Expressway electromechanical knowledge fusion and diagnosis system based on large model

Abstract

The invention provides an expressway electromechanical knowledge fusion and diagnosis system based on a large model, and relates to the technical field of information and communication, the expressway electromechanical knowledge fusion and diagnosis system comprises a knowledge fusion center module, an equipment perception and data acquisition layer and an intelligent diagnosis decision engine, and a knowledge graph construction system and a large model engine are built in the knowledge fusion center module. The intelligent diagnosis decision engine is internally provided with a real-time analysis calculation node and a predictive maintenance model, and the equipment perception and data acquisition layer comprises Internet of Things edge equipment, a monitoring camera and an equipment perception device. The device state can be monitored in real time, the fault root cause can be accurately positioned, and the potential risk can be predicted. The vehicle-mounted equipment sensing device regularly moves along the expressway, and the appearance information of the electromechanical equipment at each preset point location is scanned and recognized, so that when a fault occurs, the appearance information serves as an analysis basis to output a diagnosis result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information and communication technology, in particular to a highway electromechanical knowledge fusion and diagnosis system based on a large model. BACKGROUND

[0002] The core purpose of diagnosing the highway electromechanical system (including monitoring, toll, communication, lighting, ventilation, etc. Subsystem) is to accurately identify the equipment running state, potential failure and performance degradation trend through systematic, professional detection, analysis and evaluation, so as to realize the active control of the health state of electromechanical facilities, and ultimately guarantee the safe, efficient and reliable operation of the highway, and optimize the life cycle management cost. It is embodied in: ensuring driving safety; maintaining traffic efficiency; reducing operation and maintenance cost; supporting scientific decision-making; improving travel experience, ensuring timely information release and service facility availability, and laying a foundation for moving towards a more intelligent intelligent highway.

[0003] In the prior art, in addition to collecting a large amount of equipment parameter information, the scheme for diagnosing the highway electromechanical equipment can also establish a diagnosis system based on a large model, integrate and understand all the scattered equipment knowledge, and use these knowledge to intelligently analyze various electronic equipment (toll, monitoring, communication, lighting, power supply, etc.) on the highway, quickly diagnose faults, early warning risks, and guide maintenance decisions. However, the conventional diagnosis system can only identify and judge by collecting digital signals, and it is difficult to collect and obtain deformation and damage information in various electromechanical equipment, resulting in a large deviation in the diagnosis result. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application aims to provide a highway electromechanical knowledge fusion and diagnosis system based on a large model to solve the problems raised in the background art. The present application deeply integrates fragmented electromechanical knowledge (manuals, drawings, maintenance records, etc.) through large model technology, builds an intelligent diagnosis engine, can monitor equipment status in real time, accurately locate fault root cause, and predict potential risks. The vehicle-mounted equipment sensing device moves along the highway regularly, scans and identifies the appearance information of the electromechanical equipment at each preset point, so as to output the diagnosis result as an analysis basis when a fault occurs.

[0005] In order to achieve the above object, the application is realized by the following technical scheme: the highway electromechanical knowledge fusion and diagnosis system based on a large model, comprising a knowledge fusion hub module, a device perception and data acquisition layer, and an intelligent diagnosis decision engine, a knowledge graph construction system and a large model engine are built in the knowledge fusion hub module, real-time analysis calculation nodes and predictive maintenance models are arranged in the intelligent diagnosis decision engine, the device perception and data acquisition layer comprises an Internet of Things edge device, a monitoring camera and a device perception device, wherein the device perception device is mounted on a highway inspection vehicle, and the device perception device comprises a fixed seat, a driving mechanism, a scanning assembly and a lifting mechanism, the driving mechanism is screwed on the surface of the fixed seat, the top end of the driving mechanism is provided with the scanning assembly, one end of the surface of the fixed seat is screwed with a lifting sleeve, the top end of the lifting sleeve is inserted with the lifting mechanism, a three-dimensional scanning diagnosis unit is arranged in the scanning assembly, and the tail end of the scanning assembly is used for embedding into the inner side of the lifting mechanism.

[0006] Further, the knowledge graph construction system is used to take device parameters and fault logic, construct a device relationship network, establish a unified semantic model based on the ontology framework of the highway electromechanical field, automatically extract entities and relationships in the electromechanical knowledge, and mine the correlations in the historical fault data by using a graph neural network.

[0007] Further, the knowledge graph construction system further converts real-time sensor data streams into attribute nodes through a dynamic knowledge injection mechanism, and finally generates a dynamic graph containing millions of nodes / edges, so that fragmented electromechanical knowledge is upgraded to a decision network that provides traceability and reasoning.

[0008] Further, the large model engine is used to understand natural language and associate fragmented knowledge, and the large model engine integrates a multi-source database, the multi-source database comprises a SQL database, a NoSQL database and a real-time time series database, wherein the SQL database is used to store structured data, the NoSQL database is used to store unstructured data, and the real-time time series database is used to store device operation data streams.

[0009] Further, the scanning assembly comprises a telescopic arm, a three-dimensional laser scanner and a plug-in board, the tail end of the telescopic arm is integrally formed with the plug-in board, the tail end of the plug-in board is integrally formed with a front end clamping rod, a rear end clamping rod is inserted into the side edge of the plug-in board, and the surface of the telescopic arm is screwed with the three-dimensional laser scanner.

[0010] Further, the driving mechanism comprises a stand, a motor and a rotating shaft, a clamping groove is formed in the top of the stand, the rotating shaft is arranged in the clamping groove, the motor is screwed on one side of the top end of the stand, and the rotating shaft is inserted into the output end of the motor.

[0011] Further, the motor is started to control the rotation of the rotating shaft and drive the telescopic arm to rotate synchronously, the telescopic arm is in a three-section telescopic structure, and the front end clamping rod and the rear end clamping rod are parallel to each other.

[0012] Further, the side of the lifting sleeve is provided with a lug plate, a distance measuring module is screwed on the surface of the lug plate, and the side of the lifting sleeve is also provided with an air cylinder; the lifting mechanism comprises a lifting column, a supporting spring and a transmission mechanism, the bottom of the lifting column is welded with the supporting spring, the bottom end of the supporting spring is connected with a bottom plate, and the supporting spring and the bottom plate are embedded into the inside of the lifting sleeve; and a clamping layer is formed in the inside of the lifting column.

[0013] Further, the two sides of the lifting column are welded with an extension plate and a guide sleeve respectively, the transmission mechanism comprises a pressure bearing plate, a first gear and a second gear, the two ends of the pressure bearing plate are integrally formed with protrusions, the bottom of the pressure bearing plate is welded with a first gear rack, the bottom of the clamping layer is inserted with a supporting shaft, the surface of the supporting shaft is keyed with the first gear and the second gear, and the first gear and the first gear rack are engaged.

[0014] Further, the inside of the guide sleeve is inserted with an extension column, the surface of the extension column is integrally formed with a second gear rack, the second gear rack is used for engaging with the second gear, the end of the extension column is connected with an electrode column, the other end of the extension column is embedded into the inside of the extension plate, the bottom end of the pressure bearing plate is also welded with a spring rod, and the bottom end of the spring rod is fixedly connected with the inner wall of the lifting column.

[0015] The beneficial effects of the present application are as follows: The highway electromechanical knowledge fusion and diagnosis system based on a large model can realize real-time monitoring of equipment state, accurate positioning of fault root cause, prediction of potential risks, correlation of equipment life cycle data, and second-level response to complex queries by deeply fusing fragmented electromechanical knowledge (manuals, drawings, maintenance records, etc.) through large model technology, actively preventing instead of passively repairing, and improving operation and maintenance efficiency and significantly reducing highway interruption risks and economic losses.

[0016] In the highway electromechanical knowledge fusion and diagnosis system based on a large model, a vehicle-mounted device sensing device is regularly moved along the highway to scan and identify the appearance information of electromechanical equipment at each preset point, so that whether each electromechanical equipment has deformation or damage can be regularly obtained, and the collected three-dimensional modeling information can be updated to output diagnosis results as analysis basis when a fault occurs.

[0017] The highway electromechanical knowledge fusion and diagnosis system based on a large model realizes the scanning process of the appearance of surrounding electromechanical equipment by driving the lifting and rotation of the scanning assembly through the driving mechanism, and can also cooperate with the reverse operation of the cylinder and the driving mechanism to detect the leakage around the power equipment on the side, so as to obtain more accurate and reliable leakage information for verifying the diagnosis result and improving the accuracy of the diagnosis information. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A principle block diagram of the highway electromechanical knowledge fusion and diagnosis system based on a large model of the present application; Figure 2 A device perception device structure diagram used in the highway electromechanical knowledge fusion and diagnosis system based on a large model of the present application; Figure 3 A structure schematic diagram of the scanning assembly part of the present application; Figure 4 An installation structure diagram of the three-dimensional laser scanner part of the present application; Figure 5 A structure schematic diagram of the lifting mechanism part of the present application; Figure 6 A split diagram of the lifting mechanism of the present application; Figure 7 A Figure 2 An enlarged view of area A; Figure 8 A structure schematic diagram of the transmission mechanism part of the present application; In the figure: 1, fixed seat; 2, driving mechanism; 3, scanning assembly; 4, lifting mechanism; 5, stand; 6, clamping groove; 7, motor; 8, rotating shaft; 9, telescopic arm; 10, three-dimensional laser scanner; 11, plug-in board; 12, rear end clamping rod; 13, front end clamping rod; 14, lifting sleeve; 15, convex plate; 16, distance measuring module; 17, cylinder; 18, lifting column; 19, interlayer; 20, transmission mechanism; 21, supporting spring; 22, bottom plate; 23, extension plate; 24, guide sleeve; 25, pressure plate; 26, convex strip; 27, first rack; 28, support shaft; 29, first gear; 30, second gear; 31, second rack; 32, extension column; 33, electrode column; 34, spring rod. DETAILED DESCRIPTION

[0019] In order to make the technical means, creative features, purposes and effects realized by the present application easy to understand, the present application is further described below in combination with specific embodiments.

[0020] Please refer to Figures 1 to 8The application provides the following technical scheme: a highway electromechanical knowledge fusion and diagnosis system based on a large model, comprising a knowledge fusion hub module, a device perception and data acquisition layer, and an intelligent diagnosis decision engine, a knowledge graph construction system and a large model engine are built in the knowledge fusion hub module, real-time analysis calculation nodes and a predictive maintenance model are arranged in the intelligent diagnosis decision engine, the device perception and data acquisition layer comprises an Internet of Things edge device, a monitoring camera, and a device perception device, wherein the device perception device is mounted on a highway inspection vehicle, and the device perception device comprises a fixed seat 1, a driving mechanism 2, a scanning assembly 3, and a lifting mechanism 4, the driving mechanism 2 is screwed on the surface of the fixed seat 1, the top end of the driving mechanism 2 is provided with the scanning assembly 3, one end of the surface of the fixed seat 1 is screwed with a lifting sleeve 14, the top end of the lifting sleeve 14 is inserted with the lifting mechanism 4, a three-dimensional scanning diagnosis unit is arranged in the scanning assembly 3, and the tail end of the scanning assembly 3 is used for embedding into the inner side of the lifting mechanism 4. The system realizes the fusion of highway electromechanical knowledge based on a large model, and realizes the scanning and collection of the state of electromechanical equipment in cooperation with the vehicle-mounted device perception device, so that the purpose of more accurate and reliable system diagnosis is achieved.

[0021] In the embodiment, the knowledge graph construction system is used to obtain device parameters and fault logic, construct a device relationship network, establish a unified semantic model based on the ontology framework of the highway electromechanical field, automatically extract entities and relationships in electromechanical knowledge, and mine the association in historical fault data by using a graph neural network. The knowledge graph construction system further converts real-time sensor data streams into attribute nodes through a dynamic knowledge injection mechanism, and finally generates a dynamic graph containing millions of nodes / edges, so that fragmented electromechanical knowledge is upgraded to a decision network that provides tracing and reasoning. The large model engine is used to understand natural language and associate fragmented knowledge, and the large model engine integrates a multi-source database, the multi-source database comprising an SQL database, a NoSQL database, and a real-time time series database, wherein the SQL database is used to store structured data, the NoSQL database is used to store unstructured data, and the real-time time series database is used to store device operation data streams. The highway electromechanical knowledge fusion and diagnosis system deeply fuses fragmented electromechanical knowledge (manuals, drawings, maintenance records, etc.) through a large model technology, constructs an intelligent diagnosis engine, can monitor the state of equipment in real time, accurately locates the root cause of the fault, and predicts potential risks, changes passive repair to active prevention, associates device lifecycle data, and responds to complex queries in seconds; improves operation and maintenance efficiency, and significantly reduces the risk of highway interruption and economic loss.

[0022] The scanning assembly 3 comprises a telescopic arm 9, a three-dimensional laser scanner 10 and a plug-in plate 11, the telescopic arm 9 is integrally formed with the plug-in plate 11 at the end, the plug-in plate 11 is integrally formed with the front end clamping rod 13 at the end, the plug-in plate 11 is inserted with the rear end clamping rod 12 at the side, and the three-dimensional laser scanner 10 is screwed on the surface of the telescopic arm 9. The driving mechanism 2 comprises a column 5, a motor 7 and a rotating shaft 8, the column 5 is provided with a clamping groove 6 at the top, the rotating shaft 8 is installed in the clamping groove 6, the motor 7 is screwed on one side of the top end of the column 5, and the rotating shaft 8 is inserted into the output end of the motor 7. The motor 7 is used to control the rotating movement of the rotating shaft 8 and drive the telescopic arm 9 to run synchronously after being started, the telescopic arm 9 is in a three-section telescopic structure as a whole, and the front end clamping rod 13 and the rear end clamping rod 12 are parallel to each other. The vehicle-mounted device sensing device moves along the highway regularly, scans and identifies the appearance information of the electromechanical equipment of each preset point, so that whether the electromechanical equipment at each place has deformation or damage can be regularly obtained, and the collected three-dimensional modeling information is updated, so that the diagnosis result is output as an analysis basis when a fault occurs.

[0023] Specifically, the device sensing device is installed on the inspection vehicle of the highway, and the whole device sensing device is driven by the inspection vehicle to move along the to-be-measured highway range regularly. During the movement, a marker is erected in the area where three-dimensional scanning is needed. After the ranging module 16 irradiates the marker, an electric signal is sent to control the driving mechanism 2 to operate. At this time, the motor 7 in the driving mechanism 2 is started, the rotating shaft 8 and the telescopic arm 9 are driven to rotate by the operation of the motor 7, the three-dimensional laser scanner 10 at the end is driven to work by the rotation of the telescopic arm 9, the plug-in plate 11 at the end is moved upward from the inside of the lifting mechanism 4, so as to control the three-dimensional laser scanner 10 to lift upward, realize the scanning process of the corresponding electromechanical equipment around the marker, and finally realize the exploration of the surface deformation or damage phenomenon of the electromechanical equipment in the range and upload to the database for later diagnosis. The three-dimensional laser scanner 10 and the ranging module 16 are both existing mature technologies and do not belong to the protection range of the present application, so their internal structures and specific specifications are not described here.

[0024] In this embodiment, a protruding plate 15 is provided on the side of the lifting sleeve 14, and a ranging module 16 is screwed onto the surface of the protruding plate 15. A cylinder 17 is also installed on the side of the lifting sleeve 14. The lifting mechanism 4 includes a lifting column 18, a support spring 21, and a transmission mechanism 20. The support spring 21 is welded to the bottom of the lifting column 18, and a base plate 22 is connected to the bottom end of the support spring 21. Both the support spring 21 and the base plate 22 are embedded inside the lifting sleeve 14. A sandwich layer 19 is provided on the inner side of the lifting column 18, and the plug-in plate 11 is used to be embedded inside the sandwich layer 19. An extension plate 23 and a guide sleeve 24 are welded to both sides of the lifting column 18. The transmission mechanism 20 includes a pressure plate 25, a first gear 29, and a second gear 30. The pressure plate 25 has integrally formed protrusions 26 at both ends, and a first rack 27 is welded to the bottom of the pressure plate 25. A support shaft 28 is inserted into the bottom of the interlayer 19, and the first gear 29 and the second gear 30 are keyed to the surface of the support shaft 28. The first gear 29 and the first rack 27 mesh with each other. An extension column 32 is inserted into the inner side of the guide sleeve 24. A second rack 31 is integrally formed on the surface of the extension column 32, and the second rack 31 meshes with the second gear 30. An electrode column 33 is connected to the end of the extension column 32, and the other end of the extension column 32 is embedded into the inner side of the extension plate 23. A spring rod 34 is also welded to the bottom end of the pressure plate 25, and the bottom end of the spring rod 34 is fixedly connected to the inner wall of the lifting column 18. The scanning component 3 is raised and rotated by the drive mechanism 2 to scan the appearance of the surrounding electromechanical equipment. At the same time, it can also cooperate with the cylinder 17 and the reverse operation of the drive mechanism 2 to detect leakage current in the area around the side electrical equipment, thereby obtaining more accurate and reliable leakage current information for verification of diagnostic results and improving the accuracy of diagnostic information.

[0025] Specifically, the telescopic arm 9 and the lifting column 18 are connected by inserting the connector plate 11 into the interior of the interlayer 19 and locking it onto both sides of the lifting column 18 via the front locking rod 13 and the rear locking rod 12, respectively. When the motor 7 is started, the telescopic arm 9 rotates upward, which in turn drives the lifting column 18 to rise synchronously, ensuring that the 3D laser scanner 10 can maintain a more stable state throughout the lifting process. This continues until the connector plate 11 is completely removed from the interior of the interlayer 19. At this point, the telescopic arm 9 will reach its maximum extension state, allowing it to rotate within a wider range and scan and explore the top equipment. When the telescopic arm 9 returns to its original position, the lifting column 18 is pressed downward, causing the support spring 21 to contract.

[0026] When the air cylinder 17 is started, the lifting column 18 is pressed and clamped by the extension of the air cylinder 17, so as to achieve the locking purpose of the lifting column 18, at this time, through the reverse operation of the motor 7, the plug-in plate 11 at the end can be pressed downward on the pressure plate 25, so as to drive the pressure plate 25 and the first rack 27 at the bottom to move downward, and through the first gear 29, the support shaft 28 is driven to rotate, the second gear 30 drives the second rack 31 at the bottom to move, and finally the electrode column 33 at the end is extended outward from the inside of the guide sleeve 24, and clamped on the corresponding electromechanical equipment around the side, and whether there is current at the contact position is probed, so as to achieve the purpose of leakage detection and diagnosis.

[0027] The above shows and describes the basic principles and main features of the present application and the advantages of the present application, and it is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or essential characteristics of the present application.

[0028] In addition, it should be understood that although the present application is described in the form of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be combined appropriately to form other embodiments that those skilled in the art can understand.

Claims

1. A highway electromechanical knowledge fusion and diagnosis system based on a large model, characterized in that: The application relates to a knowledge fusion center module, a device sensing and data acquisition layer and an intelligent diagnosis decision engine, wherein a knowledge graph construction system and a large model engine are arranged in the knowledge fusion center module, real-time analysis calculation nodes and a predictive maintenance model are arranged in the intelligent diagnosis decision engine, and the device sensing and data acquisition layer comprises Internet of Things edge devices, monitoring cameras and device sensing devices, wherein the device sensing devices are mounted on highway inspection vehicles, and the device sensing devices comprise a fixed seat (1), a driving mechanism (2), a scanning assembly (3) and a lifting mechanism (4), the driving mechanism (2) is screwed on the surface of the fixed seat (1), the top end of the driving mechanism (2) is provided with the scanning assembly (3), one end of the surface of the fixed seat (1) is screwed with a lifting sleeve (14), the top end of the lifting sleeve (14) is inserted with the lifting mechanism (4), and the scanning assembly (3) is provided with a three-dimensional scanning diagnosis unit, and the tail end of the scanning assembly (3) is used for being embedded into the inner side of the lifting mechanism (4).

2. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 1, characterized in that: The knowledge graph construction system is used for obtaining device parameters and fault logic, constructing a device relationship network, establishing a unified semantic model based on a highway electromechanical field ontology framework, automatically extracting entities and relationships in electromechanical knowledge, and utilizing a graph neural network to mine correlations in historical fault data.

3. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 2, characterized in that: The knowledge graph construction system further converts real-time sensor data streams into attribute nodes through a dynamic knowledge injection mechanism, finally generates a dynamic graph containing million-level nodes / edges, and upgrades fragmented electromechanical knowledge into a decision network providing tracing and reasoning.

4. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 2, characterized in that: The large model engine is used for understanding natural language and correlating fragmented knowledge, and the large model engine integrates a multi-source database, wherein the multi-source database comprises an SQL database, a NoSQL database and a real-time time series database, the SQL database is used for storing structured data, the NoSQL database is used for storing unstructured data, and the real-time time series database is used for storing device operation data streams.

5. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 1, characterized in that: The scanning assembly (3) comprises a telescopic arm (9), a three-dimensional laser scanner (10) and a plug-in plate (11), the tail end of the telescopic arm (9) is integrally formed with the plug-in plate (11), the tail end of the plug-in plate (11) is integrally formed with a front end clamping rod (13), the side edge of the plug-in plate (11) is inserted with a rear end clamping rod (12), and the surface of the telescopic arm (9) is screwed with the three-dimensional laser scanner (10).

6. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 5, characterized in that: The driving mechanism (2) comprises a stand (5), a motor (7) and a rotating shaft (8), the top of the stand (5) is provided with a clamping groove (6), the clamping groove (6) is internally provided with the rotating shaft (8), one side of the top end of the stand (5) is screwed with the motor (7), and the rotating shaft (8) is inserted into the output end of the motor (7).

7. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 6, characterized in that: The motor (7) is used for controlling the rotating movement of the rotating shaft (8) and driving the telescopic arm (9) to synchronously run after being started, the telescopic arm (9) is in a three-section telescopic structure as a whole, and the front end clamping rod (13) and the rear end clamping rod (12) are parallel to each other.

8. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 5, characterized in that: The side of the lifting sleeve (14) is provided with a lug plate (15), the surface of the lug plate (15) is screwed with a distance measuring module (16), the side of the lifting sleeve (14) is also provided with a cylinder (17), the lifting mechanism (4) comprises a lifting column (18), a supporting spring (21) and a transmission mechanism (20), the bottom of the lifting column (18) is welded with a supporting spring (21), the bottom end of the supporting spring (21) is connected with a bottom plate (22), the supporting spring (21) and the bottom plate (22) are embedded into the inside of the lifting sleeve (14), the inside of the lifting column (18) is provided with a sandwich layer (19), the plug-in plate (11) is used for embedding into the inside of the sandwich layer (19).

9. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 8, characterized in that: The two sides of the lifting column (18) are respectively welded with an extension plate (23) and a guide sleeve (24), the transmission mechanism (20) comprises a pressure plate (25), a first gear (29) and a second gear (30), the two ends of the pressure plate (25) are integrally formed with a convex strip (26), the bottom of the pressure plate (25) is welded with a first gear rack (27), the bottom of the sandwich layer (19) is inserted with a supporting shaft (28), the surface of the supporting shaft (28) is keyed with the first gear (29) and the second gear (30), the first gear (29) and the first gear rack (27) are engaged.

10. The large model-based highway electromechanical knowledge fusion and diagnosis system according to claim 9, characterized in that: The inside of the guide sleeve (24) is inserted with an extension column (32), the surface of the extension column (32) is integrally formed with a second gear rack (31), the second gear rack (31) is used for engaging with the second gear (30), the end of the extension column (32) is connected with an electrode column (33), the other end of the extension column (32) is embedded into the inside of the extension plate (23), the bottom end of the pressure plate (25) is also welded with a spring rod (34), the bottom end of the spring rod (34) is fixedly connected with the inner wall of the lifting column (18).

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