Ship operation and maintenance guidance animation generation method based on AI and knowledge graph

Abstract

The invention relates to the technical field of ship operation and maintenance guidance, in particular to a ship operation and maintenance guidance animation generation method based on AI and a knowledge graph, by combining a natural language large model, an operation and maintenance large model and a knowledge graph network, an operator can ask questions through a natural and fuzzy language, and the knowledge graph network is optimized. And the system can automatically push the solution only by simply speaking the operation and maintenance requirements and without actively searching the problem by an operator. And meanwhile, the solution is processed to generate a complete operation and maintenance guidance animation, so that an operator can be intuitively and quickly guided to process and operate the operation and maintenance requirements, and the operation is more convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of ship operation and maintenance guidance, and in particular to a method for generating a ship operation and maintenance guidance animation based on AI and a knowledge graph. Background Art

[0002] At present, domestic ships are becoming larger, more complex, and more high-value-added. The number of parts they contain is also increasing, and their operation and maintenance are becoming more and more difficult. In the past, shipyards generally delivered drawings, documents, and manuals to shipowners to serve operation and maintenance. Subsequently, against the background of rapid digital development, the IETM (Interactive Electronic Technical Manual) interactive electronic manual came into being. It uses electronic methods to structure and integrate multimodal information such as text, models, pictures, videos, animations, etc. in the operation and maintenance manuals in traditional paper or technical materials to build a standardized structure tree for equipment use, maintenance, training, and other work. However, the solution pushed in this way is only a word-like document, but where to operate what equipment at each step on the ship needs to be further determined, making operation and maintenance more troublesome. Summary of the invention

[0003] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide a method for generating ship operation and maintenance guidance animation based on AI and knowledge graph, which is used to solve the problem that the prior art uses interactive electronic manuals for operation and maintenance guidance, and the pushed solution is only a word-like document, which makes operation and maintenance more troublesome.

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention provides a method for generating a ship operation and maintenance guidance animation based on AI and knowledge graph, and the animation generation method comprises:

[0005] Build a large model in the field of operation and maintenance to analyze failures and problems in the operation and maintenance process, and proactively push answers or solutions;

[0006] Constructing a ship operation and maintenance knowledge graph network, wherein the knowledge graph network includes a relationship between a three-dimensional model and a name corresponding to the three-dimensional model and a relationship between a logical action and an action library;

[0007] Input operation and maintenance requirements and process the requirements;

[0008] Performing retrieval and reasoning on the processed requirements to obtain solutions;

[0009] Standardizing the solution, associating the name of the object model in the solution with the three-dimensional model and the logical action in the solution with the action library, so as to respectively construct operation animations of each of the object models;

[0010] The knowledge graph network is used to plan routes in a virtual environment according to the order in which the object models appear. A virtual camera is used to follow and shoot the operation animations of each object model along the route to generate a complete operation and maintenance guidance animation.

[0011] Optionally, the animation generation method further comprises constructing a large natural language model;

[0012] The method for processing the demand is to use the natural language large model to process the demand.

[0013] Optionally, the operation and maintenance big model is a cross-modal big model, and the training sources of the operation and maintenance big model include fault trees, mechanism models of various systems, standard operation and maintenance processes, vibration and noise data, and fault images.

[0014] Optionally, the knowledge graph network is a multimodal knowledge graph network.

[0015] Optionally, the requirements include inquiries into system functions, equipment parameters, and solutions to operational and maintenance issues.

[0016] Optionally, the input operation and maintenance requirements may be input in a human-computer interaction manner.

[0017] Optionally, the human-computer interaction method includes using a computer and a keyboard to input text.

[0018] Optionally, the human-computer interaction method includes using an AR terminal to input through AR voice or AR gestures.

[0019] Optionally, the AR terminal is AR glasses.

[0020] In the method for generating ship operation and maintenance guidance animation based on AI and knowledge graph of the present invention, by combining the natural language big model, the operation and maintenance big model and the knowledge graph network, the operator can ask questions in natural and ambiguous language, and only needs to simply state the operation and maintenance requirements, without the operator actively searching for the problem, and the system will automatically push the solution. At the same time, the solution is processed to generate a complete operation and maintenance guidance animation, so that the operator can be intuitively and quickly guided to handle the operation and maintenance requirements, which is more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1It is a flow chart of a method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Refer to the following Figure 1 To describe a method for generating a ship operation and maintenance guidance animation based on AI and knowledge graph of the present invention. In the description of this embodiment, it should be understood that although the various steps in the flowcharts of the various embodiments of the present invention are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in each embodiment may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0023] Unless otherwise clearly defined and limited, the terms "set", "install", "connect", "connect", "fix", "couple" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. A person skilled in the art should be able to understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0024] In addition, in the description of this embodiment, the first feature being "above" or "below" the second feature may include the first and second features being in direct contact, or may include the first and second features not being in direct contact but being in contact through another feature between them. That is, in the description of this embodiment, the first feature being "above", "above", and "above" the second feature includes the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. The first feature being "below", "below", or "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0025] In the description of the present embodiment, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a method for generating a ship operation and maintenance guidance animation based on AI and knowledge graph, and the animation generation method includes:

[0027] Step S10, constructing a large natural language model for recognizing and processing text generated by human-computer interaction.

[0028] Step S20, constructing a large model in the field of operation and maintenance. The large model in the field of operation and maintenance is used to analyze faults and problems in the process of operation and maintenance, and actively push answers or solutions. The training sources of the large model of operation and maintenance include fault trees, mechanism models of various systems, standard operation and maintenance processes, vibration and noise data, and fault images, so that the analysis of the large model of operation and maintenance is more comprehensive and the input types accepted are more diversified.

[0029] Step S30, constructing a ship operation and maintenance knowledge graph network. The knowledge graph network includes the relationship between the three-dimensional model and the corresponding name of the three-dimensional model, and the relationship between the logical action and the action library. Optionally, the knowledge graph network is a multimodal knowledge graph network.

[0030] Step S40, input the operation and maintenance requirements, and process the operation and maintenance requirements. The input operation and maintenance requirements can be input in a human-computer interaction manner. It can be text input using a computer and keyboard. It can also be input using an AR terminal through AR voice or AR gestures. Optionally, the AR terminal is AR glasses. At the same time, the operation and maintenance requirements can be processed using a natural language large model to convert the operation and maintenance requirements into a form that is convenient for knowledge graph network retrieval and reasoning. The operation and maintenance requirements include inquiries about system functions, equipment parameters, and solutions to operation and maintenance problems. For example, the crew of the generator room inputs "We have just boarded the ship and are not familiar with the environment. How do we warm up the ship?" through the computer keyboard. The natural language large model converts the above questions into text, and then simplifies them into the following content: "How to warm up the ship", "What equipment is involved in warming up", "What to pay attention to in warming up", "Warming up operation process", etc., for subsequent retrieval and reasoning.

[0031] Step S50, searching and reasoning the processed operation and maintenance requirements to obtain corresponding solutions.

[0032] For example, the knowledge graph network performs inference retrieval based on the processing of the natural language large model. It first searches for generators, generator inlet water valves, engine inlet valves, hot water pumps, etc. related to warm-up; then searches for precautions before warm-up, and finally searches for the general operating procedures for warm-up. The inferred solution is: ① Fully open the fuel valve; ② Close 80% of the water inlet valve; ③ Click the start button in the remote control room.

[0033] Step S60, standardize the solution, that is, associate the name of the object model in the solution with the three-dimensional model. Then associate the logical action in the solution with the action library, which includes animation files. According to the verbs in the solution, the animation files in the action library are associated with the operation, so as to construct the operation animation of each object model. For example, after reading the solution, the system will first establish the corresponding relationship between the name of the fuel valve, water inlet valve and start button and the three-dimensional model, and then associate the animation files in the operation action library according to the verbs "open, close, click" in the solution. Finally, each object model is combined with the corresponding animation file to construct the operation animation of each object model.

[0034] Step S70, using the knowledge graph network, route planning is performed in the virtual environment according to the order in which the object models appear. For example, go to the fuel valve station first, then to the water inlet valve station, and finally to the remote control room station. Using a virtual camera, follow and shoot the operation animations of each object model along the route to generate a complete operation and maintenance guidance animation. Based on the association relationship, the system obtains the model, position data, rotation angle, pressing distance and other action libraries of each operation object, and then combines the path perspective images generated in sequence by the virtual camera to synthesize these into a complete warm-up operation animation to guide the operator to learn the relevant operation essentials.

[0035] By combining the natural language big model, the operation and maintenance big model with the knowledge graph network, operators can ask questions in natural and ambiguous language, and only need to simply state the operation and maintenance requirements. The system will automatically push solutions without the operator actively searching for the problem. At the same time, the solution is processed to generate a complete operation and maintenance guidance animation, which can intuitively and quickly guide operators to handle operation and maintenance requirements, which is more convenient.

[0036] 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 art may 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 a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for generating ship operation and maintenance guidance animation based on AI and knowledge graph, characterized in that: The animation generation method comprises: Build a large model in the field of operation and maintenance to analyze failures and problems in the operation and maintenance process, and proactively push answers or solutions; Constructing a ship operation and maintenance knowledge graph network, wherein the knowledge graph network includes a relationship between a three-dimensional model and a name corresponding to the three-dimensional model, and a relationship between a logical action and an action library; Input operation and maintenance requirements and process the requirements; Performing retrieval and reasoning on the processed requirements to obtain solutions; Standardizing the solution, associating the name of the object model in the solution with the three-dimensional model and the logical action in the solution with the action library, so as to respectively construct operation animations of each of the object models; The knowledge graph network is used to plan routes in a virtual environment according to the order in which the object models appear. A virtual camera is used to follow and shoot the operation animations of each object model along the route to generate a complete operation and maintenance guidance animation.

2. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 1 is characterized in that: The animation generation method also includes constructing a large natural language model; The method for processing the demand is to use the natural language large model to process the demand.

3. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 1 is characterized in that: The operation and maintenance big model is a cross-modal big model, and the training sources of the operation and maintenance big model include fault trees, mechanism models of various systems, standard operation and maintenance processes, vibration and noise data, and fault images.

4. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 1 is characterized in that: The knowledge graph network is a multimodal knowledge graph network.

5. The ship hull welding auxiliary method based on AI and knowledge graph according to claim 1 is characterized in that: The requirements include inquiring about system functions, equipment parameters, and solutions to operational and maintenance issues.

6. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 1 is characterized in that: The input operation and maintenance requirements can be input in a human-computer interaction manner.

7. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 6 is characterized in that: The human-computer interaction method includes using a computer and a keyboard to input text.

8. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 6 is characterized in that: The human-computer interaction method includes using an AR terminal to input through AR voice or AR gestures.

9. The method for generating ship operation and maintenance guidance animation based on AI and knowledge graph according to claim 8 is characterized in that: The AR terminal is AR glasses.

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