elevator to report a trapped person fault. The cause of the fault was diagnosed as: pouring water on the elevator button caused the elevator to report a trapped person fault.

[0115] Embodiment three:

[0116] The difference between implementation three and implementation one is the reverse analysis. The example of implementation three is:

[0117] Elevator C had a trapped person fault at 15:00. Through reverse analysis of the diagnostic timeline of the fault diagnosis model M3, it was found that a stop failure occurred at 14:58 and an electric vehicle fault occurred at 14:56. The video behavior analysis model M2 then analyzed that an elevator entered an electric vehicle at 9:54, which met the rule that the elevator entering an electric vehicle causes the elevator to report a trapped person fault. The cause of the fault was diagnosed as: the elevator entering an electric vehicle caused the elevator to report a trapped person fault.

[0118] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A system for rapid diagnosis of elevator faults based on artificial intelligence, characterized in that: It includes a mobile phone A1, a remote monitoring client C1, an intelligent remote monitoring system Y1 set in the cloud, an elevator E1, a DTU terminal T1, a maintenance personnel P1 and an expert P2. The intelligent remote monitoring system includes a remote monitoring server S1, a communication server Z1, a database B1 and an elevator large model X1. The remote monitoring client C1 is installed on the mobile phone A1, and the DTU terminal T1 is connected to the elevator E1 for uploading faults.

2. The system for rapid diagnosis of elevator faults based on artificial intelligence according to claim 1, characterized in that: The elevator model includes a state and fault model M1, a video behavior analysis model M2, a fault diagnosis model M3 and a fault case model M4; The video behavior analysis model M2 is used to accurately identify the behavior in the elevator; The elevator E1 is used to upload the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally push it to the status and fault model M1 on the elevator large model X1; The DTU terminal T1 is used to collect the status, faults and video inside the elevator E1, and upload the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1; After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

3. The system for rapid diagnosis of elevator faults based on artificial intelligence according to claim 2 is characterized in that: After receiving the elevator fault and video information, the elevator large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline.

4. The system for rapid diagnosis of elevator faults based on artificial intelligence according to claim 3 is characterized in that: The reverse analysis is used to analyze whether the fault meets the fault rules, and start the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnose the cause of the fault and recommend solutions.

5. A method for rapid diagnosis of elevator faults based on artificial intelligence, characterized in that: The steps include: S1: pre-set the relationship rules between the behavior and faults and the recommended solutions of the fault diagnosis model M3; S2: Train the video behavior analysis model M2 through video samples to accurately identify the behavior in the elevator; S3: The elevator E1 uploads the elevator status and fault information to the intelligent remote monitoring system Y1 in real time, and finally pushes it to the status and fault model M1 on the elevator large model X1; S4: After receiving the fault and video, the elevator model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault meets the fault rules, and then starts the video behavior analysis model M2 to analyze the behavior before the fault occurs, and finally diagnoses the cause of the fault and recommends a solution; S5: The elevator model X1 pushes the fault information, fault cause and recommended solution to the remote monitoring client C1; S6: After receiving the information, maintenance personnel P1 selects the recommended solution for quick processing and rates the recommended solution; S7: The remote monitoring client C1 uploads the fault information, solution and score to the elevator fault case M4 for long-term storage. S8: The solution scores are used as the order of recommended solutions for the next time; S9: The elevator fault has been resolved and the elevator has returned to normal.

6. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 5, characterized in that: The S1 includes the rules that cleaning behavior causes the elevator to report a trapped person fault, the rules that pouring water on the elevator buttons causes the elevator to report a trapped person fault, and the rules that an electric car enters the elevator causes the elevator to report a trapped person fault. The behaviors in the elevator in S2 include cleaning and blocking the elevator door with an obstacle, pouring water on the elevator buttons, and an electric car entering the elevator. The specific rules for the elevator to report a trapped person fault due to cleaning behavior are: S10: Cleaning staff blocked the elevator door with an obstacle; S11: Elevator sends an alarm for door opening and closing failure; S12: The elevator sends an alarm to stop running fault; S13: The elevator reports a trapped person fault; S14: The diagnosis result is that the cleaning behavior caused the elevator to send out an alarm of trapped person failure; S15: The recommended solution is to explain the situation through the remote monitoring client and file the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site; The specific rules for causing the elevator to report a trapped person fault due to pouring water on the elevator buttons are: S16: Throwing water on the elevator buttons; S17: Report button failure to the elevator; S18: Elevator alarm mainboard self-protection fault; S19: The elevator sends an alarm to stop running fault; S20: The elevator reports a trapped person fault; S21: Pouring water on the elevator buttons causes the elevator to report a trapped person fault; S22: The recommended solution is to replace the elevator mainboard, replace the damaged parts of the elevator mainboard, or replace the elevator buttons; The specific rules for the elevator to report a trapped person fault caused by the electric vehicle entering the elevator are: S23: Elevator entering electric vehicle behavior; S24: The elevator reports an electric vehicle failure; S25 elevator sends an alarm to stop running fault; S26: Elevator reports a trapped person fault; S27: The electric vehicle enters the elevator, causing the elevator to report a trapped person fault; S28: ① The recommended solution is to explain the situation through the remote monitoring client and archive the trapped fault, report the situation to the property management, report the situation to the remote monitoring customer service phone, or handle the situation on site.

7. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 5, characterized in that: Specifically, in S3, the DTU terminal T1 collects the status, faults and video inside the elevator E1, and uploads the status, faults and video of the elevator E1 to the communication service terminal Z1 of the intelligent remote monitoring system Y1; After receiving the fault, the communication server Z1 is used to write the fault information into the database B1 for long-term storage. At the same time, the communication server Z1 pushes the elevator status and fault information to the status and fault model M1 of the elevator large model X1, and pushes the video information to the video behavior analysis model M2 for long-term storage.

8. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 5, characterized in that: The scoring system in S6 is a percentage system, with 100 being the best and 0 being the worst.

9. An artificial intelligence-based device for rapid diagnosis of elevator faults, characterized in that: It includes any mobile phone, computer, communication device and storage device that can run the software model in claims 1 to 7.

10. A device for rapid diagnosis of elevator faults based on artificial intelligence, characterized in that: It includes any mobile phone, computer, communication device and storage device that can run the software model in claims 1 to 7.

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