Automatic report generation system and method based on large language model combined with digital twin

Abstract

The report automatic generation system and method based on large-scale language model combined with digital twin belongs to the field of digital twin technology. In order to use large-scale language model technology to quickly summarize and filter the large amount of information and data generated by the digital twin model, the present invention includes a reporting system service layer including a digital twin model connection module, a user information database, a system scheduling module, an information sending module, and an inter-level connection module. The reporting system service layer processes user needs, communicates with the natural language model layer, the data processing layer, the early warning information processing layer, and the file service layer, and outputs and pushes the report to the digital twin system; the natural language model layer includes a large language model, a role selection module, a language model reasoning module, and a language model role module. The natural language model layer uses a prompt project to build a role, generate a data analysis summary and report, and send it back to the reporting system service layer. The present invention can directly perform text summarization.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin technology, and specifically relates to a system and method for automatically generating reports based on a large-scale language model combined with digital twins. Background Art

[0002] Digital twins are an industrial monitoring and management technology that leverages the Industrial Internet of Things (IIoT), computer simulation, and 3D visualization. They are widely used in a variety of fields, including manufacturing, energy and power, social governance, and transportation. Digital twin technology has proven effective in production scenarios. However, for managers, locating the required information from complex digital twin models is inefficient.

[0003] Advances in computer processing power have made large-scale language models possible, enabling automated, intelligent text generation programs with deep semantic understanding. These models can be used in digital twins to quickly and instantly generate meaningful, accurate reports. However, large-scale language models have not yet been effectively applied in digital twin systems, which already have vast amounts of available data and descriptive information. To address these challenges, it is crucial to find ways to leverage large-scale language model technology to rapidly summarize and filter the vast amounts of information and data generated by digital twins. Summary of the Invention

[0004] The problem to be solved by the present invention is to use large-scale language model technology to quickly summarize and filter the large amount of information and data generated by the digital twin model, and propose an automatic report generation system and method based on large-scale language model combined with digital twin.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] An automatic report generation system based on a large language model combined with digital twins, including a reporting system service layer, a natural language model layer, a data processing layer, an early warning information processing layer, and a file service layer;

[0007] The reporting system service layer includes a digital twin model connection module, a user information database, a system scheduling module, an information sending module, and an inter-level connection module. The reporting system service layer processes user needs, communicates with the natural language model layer, the data processing layer, the warning information processing layer, and the file service layer, and outputs and pushes reports to the digital twin system.

[0008] The natural language model layer includes a large language model, a role selection module, a language model reasoning module, and a language model role module. The natural language model layer uses prompt engineering to build roles, generate data analysis summaries and reports, and send them back to the reporting system service layer;

[0009] The data processing layer processes the collected data or generates charts based on data statistics and processing algorithms, and sends the reports back to the system service layer;

[0010] The warning information processing layer screens and summarizes the information based on the warning information screening algorithm, organizes it into warning text information, and sends it back to the reporting system service layer;

[0011] The file service layer is connected to the file system in the application scenario of the digital twin system, finds the corresponding file according to the final demand description vector received from the reporting system service layer, and returns the file to the reporting system service layer.

[0012] Furthermore, the language model inference module includes a large inference language model and a simplified dialogue request module; the language model role module includes a role prompt word module, a role description module, and a role identifier module.

[0013] Furthermore, the reporting system service layer stores and identifies user information through a user information database, communicates with the natural language model layer, the data processing layer, the warning information processing layer, and the file service layer using a digital twin model connection module and an inter-layer connection module based on the demand description vector, and sends the communication results to the user device through an information sending module;

[0014] The demand description vector D is expressed as follows:

[0015] D=[D EQ ,D EV ,D W ,D M ,D F ,D T ,T]

[0016] Among them, D EQ Represents the equipment demand vector, D EV Represents the environmental demand vector, D W represents the worker demand vector, D M Represents the material demand vector, D F Represents the file demand vector, D T represents the time period demand vector, and T represents the time when the request is initiated.

[0017] Furthermore, a trigger is provided in the system scheduling module.

[0018] Furthermore, the language model role module of the natural language model layer uses the prompt project to construct the language model role, which is then passed to the language model inference module for model input. The role prompt word module is used to call the language inference capability in the large inference language model to generate model input. The model output is input into the streamlined dialogue request module to request streamlined dialogue. The final output is used as part of the final report or connected to the digital twin system. The expression is:

[0019]

[0020] in, is the completed text content, T roleprompt For large language models, T input To input the prompt word, LLM is the large language model text completion function.

[0021] Furthermore, the language model role module uses a code generation role to perform information processing within the system. The code generation role is a language model role that contains code generation prompt words. It generates code through an interface call and hands it over to the digital twin system, which is then executed by the digital twin system.

[0022] The role selection module is composed of a structured output language model constructed by the prompt project and the corresponding control program. The multiple selection roles in the role selection module correspond one-to-one to the model roles in the language model role module. All selection roles judge whether they need the corresponding model role. If necessary, the data list and prompt words required by the model role are structured and output. The structured output is read and the language model code list and the corresponding data list and prompt word list are returned.

[0023] Furthermore, the data processing layer establishes connections with different databases in the digital twin system, generates structured query language statements based on the final demand description vector received in the reporting system service layer, executes the statements to query data and obtain descriptive statistical data, and sends them back to the reporting system service layer.

[0024] Furthermore, the warning information processing layer establishes connections with different warning data interfaces in the digital twin system, and based on the final demand description vector received in the reporting system service layer, calls the application programming interface provided by the interface to complete the warning information query and the generation or acquisition of descriptive statistical data, and sends it back to the reporting system service layer.

[0025] A method for automatically generating reports based on a large language model combined with digital twins is implemented based on the aforementioned automatic report generation system based on a large language model combined with digital twins, and includes the following steps:

[0026] S1. The user triggers the trigger in the system scheduling module, and the system scheduling module issues a report generation instruction;

[0027] S2. The reporting system service layer queries the user information database to obtain user information. The reporting system service layer aggregates the user information, user request, and current time to the digital twin model connection module for digital twin model response, generates a demand description vector, and sends it to the data processing layer, warning information processing layer, and file service layer.

[0028] S3. The reporting system service layer receives the response files from the data processing layer, warning information processing layer, and file service layer;

[0029] S4. The reporting system service layer combines the response files received from the data processing layer, the warning information processing layer, and the file service layer with the user request to obtain a response request and sends it to the natural language model layer;

[0030] S5. The role selection module in the natural language model layer receives the response request and generates a language model code list in the language model role module, and a data list and a prompt word list corresponding thereto;

[0031] S6. The natural language model layer generates a response combination based on the language model code list, data list, and prompt word list obtained in step S5 and sends it to the reporting system service layer;

[0032] S7. The reporting system service layer combines the obtained response combination with the response files from the data processing layer, the early warning information processing layer, and the file service layer into a document;

[0033] S8. The information sending module in the reporting system service layer sends the document to the user, completing the automatic generation of reports based on the large language model combined with digital twins.

[0034] Beneficial effects of the present invention:

[0035] The report automatic generation system based on a large language model combined with digital twins described in the present invention can be used directly in different application scenarios without any changes. When the large language model deployed in the cloud is reused, large-scale, real-time report generation and sending operations can be achieved, which greatly improves the practical value and popularity of the current digital twin model. It uses text to quickly, timely and customizedly reach staff in various positions, meet data needs, reduce the use cost of digital twins and data visualization, and use prompt engineering for role setting to directly perform text summaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a structural diagram of an automatic report generation system based on a large language model combined with digital twins according to the present invention;

[0037] Figure 2 This is a schematic diagram of the structure of the service layer of the reporting system of the present invention;

[0038] Figure 3 Schematic diagram of the structure of the natural language model layer of the present invention;

[0039] Figure 4 Schematic diagram of the structure of the language model thrust module of the present invention;

[0040] Figure 5 Schematic diagram of the structure of the language model role module of the present invention;

[0041] Figure 6 Schematic diagram of the processing flow of the data processing module. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. That is, the specific embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the specific embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0043] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] In order to further understand the content, features and effects of the present invention, the following specific embodiments are given as examples, and the attached Figure 1 -Attached Figure 6 The detailed instructions are as follows:

[0045] Example 1:

[0046] An automatic report generation system based on a large language model combined with digital twins, comprising a report system service layer 10, a natural language model layer 20, a data processing layer 30, an early warning information processing layer 40, and a file service layer 50;

[0047] The reporting system service layer 10 includes a digital twin model connection module 110, a user information database 120, a system scheduling module 130, an information sending module 140, and an inter-level connection module 150. The reporting system service layer 10 processes user needs, communicates with the natural language model layer 20, the data processing layer 30, the warning information processing layer 40, and the file service layer 50, and outputs and pushes reports to the digital twin system;

[0048] The natural language model layer 20 includes a large language model 210, a role selection module 220, a language model reasoning module 230, and a language model role module 240. The natural language model layer 20 uses prompt engineering to build roles, generates data analysis summaries and reports, and sends them back to the reporting system service layer 10;

[0049] The data processing layer 30 processes the collected data or generates charts based on data statistics and processing algorithms, and sends reports back to the system service layer 10;

[0050] The warning information processing layer 40 screens and summarizes the information based on the warning information screening algorithm, organizes it into warning text information, and sends it back to the reporting system service layer 10;

[0051] The file service layer 50 is connected to the file system in the application scenario of the digital twin system, finds the corresponding file according to the final demand description vector received from the reporting system service layer 10, and returns the file to the reporting system service layer 10.

[0052] Furthermore, the language model inference module 230 includes a large inference language model 231 and a simplified dialogue request module 232 ; the language model role module 240 includes a role prompt word module 241 , a role description module 242 , and a role identifier module 243 .

[0053] Furthermore, the reporting system service layer 10 stores and identifies user information through the user information database 120, and communicates with the natural language model layer 20, the data processing layer 30, the warning information processing layer 40, and the file service layer 50 using the digital twin model connection module 110 and the inter-level connection module 150 based on the demand description vector, and sends the communication results to the user device through the information sending module 140;

[0054] The demand description vector D is expressed as follows:

[0055] D=[D EQ ,D EV ,D W ,D M ,D F ,D T ,T]

[0056] Among them, D EQ Represents the equipment demand vector, D EV Represents the environmental demand vector, D W represents the worker demand vector, D M Represents the material demand vector, D F Represents the file demand vector, D T represents the time period demand vector, and T represents the time when the request is initiated.

[0057] Furthermore, a trigger is provided in the system scheduling module 130 .

[0058] Furthermore, the language model role module 240 of the natural language model layer 20 uses the prompt engineering to construct the language model role, which is then passed to the language model inference module 230 for model input. The role prompt word module 241 is used to call the language inference capability in the large inference language model 231 to generate the model input. The model output is input to the simplified dialogue request module 232 to request a simplified dialogue. The final output obtained is used as part of the final report or connected to the digital twin system. The expression is:

[0059]

[0060] in, is the completed text content, T roleprompt For large language models, T input To input the prompt word, LLM is the large language model text completion function.

[0061] Furthermore, the language model role module 240 uses a code generation role to perform information processing within the system. The code generation role is a language model role that contains code generation prompt words. It generates code through an interface call and hands it over to the digital twin system, which is then executed by the digital twin system.

[0062] The role selection module 220 is composed of a structured output language model constructed by the prompt project and a corresponding control program. The multiple selection roles in the role selection module 220 correspond one-to-one to the model roles in the language model role module 240. All selection roles determine whether they need the model role corresponding to them. If necessary, the data list and prompt words required by the model role are structured and output. The structured output is read and a language model code list and a data list and prompt word list corresponding to them are returned.

[0063] Furthermore, the data processing layer 30 establishes connections with different databases in the digital twin system, generates structured query language statements based on the final demand description vector received in the reporting system service layer 10, executes the statements to query data and obtain descriptive statistical data, and sends them back to the reporting system service layer 10.

[0064] Furthermore, the warning information processing layer 40 establishes connections with different warning data interfaces in the digital twin system, and based on the final demand description vector received in the reporting system service layer 10, calls the application programming interface provided by the interface to complete the warning information query and the generation or acquisition of descriptive statistical data, and sends it back to the reporting system service layer 10.

[0065] Example 2:

[0066] A method for automatically generating reports based on a large language model combined with digital twins is implemented based on the system for automatically generating reports based on a large language model combined with digital twins described in Example 1, and includes the following steps:

[0067] S1. The user triggers the trigger in the system scheduling module 130, and the system scheduling module 130 issues a report generation instruction;

[0068] S2. The reporting system service layer 10 queries the user information database 120 to obtain user information. The reporting system service layer 10 aggregates the user information, user request, and current time to the digital twin model connection module 110 for digital twin model response, generates a demand description vector, and sends it to the data processing layer 30, the warning information processing layer 40, and the file service layer 50;

[0069] S3 reporting system service layer 10 accepts the data processing layer 30, the warning information processing layer 40, the file service layer 50 response file;

[0070] S4. The reporting system service layer 10 combines the response file received from the data processing layer 30, the warning information processing layer 40, and the file service layer 50 with the user request to obtain a response request and sends it to the natural language model layer 20;

[0071] S5. The role selection module 220 in the natural language model layer 20 receives the response request and generates a language model role module 240 in the language model code list, and its one-to-one corresponding data list and prompt word list;

[0072] S6. The natural language model layer 20 generates a response combination based on the language model code list, data list, and prompt word list obtained in step S5 and sends it to the reporting system service layer 10;

[0073] S7. The reporting system service layer 10 combines the obtained response with the response file from the data processing layer 30, the warning information processing layer 40, and the file service layer 50 into a document;

[0074] S8. The information sending module 140 in the reporting system service layer 10 sends the document to the user, completing the automatic generation of the report based on the large language model combined with the digital twin.

[0075] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0076] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. An automatic report generation system based on a large language model combined with digital twins, characterized by: It includes a reporting system service layer (10), a natural language model layer (20), a data processing layer (30), an early warning information processing layer (40), and a file service layer (50); The reporting system service layer (10) includes a digital twin model connection module (110), a user information database (120), a system scheduling module (130), an information sending module (140), and an inter-level connection module (150). The reporting system service layer (10) processes user needs, communicates with the natural language model layer (20), the data processing layer (30), the warning information processing layer (40), and the file service layer (50), and outputs and pushes reports to the digital twin system. The natural language model layer (20) includes a large language model (210), a role selection module (220), a language model reasoning module (230), and a language model role module (240). The natural language model layer (20) uses prompt engineering to construct roles, generates data analysis summaries and reports, and sends them back to the reporting system service layer (10); The data processing layer (30) processes the collected data or generates charts based on data statistics and processing algorithms, and sends the report back to the system service layer (10); The warning information processing layer (40) screens and summarizes the information based on the warning information screening algorithm, compiles the information into warning text information, and sends it back to the reporting system service layer (10); The file service layer (50) is connected to the file system in the application scenario of the digital twin system, finds the corresponding file according to the final demand description vector received from the reporting system service layer (10), and returns the file to the reporting system service layer (10).

2. The automatic report generation system based on a large language model combined with digital twins according to claim 1 is characterized in that: The language model inference module (230) includes a large inference language model (231) and a simplified dialogue request module (232); the language model role module (240) includes a role prompt word module (241), a role description module (242), and a role identifier module (243).

3. The automatic report generation system based on a large language model combined with digital twins according to claim 2 is characterized in that: The reporting system service layer (10) stores and identifies user information through a user information database (120), communicates with a natural language model layer (20), a data processing layer (30), an early warning information processing layer (40), and a file service layer (50) based on a demand description vector using a digital twin model connection module (110) and an inter-level connection module (150), and sends the communication result to the user device through an information sending module (140); The demand description vector D is expressed as follows: D=[D EQ ,D EV ,D W ,D M ,D F ,D T ,T] Among them, D EQ Represents the equipment demand vector, D EV Represents the environmental demand vector, D W represents the worker demand vector, D M Represents the material demand vector, D F Represents the file demand vector, D T represents the time period demand vector, and T represents the time when the request is initiated.

4. The automatic report generation system based on a large language model combined with digital twins according to claim 3 is characterized in that: The system scheduling module (130) is provided with a trigger.

5. The automatic report generation system based on a large language model combined with digital twins according to claim 4 is characterized in that: The language model role module (240) of the natural language model layer (20) uses the prompt engineering to construct the language model role, and then passes it to the language model reasoning module (230) for model input, and uses the role prompt word module (241) to call the language reasoning ability in the large reasoning language model (231) to generate model input, and inputs the model output into the simplified dialogue request module (232) to request simplified dialogue. The final output obtained is used as part of the final report or connected to the digital twin system, and the expression is: in, is the completed text content, T roleprompt For large language models, T input To input the prompt word, LLM is the large language model text completion function.

6. The automatic report generation system based on a large language model combined with digital twins according to claim 5 is characterized in that: The language model role module (240) uses a code generation role to perform information processing within the system. The code generation role is a language model role containing a code generation prompt word. The code is generated by calling an interface and handed over to the digital twin system, and the digital twin system executes it. The role selection module (220) is composed of a structured output language model constructed by the prompt project and a corresponding control program. The multiple selection roles in the role selection module (220) correspond one-to-one to the model roles in the language model role module (240). All selection roles judge whether they need the model role corresponding to them. If necessary, the data list and prompt words required by the model role are structured and output. The structured output is read and a language model code list and a data list and prompt word list corresponding to them are returned.

7. The automatic report generation system based on a large language model combined with digital twins according to claim 6 is characterized in that: The data processing layer (30) establishes connections with different databases in the digital twin system, generates structured query language statements based on the final demand description vector received in the reporting system service layer (10), executes the statements to query data and obtain descriptive statistical data, and sends them back to the reporting system service layer (10).

8. The automatic report generation system based on a large language model combined with digital twins according to claim 7 is characterized in that: The warning information processing layer (40) establishes a connection with different warning data interfaces in the digital twin system, and based on the final demand description vector received in the reporting system service layer (10), calls the application programming interface provided by the interface to complete the warning information query and the generation or acquisition of descriptive statistical data, and sends it back to the reporting system service layer (10).

9. A method for automatically generating reports based on a large language model combined with digital twins, implemented by a system for automatically generating reports based on a large language model combined with digital twins according to any one of claims 1 to 8, characterized in that: The steps include: S1. The user triggers the trigger in the system scheduling module (130), and the system scheduling module (130) issues a report generation instruction; S2. The reporting system service layer (10) queries the user information database (120) to obtain user information, and the reporting system service layer (10) aggregates the user information, user request, and current time to the digital twin model connection module (110) for digital twin model response, generates a demand description vector, and sends it to the data processing layer (30), the warning information processing layer (40), and the file service layer (50); S3. The reporting system service layer (10) receives the response file from the data processing layer (30), the warning information processing layer (40), and the file service layer (50); S4. The reporting system service layer (10) combines the response files received from the data processing layer (30), the warning information processing layer (40), and the file service layer (50) with the user request to obtain a response request, and sends it to the natural language model layer (20); S5. The role selection module (220) in the natural language model layer (20) receives the response request and generates a language model code list in the language model role module (240), and a data list and a prompt word list corresponding thereto; S6. The natural language model layer (20) generates a response combination based on the language model code list, data list, and prompt word list obtained in step S5 and sends it to the reporting system service layer (10); S7. The reporting system service layer (10) combines the obtained response combination with the response files from the data processing layer (30), the warning information processing layer (40), and the file service layer (50) into a document; S8. The information sending module (140) in the reporting system service layer (10) sends the document to the user, completing the automatic generation of reports based on the large language model combined with digital twins.

Images