Natural gas long-distance pipeline leakage intelligent early warning system

Abstract

The invention provides an intelligent early warning system for natural gas long-distance pipeline leakage. The system generates detailed consequence evaluation and processing suggestions by using a structured knowledge base, storing historical cases, expert experience and standard operation regulations and combining technologies such as retrieval enhancement generation and the like. The user interface module provides a visual interaction interface, displays a detection result, a processing suggestion and an operation process, improves the emergency processing efficiency and safety, and remarkably improves the safety management level of pipeline operation.

Description

Technical Field

[0001] The invention belongs to the technical field of natural gas long-distance pipeline leakage early warning, and relates to an intelligent early warning system for natural gas long-distance pipeline leakage. Background Art

[0002] Natural gas long-distance pipelines are an important way of modern energy transmission, and their safe operation is of great significance to economic development and environmental protection. Existing natural gas pipeline leakage detection technologies mainly rely on physical sensors and data analysis technologies, which can detect pipeline leakage to a certain extent, but have great limitations in subsequent treatment suggestions and process guidance.

[0003] The existing technology mainly has the following deficiencies:

[0004] 1. Limited to leak alerts: The main function of the existing system is to detect leaks and sound an alarm to alert operators that there may be problems with the pipeline. However, this alert is limited to initial notification of the leak and fails to provide further guidance and information.

[0005] 2. Lack of consequence assessment: The existing system is unable to conduct a detailed assessment of the consequences of a leak and is unable to tell operators the specific scope of impact, possible economic losses and environmental hazards of the leak.

[0006] 3. Lack of treatment suggestions: For emergency treatment after a leak, the existing system is unable to provide detailed treatment suggestions and operating procedures, resulting in a lack of scientific guidance for on-site personnel when responding to emergencies, which may delay the treatment or take inappropriate measures.

[0007] 4. Insufficient utilization of knowledge base: Although a large amount of knowledge and experience has been accumulated in the operation and maintenance of natural gas pipelines, the existing system fails to effectively utilize this knowledge and fails to apply expert experience and standard operating procedures to the leak handling process.

[0008] 5. Lack of comprehensiveness: Existing leak detection systems are often single-function and can only achieve a combination of one or several detection methods. They fail to form a comprehensive intelligent early warning system and are difficult to adapt to complex and changeable leak situations. Summary of the invention

[0009] In summary, although the existing long-distance natural gas pipeline leakage detection system has achieved some results in terms of reminder function, it has obvious deficiencies in consequence assessment, treatment suggestions and operation process guidance. These deficiencies may lead to inaccurate leakage consequence assessment, untimely emergency treatment, improper operation and other problems in practical applications, which seriously affect the safe operation of the pipeline and environmental protection. Therefore, it is necessary to develop a comprehensive intelligent early warning system based on advanced technology to make up for the deficiencies of existing technologies and improve the safety management level of natural gas pipelines.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0011] A leakage detection module, including a data acquisition module and a data processing module;

[0012] Data acquisition module, which uses fiber-optic signal analysis to detect and locate gas pipeline leaks;

[0013] The data processing module pre-processes and extracts features from the collected data, uses machine learning and deep learning algorithms to analyze feature data, identifies leaks and determines their specific locations, and generates leak warning information;

[0014] The knowledge base module builds and maintains a structured database to store and manage professional knowledge on natural gas pipeline operations, leakage consequences and treatment methods;

[0015] The large language model and RAG module, by combining the pre-trained large language model with retrieval enhancement generation technology, realizes the expansion and intelligent processing of leakage warning information;

[0016] The processing suggestion module adopts multi-level analysis and decision-making, combining the knowledge base module and the large language model with the RAG module to provide processing suggestions for on-site personnel;

[0017] The user interface module is implemented using Web technology and framework, providing an interactive interface to display various types of system information.

[0018] Furthermore, the data processing module extracts features from the collected data, including time domain features, frequency domain features, time-frequency domain features and statistical features.

[0019] Furthermore, the retrieval function of the knowledge base module adopts indexing technology and natural language processing algorithm;

[0020] The document score calculation formula is:

[0021]

[0022] Where D is the document, Q is the query, and q i is the query word, f(q i , D) is the word frequency, |D| is the document length, and avgdl is the average document length;

[0023] Then, the BERT model is used to calculate the semantic similarity. The specific formula is:

[0024] sim(Q,D)=cosine(BERT(Q),BERT(D))

[0025] Finally, the BM25 score and semantic similarity score are integrated to get the ranking of retrieval results.

[0026] Furthermore, the pre-training method of the large language model and the large language model in the RAG module is:

[0027] Domain adaptive pre-training uses large-scale leakage accident reports, safety operating procedures, emergency plans and other related texts for continued pre-training. The pre-training objective function is defined as:

[0028] L D AP(θ)=E[lo g P(x|c;θ)]

[0029] Where θ is the model parameter, x represents the input text, and c is the context information;

[0030] Contrastive learning enhancement, constructing positive and negative sample pairs, strengthens the model's representation learning of leakage-related concepts, and the contrast loss function is designed as follows:

[0031] L C L(θ)=-log[exp(s(h, h+) / τ) / Σ i exp(s(h,h i ) / τ)]

[0032] h represents the representation of the current sample, h+ is the positive sample, and h i is a negative sample, s(x) is the cosine similarity function, and τ is the temperature parameter;

[0033] Hint learning fine-tuning, designing a series of hint templates P = p1, ..., p specific to the leakage handling task m , the optimization goal is:

[0034] L P L(θ)=E x [-logP(y|x,p;θ)]

[0035] y is the expected output, p∈P is the selected prompt template;

[0036] Multi-task joint optimization combines the above three tasks to design a multi-task learning objective:

[0037] L t otal(θ)=α·L D AP(θ)+β·L C L(θ)+γ·L P L(θ)

[0038] Among them, α, β, and γ are the weight coefficients of each task.

[0039] Furthermore, it is characterized in that the large language model and the RAG module also support self-optimization and continuous learning, which is achieved by the following methods:

[0040] Online fine-tuning, when the system processes new leak warning information, it will automatically record user feedback and corrections. When enough new data is accumulated, the online fine-tuning process is triggered and the fine-tuning loss function is defined:

[0041] L online (θ) = E[(yf(x;θ)) 2 ]+λ||θ-θ0|| 2

[0042] Where f(x; θ) is the model output, y is the result after user correction, θ0 is the original parameter, and λ is the regularization coefficient;

[0043] Active learning: The system actively selects samples that need to be manually labeled through an uncertainty sampling strategy and defines the uncertainty measure of the samples:

[0044] U(x)=1-max y P(y|x;θ)

[0045] Select the k samples with the highest uncertainty for manual labeling.

[0046] Knowledge distillation, assuming the teacher model is T, the student model is S, and the distillation loss is:

[0047] L K D(S)=α·H(y, S(x))+(1-α)·KL(T(x) / τ, S(x) / τ)

[0048] Where v is the cross entropy, KL is the KL divergence, τ is the temperature parameter, and α is the balance coefficient.

[0049] Furthermore, the method for the processing suggestion module to perform scene classification and risk scoring is: classifying the leakage scene through a multi-level classifier based on a decision tree;

[0050] The classifier considers inputs including leak location, size, pressure change data, and environment to categorize leaks into predefined typical scenarios;

[0051] The system initiates a comprehensive risk assessment, taking into account factors including the estimated leakage volume, surrounding population density, terrain characteristics, weather conditions and distribution of important facilities, and calculates a risk score to provide a basis for subsequent decision-making.

[0052] Furthermore, the specific method for the processing suggestion module to make suggestions based on scenario classification and risk scoring is:

[0053] The intelligent plan matching system selects the most similar treatment plan from the plan library and supports dynamic combination to form a customized response strategy;

[0054] The system generates detailed treatment steps, including emergency shut-off and isolation procedures, personnel evacuation plans, surrounding traffic control recommendations, leak point repair technology selection and environmental monitoring deployment recommendations

[0055] Each step is accompanied by execution priority, estimated time and potential risks.

[0056] The intelligent resource allocation system calculates the required manpower and materials based on the leakage characteristics, including professional personnel, equipment and tools, emergency materials and logistical support requirements.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The intelligent early warning system for long-distance natural gas pipeline leakage provided by the present invention, combined with the detailed consequence assessment and scientific treatment suggestions provided by the knowledge base module, significantly improves the efficiency and standardization of emergency treatment and ensures the safety and stability of pipeline operation.

[0059] In addition, the user interface module enables users to quickly obtain and understand leak-related information through intuitive interactive design and rich visualization functions, supports multi-device access and collaborative operation, and improves user experience and operational convenience. Overall, the present invention provides comprehensive support in consequence assessment and emergency response, greatly improving the safety management level and accident response capabilities of long-distance natural gas pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, the present invention provides an intelligent early warning system for leakage of a natural gas long-distance pipeline, including the following main modules:

[0063] The leakage detection module includes a data acquisition module and a data processing module.

[0064] Data acquisition module that uses fiber optic signal analysis to detect and locate natural gas pipeline leaks.

[0065] The data processing module preprocesses and extracts features from the collected data, uses machine learning and deep learning algorithms to analyze feature data, identifies leaks and determines their specific locations, and generates leak warning information.

[0066] The knowledge base module builds and maintains a structured database to store and manage professional knowledge on natural gas pipeline operation, leakage consequences and treatment methods.

[0067] The large language model and RAG module, by combining the pre-trained large language model with retrieval enhancement generation technology, realizes the expansion and intelligent processing of leakage warning information.

[0068] The processing suggestion module adopts multi-level analysis and decision-making, combining the knowledge base module and the large language model with the RAG module to provide processing suggestions for on-site personnel.

[0069] The user interface module is implemented using Web technology and framework, providing an interactive interface to display various types of system information.

[0070] The details of each module are as follows:

[0071] The Leak Detection Module uses fiber optic signal analysis to detect and locate natural gas pipeline leaks.

[0072] The data acquisition module uses an ultra-narrow linewidth laser to emit an optical signal, which is divided into detection light and local light through a coupler, and then modulated into detection pulse light through a modulator. After the detection pulse light is amplified by a low-noise optical pulse amplifier, the optical signal enters the sensing fiber and returns to generate backward Rayleigh scattering. The optical signal processed by the acousto-optic frequency shifter coherently interferes with the Rayleigh scattered light, and data is collected through a bipolar cooling detector and a high-speed data acquisition card.

[0073] The data processing module preprocesses and extracts features from the collected data, uses machine learning and deep learning algorithms to analyze feature data, identifies leaks and determines specific locations, and generates leak warning information. Finally, the leak detection module outputs detection results that include leak location information and the possibility of leaks at that location.

[0074] The leakage detection module includes a data processing module, which uses machine learning and deep learning algorithms to analyze the collected feature data. The data processing module first performs feature engineering to extract four types of features from the original data, including time domain features, frequency domain features, time-frequency domain features, and statistical features. Each type of feature contains multiple specific feature parameters. For time series data x(t), the extracted feature set F is defined as:

[0075] F={F time , F freq , F time-freq, F stat}

[0076] in,

[0077] F time ={f time，1 (x(t)),f time，2 (x(t)),...,f time，m (x(t))}

[0078] F freq ={f freq，1 (x(t)),f freq，2 (x(t)),...,f freq，n (x(t))}

[0079] F time-freq ={f time-freq，1 (x(t)),f time - freq，2 (x(t)),...,f time - freq，p (x(t))}

[0080] F stat ={f stat，1 (x(t)),f stat，2 (x(t)),...,f stat，q (x(t))}

[0081] Here, f category，i (x(t)) represents the i-th feature extraction function in each category of features.

[0082] Each feature is extracted in the following way:

[0083] 1. Time domain characteristics:

[0084] Let f time，1 (x(t)) is the root mean square (RMS) value of the signal:

[0085]

[0086] When a leak occurs, the RMS value will show a significant jump due to the sudden drop in pressure.

[0087] 2. Frequency domain characteristics:

[0088] Let f freq，1 (x(t)) is the integral of the signal's power spectral density (PSD) in a specific frequency band:

[0089]

[0090] Leakage usually produces additional energy in a specific frequency band (such as 20-100Hz), causing this characteristic value to increase.

[0091] 3. Time-frequency domain characteristics:

[0092] Let f time-freq，1 (x(t)) is the energy of the wavelet transform coefficient:

[0093]

[0094] Where W x (a, b) is the continuous wavelet transform of the signal x(t). Leakage will produce characteristic patterns in the time-frequency diagram, which is reflected in the change of the energy value.

[0095] 4. Statistical characteristics:

[0096] Let f stat，1 (x(t)) is the kurtosis of the signal:

[0097]

[0098] Leakage may cause an increase in the peakiness of the signal distribution, reflected in an increase in the kurtosis value.

[0099] The leak detection algorithm takes these features into consideration and constructs a feature vector:

[0100] v=[f time，1 (x(t)),f freq，1 (x(t)),f time-freq，1 (x(t)),f stat，1 (x(t))]

[0101] Then use Support Vector Machine (SVM) for classification:

[0102]

[0103] Where K(v i , v) is the kernel function, α i and b are SVM model parameters, y i is the label of the training sample. When y>0, the system determines it as leakage.

[0104] Through this method, the system can capture the performance of leakage in different feature domains and improve the accuracy and robustness of detection. For example, even if the leakage performance in a certain feature domain is not obvious, such as the RMS change in the time domain is not large, it may be significant in other feature domains, such as the increase of frequency domain energy. Combining these features can effectively reduce false positives and false negatives.

[0105] The knowledge base module builds and maintains a structured database to store and manage professional knowledge on natural gas pipeline operations, leakage consequences and treatment methods. The knowledge base adopts a relational structure and is divided into multiple tables, including historical case tables, expert experience tables, standard operating procedures tables, etc. Each table contains detailed fields such as case descriptions, processing steps, impact assessments, etc. The knowledge base is regularly updated and expanded to ensure the timeliness and accuracy of data. The retrieval function uses advanced indexing technology and natural language processing (NLP) algorithms to achieve fast and accurate information retrieval and related knowledge extraction. The module also integrates data security and backup mechanisms to ensure the stability and reliability of the knowledge base.

[0106] Among them, the search function uses inverted index technology combined with BM25 algorithm for preliminary screening, and the document score calculation formula is:

[0107]

[0108] Where D is the document, Q is the query, and q i is the query word, f(q i , D) is the word frequency, |D| is the document length, and avgdl is the average document length.

[0109] Then, the BERT model is used to calculate the semantic similarity. The specific formula is:

[0110] sim(Q,D)=cosine(BERT(Q),BERT(D))

[0111] Finally, the BM25 score and semantic similarity score are integrated to get the ranking of retrieval results.

[0112] The large language model and RAG module combine pre-trained large language models, such as ChatGLM4, with retrieval-enhanced generation technology to achieve expansion and intelligent processing of leakage warning information.

[0113] First, the warning information generated by the leak detection module is input into the large language model, and the relevant information is retrieved from the knowledge base through RAG technology, combined with the generation capability of the large language model, to generate a detailed leak consequence assessment, treatment suggestions and processing procedures. This module uses advanced natural language processing and machine learning algorithms to ensure the accuracy and practicality of the generated content, and provide comprehensive and scientific emergency treatment guidance for on-site personnel.

[0114] At the same time, this module supports self-optimization and continuous learning. With the updating of the knowledge base and the training of the model, the quality of generated content and the intelligence level of the system are continuously improved.

[0115] The further pre-training process adopts a multi-stage, multi-task optimization strategy to enhance the model's ability to process leak warning information. Specifically, we designed the following pre-training process:

[0116] 1. Domain Adaptive Pre-training:

[0117] Use large-scale leakage accident reports, safety operating procedures, emergency plans and other related texts for continued pre-training. The pre-training objective function is defined as:

[0118] L D AP(θ)=E[logP(x|c;θ)]

[0119] Where θ is the model parameter, x represents the input text, and c is the context information.

[0120] 2. Contrastive Learning Enhancement:

[0121] Construct positive and negative sample pairs to strengthen the model's representation learning of leakage-related concepts. The contrast loss function is designed as follows:

[0122] L C L(θ)=-log[exp(s(h, h+) / τ) / Σ i exp(s(h,h i ) / τ)]

[0123] h represents the representation of the current sample, h+ is the positive sample, and h i is a negative sample, s(x) is the cosine similarity function, and τ is the temperature parameter.

[0124] 3. Tips for learning fine-tuning:

[0125] Design a series of prompt templates P=p1,...,p specific to the leak handling task m , such as "Assess the consequences of [leaked material] leakage", "Provide treatment suggestions for [leakage scenario]", etc. The optimization goals are:

[0126] L P L(θ)=E x [-logP(y|x,p;θ)]

[0127] y is the expected output, and p∈P is the selected prompt template.

[0128] 4. Multi-task joint optimization:

[0129] Combining the above three tasks, design a multi-task learning objective:

[0130] L t otal(θ)=α·L D AP(θ)+β·LC L(θ)+γ·L P L(θ)

[0131] Among them, α, β, and γ are the weight coefficients of each task.

[0132] Through this series of carefully designed pre-training steps, the model can fully absorb domain knowledge, enhance its understanding of leak-related concepts, and adapt to specific task requirements. This multi-stage, multi-task pre-training method significantly improves the model's performance in leak warning information processing and provides a strong basic model support for the subsequent RAG module.

[0133] The self-optimization and continuous learning mechanism of the large language model and RAG module is achieved through the following technologies:

[0134] 1. Online fine-tuning:

[0135] When the system processes new leak warning information, it will automatically record user feedback and corrections. When enough new data is accumulated, the online fine-tuning process is triggered. Define the fine-tuning loss function:

[0136] L online (θ) = E[(yf(x;θ)) 2 ]+λ||θ-θ0|| 2

[0137] Where f(x; θ) is the model output, y is the result after user correction, θ0 is the original parameter, and λ is the regularization coefficient.

[0138] 2. Active Learning:

[0139] The system uses an uncertainty sampling strategy to actively select samples that need to be manually labeled. Define the uncertainty measure of the sample:

[0140] U(x)=1-max y P(y|x;θ)

[0141] Select the k samples with the highest uncertainty for manual labeling.

[0142] 3. Knowledge Distillation:

[0143] In order to keep the model concise and efficient, knowledge distillation is performed regularly. Let the teacher model be T, the student model be S, and the distillation loss be:

[0144] L K D(S)=α·H(y, S(x))+(1-α)·KL(T(x) / τ, S(x) / τ)

[0145] Where H is the cross entropy, KL is the KL divergence, τ is the temperature parameter, and α is the balance coefficient.

[0146] Through the combined application of these technologies, the module is able to continuously learn from new data and user feedback, continuously optimize its performance, while maintaining memory of original knowledge, achieving true self-optimization and continuous learning capabilities.

[0147] The treatment suggestion module of the intelligent early warning system for natural gas long-distance pipeline leakage adopts a multi-level intelligent analysis and decision-making system, combined with an expert knowledge base and data-driven methods, to provide on-site personnel with accurate and actionable treatment suggestions.

[0148] The treatment suggestion module first classifies the leak scenario through a multi-level classifier based on a decision tree. The classifier considers inputs such as leak location, scale, pressure change data and environmental factors to classify the leak into predefined typical scenarios. Subsequently, the system initiates a comprehensive risk assessment, taking into account factors such as the estimated leak volume, surrounding population density, terrain characteristics, weather conditions and distribution of important facilities, and calculates a risk score to provide a basis for subsequent decision-making.

[0149] Based on scenario classification and risk scoring, the intelligent plan matching system selects the most similar treatment plan from the plan library and supports dynamic combination to form a customized response strategy. The treatment suggestion module then generates detailed treatment steps, including emergency cut-off and isolation procedures, personnel evacuation plans, surrounding traffic control suggestions, leakage point repair technology selection and environmental monitoring deployment suggestions. Each step is accompanied by execution priority, estimated time required and potential risk prompts. At the same time, the intelligent resource allocation system calculates the required manpower and materials according to the leakage characteristics, including professional personnel, equipment and tools, emergency materials and logistics support requirements.

[0150] To ensure safety, the system integrates an advanced gas diffusion model. The processing suggestion module calculates the recommended safe isolation range and generates a dynamically updated electronic map showing the isolation area, evacuation routes, and temporary assembly point locations. In addition, the real-time monitoring and dynamic adjustment mechanism continuously receives on-site data and manual feedback, enabling the processing suggestion module to adjust the processing suggestions in a timely manner. To ensure the accuracy of key decisions, the processing suggestion module also implements a multi-level review workflow, automatically selects the approval process according to the risk level, and supports remote approval to speed up the decision-making process. Through the comprehensive application of these technologies, the processing suggestion module can provide comprehensive, accurate, and operational emergency response guidance for natural gas long-distance pipeline leakage accidents, effectively improving the efficiency and safety of emergency response.

[0151] The user interface module is implemented by adopting modern web technologies and frameworks, such as React, Angular or Vue.js, to provide an intuitive and friendly interactive interface to display various information of the system. The module includes multiple views and dashboards for real-time display of leak detection results, leak location information, consequence assessment, treatment suggestions and specific operation procedures. Users can query historical data, view knowledge base content, obtain treatment guidelines, and perform rapid response operations in emergency situations through the interface. The interface design focuses on user experience, supports multi-device access and multi-user collaboration, integrates interactive charts and maps, and presents complex data in a visual form, helping users understand and handle pipeline leaks more efficiently.

[0152] Through the organic combination of the above modules, the present invention provides an intelligent, comprehensive and efficient natural gas long-distance pipeline leakage early warning and emergency treatment system.

[0153] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent early warning system for long-distance natural gas pipeline leakage. It is characterized in that Included are: A leakage detection module, including a data acquisition module and a data processing module; Data acquisition module, which uses fiber-optic signal analysis to detect and locate gas pipeline leaks; The data processing module pre-processes and extracts features from the collected data, uses machine learning and deep learning algorithms to analyze feature data, identifies leaks and determines their specific locations, and generates leak warning information; The knowledge base module builds and maintains a structured database to store and manage professional knowledge on gas pipeline operations, leakage consequences and treatment methods; The large language model and RAG module, by combining the pre-trained large language model with retrieval enhancement generation technology, can achieve the expansion and intelligent processing of leakage warning information; The processing suggestion module adopts multi-level analysis and decision-making, combining the knowledge base module and the large language model with the RAG module to provide processing suggestions for on-site personnel; The user interface module is implemented using Web technology and framework, providing an interactive interface to display various types of system information.

2. According to claim 1, an intelligent early warning system for leakage of a long-distance natural gas pipeline, Features ,The data processing module extracts features from the collected data, including time ,domain features, frequency domain features, time-frequency domain features and ,statistical features.

3. According to claim 1, a natural gas long-distance pipeline leakage intelligent early warning system, Features ,The retrieval function of the knowledge base module adopts indexing technology and natural language processing algorithm; The document score calculation formula is: Where D is the document, Q is the query, and q i is the query word, f(q i , D) is the word frequency, |D| is the document length, and avgdl is the average document length; Then, the BERT model is used to calculate the semantic similarity. The specific formula is: sim(Q,D)=cosine(BERT(Q),BERT(D)) Finally, the BM25 score and semantic similarity score are integrated to get the ranking of retrieval results.

4. According to claim 1, a natural gas long-distance pipeline leakage intelligent early warning system, It is characterized in that The pre-training method of the large language model and the large language model in the RAG module is: Domain adaptive pre-training uses large-scale leakage accident reports, safety operating procedures, and emergency plans for further pre-training. The pre-training objective function is defined as: L D AP(θ)=E[lo g P(x|c;θ)] Where θ is the model parameter, x represents the input text, and c is the context information; Contrastive learning enhancement, constructing positive and negative sample pairs, strengthens the model's representation learning of leakage-related concepts, and the contrast loss function is designed as follows: L C L(θ)=-log[exp(s(h,h+) / τ) / Σ i exp(s(h,h i ) / t)] h represents the representation of the current sample, h+ is the positive sample, and h i is a negative sample, s(x) is the cosine similarity function, and τ is the temperature parameter; Hint learning fine-tuning, designing a series of hint templates specific to the leakage handling task P = p 1 ,…,p m , the optimization goal is: L P L(θ)=E x [-logP(y|x,p; θ)] y is the expected output, p∈P is the selected prompt template; Multi-task joint optimization combines the above three tasks to design a multi-task learning objective: L t otal(θ)=α·L D AP(θ)+β·L C L(θ)+γ·L P L(θ) Among them, α, β, and γ are the weight coefficients of each task.

5. An intelligent early warning system for leakage of a long-distance natural gas pipeline according to claim 1 or 4, It is characterized in that The large language model and RAG module also support self-optimization and continuous learning, which is achieved through the following methods: Online fine-tuning, when the system processes new leak warning information, it will automatically record user feedback and corrections. When enough new data is accumulated, the online fine-tuning process is triggered and the fine-tuning loss function is defined: L online (θ)=E[(yf(x;θ)) 2 ]+λ‖θ-θ 0 ‖ 2 Where f(x; θ) is the model output, y is the result after user correction, and θ 0 is the original parameter, λ is the regularization coefficient; Active learning: The system actively selects samples that need to be manually labeled through an uncertainty sampling strategy and defines the uncertainty measure of the samples: U(x)=1-maxyP(y|x;θ) Select the k samples with the highest uncertainty for manual labeling. Knowledge distillation, assuming the teacher model is T, the student model is S, and the distillation loss is: L K D(S)=α·H(y, S(x))+(1-α)·KL(T(x) / τ, S(x) / τ) Where H is the cross entropy, KL is the KL divergence, τ is the temperature parameter, and α is the balance coefficient.

6. According to claim 1, an intelligent early warning system for leakage of a long-distance natural gas pipeline, It is characterized in that The method for the processing suggestion module to perform scene classification and risk scoring is: Classify the leakage scenarios through a multi-level classifier based on a decision tree; The classifier considers inputs including leak location, size, pressure change data, and environment to categorize leaks into predefined typical scenarios; The system initiates a comprehensive risk assessment, taking into account factors including the estimated leakage volume, surrounding population density, terrain characteristics, weather conditions and distribution of important facilities, and calculates a risk score to provide a basis for subsequent decision-making.

7. According to claim 1, a natural gas long-distance pipeline leakage intelligent early warning system, Features ,The specific method of the processing suggestion module to make suggestions based on ,scenario classification and risk scoring is: The intelligent plan matching system selects the most similar treatment plan from the plan library and supports dynamic combination to form a customized response strategy; The system generates detailed processing steps, including emergency shut-off and isolation procedures, personnel evacuation plans, surrounding traffic control recommendations, leak point repair technology selection, and environmental monitoring deployment recommendations; Each step is accompanied by execution priority, estimated time and potential risks. The intelligent resource allocation system calculates the required manpower and materials based on the leakage characteristics, including professional personnel, equipment and tools, emergency materials and logistical support requirements.

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