Refrigerating machine room vertical knowledge question answering system and method based on RAG technology, medium, terminal and program product

Abstract

The invention provides a refrigerating machine room vertical knowledge question answering system and method based on the RAG technology, a medium, a terminal and a program product, and the system comprises a knowledge base construction module which is used for obtaining static data in a pre-constructed static database and equipment operation data in a refrigerating machine room, and constructing a knowledge base according to the static data and the equipment operation data; the retrieval module is used for retrieving corresponding knowledge fragments from a knowledge base according to the received to-be-answered question based on the RAG technology so as to obtain mixed knowledge information; the special tool construction module is used for constructing special tools of the refrigerating machine room by using the large model platform; the special tool is used for accessing a predefined refrigerating machine room rule and performing calculation and verification according to the equipment operation data to obtain calculation and verification information; and the generation module is used for inputting the mixed knowledge information and the calculation verification information into a large language model so as to generate response content of the to-be-answered question. According to the method, the accurate, professional and highly-targeted question and answer result can be generated.

Description

Technical Field

[0001] The present application relates to the field of industrial refrigeration technology, and in particular to a vertical knowledge question-and-answer system, method, medium, terminal, and program product for a refrigeration room based on RAG technology. Background Art

[0002] Refrigeration rooms play a critical role in many high-tech and precision manufacturing industries, including high-efficiency computer rooms, semiconductor factories, and panel factories. Their internal operating systems are highly complex and require extensive expertise. However, traditional technical training and troubleshooting often rely on manual research and personal experience, making it difficult for new employees to quickly grasp the operating principles and troubleshooting methods of the entire refrigeration room system.

[0003] Although large language models have performed well in general knowledge question answering in recent years, they often find it difficult to provide accurate, professional, and targeted answers in the specialized field of refrigeration systems due to the lack of rich expertise in the field.

[0004] Therefore, it is necessary to provide a refrigeration room vertical knowledge question and answer system, method, medium, terminal and program product based on RAG technology to solve the above-mentioned problems existing in the prior art. Summary of the Invention

[0005] In view of the shortcomings of the existing technology mentioned above, the purpose of this application is to provide a vertical knowledge question and answer system, method, medium, terminal and program product for refrigeration rooms based on RAG technology, which is used to solve the technical problem that the existing technology cannot provide accurate, professional and targeted answers when applied to the refrigeration system.

[0006] To achieve the above-mentioned and other related objectives, the first aspect of the present application provides a refrigeration room vertical knowledge question-answering system based on RAG technology, comprising:

[0007] A knowledge base construction module is used to obtain static data in a pre-built static database and equipment operation data in the refrigeration room, and construct a knowledge base based on the data;

[0008] A retrieval module, configured to retrieve corresponding knowledge fragments from the knowledge base according to the received question to be answered based on the RAG technology, so as to obtain hybrid knowledge information;

[0009] A dedicated tool construction module is used to construct a dedicated tool for the refrigeration room using a large model platform; the dedicated tool is used to access predefined refrigeration room rules and perform calculation verification based on the equipment operation data to obtain calculation verification information;

[0010] A generation module is used to input the mixed knowledge information and the calculation verification information into a large language model to generate a response content for the question to be answered.

[0011] In some embodiments of the first aspect of the present application, the knowledge base construction module includes: a static database construction unit, used to acquire multimodal data of the refrigeration industry and private domain data of the refrigeration room, and construct a static database based on this; a dynamic data acquisition unit, used to acquire equipment operation data in the refrigeration room in real time; a fusion unit, used to fuse the equipment operation data acquired in real time into the static database to form the knowledge base.

[0012] In some embodiments of the first aspect of the present application, the large model platform is a Dify platform.

[0013] In some embodiments of the first aspect of the present application, the special tool construction module includes: a refrigeration room rule access unit, used to use the Dify platform to build a rule customization interface for the refrigeration room; the rule customization interface is used to access predefined refrigeration room rules; a real-time calculation unit, used to perform real-time calculations based on the equipment operation data to obtain key performance indicators; a verification unit, used to verify the key performance indicators obtained by real-time calculation according to the predefined refrigeration room rules to obtain the calculation verification information.

[0014] In some embodiments of the first aspect of the present application, the key performance indicators include any one or more combinations of energy efficiency ratio, thermal balance rate, subsystem energy consumption ratio, and performance coefficient of each device.

[0015] In some embodiments of the first aspect of the present application, a feedback optimization module is further included, which is used to provide feedback based on the generated response content to generate feedback data, and optimize the response content based on the feedback data.

[0016] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a vertical knowledge question-answering method for a refrigeration room based on RAG technology, including: obtaining static data in a pre-built static database and equipment operation data in the refrigeration room, and constructing a knowledge base based on this; retrieving corresponding knowledge fragments from the knowledge base based on the received questions to be answered based on RAG technology to obtain hybrid knowledge information; using a large model platform to build a special tool for the refrigeration room; the special tool is used to access predefined refrigeration room rules, and perform calculation verification based on the equipment operation data to obtain calculation verification information; the hybrid knowledge information and the calculation verification information are input into a large language model to generate the response content of the question to be answered.

[0017] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the method when executed by a processor.

[0018] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes computer program code. When the computer program code is run on a computer, the computer implements the method.

[0019] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the method.

[0020] As described above, the RAG-based refrigeration room vertical knowledge question-and-answer system, method, medium, terminal, and program product of the present application have the following beneficial effects:

[0021] By obtaining static data from a pre-built static database and real-time equipment operation data in the refrigeration room, the data are integrated to construct a knowledge base. Based on RAG technology, the knowledge fragments corresponding to the questions to be answered are retrieved from the constructed knowledge base according to the received questions to be answered to obtain mixed knowledge information. A special tool for the refrigeration room is constructed using a large model platform to access predefined refrigeration room rules, and calculation verification is performed based on the real-time acquired equipment operation data to obtain calculation verification information. The mixed knowledge information and calculation verification information are input into the large language model to generate the response content of the questions to be answered. This application integrates static data with real-time dynamic data in the refrigeration room, and combines the predefined refrigeration room rules accessed by the special tool and calculation verification, so that the refrigeration room vertical knowledge question and answer system generates accurate, professional and targeted question and answer results, thereby assisting the daily operation, energy-saving control and fault diagnosis of the refrigeration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Shown is a structural diagram of a vertical knowledge question-and-answer system for a refrigeration room based on RAG technology in one embodiment of the present application.

[0023] Figure 2 Shown is a schematic diagram of the framework of the knowledge base construction module in one embodiment of the present application.

[0024] Figure 3 Shown is a schematic diagram of a framework of a dedicated tool construction module in one embodiment of the present application.

[0025] Figure 4Shown is a framework flow chart of a vertical knowledge question-and-answer system for a refrigeration room based on RAG technology in one embodiment of the present application.

[0026] Figure 5 Shown is a flow chart of a vertical knowledge question-and-answer method for a refrigeration room based on RAG technology in one embodiment of the present application.

[0027] Figure 6 Shown is a structural schematic diagram of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0029] In the embodiments of this application, terms such as "first" and "second" are used to distinguish between identical or similar items with substantially the same function or effect. For example, the first XX and the second XX are merely used to distinguish between different XXs and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0030] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" represent examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0031] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0032] Before further explaining the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations:

[0033] <1> RAG (Retrieval-Augmented Generation) technology: is a natural language processing framework that combines retrieval technology and generation technology. It aims to enhance the generation capability of large language models (LLMs) by retrieving relevant information from external knowledge bases.

[0034] <2> Vertical category: short for "vertical field", usually used to describe a specific industry or field.

[0035] <3> Large Language Model (LLM): An artificial intelligence model built based on deep learning technology, specifically used to process and generate natural language text.

[0036] <4> Dify Platform: An open-source large-model application development platform that combines Backend-as-a-Service with the LLMOps (Large Language Model Operations) concept. It supports the integration of multiple large-scale language models and provides visualization tools and zero-code development capabilities.

[0037] <5> The Energy Efficiency Ratio (EER) is a metric used to measure the energy efficiency of refrigeration equipment, such as air conditioners. It is defined as the ratio of the cooling capacity of the equipment to the electrical energy consumed over a certain period of time under specific conditions. The EER is commonly used to evaluate the energy efficiency of air conditioning systems. A higher EER value indicates better energy efficiency, meaning that the air conditioner consumes less energy while providing the same cooling capacity.

[0038] To facilitate understanding of the embodiments of this application, first Figure 1Detailed description. Figure 1 The schematic diagram of the structure of a vertical knowledge question-and-answer system for refrigeration rooms based on RAG technology in an embodiment of the present invention is shown. The vertical knowledge question-and-answer system 100 for refrigeration rooms based on RAG technology in this embodiment includes: a knowledge base construction module 101, a retrieval module 102, a dedicated tool construction module 103, and a generation module 104.

[0039] The knowledge base construction module 101 is used to obtain static data in a pre-constructed static database and equipment operation data in a refrigeration room, and construct a knowledge base based on the data.

[0040] The retrieval module 102 is used to retrieve corresponding knowledge fragments from the knowledge base according to the received questions to be answered based on the RAG technology to obtain mixed knowledge information.

[0041] The dedicated tool construction module 103 is used to construct the dedicated tool for the refrigeration room using the large model platform; the dedicated tool is used to access the predefined refrigeration room rules and perform calculation verification based on the equipment operation data to obtain calculation verification information.

[0042] The generation module 104 is used to input the mixed knowledge information and the calculation verification information into the large language model to generate the answer content of the question to be answered.

[0043] By obtaining static data from a pre-built static database and real-time equipment operation data in the refrigeration room, the data are integrated to construct a knowledge base. Based on RAG technology, the knowledge fragments corresponding to the questions to be answered are retrieved from the constructed knowledge base according to the received questions to be answered to obtain mixed knowledge information. A special tool for the refrigeration room is constructed using a large model platform to access predefined refrigeration room rules, and calculation verification is performed based on the real-time obtained equipment operation data to obtain calculation verification information. The mixed knowledge information and calculation verification information are input into the large language model to generate the response content of the questions to be answered. This application integrates static data with real-time dynamic data in the refrigeration room, and combines the predefined refrigeration room rules accessed by the special tool to expand professional thinking and perform calculation verification, so that the refrigeration room vertical knowledge question and answer system generates accurate, professional and targeted question and answer results, thereby assisting the daily operation, energy-saving control and fault diagnosis of the refrigeration system.

[0044] In some embodiments of the present application, Figure 2As shown, the knowledge base construction module 101 includes: a static database construction unit 1011, which is used to obtain multimodal data of the refrigeration industry and private domain data of the refrigeration room, and construct a static database based on this; a dynamic data acquisition unit 1012, which is used to obtain equipment operation data in the refrigeration room in real time; and a fusion unit 1013, which is used to fuse the real-time acquired equipment operation data into the static database to form the knowledge base. The retrieval module matches the questions to be answered by the user with the static data in the knowledge base, and introduces the retrieval results of the real-time acquired equipment operation data, thereby forming hybrid knowledge information, ensuring that the answers to the questions to be answered are real-time.

[0045] In some specific embodiments, in the process of building a static database, not only private domain data about the refrigeration room is acquired, such as multimodal information such as bidding documents, design drawings, construction plans, historical fault records, etc. within the refrigeration room project, but also public multimodal data of the refrigeration industry and related industries are integrated. By integrating these data information to build a static database covering the entire life cycle of the refrigeration room project, the large language model's ability to understand and answer professional domain knowledge is enhanced, thereby improving the professionalism and practicality of the refrigeration room vertical knowledge question-and-answer system based on RAG technology. The acquired multimodal data (such as text, pictures, drawings, etc.) is pre-processed in a unified format, annotated according to specific application requirements, and mapped to a vector space using a domain-adaptive embedding algorithm, and stored to form a static database. Specifically, the pre-processing method for text data is: converting texts of different formats into a unified format, and performing text cleaning to remove noise information in the text. The pre-processed and annotated text data is mapped to a vector space using, for example, a BERT model to store it as part of the static database. It should be understood that the BERT model is a pre-trained language model based on the Transformer architecture. It learns deep semantic representations of text by pre-training on large amounts of text data. Specifically, for image data, it uses OCR and image recognition technologies to process unstructured data and then uses a domain-specific embedding model to store it in a static database.

[0046] In some specific embodiments, a real-time data acquisition module is integrated into the PLC (Programmable Logic Controller) of the refrigeration equipment to acquire real-time operational data of equipment such as the refrigeration system and the air compressor system, such as operating parameters such as temperature and pressure. This real-time operational data is transmitted via an industrial bus and IoT (Internet of Things) devices, converted into text or other structured data, and uploaded to a data fusion platform. This real-time dynamic data is then integrated into a static database, supplementing the contextual information used when generating questions and answers, enabling accurate responses to the context and real-time status of the questions being answered. Furthermore, relevant evaluation criteria and early warning indicators are generated based on the acquired real-time dynamic data to determine whether there are any operational issues with each device in real time.

[0047] At the same time, the RAG-based refrigeration room vertical knowledge question-and-answer system 100 of this application has an automated data transfer function, regularly updating the dynamic data obtained by the real-time data acquisition module to the static database, ensuring the timeliness and integrity of the constructed knowledge base content, and gradually building a comprehensive knowledge resource covering both historical and current status. Utilizing the retrieval mechanism of RAG technology, information fragments related to the questions raised by users can be quickly retrieved from the knowledge base.

[0048] In some embodiments of the present application, the large model platform is the Dify platform. Using the Dify platform as a development foundation, a tool chain dedicated to refrigeration rooms is constructed. This specialized tool not only provides customized refrigeration domain knowledge and data parsing interfaces to the large language model, but also embeds predefined refrigeration room rules and specialized domain calculations, embedding refrigeration domain knowledge and customized rules into the large language model's question-and-answer process. This imbues the large language model with professional thinking patterns and computational verification capabilities, thereby generating professional and accurate question-and-answer results.

[0049] In some embodiments of the present application, Figure 3 As shown, the dedicated tool construction module 103 includes: a refrigeration room rule access unit 1031, which is used to use the Dify platform to build a rule customization interface for the refrigeration room; the rule customization interface is used to access predefined refrigeration room rules; a real-time calculation unit 1032, which is used to perform real-time calculations based on the equipment operation data to obtain key performance indicators; a verification unit 1033, which is used to verify the key performance indicators obtained by real-time calculation according to the predefined refrigeration room rules to obtain the calculation verification information.

[0050] In this embodiment, the Dify platform is used to customize the interactive interface to access predefined refrigeration room rules, thereby integrating the knowledge and experience of refrigeration industry experts and professionally customizing the question-and-answer process, enabling the large language model to analyze and answer questions from a more accurate and professional perspective. At the same time, the Dify platform is used to develop calculation verification for key performance indicators. By embedding dedicated calculation verification tools, it supports real-time calculation and comparison of key performance indicators in the refrigeration room, realizing real-time data comparison with historical data, numerical calculation, and abnormal warning. This provides a basis for the analysis of the refrigeration room vertical knowledge question-and-answer system based on RAG technology, improves the scientificity and accuracy of the answers, and enhances the credibility of the question-and-answer results.

[0051] In some embodiments of the present application, the key performance indicators include any one or more combinations of energy efficiency ratio, thermal balance rate, subsystem energy consumption ratio, and performance coefficient of each device.

[0052] In some specific embodiments, the energy efficiency ratio calculation unit is embedded to perform energy efficiency ratio calculation, and the specific formula is as follows:

[0053]

[0054] Among them, EER is energy efficiency ratio, referred to as energy efficiency ratio; Q 冷 is the cooling capacity; P 输入 It is the total electrical power of each unit in the refrigeration room, including chiller power, pump power and cooling tower power.

[0055] In some specific embodiments, a heat balance rate calculation unit is embedded to calculate the heat balance rate. In the refrigeration system, according to the law of energy conservation, the following formula is obtained:

[0056] Q 散热 =P 总功 +P 总负荷 ;Formula (2)

[0057] Among them, Q 散热 is the total heat dissipation in the refrigeration system; P 总功 is the total power consumption in the refrigeration system, including chillers, pumps, and towers; P 总负荷 is the cooling capacity of the refrigeration system.

[0058] The thermal equilibrium rate β is defined according to formula (2):

[0059]

[0060] The absolute value of the difference between the input (total power consumption + cooling capacity) and output (heat dissipation) of the refrigeration system is the energy imbalance of the system; Q 散热 The measured heat dissipation is calculated from the cooling water flow and temperature difference. The specific calculation formula is as follows:

[0061] Q 散热 =mc p ΔT; Formula (4)

[0062] Where m is the mass flow rate of the working fluid (usually water); c p is the specific heat at constant pressure; ΔT is the temperature difference.

[0063] When β approaches 0, it indicates that “input = output” has been well closed-loop verified, and the original measurement data (flow rate, temperature difference, electric power, cooling load, etc.) are highly consistent. If β is large, it indicates that the measurement error or system heat loss (unaccounted heat dissipation, pipeline thermal bridges, etc.) is more obvious, and further correction or additional measurement points are needed.

[0064] In some specific embodiments, a subsystem energy consumption ratio calculation unit is embedded to calculate the subsystem energy consumption ratio. The centralized refrigeration system such as a central air conditioner or a computer room is divided into circuits (subsystems), such as a fan circuit, a chilled water pump circuit, a compressor circuit, a cooling water pump circuit, a cooling tower fan circuit, etc. Then:

[0065] The total energy consumption of the system is: E total =∑ i E i ; Formula (5)

[0066] The energy consumption ratio of the i-th subsystem is:

[0067] Among them, E i is the energy consumption of the i-th subsystem.

[0068] In some specific embodiments, the performance coefficient of each device is calculated by embedding a performance coefficient calculation unit for each device, and the performance coefficient of each device includes: any one or more of the compressor isentropic efficiency, water pump efficiency, fan efficiency, and cooling tower (CT) efficiency.

[0069] The compressor isentropic efficiency is the ratio of ideal isentropic compression to actual compression power consumption. The specific formula is as follows:

[0070]

[0071] Among them, η s is the isentropic efficiency of the compressor; h1 is the enthalpy value at the compressor inlet; h2 is the actual enthalpy value at the compressor outlet; h 2s is the ideal isentropic enthalpy at the compressor outlet, that is, the enthalpy value after the compression process is completed if the compression process is isentropic (no heat loss and reactive power loss); s is isentropic.

[0072] The pump efficiency is the ratio of hydraulic output power to input shaft power. The specific formula is as follows:

[0073]

[0074] Among them, η pump is the pump efficiency, which indicates the pump's ability to convert input energy into hydraulic output energy; ρ is the density of the fluid; g is the acceleration due to gravity; Q is the flow rate, i.e., the volume flow rate of the fluid per unit time; H is the head, i.e., the height to which the pump can lift the fluid; P input is the input power, that is, the shaft power of the pump.

[0075] The fan efficiency is the ratio of the fan aerodynamic efficiency to the motor input power. The specific formula is as follows:

[0076]

[0077] Among them, η fan is the fan efficiency, which indicates the efficiency of the fan in converting the electrical energy input by the motor into aerodynamic power (i.e., the ability to do work on the air); ρ air is the air density, which indicates the mass of air per unit volume; Q air is the air mass flow rate, that is, the amount of air passing through the fan per unit time; Δ P is the static pressure difference generated by the fan, that is, the pressure difference between the fan inlet and outlet; P input is the input power, that is, the input power of the fan motor.

[0078] The efficiency of a cooling tower (CT) is the ratio of the cooling water temperature drop to the theoretical maximum temperature drop. The specific formula is as follows:

[0079]

[0080] Among them, η ct T is the cooling tower efficiency, which indicates the cooling tower's ability to reduce the cooling water temperature; ω,in is the cooling water inlet temperature, that is, the temperature of the water entering the cooling tower; T ω,out is the cooling water outlet temperature, that is, the temperature of the water leaving the cooling tower; T ωb,air It is the wet bulb temperature, that is, the wet bulb temperature of the air around the cooling tower, which reflects the humidity and temperature of the air.

[0081] By using the Dify platform to build a rule customization interface to access predefined refrigeration room rules, and embedding the above-mentioned energy efficiency ratio calculation unit, thermal balance rate calculation unit, subsystem energy consumption proportion calculation unit and each equipment performance coefficient calculation unit, the key performance indicators in the refrigeration room are calculated in real time. According to the predefined refrigeration room rules that are accessed, the key performance indicators calculated in real time are verified to obtain calculation verification information, and the calculation verification information is input into the large language model, so that the large language model has a professional thinking mode and calculation verification capabilities, thereby providing a real-time analysis basis for answering questions to be answered, and generating accurate, professional and targeted question and answer results.

[0082] In some embodiments of the present application, Figure 1 As shown, the refrigeration room vertical knowledge question and answer system 100 based on RAG technology also includes a feedback optimization module 105, which is used to provide feedback based on the generated response content to generate feedback data and optimize the response content based on the feedback data.

[0083] In this embodiment, by introducing the feedback optimization module 105, the quality of the answer is evaluated according to the response content generated by the generation module 104, and the knowledge base, question-answering algorithm and special tool rules are continuously optimized through the feedback mechanism.

[0084] Figure 4 The following is a schematic diagram of the framework flow of the vertical knowledge question-answering system for the refrigeration room based on RAG technology in one embodiment of the present application. Figure 4A specific example is given. For example, the question to be answered raised by the user is "Does the current abnormal temperature in the computer room affect the operation of the refrigeration system?" The retrieval module of the vertical knowledge question-and-answer system for the refrigeration room based on RAG technology of this application retrieves static data in the static database according to the question raised by the user "Does the current abnormal temperature in the computer room affect the operation of the refrigeration system?" For example, the knowledge fragment "abnormal temperature in the computer room may cause equipment failure and system performance degradation" is retrieved, and combined with the real-time acquired equipment operation data, for example, the acquired real-time dynamic data is "the current computer room temperature is 30°C, which exceeds the normal operating range (usually 18-25°C)", to form hybrid knowledge information; access predefined refrigeration room rules, such as industry standards and equipment specifications, through the rule customization interface, and call the real-time calculation unit and verification unit, and verify according to industry standards and equipment specifications to obtain calculation verification information, such as "In an environment of 30°C, the heat dissipation efficiency of the server will decrease, which may cause CPU overheating"; through The large language model combines the hybrid knowledge information retrieved above and the calculation verification information obtained through verification to generate detailed and verified response content, such as "According to the computer room environmental standards, the computer room temperature should be maintained between 18-25°C to ensure the normal operation of the equipment. The current computer room temperature is 30°C, which exceeds the normal operating range. According to the equipment specification, the heat dissipation efficiency of the server will decrease in an environment of 30°C, which may cause the CPU to overheat and affect the stability of the system." The vertical knowledge question and answer system for refrigeration rooms based on RAG technology in this application prompts the user whether it is necessary to dispatch maintenance personnel for further inspection and maintenance based on the generated answer results, such as "It is recommended to take cooling measures immediately and consider dispatching maintenance personnel for inspection and maintenance." The entire question and answer process of the vertical knowledge question and answer system for refrigeration rooms based on RAG technology in this application includes knowledge retrieval, dynamic data fusion, special tool calculation verification and feedback mechanism to ensure that the answers are accurate, professional and highly credible.

[0085] In the technical solution of this application, a vertical field knowledge question and answer system for refrigeration rooms is constructed by using retrieval + enhancement + large language model generation based on RAG technology, integrating static data and real-time dynamic data, and combining with the Dify platform to develop special tools, giving the large language model professional thinking and calculation verification capabilities, generating highly professional and targeted question and answer results, which can be applied to energy-saving control and fault diagnosis in high-efficiency computer rooms, high-efficiency air compressors, semiconductor factories, panel factories and other high-precision manufacturing industries.

[0086] The RAG-based refrigeration room vertical knowledge question-and-answer system 100 of this application can be applied to various application scenarios in refrigeration rooms, as shown below:

[0087] Application Scenario 1: Routine maintenance and training for refrigeration rooms. New employees can quickly learn about the structure, operating principles, and common troubleshooting methods of refrigeration rooms through this RAG-based refrigeration room vertical knowledge Q&A system. They can also calculate and verify equipment operating parameters using specialized tools built on the Dify platform, reducing training and learning costs.

[0088] Application Scenario 2: Equipment Fault Diagnosis and Emergency Response in Refrigeration Rooms. This application's RAG-based refrigeration room knowledge question-and-answer system integrates historical fault records and real-time monitoring data. Leveraging computational verification capabilities and refrigeration room rules developed on the Dify platform, it assists engineers in quickly locating the cause of equipment failures, thereby improving the efficiency of fault diagnosis responses.

[0089] Application Scenario 3: Energy-saving Optimization and Operation Evaluation of Refrigeration Rooms. This RAG-based refrigeration room vertical knowledge question-and-answer system generates scientific evaluation reports using specialized tools by comparing and analyzing real-time equipment operation data with historical data. This provides a scientific basis for energy consumption control and optimization, enabling energy-efficient operation of industrial refrigeration systems.

[0090] Application Scenario 4: Cross-domain Intelligent Q&A. The RAG-based vertical knowledge Q&A system for refrigeration rooms in this application can be expanded to other high-precision manufacturing fields, such as high-efficiency air compression systems and semiconductor production lines, providing customized intelligent Q&A support for related industrial systems and providing professionalism through rule customization.

[0091] Figure 5 This is a flow chart of the vertical knowledge question-answering method for a refrigeration room based on RAG technology provided by an embodiment of the present application. Figure 5 As shown in the figure, the vertical knowledge question-answering method for the refrigeration room based on RAG technology includes the following steps:

[0092] Step S51: Obtain static data in a pre-built static database and equipment operation data in a refrigeration room, and build a knowledge base based on the data.

[0093] Step S52: Retrieve corresponding knowledge fragments from the knowledge base according to the received question to be answered based on the RAG technology to obtain mixed knowledge information.

[0094] Step S53: constructing a special tool for the refrigeration room using the large model platform; the special tool is used to access predefined refrigeration room rules and perform calculation verification based on the equipment operation data to obtain calculation verification information.

[0095] Step S54: inputting the mixed knowledge information and the calculation verification information into the large language model to generate the answer content of the question to be answered.

[0096] It should be understood that the refrigeration room vertical knowledge question and answer method based on RAG technology provided in the embodiment of the present application can realize the functions of the above-mentioned refrigeration room vertical knowledge question and answer system based on RAG technology, and will not be repeated here.

[0097] It should also be understood that the division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0098] Figure 6 This is a schematic diagram of the structure of the electronic terminal provided in the embodiment of the present application. Figure 6 As shown, the electronic terminal 600 includes: at least one processor 601, a memory 602, at least one network interface 603 and a user interface 605. The various components in the electronic terminal 600 are coupled together via a bus system 604. It is understood that the bus system 604 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 604 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 604 is not shown in FIG. Figure 6 Various buses are labeled as bus systems.

[0099] The user interface 605 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0100] It will be appreciated that the memory 602 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM) or a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0101] The memory 602 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 600. Examples of these data include: any executable program for operating on the electronic terminal 600, such as an operating system 6021 and an application 6022; the operating system 6021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 6022 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The vertical knowledge question and answer method for the refrigeration room based on RAG technology provided in the embodiment of the present invention can be included in the application 6022.

[0102] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 601 or by software instructions. The above processor 601 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 601 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor 601 can be a microprocessor or any conventional processor. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium located in a memory. The processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0103] In an exemplary embodiment, the electronic terminal 600 can be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), and complex programmable logic devices (CPLDs) to execute the aforementioned refrigeration room vertical knowledge question and answer method based on RAG technology.

[0104] According to the refrigeration room vertical knowledge question-answering method based on RAG technology provided in an embodiment of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes Figure 5 The method of any of the embodiments shown.

[0105] According to the refrigeration room vertical knowledge question-answering method based on RAG technology provided by the embodiment of the present application, the present application also provides a computer-readable storage medium, which stores program code, and when the program code is run on a computer, the computer executes Figure 5 The method of any of the embodiments shown.

[0106] As used in this specification, the terms "component," "module," "system," and the like are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. By way of illustration, both an application running on a computing device and a computing device can be a component. One or more components can reside in a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures stored thereon. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component on a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0107] Those skilled in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (program) are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0113] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0114] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0115] In summary, in response to the technical problem that the existing technology cannot give accurate, professional and targeted answers when applied to the refrigeration system, the present invention provides a vertical knowledge question-answering system, method, medium, terminal and program product for a refrigeration room based on RAG technology, which obtains static data from a pre-constructed static database and equipment operation data in the refrigeration room obtained in real time to construct a knowledge base, and retrieves knowledge fragments corresponding to the questions to be answered from the constructed knowledge base based on the received questions to be answered based on RAG technology to obtain mixed knowledge information, and uses the large model platform to build a special tool for the refrigeration room to access predefined refrigeration room rules, and performs calculation verification based on the equipment operation data obtained in real time to obtain calculation verification information, and inputs the mixed knowledge information and calculation verification information into the large language model to generate the answer content of the questions to be answered. Specifically, through Di The fy platform builds customized rules for refrigeration rooms with professional thinking logic to ensure that the large language model can call predefined professional knowledge and calculation verification results when answering. This application integrates static data with real-time dynamic data in the refrigeration room, and combines special tools developed by the Dify platform to access predefined refrigeration room rules and calculation verification of key performance indicators to obtain calculation verification information and build comprehensive context information, so that the vertical knowledge question and answer system of the refrigeration room can generate accurate, professional and targeted question and answer results, and realize that the large language model has professional thinking mode and calculation verification ability, so as to provide professional answers and intelligent auxiliary decision-making for the complex system of the refrigeration room, which not only improves the technical personnel's cognition and operation efficiency of complex systems, but also has significant advantages in fault diagnosis, energy-saving control and system optimization. It can also be extended to other industrial automation and high-precision manufacturing fields, providing strong support for the intelligent transformation of the industry. Therefore, this application effectively overcomes the various shortcomings of the existing technology and has high industrial utilization value.

[0116] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A vertical knowledge question-answering system for refrigeration rooms based on RAG technology, characterized by: include: A knowledge base construction module is used to obtain static data in a pre-built static database and equipment operation data in the refrigeration room, and construct a knowledge base based on the data; A retrieval module, configured to retrieve corresponding knowledge fragments from the knowledge base according to the received question to be answered based on the RAG technology, so as to obtain hybrid knowledge information; A dedicated tool construction module is used to construct a dedicated tool for the refrigeration room using a large model platform; the dedicated tool is used to access predefined refrigeration room rules and perform calculation verification based on the equipment operation data to obtain calculation verification information; A generation module is used to input the mixed knowledge information and the calculation verification information into a large language model to generate a response content for the question to be answered.

2. The refrigeration room vertical knowledge question-answering system based on RAG technology according to claim 1 is characterized in that: The knowledge base building module includes: A static database construction unit is used to obtain multimodal data of the refrigeration industry and private domain data of refrigeration rooms, and build a static database based on this data; A dynamic data acquisition unit, used to acquire the equipment operation data in the refrigeration room in real time; A fusion unit is used to fuse the equipment operation data acquired in real time into the static database to form the knowledge base.

3. The refrigeration room vertical knowledge question and answer system based on RAG technology according to claim 1 is characterized in that: The large model platform is the Dify platform.

4. The refrigeration room vertical knowledge question-answering system based on RAG technology according to claim 3 is characterized in that: The dedicated tool building blocks include: A refrigeration room rule access unit, configured to utilize the Dify platform to construct a refrigeration room rule customization interface; the rule customization interface is configured to access predefined refrigeration room rules; A real-time calculation unit, configured to perform real-time calculations based on the equipment operation data to obtain key performance indicators; A verification unit is used to verify the key performance indicators obtained by real-time calculation according to the predefined refrigeration room rules to obtain the calculation verification information.

5. The refrigeration room vertical knowledge question-answering system based on RAG technology according to claim 4 is characterized in that: The key performance indicators include any one or more combinations of energy efficiency ratio, thermal balance rate, subsystem energy consumption ratio, and performance coefficient of each device.

6. The refrigeration room vertical knowledge question-answering system based on RAG technology according to claim 1 is characterized in that: It also includes a feedback optimization module for providing feedback based on the generated response content to generate feedback data, and optimizing the response content based on the feedback data.

7. A refrigeration room vertical knowledge question-answering method based on RAG technology, characterized by: include: Obtain static data from a pre-built static database and equipment operation data in the refrigeration room, and build a knowledge base based on this data; Retrieving corresponding knowledge fragments from the knowledge base according to the received question to be answered based on the RAG technology to obtain hybrid knowledge information; A dedicated tool for the refrigeration room is constructed using a large model platform; the dedicated tool is used to access predefined refrigeration room rules and perform calculation verification based on the equipment operation data to obtain calculation verification information; The mixed knowledge information and the calculation verification information are input into a large language model to generate a response content to the question to be answered.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method as claimed in claim 7 is implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program code, which, when executed on a computer, enables the computer to implement the method as claimed in claim 7 .

10. An electronic terminal comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method as claimed in claim 7.

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