An elevator fault rapid diagnosis method, device, equipment and system based on artificial intelligence

Abstract

The application discloses a kind of based on artificial intelligence's elevator fault rapid diagnosis method, device, equipment and system, including mobile phone A1, remote monitoring client C1, set in the intelligent remote monitoring system Y1 of cloud, elevator E1, DTU terminal T1, maintenance personnel P1 and expert P2, intelligent remote monitoring system includes remote monitoring server S1, communication server Z1, database B1 and elevator big model X1, remote monitoring client C1 is installed on mobile phone A1;The scheme is by constructing elevator big model system, setting the cause-effect relationship of behavior and failure, automatically dynamic analysis the behavior in elevator before failure and associated failure, quickly diagnose the accurate reason of failure occurrence, realize elevator fault automatic research and accurate positioning, and recommend optimal failure solution, intelligent and process-oriented guide maintenance personnel to carry out elevator fault processing, greatly improve fault processing efficiency, shorten fault processing time, quickly restore the use of elevator, far superior to traditional fault processing method, with innovativeness.

Description

Technical Field

[0001] This invention belongs to the field of elevator maintenance technology, specifically relating to a method, device, equipment, and system for rapid diagnosis of elevator faults based on artificial intelligence. Background Technology

[0002] Elevators, as an indispensable vertical transportation tool in modern buildings, are directly related to passenger safety and reliability. Therefore, regular maintenance and upkeep not only extend their lifespan but also effectively reduce malfunctions and accidents, ensuring passenger safety. However, with the increasing number of elevator maintenance personnel, high turnover rates and varying skill levels mean that troubleshooting based on personal experience, consulting manuals, or requesting remote assistance is often time-consuming and laborious. Analyzing the causes of malfunctions and finding solutions takes considerable time, resulting in low efficiency and prolonged troubleshooting times, impacting customer elevator usage.

[0003] Chinese invention patent CN106586753B discloses an intelligent elevator fault reporting and processing system and method, which aims to reduce elevator operating costs and ensure the safety of life and property. The method includes the following steps: one-click reporting or QR code reporting; one-click reporting involves pressing a button inside the elevator car, which sends fault information, including the elevator name, to a cloud platform via the network; QR code reporting involves scanning a QR code with a mobile phone, linking to the cloud platform, and sending the fault information to the cloud platform via the network; after receiving the fault information, the cloud platform sends the elevator location and fault information to the property management terminal and maintenance personnel terminal via the network; after seeing the fault information on their terminal, maintenance personnel can accept or refuse the repair task.

[0004] The system assigns maintenance tasks to maintenance personnel and feeds the information back to the cloud platform. Maintenance personnel then report the progress and status of the maintenance tasks through their terminals. The cloud platform then transmits the progress and status of the maintenance tasks to the property management terminals and maintenance personnel terminals via the network. However, this approach only provides a basic way to assign maintenance tasks to maintenance personnel and lacks the ability to develop optimal fault solutions.

[0005] Therefore, the present invention provides a method, apparatus, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence, which can quickly diagnose the accurate cause of the fault and recommend the optimal fault solution. Summary of the Invention

[0006] To address the problems raised in the background technology, the purpose of this invention is to provide a method, device, equipment, and system for rapid elevator fault diagnosis based on artificial intelligence. By constructing a large elevator model system and establishing causal relationships between behavior and faults, the system automatically and dynamically analyzes the behavior and associated faults within the elevator before a fault occurs, quickly diagnoses the accurate cause of the fault, achieves automatic fault assessment and precise location, and recommends the optimal fault solution. This provides intelligent and process-oriented guidance for maintenance personnel in handling elevator faults, thereby significantly improving fault handling efficiency, shortening fault handling time, and quickly restoring elevator usability. This is far superior to traditional fault handling methods, innovative, and enhances the company's brand image.

[0007] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0008] An AI-based system for rapid elevator fault diagnosis includes a mobile phone A1, a remote monitoring client C1, a cloud-based intelligent remote monitoring system Y1, an elevator E1, a DTU terminal T1, 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 a large elevator 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 fault information.

[0009] Further defined, the elevator large model X1 includes a state and fault model M1, a video behavior analysis model M2, a fault diagnosis model M3, and a fault case model M4;

[0010] The video behavior analysis model M2 is used to accurately identify behaviors inside the elevator;

[0011] The elevator E1 is used to upload 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.

[0012] The DTU terminal T1 is used to collect the status, faults and video inside the elevator E1, and upload the status, faults and video inside the elevator E1 to the communication server Z1 of the intelligent remote monitoring system Y1.

[0013] After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator's status and fault information to the elevator's large model X1 and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time.

[0014] Further specifying, after receiving elevator fault and video information, the elevator large model X1 activates the fault diagnosis model M3 to perform reverse analysis through the timeline.

[0015] Further specifying, the reverse analysis is used to analyze whether the fault conforms to the fault rules, and to 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.

[0016] A method for rapid diagnosis of elevator faults based on artificial intelligence includes the following steps:

[0017] S1: Pre-set the relationship rules between the behavior and faults of the fault diagnosis model M3 and recommend solutions;

[0018] S2: Train the video behavior analysis model M2 using video samples to accurately identify behaviors inside the elevator;

[0019] 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.

[0020] S4: After receiving the fault and video, the elevator large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault conforms to the fault rules. Then, it starts the video behavior analysis model M2 to analyze the behavior before the fault occurred, and finally diagnoses the cause of the fault and recommends a solution.

[0021] S5: The elevator large model X1 pushes fault information, fault causes and recommended solutions to the remote monitoring client C1;

[0022] S6: After receiving the information, maintenance personnel P1 selects the recommended solution for quick processing and rates the recommended solution;

[0023] S7: The remote monitoring client C1 uploads fault information, solutions, and ratings to the elevator fault case M4 for long-term storage.

[0024] S8: The score of the solution is used as the order of recommended solutions in the next round;

[0025] S9: The elevator malfunction has been resolved and the elevator is back to normal.

[0026] Further defined, S1 includes rules for elevator entrapment caused by cleaning behavior, rules for elevator entrapment caused by splashing water on elevator buttons, and rules for elevator entrapment caused by electric vehicle entering elevator. The behaviors inside the elevator in S2 include cleaning and blocking the elevator car door with an obstacle, splashing water on elevator buttons, and electric vehicle entering elevator.

[0027] The specific rules governing elevator entrapment incidents caused by cleaning activities are as follows:

[0028] S10: Cleaning staff blocking elevator doors with obstacles;

[0029] S11: Elevator reports door opening / closing malfunction;

[0030] S12: Elevator reports a stoppage fault;

[0031] S13: Elevator reports entrapment fault;

[0032] S14: The diagnosis was that the cleaning staff's actions caused the elevator to report a entrapment malfunction.

[0033] S15: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site.

[0034] The specific rules governing the elevator reporting a entrapment fault due to the act of splashing water on elevator buttons are as follows:

[0035] S16: The act of splashing water on elevator buttons;

[0036] S17: Report a button malfunction to the elevator;

[0037] S18: Elevator reports a mainboard self-protection fault;

[0038] S19: Elevator reports a stoppage fault;

[0039] S20: Elevator reports entrapment fault;

[0040] S21: Throwing water at elevator buttons caused the elevator to report a entrapment fault.

[0041] S22: The recommended solution is to replace the elevator mainboard, replace the damaged parts of the elevator mainboard, or replace the elevator buttons;

[0042] The specific rules for triggering an elevator entrapment fault report due to an electric vehicle entering the elevator are as follows:

[0043] S23: Electric vehicle entering the elevator;

[0044] S24: The elevator reported an electric vehicle malfunction;

[0045] Elevator S25 reported a stop-operation fault;

[0046] S26: Elevator reports entrapment malfunction;

[0047] S27: The act of an electric vehicle entering the elevator caused the elevator to report a entrapment malfunction.

[0048] S28: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site.

[0049] Further specifying, S3 specifically involves the DTU terminal T1 collecting the status, faults, and video inside the elevator E1, and uploading the status, faults, and video of the elevator E1 to the communication server Z1 of the intelligent remote monitoring system Y1;

[0050] After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator's status and fault information to the elevator's large model X1 and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time.

[0051] Furthermore, the scoring system in S6 adopts a percentage system, with 100 points being the best and 0 points being the worst.

[0052] The beneficial effects of this invention are:

[0053] 1. This solution uses behavioral analysis and fault information to diagnose the exact cause of a fault, reducing the time spent searching for fault information, effectively improving the efficiency of fault diagnosis, shortening the fault handling cycle, thereby reducing costs and increasing customer satisfaction.

[0054] 2. This solution dynamically recommends solutions, reducing the time spent by on-site personnel seeking experts and searching for information, and greatly improving the speed of troubleshooting.

[0055] 3. This solution enables knowledge sharing by automatically uploading fault cases to the elevator large model X1, making them available to maintenance personnel nationwide.

[0056] 4. This solution addresses the challenge of improving fault handling quality by enhancing the quality of fault handling, and further improves the quality of fault handling through the optimal recommended solution.

[0057] 5. In summary, this solution constructs a large-scale elevator model system, establishes causal relationships between behavior and faults, automatically and dynamically analyzes elevator behavior and related faults before a fault occurs, quickly diagnoses the accurate cause of the fault, achieves automatic fault assessment and precise location, and recommends the optimal fault solution. It provides intelligent and process-oriented guidance for maintenance personnel in handling elevator faults, thereby significantly improving fault handling efficiency, shortening fault handling time, and quickly restoring elevator usability. This is far superior to traditional fault handling methods, demonstrates innovation, and enhances the company's brand image. Attached Figure Description

[0058] The present invention can be further illustrated by the non-limiting embodiments given in the accompanying drawings;

[0059] Figure 1This is a schematic diagram of an embodiment of the method, apparatus, equipment and system for rapid diagnosis of elevator faults based on artificial intelligence according to the present invention. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments. The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0062] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] Example 1: As Figure 1 As shown, the present invention discloses an artificial intelligence-based rapid elevator fault diagnosis system, comprising 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, 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 fault information.

[0064] Preferably, the elevator large model X1 includes a state and fault model M1, a video behavior analysis model M2, a fault diagnosis model M3, and a fault case model M4;

[0065] The video behavior analysis model M2 is used to accurately identify behaviors inside the elevator;

[0066] The elevator E1 is used to upload 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.

[0067] The DTU terminal T1 is used to collect the status, faults and video inside the elevator E1, and upload the status, faults and video inside the elevator E1 to the communication server Z1 of the intelligent remote monitoring system Y1.

[0068] After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator's status and fault information to the elevator's large model X1 and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time.

[0069] Preferably, after receiving elevator fault and video information, the elevator large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline.

[0070] Preferably, the reverse analysis is used to analyze whether the fault conforms to the fault rules, and to 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.

[0071] A method for rapid diagnosis of elevator faults based on artificial intelligence includes the following steps:

[0072] S1: Pre-set the relationship rules between the behavior and faults of the fault diagnosis model M3 and recommend solutions;

[0073] S2: Train the video behavior analysis model M2 using video samples to accurately identify behaviors inside the elevator;

[0074] 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.

[0075] S4: After receiving the fault and video, the elevator large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault conforms to the fault rules. Then, it starts the video behavior analysis model M2 to analyze the behavior before the fault occurred, and finally diagnoses the cause of the fault and recommends a solution.

[0076] For example, if elevator A experiences a passenger entrapment fault at 10:00, reverse analysis of the diagnostic timeline using fault diagnosis model M3 reveals that a stoppage fault occurred at 9:58 and a door opening / closing fault occurred at 9:56. Further analysis using video behavior analysis model M2 shows that cleaning was performed at 9:54 and the elevator car door was blocked by an obstacle. This behavior aligns with the rule that cleaning activities cause the elevator to report a passenger entrapment fault. Therefore, the diagnosis concludes that the cause of the fault is: cleaning activities caused the elevator to report a passenger entrapment fault.

[0077] S5: The elevator large model X1 pushes fault information, fault causes and recommended solutions to the remote monitoring client C1;

[0078] S6: After receiving the information, maintenance personnel P1 selects the recommended solution for quick processing and rates the recommended solution;

[0079] S7: The remote monitoring client C1 uploads fault information, solutions, and ratings to the elevator fault case M4 for long-term storage.

[0080] S8: The score of the solution is used as the order of recommended solutions in the next round;

[0081] Specifically, for example, if cleaning work leads to an elevator entrapment report, there are four solutions. The default order is: ① Explain the situation and file the entrapment report through the remote monitoring client; ② Report the situation to the property management; ③ Report the situation to the remote monitoring customer service hotline; ④ Handle the situation on-site. However, after scoring, if the solutions score ① 89 points, ② 95 points, ③ 92 points, and ④ 90 points, then the recommended solution for the next time will automatically become: ① Report the situation to the property management; ② Report the situation to the remote monitoring customer service hotline; ③ Handle the situation on-site; ④ Explain the situation and file the entrapment report through the remote monitoring client.

[0082] S9: The elevator malfunction has been resolved and the elevator is back to normal.

[0083] Preferably, S1 includes rules for elevator entrapment caused by cleaning behavior, rules for elevator entrapment caused by splashing water on elevator buttons, and rules for elevator entrapment caused by electric vehicle entering elevator. The behaviors inside the elevator in S2 include cleaning and blocking the elevator car door with an obstacle, splashing water on elevator buttons, and electric vehicle entering elevator.

[0084] The specific rules governing elevator entrapment incidents caused by cleaning activities are as follows:

[0085] S10: Cleaning staff blocking elevator doors with obstacles;

[0086] S11: Elevator reports door opening / closing malfunction;

[0087] S12: Elevator reports a stoppage fault;

[0088] S13: Elevator reports entrapment fault;

[0089] S14: The diagnosis was that the cleaning staff's actions caused the elevator to report a entrapment malfunction.

[0090] S15: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site.

[0091] The specific rules governing the elevator reporting a entrapment fault due to the act of splashing water on elevator buttons are as follows:

[0092] S16: The act of splashing water on elevator buttons;

[0093] S17: Report a button malfunction to the elevator;

[0094] S18: Elevator reports a mainboard self-protection fault;

[0095] S19: Elevator reports a stoppage fault;

[0096] S20: Elevator reports entrapment fault;

[0097] S21: Throwing water at elevator buttons caused the elevator to report a entrapment fault.

[0098] S22: The recommended solution is to replace the elevator mainboard, replace the damaged parts of the elevator mainboard, or replace the elevator buttons;

[0099] The specific rules for triggering an elevator entrapment fault report due to an electric vehicle entering the elevator are as follows:

[0100] S23: Electric vehicle entering the elevator;

[0101] S24: The elevator reported an electric vehicle malfunction;

[0102] Elevator S25 reported a stop-operation fault;

[0103] S26: Elevator reports entrapment malfunction;

[0104] S27: The act of an electric vehicle entering the elevator caused the elevator to report a entrapment malfunction.

[0105] S28: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site.

[0106] Preferably, in step 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 server Z1 of the intelligent remote monitoring system Y1.

[0107] After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator's status and fault information to the elevator's large model X1 and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time.

[0108] Preferably, the scoring system in S6 is a percentage system, with 100 points being the best and 0 points being the worst.

[0109] An artificial intelligence-based device for rapid diagnosis of elevator faults includes any mobile phone, computer, communication device, and storage device capable of running the software model of this solution.

[0110] Example 2:

[0111] The difference between Example 2 and Example 1 lies in the reverse analysis. Example 2 is illustrated below:

[0112] Elevator B experienced a passenger entrapment fault at 12:00. Through reverse analysis of the diagnostic timeline using fault diagnosis model M3, it was found that a stop-operation fault occurred at 11:58, a mainboard self-protection fault occurred at 11:56, and a button malfunction occurred at 11:54. Further analysis using video behavior analysis model M2 revealed that at 11:52, there was an act of splashing water on the elevator buttons, which conforms to the rule that splashing water on elevator buttons causes the elevator to report a passenger entrapment fault. Therefore, the diagnosis concluded that the cause of the fault was: splashing water on the elevator buttons caused the elevator to report a passenger entrapment fault.

[0113] Example 3:

[0114] The difference between Implementation 3 and Implementation 1 lies in the reverse analysis. An example of Implementation 3 is as follows:

[0115] Elevator C experienced a passenger entrapment fault at 15:00. Through reverse analysis of the diagnostic timeline using fault diagnosis model M3, it was found that a stoppage fault occurred at 14:58, and an electric vehicle fault occurred at 14:56. Further analysis using video behavior analysis model M2 revealed that there was an elevator entering an electric vehicle at 9:54, which conforms to the rule that elevator entering an electric vehicle causes the elevator to report a passenger entrapment fault. Therefore, the cause of the fault was diagnosed as: elevator entering an electric vehicle caused the elevator to report a passenger entrapment fault.

[0116] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in 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: The system 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, 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 to upload fault information. The elevator model X1 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 behaviors inside the elevator; The elevator E1 is used to upload 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 inside the elevator E1 to the communication server Z1 of the intelligent remote monitoring system Y1. After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator status and fault information to the elevator big model X1 status and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time. After receiving elevator fault and video information, the elevator large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline. The reverse analysis is used to analyze whether the fault conforms to the fault rules, and to start the video behavior analysis model M2 to analyze the behavior before the fault occurred, and finally diagnose the cause of the fault and recommend solutions.

2. A method for rapid elevator fault diagnosis based on artificial intelligence, used to implement the system for rapid elevator fault diagnosis based on artificial intelligence as described in claim 1, characterized in that, Includes the following steps: S1: Pre-set the relationship rules between the behavior and faults of the fault diagnosis model M3 and recommend solutions; S2: Train the video behavior analysis model M2 using video samples to accurately identify behaviors inside 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 large model X1 starts the fault diagnosis model M3 to perform reverse analysis through the timeline to analyze whether the fault conforms to the fault rules. Then, it starts the video behavior analysis model M2 to analyze the behavior before the fault occurred, and finally diagnoses the cause of the fault and recommends a solution. S5: The elevator large model X1 pushes fault information, fault causes and recommended solutions 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 fault information, solutions, and scores to the elevator's fault case model M4 for long-term storage; S8: The score of the solution is used as the order of recommended solutions in the next round; S9: The elevator malfunction has been resolved and the elevator is back to normal.

3. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 2, characterized in that: S1 includes rules for elevators to report entrapment faults due to cleaning behavior, rules for elevators to report entrapment faults due to splashing water on elevator buttons, and rules for elevators to report entrapment faults due to electric vehicles entering the elevator. S2 includes behaviors inside the elevator, such as cleaning and blocking the elevator car door with obstacles, splashing water on elevator buttons, and electric vehicles entering the elevator. The specific rules governing elevator entrapment incidents caused by cleaning activities are as follows: S10: Cleaning staff blocking elevator doors with obstacles; S11: Elevator reports door opening / closing malfunction; S12: Elevator reports a stoppage fault; S13: Elevator reports entrapment fault; S14: The diagnosis was that the cleaning staff's actions caused the elevator to report a entrapment malfunction. S15: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site. The specific rules governing the elevator reporting a entrapment fault due to the act of splashing water on elevator buttons are as follows: S16: The act of splashing water on elevator buttons; S17: Elevator up button malfunction; S18: Elevator reports a mainboard self-protection fault; S19: Elevator reports a stoppage fault; S20: Elevator reports entrapment fault; S21: Throwing water at elevator buttons caused the elevator to report a entrapment 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 triggering an elevator entrapment fault report due to an electric vehicle entering the elevator are as follows: S23: Electric vehicle entering the elevator; S24: The elevator reported an electric vehicle malfunction; S25: Elevator reports a stoppage fault; S26: Elevator reports entrapment malfunction; S27: The act of an electric vehicle entering the elevator caused the elevator to report a entrapment malfunction. S28: The recommended solution is to explain the situation and file the entrapment fault through the remote monitoring client, report the situation to the property management, report the situation to the remote monitoring customer service hotline, or handle the situation on-site.

4. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 2, 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 inside the elevator E1 to the communication server Z1 of the intelligent remote monitoring system Y1. After receiving a fault, the communication server Z1 writes the fault information into the database B1 and stores it for a long time. At the same time, the communication server Z1 pushes the elevator's status and fault information to the elevator's large model X1 and fault model M1, and pushes the video information to the video behavior analysis model M2 and stores it for a long time.

5. The method for rapid diagnosis of elevator faults based on artificial intelligence according to claim 2, characterized in that: The scoring system in S6 uses a percentage system, with 100 points being the best and 0 points being the worst.

Images