Digital agent oil field intelligent optimization system and method based on large model

Abstract

The invention discloses a digital agent oil field intelligent optimization system based on a large model. The invention discloses a digital agent oil field intelligent optimization system based on a large model, and the system comprises a digital twinborn layer which is used for carrying out the real-time monitoring, optimization and prediction of the operation state of oil field equipment; the intelligent agent system is used for decomposing tasks according to user requirements, calling small models and interacting with a user; the identification type small model is used for processing real-time data of equipment operation and providing a prediction analysis result; the generation type large model is used for processing a user instruction through a natural language and generating an operation instruction, an optimization suggestion and a report in combination with a result of the identification type small model; the invention aims to solve the problem of complex optimization in oil field operation by constructing an intelligent agent combining a large model and a field-specific identification type small model.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a digital intelligent agent oilfield intelligent optimization system and method based on a large model. Background Art

[0002] In the current technical status of oilfield management, tasks such as operating status monitoring, predictive maintenance, energy efficiency optimization, and fault diagnosis of key equipment such as pumps, heaters, furnaces, and PLC control systems mainly rely on manual experience and isolated analysis of single equipment. Although this method can maintain the basic operation of the oilfield to a certain extent, it is limited by the limitations of manual judgment and the existence of information islands, making it difficult to achieve global optimization and efficient coordination of the entire oilfield system. Specifically, the traditional oilfield management method lacks an integrated management platform that can comprehensively consider the interactions between multiple devices and multiple systems, resulting in low operating efficiency, high equipment maintenance costs, and fault detection and processing often lags behind actual needs. Therefore, it is necessary to propose a digital agent oilfield intelligent optimization system and method based on a large model to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to address the deficiencies of the prior art and provide a digital intelligent agent oilfield intelligent optimization system and method based on a large model to solve the problem that the prior art is difficult to achieve global optimization and efficient coordination of the entire oilfield system.

[0004] The present invention provides a digital intelligent agent oilfield intelligent optimization system based on a large model, comprising:

[0005] The digital twin layer is used to monitor, optimize and predict the operating status of oilfield equipment in real time;

[0006] The agent system is used to decompose tasks according to user needs, call small models, and interact with users;

[0007] Small identification model, used to process real-time data of equipment operation and provide predictive analysis results;

[0008] The generative large model is used to process user instructions through natural language processing and generate operational guidance, optimization suggestions and reports based on the results of the discriminative small model.

[0009] Furthermore, the digital twin layer includes:

[0010] Maintenance monitoring unit, used to continuously collect operating data of oilfield equipment and transmit the data to the analysis and detection unit in real time;

[0011] An analysis and detection unit, used to receive data from the maintenance and monitoring unit, process the data using a data analysis algorithm, and identify abnormal data;

[0012] The load optimization unit is used to calculate the optimal operating load of the equipment by applying the load optimization algorithm according to the oilfield production plan and the actual operation data of the equipment, and generate load adjustment instructions;

[0013] The linkage optimization unit is used to collect the operating data of each device, analyze the mutual influence relationship between devices, apply the linkage optimization algorithm to calculate the optimal coordination strategy between devices, and generate linkage adjustment instructions;

[0014] The performance optimization unit is used to regularly collect the performance data of the equipment, apply the performance evaluation algorithm to evaluate the performance of the equipment, and generate performance optimization suggestions or adjust parameters based on the evaluation results;

[0015] Response optimization unit, which is used to monitor equipment failures or abnormal conditions and immediately trigger emergency plans once an abnormality is detected;

[0016] The adjustment management unit is used to calculate the optimal operating parameters of the equipment and generate adjustment instructions based on changes in production demand and environmental changes using intelligent control algorithms;

[0017] Energy efficiency optimization unit, used to collect energy consumption data of equipment, apply energy efficiency evaluation algorithm to evaluate the energy efficiency of equipment, generate energy efficiency optimization suggestions or adjust the working mode of equipment to reduce energy consumption according to the evaluation results;

[0018] The prediction and scheduling unit is used to predict the future operating status of the equipment based on historical data and real-time data using prediction algorithms, and generate maintenance and overhaul plans based on the prediction results;

[0019] The real-time adjustment unit is used to collect the equipment's operating data in real time according to changes in production demand and environmental changes, apply the real-time adjustment algorithm to calculate the equipment's optimal operating strategy, and generate real-time adjustment instructions to ensure that the equipment is always in the best operating state.

[0020] Furthermore, the intelligent agent system comprises:

[0021] Action unit, used to perform operations;

[0022] The planning and tool unit is used to generate task plans according to user needs; through the combination of small models and large models, data analysis, fault diagnosis and optimization suggestions are completed to generate memory agents;

[0023] Memory unit, used to interact with users based on memory agents.

[0024] Furthermore, the identification model is used for the preventive maintenance and real-time monitoring unit of the pump, and the preventive maintenance and real-time monitoring workflow of the pump is as follows:

[0025] Natural language input and demand understanding: The operator asks the system whether the pump needs preventive maintenance. The generative large model understands the user's natural language input and extracts key requirements. It determines that the user wants to know the health status of the pump equipment and determines whether preventive maintenance is needed. It decomposes the requirements into specific tasks and identifies the small identification models that need to be called.

[0026] Task allocation and small model calling: The intelligent agent decides to call the pump fault diagnosis small model according to the task decomposition of the generative large model; obtains the real-time operation data and historical data of the pump through sensors and data interfaces; the pump fault diagnosis small model analyzes the real-time and historical operation data of the pump, identifies abnormal patterns and fault signs; evaluates the current health status of the pump, calculates the health index or fault risk level; predicts the time range in which the pump may fail, and provides recommended time points for preventive maintenance;

[0027] Data analysis and result feedback: The small model provides health status assessment and failure risk level of the pump; predicts the time period when the pump may fail in the future; the intelligent agent receives the analysis results of the small model, summarizes and verifies them; and verifies the analysis results to ensure accuracy and reliability;

[0028] Preventive maintenance suggestion generation: The large model combines the diagnosis and prediction results of the small model to evaluate the necessity and urgency of preventive maintenance; it provides specific suggestions to the operator in natural language and provides analysis basis to help the operator understand the importance and necessity of the suggestions;

[0029] Execute maintenance plan and continuous monitoring: Users arrange preventive maintenance plans based on recommendations and notify the maintenance team to carry out related work; the agent records the maintenance plan and sets reminders to ensure that maintenance work is carried out on time; before and after maintenance, the pump's operating status is continuously monitored to verify the maintenance effect; if anomalies still exist after maintenance, the agent will call the small model again for in-depth analysis and provide further suggestions;

[0030] The identification model is used for vibration analysis and RMS detection of the pump. The vibration analysis and RMS detection workflow of the pump is as follows:

[0031] Natural language input and demand understanding: The operator issues instructions to the system through natural language: Please monitor the vibration of the pump to ensure safe operation; the generative large model understands the natural language input by the user, extracts keywords and core intent; determines that the user wants to monitor the vibration of the pump equipment in real time, evaluate its safety, and take necessary measures to prevent failures; decomposes the demand into specific tasks and identifies the small identification model that needs to be called;

[0032] Task allocation and small model calling: The intelligent agent decides to call the pump effective value RMS detection small model based on the task decomposition of the large model; obtains the real-time vibration data, historical vibration records and related operating parameters of the pump through sensors and data interfaces; the pump effective value RMS detection small model collects and processes the vibration signal of the pump in real time and calculates the effective value of the vibration; compares the real-time RMS value with the preset safety threshold to evaluate whether the vibration exceeds the safety range; analyzes historical data, predicts vibration trends, determines whether there is a trend of gradual increase, and predicts potential wear or failure;

[0033] Data analysis and result feedback: The small model provides the current pump vibration RMS value and historical trend chart; if the vibration approaches or exceeds the warning line, it generates risk warning information and puts forward specific operational suggestions; the intelligent agent receives the analysis results of the small model, summarizes and generates a complete monitoring report; combined with other operating data, it verifies whether the vibration abnormality is caused by other factors;

[0034] Optimization suggestion generation: The generative large model combines the output of the small model with other information from the system for comprehensive evaluation; provides specific suggestions to the operator in natural language, and recommends that technicians conduct equipment inspections to eliminate potential faults; and explains possible causes of increased vibration;

[0035] Implementation of optimization measures and continuous monitoring: Users adjust the operating parameters of the pump according to the suggestions provided by the system; arrange equipment inspection and maintenance to prevent failures; the intelligent agent monitors the vibration of the pump in real time and evaluates the effectiveness of the adjustment measures; if the vibration value does not decrease or continues to increase, the intelligent agent will call the small model again for in-depth analysis and provide further suggestions; if the vibration exceeds the safety threshold, the intelligent agent will immediately issue an alarm and notify relevant personnel to take emergency measures.

[0036] Furthermore, the identification model is used for outlet temperature and equipment load optimization, and the outlet temperature and equipment load optimization workflow is as follows:

[0037] Natural language input and demand understanding: The operator issues instructions to the system through natural language: Please optimize the outlet temperature of the heating furnace to reduce the load of subsequent equipment; the generative large model understands the natural language input by the user, extracts keywords and core intentions; determines that the user wants to optimize the operating load of subsequent equipment by adjusting the outlet temperature of the heating furnace; decomposes the overall task into specific subtasks, and identifies the small identification model that needs to be called;

[0038] Task allocation and small model calling: The intelligent agent decides to call the outlet temperature time series prediction small model according to the task decomposition of the large model; collects the historical and real-time outlet temperature data of the heating furnace and the operating parameters of the subsequent equipment through sensors and data interfaces; the outlet temperature time series prediction small model analyzes the outlet temperature change trend of the heating furnace and identifies the relationship between temperature fluctuations and subsequent equipment loads; predicts outlet temperature changes in the future based on current working conditions; calculates the optimal outlet temperature setting value to achieve balance and optimization of subsequent equipment loads;

[0039] Data analysis and result feedback: The small model provides time-series forecast charts and data of the outlet temperature; proposes specific suggestions for temperature adjustment; the intelligent agent receives the analysis results of the small model, conducts preliminary verification and integration; evaluates the impact of adjusting the outlet temperature on the entire process flow to ensure that no new problems are introduced;

[0040] Optimization suggestion generation: The large model combines the output of the small model with other relevant information; provides optimization suggestions to the operator in natural language; explains the reasons and expected effects of the adjustments to help the operator understand and adopt the suggestions;

[0041] Execution of optimization plan and continuous monitoring: The user manually adjusts the outlet temperature setting of the heating furnace according to the optimization suggestions provided by the system; the intelligent agent automatically controls the temperature setting through the control system; the outlet temperature of the heating furnace and the operating status of subsequent equipment are monitored in real time to ensure that the adjustment achieves the expected effect; if new deviations or problems are found, the intelligent agent will re-call the small model for analysis and provide further optimization suggestions;

[0042] The identification model is used for multi-region PLC equipment linkage optimization. The multi-region PLC equipment linkage optimization workflow is as follows:

[0043] Natural language input and demand understanding: The operator issues instructions through natural language: Please optimize the equipment linkage in multiple PLC areas to improve production efficiency; the generative large model understands the user's input, extracts keywords and core intentions; determines that the user wants to optimize the collaborative work between equipment in multiple PLC-controlled areas to ensure efficient operation of the process; decomposes the overall demand into specific subtasks and identifies the small identification model that needs to be called:

[0044] Task allocation and small model calling: The intelligent agent decomposes the tasks of the large model and decides to call the relevant identification small model; collects the operation data of the equipment through the sensors and PLC systems in each area; the block PLC timing prediction small model analyzes the equipment operation timing in each PLC-controlled area, identifies the potential bottlenecks and optimization space in the area; predicts the operation of the equipment in the future and finds possible resource conflicts or idleness; the full-site multi-PLC timing prediction small model analyzes the equipment linkage in multiple PLC areas across the entire site, identifies cross-regional timing conflicts, resource competition or energy waste; proposes global optimization strategies to ensure efficient coordination of equipment in each area;

[0045] Data analysis and result feedback: The small model provides timing analysis results for each area and the entire site, including potential bottlenecks, conflicts, and optimization opportunities; and proposes preliminary optimization solutions for the problems found;

[0046] The agent aggregates the analysis results of each small model to form a global view; if there are conflicts in the suggestions of different small models, the agent is responsible for coordination and seeking the best balance solution;

[0047] Optimization suggestion generation: The generative large model combines the analysis of the small model and the coordination results of the intelligent agent to evaluate the feasibility and expected effect of the overall optimization plan; generate optimization suggestions and explanations;

[0048] Execution and monitoring: Users adjust the operating parameters and timing settings of equipment in each area based on the optimization suggestions provided by the system; the intelligent agent monitors the operating status and performance indicators of the equipment in real time and evaluates the actual effect of the optimization measures; if the expected effect is not achieved, the intelligent agent will call the small model again for analysis and provide further optimization suggestions.

[0049] Furthermore, the identification type small model is used for multi-device comprehensive performance optimization, and the multi-device comprehensive performance optimization workflow is as follows:

[0050] Natural language input and demand understanding: The operator makes a request to optimize the operating efficiency of the entire station equipment and reduce energy consumption. The generative large model uses natural language processing technology to understand the user's core needs and extract keywords. It determines that the user wants to optimize the comprehensive performance of all equipment in the well station to improve production efficiency and reduce energy consumption. The overall demand is broken down into specific subtasks to identify the small identification model that needs to be called.

[0051] Task allocation and small model calling: The intelligent agent decomposes the tasks of the large model and decides to call the relevant small identification models, coordinates their execution order and data interaction, and collects real-time operation data from various devices and sensors;

[0052] The small model for predicting the timing of multiple PLCs in the entire site performs timing analysis on the PLC devices in the entire site, identifies the interaction and coordination relationship between the devices, and discovers possible energy waste or redundant equipment operation.

[0053] The outlet temperature time series prediction model analyzes the outlet temperature of equipment such as heating furnaces and predicts its impact on subsequent equipment;

[0054] The pump fault diagnosis model detects the pump's operating efficiency and potential faults to ensure that the pump operates in the best condition;

[0055] The furnace temperature time series prediction model ensures that the heating furnace operates within a safe and efficient temperature range;

[0056] Data analysis and result feedback: Each small model provides the operating efficiency, energy consumption level, and potential problem analysis results of the equipment; it proposes preliminary optimization measures for its respective field; the intelligent agent summarizes the analysis results of each small model to form a global view; if there is a conflict between the suggestions of different small models, the intelligent agent is responsible for coordination and seeking the best balance point;

[0057] Generation of optimization suggestions: The large model combines the outputs of each small model and the coordination results of the intelligent agent to perform global performance optimization analysis; it provides a comprehensive performance optimization report to the operator, detailing the basis and expected effects of each adjustment, to help the operator understand and adopt the suggestions;

[0058] Execution and monitoring: The operator adjusts the operating parameters of the relevant equipment according to the suggestions; the intelligent agent adjusts the equipment parameters; the operating status and energy consumption indicators of the adjusted equipment are monitored in real time to evaluate the optimization effect; if the expected effect is not achieved, the intelligent agent will call the small model again for analysis and provide further optimization suggestions;

[0059] The identification model is used for equipment response and operation optimization in emergency situations. The equipment response and operation optimization workflow in emergency situations is as follows:

[0060] Natural language input and demand understanding: The operator issues an emergency command through natural language: The current heating furnace temperature is too high, please provide an emergency treatment plan; the generative large model uses natural language processing technology to understand the user's emergency request and extract key information; it determines that the user needs to immediately obtain an emergency treatment plan for heating furnace overheating to ensure safety and minimize equipment damage; it decomposes the demand into specific subtasks and identifies the small identification model that needs to be called;

[0061] Task allocation and small model calling: Based on the task decomposition of the large model, the intelligent agent quickly decides to call the relevant identification small model in parallel to obtain the analysis results; obtains data from sensors and equipment interfaces in real time; the furnace temperature time series prediction small model analyzes the current furnace temperature data and predicts the future temperature change trend; evaluates whether the temperature will continue to rise and identifies the cause of overheating; the pump fault diagnosis small model analyzes the real-time operation data of the pump to determine whether there is a fault or abnormality; evaluates the impact of the pump status on the entire system, especially in emergency situations;

[0062] Data analysis and result feedback: The small model provides a temperature trend chart, indicating the time point and possible reasons when the temperature exceeds the safety threshold; reports the health status of the pump, indicating any abnormalities, failures and their potential impact on the system; the intelligent agent quickly integrates the analysis results of the small model to provide comprehensive information support for the generative large model; determines the priority of emergency treatment according to the urgency of the problem;

[0063] Generation of emergency treatment plan: The large model combines the overheating of the heating furnace and the operating status of the pump to formulate a comprehensive emergency treatment plan; provide clear emergency suggestions to the operator, explain the necessity and expected effect of the above measures, and help the operator understand and quickly implement them;

[0064] Execution and monitoring: Based on the emergency recommendations, the operator immediately adjusts the fuel supply, starts the cooling system, load-adjusts or shuts down the pump, and notifies maintenance personnel to perform maintenance. The intelligent agent performs some emergency operations. It monitors the furnace temperature, pump operating status, and overall system status in real time to evaluate the effectiveness of emergency measures. If the problem is not effectively alleviated, the intelligent agent will call the small model again for analysis and provide further response measures or issue an alarm.

[0065] Furthermore, the identification model is used for furnace temperature regulation and safety management, and the furnace temperature regulation and safety management workflow is as follows:

[0066] Natural language input and demand understanding: The operator asks a question: How to ensure that the furnace body temperature of the heating furnace operates within a safe range? The generative large model understands the user's natural language input and extracts key requirements. It recognizes that the user wants to monitor the furnace body temperature of the heating furnace to prevent the equipment from overheating and ensure safe operation. It decomposes the overall requirements into specific tasks and determines the small identification model that needs to be called.

[0067] Task allocation and small model calling: The intelligent agent disassembles the tasks of the large model and decides to call the small model for furnace temperature time series prediction. Through sensors and data interfaces, the real-time and historical data of the heating furnace temperature are obtained. The small model for furnace temperature time series prediction analyzes the current furnace temperature and identifies the temperature variation pattern based on historical data. It predicts the temperature variation trend of the furnace in the future and identifies possible overheating risks. It determines whether the furnace temperature is close to the safety upper limit and assesses the possibility and severity of overheating.

[0068] Data analysis and result feedback: The small model provides future trend charts and data of the furnace temperature; points out the time point and reason when the temperature may exceed the safe range; the intelligent agent receives the analysis results of the small model, summarizes and preliminarily evaluates them; verifies the accuracy of the prediction results and cross-validates them with other relevant data;

[0069] Safety management recommendation generation: The large model combines the predictions of the small model and the verification results of the intelligent agent to comprehensively evaluate the operating status of the heating furnace; provide specific operational recommendations to the operator in natural language; and detail the reasons for the temperature increase, possible consequences, and how the recommended measures can effectively prevent overheating;

[0070] Execution of safety measures and continuous monitoring: The user adjusts the fuel supply and starts or adjusts the cooling system according to the suggestions provided by the system; the agent controls the fuel supply valve and the operating parameters of the cooling system; monitors the furnace temperature and related parameters in real time to ensure that the adjustment measures are effective; if the temperature still does not fall within the safe range, the agent will call the small model again for analysis and provide further suggestions;

[0071] The identification model is used for exhaust temperature and energy efficiency optimization. The exhaust temperature and energy efficiency optimization workflow is as follows:

[0072] Natural language input and demand understanding: The operator makes a request: how to optimize the exhaust temperature to improve energy efficiency; the generative large model performs semantic analysis on the user's natural language input, extracts keywords and core intent; understands that the user's demand is to optimize the exhaust temperature of the heating system to reduce energy waste and improve efficiency; decomposes the task and determines the small identification model that needs to be called;

[0073] Task decomposition and small model calling: Based on the task decomposition of the large model, the intelligent agent decides to call the small model for exhaust temperature time series prediction; the small model for exhaust temperature time series prediction collects current exhaust temperature data; performs time series analysis to predict future exhaust temperature change trends; and identifies unreasonable temperature fluctuations and potential energy waste factors.

[0074] Data analysis and result feedback: The small model provides detailed exhaust temperature time series prediction results; points out the specific time period and possible reasons for excessively high temperature or abnormal fluctuations; the intelligent agent receives the analysis results of the small model; integrates data to provide decision support for the generative large model;

[0075] Optimization suggestion generation: The large model combines the predictions of the small model to evaluate different adjustment plans and their impact on energy efficiency; generate optimization suggestions;

[0076] Execution of optimization plan and continuous monitoring: The user adjusts the fuel input of the burner according to the optimization suggestions; the intelligent agent continues to monitor the exhaust temperature and energy efficiency indicators; if the system supports automatic control, the intelligent agent can directly adjust the burner parameters through the API interface; the adjusted data is collected in real time to evaluate the optimization effect.

[0077] Furthermore, the identification model is used for equipment scaling prediction and cleaning arrangement, and the equipment scaling prediction and cleaning arrangement workflow is as follows:

[0078] Natural language input and demand understanding: The operator makes a request: Please predict the scaling in the pipeline and recommend a cleaning time. The generative large model understands the user's natural language input and extracts key requirements. It determines that the user wants to understand the scaling trend of equipment such as pipelines and heaters, and get a recommended cleaning time. The overall demand is broken down into specific tasks, and the identification small model that needs to be called is determined.

[0079] Task allocation and small model calling: The intelligent agent decides to call the small scaling prediction model based on the task decomposition of the large model; obtains relevant data, including current water quality parameters, through sensors and data interfaces; the small scaling prediction model analyzes the current process parameters and water quality characteristics to evaluate scaling tendency; predicts the growth trend of scaling in the equipment based on historical data and current operating conditions; determines the impact of scaling on equipment performance and energy efficiency, and predicts when scaling will reach a critical value that requires cleaning;

[0080] Data analysis and result feedback: The small model provides prediction curves and data on the equipment scaling degree, estimates the time when scaling reaches the critical value, and points out the risks that may be caused by scaling. The intelligent agent receives the analysis results of the small model, summarizes and verifies them; considers the production plan and equipment operation status, and preliminarily formulates suggestions for cleaning arrangements;

[0081] Cleaning suggestion generation: The large model combines the prediction results of the small model with the company's production plan, maintenance cycle and other information; it provides specific suggestions to the operator in natural language, explains the impact of scaling and the necessity of early cleaning, and provides optimization suggestions;

[0082] Execute cleaning plan and continuous monitoring: Users coordinate with relevant departments to develop and arrange cleaning plans based on system recommendations; the agent records the cleaning plan and sets reminders to ensure that cleaning work is carried out on time; before and after cleaning, the scaling and operating efficiency of the equipment are continuously monitored to evaluate the cleaning effect; if abnormal scaling growth is found, the agent will call the small model again for analysis and provide new suggestions;

[0083] The identification model is used for real-time adjustment of oxygen content and combustion efficiency. The real-time adjustment process of oxygen content and combustion efficiency is as follows:

[0084] Natural language input and demand understanding: The operator uses natural language instructions, and the generative large model parses the user input and extracts key requirements; breaks down the requirements into multiple subtasks; determines the identification small model that needs to be called, and generates a task scheduling request;

[0085] Task allocation and small model calling: The intelligent agent calls the corresponding identification small model according to the task decomposition of the generative large model; the oxygen content time series prediction small model analyzes the current oxygen content and predicts future changes; the combustion efficiency analysis model evaluates the combustion efficiency under different oxygen supply levels; the oxygen content time series prediction small model receives real-time sensor data and analyzes the current state of oxygen content; uses historical data and algorithms to predict future changes in oxygen content; and identifies the optimal oxygen supply;

[0086] Data analysis and result feedback: The real-time oxygen content data of the combustion system is obtained through sensors and data interfaces; the small model feeds back the analysis results to the intelligent agent; the intelligent agent summarizes the information and prepares to provide optimization solutions;

[0087] Generate optimization suggestions and execute them: The generative large model generates optimization suggestions in natural language based on the feedback from the small model; the agent adjusts the parameters of the oxygen supply equipment; and displays the optimization suggestions to the operator to assist him in making manual adjustments;

[0088] Continuous monitoring and dynamic adjustment: The intelligent agent continuously obtains data on oxygen content and combustion efficiency to monitor the system status. If new changes are detected, the intelligent agent calls the small model again for analysis. The generative large model updates optimization suggestions to ensure that the system always runs in the best state.

[0089] The beneficial effects of the present invention are as follows: The digital intelligent agent oilfield intelligent optimization system based on a large model of the present invention has the following advantages:

[0090] Global optimization capability: Traditional technologies mostly rely on local optimization of a single device, while the present invention achieves linkage optimization among devices through the collaborative work of large and small models, such as comprehensive performance optimization of multiple devices and linkage optimization of multi-region PLC devices.

[0091] Higher level of intelligence: Through generative big models and natural language processing technology, operators can interact with the system through simple language instructions, reducing the complexity and error rate of manual operations.

[0092] Real-time monitoring and prediction: Through real-time data analysis and trend prediction of small models, the present invention can make accurate predictions on equipment failures, energy efficiency, etc., and generate maintenance recommendations in a timely manner, reducing the risk of equipment failures and unplanned downtime.

[0093] Flexible response to complex scenarios: The present invention is specifically designed for the complex and changeable environment of oil fields. Through the collaborative work of intelligent bodies, it can deal with complex problems such as equipment scaling, vibration, temperature fluctuations, etc., and has a high degree of adaptability and emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 A schematic diagram of a digital intelligent agent oilfield intelligent optimization system based on a large model of the present invention;

[0095] Figure 2 A schematic diagram of an actual application scenario of a large-model-based digital intelligent agent oilfield intelligent optimization system in an interwell station provided by the present invention. DETAILED DESCRIPTION

[0096] The following non-limiting embodiments can enable ordinary technicians in the field to understand the present invention more comprehensively, but do not limit the present invention in any way. It is necessary to point out that the following embodiments cannot be understood as limiting the scope of protection of the present invention. If technical personnel skilled in the art make some non-essential improvements and adjustments to the present invention based on the above-mentioned content of the present invention, they still fall within the scope of protection of the present invention.

[0097] The present invention provides a digital agent oilfield intelligent optimization system based on a large model. The large model is responsible for macro semantic understanding, complex problem solving and decision support, while the small model focuses on specific field tasks, such as pump preventive maintenance, vibration analysis, temperature regulation, energy efficiency optimization, etc. Through the scheduling and management of the agent, the large model is closely integrated with the small model, which can realize real-time monitoring, fault prediction, energy efficiency optimization and emergency response of oilfield equipment.

[0098] The specific plan is as follows:

[0099] Digital twin layer: responsible for real-time status monitoring and optimization of oilfield equipment, including maintenance monitoring, performance optimization, predictive scheduling, etc.

[0100] Agent system: includes actions, plans and tools, responsible for decomposing tasks according to user needs, calling small models, and interacting with users.

[0101] Identification small model: Its specific functions include preventive maintenance of pumps, vibration analysis, temperature regulation, energy efficiency optimization and other tasks. It processes equipment status data and generates optimization suggestions.

[0102] Generative large model: Processes user instructions through natural language and generates operation instructions, optimization suggestions and reports based on the results of small models.

[0103] The technical solution of the present invention combines a large generative model with a small identification model, so that the intelligent agent can efficiently complete the fault prediction and optimization operation of the equipment, reduce human intervention, and realize the automatic adjustment and management of the equipment.

[0104] Please combine Figure 1 The present invention provides a large-model-based digital agent oilfield intelligent optimization system, which includes the following aspects:

[0105] 1. Digital twin layer: This layer is responsible for real-time monitoring, optimization and prediction of oilfield equipment. It includes multiple functional modules, such as maintenance monitoring, analysis and detection, load optimization, linkage optimization, performance optimization, energy efficiency optimization, etc., to ensure efficient operation and maintenance of equipment.

[0106] 2. Agent system: It consists of three major parts: action, planning, tool module, and memory. The action part is responsible for executing operations, the plan generates task plans based on user needs, and the tool part combines small models with large models to complete data analysis, fault diagnosis, and optimization suggestions to generate memory agents.

[0107] 3. Identification model: mainly includes sub-models for multiple specific tasks such as pump preventive maintenance, vibration analysis, temperature regulation, etc. These models can process real-time data of equipment operation and provide high-precision predictive analysis results, such as pump health assessment and temperature change prediction.

[0108] 4. Large model (LLM): The generative large model processes the user's needs through natural language processing and generates specific operation suggestions and reports based on the results of the small models. For example, when a user requests to optimize the operation of a pump, the large model will call the relevant small models for data analysis and generate maintenance or optimization suggestions.

[0109] Through the collaborative work of these levels and modules, the intelligent system can efficiently manage the complex equipment and tasks of the oil field and realize intelligent optimization management of the oil field.

[0110] Figure 2 The actual application scenario of the well station is shown, including the following ten small models, which are described in detail as follows:

[0111] 1. Preventive maintenance and real-time monitoring of pumps:

[0112] 1. Process description

[0113] (1) Natural language input and demand understanding

[0114] User Action: The operator asks the system whether the pump requires preventive maintenance.

[0115] The role of the generative large model:

[0116] Semantic parsing: Understand the user's natural language input and extract key requirements, such as "preventive maintenance of the pump" and "current status of the pump".

[0117] Intent recognition: Determine if the user wants to know the health status of the pump equipment and whether preventive maintenance is needed.

[0118] Task decomposition: break down the requirements into specific tasks and identify the small identification models that need to be called: pump fault diagnosis small model.

[0119] (2) Task allocation and small model calling

[0120] The role of the agent:

[0121] Task scheduling: Based on the task decomposition of the large model, decide to call the small pump fault diagnosis model.

[0122] Data collection: Through sensors and data interfaces, obtain real-time pump operation data such as vibration, temperature, pressure, current, etc. and historical data.

[0123] The role of the identification model:

[0124] Small model for pump fault diagnosis:

[0125] Data Analysis: Analyze real-time and historical pump operating data to identify abnormal patterns and signs of failure.

[0126] Health Assessment: Evaluate the current health status of the pump and calculate the health index or failure risk level.

[0127] Failure prediction: Predict the time frame when a pump may fail and provide recommended time points for preventive maintenance.

[0128] (3) Data analysis and result feedback

[0129] Small model output:

[0130] Diagnostic results: Provides pump health status assessment and failure risk level.

[0131] Prediction results: Predict the time period in the future when the pump is likely to fail.

[0132] The role of the agent:

[0133] Result integration: Receive the analysis results of the small model and summarize and verify them.

[0134] Data verification: Verify the analysis results to ensure accuracy and reliability.

[0135] (4) Preventive maintenance recommendation generation

[0136] Large model integration analysis results:

[0137] Combine the diagnostic and prediction results of the small model to evaluate the necessity and urgency of preventive maintenance.

[0138] Generate maintenance recommendations:

[0139] Provides specific recommendations to operators in natural language, such as:

[0140] Based on the analysis of the pump's operating data, it is predicted that a failure may occur within two weeks. It is recommended to schedule preventive maintenance within one week to avoid unplanned downtime and production loss.

[0141] Explanation: Provide detailed analysis basis to help operators understand the importance and necessity of the recommendation.

[0142] (5) Execute maintenance plan and continuous monitoring

[0143] User Action:

[0144] Based on the recommendations, arrange preventive maintenance plans and notify the maintenance team to carry out related work.

[0145] The role of the agent:

[0146] Maintenance plan tracking: Record maintenance plans and set reminders to ensure maintenance work is performed on time.

[0147] Continuous monitoring: Before and after maintenance, the operating status of the pump is continuously monitored to verify the maintenance effect.

[0148] Feedback loop: If anomalies still exist after maintenance, the intelligent body will call the small model again for in-depth analysis and provide further suggestions.

[0149] Advantages of the above solution:

[0150] (1) Reduce operational risks

[0151] Avoid unplanned downtime: Through preventive maintenance, the downtime caused by sudden failures can be reduced to ensure the continuity of production.

[0152] (2) Save maintenance costs

[0153] Optimize maintenance resources: Plan maintenance work in advance to reduce the high costs of emergency repairs.

[0154] Reduce equipment damage: Preventive maintenance helps identify and resolve potential problems before they cause serious damage to equipment.

[0155] (3) Improve equipment life

[0156] Extended service life: Regular maintenance can reduce wear and tear on equipment and extend the overall service life of the pump.

[0157] (4) Improve production efficiency

[0158] Stabilize production process: Improve equipment reliability, make production process smoother, and reduce interruptions to production plans.

[0159] (5) Data-driven decision making

[0160] Scientific Management: Use real-time data and predictive models to support management in making informed maintenance and operational decisions.

[0161] Reduce human errors: Reduce reliance on human judgment and reduce errors caused by inexperience or negligence.

[0162] (6) Security

[0163] Prevent safety accidents: timely discover equipment hidden dangers, avoid safety problems caused by pump failures, and protect the safety of employees and equipment.

[0164] (7) Cost savings

[0165] Save energy consumption: Well-maintained equipment operates more efficiently, improves energy utilization, and reduces energy costs.

[0166] Optimize inventory management: Accurately predict spare parts needs and reduce unnecessary inventory backlogs.

[0167] 2. Pump vibration analysis and RMS detection:

[0168] 1. Process description

[0169] (1) Natural language input and demand understanding

[0170] User operation: The operator issues instructions to the system through natural language:

[0171] Monitor the pump for vibration to ensure safe operation.

[0172] The role of the generative large model:

[0173] Semantic parsing: Understand the natural language input by the user and extract keywords and core intent, such as "monitor the vibration of the pump" and "ensure safe operation".

[0174] Intent recognition: Identify that the user wants to monitor the vibration of the pump equipment in real time, assess its safety, and take necessary actions to prevent failure.

[0175] Task decomposition: Decompose the requirements into specific tasks and identify the small identification model that needs to be called: pump effective value RMS detection small model.

[0176] (2) Task allocation and the role of small model calling agents:

[0177] Task scheduling: Based on the task decomposition of the large model, it is decided to call the pump effective value RMS detection small model.

[0178] Data collection: Through sensors and data interfaces, the real-time vibration data, historical vibration records and related operating parameters of the pump, such as flow, pressure, temperature, etc., are obtained.

[0179] The role of the identification model:

[0180] The pump RMS detection model can achieve the following functions based on the patent CN116950905A: A centrifugal pump fault diagnosis and early warning method, system, device and storage medium:

[0181] Real-time analysis: collect and process the vibration signal of the pump in real time and calculate the effective value (RMS) of the vibration.

[0182] Safety Assessment: Compares the real-time RMS value with the preset safety threshold to assess whether the vibration exceeds the safe range.

[0183] Trend prediction: Analyze historical data, predict vibration trends, determine whether there is a gradual increase in the trend, and predict potential wear or failure.

[0184] (3) Data analysis and result feedback

[0185] Small model output:

[0186] Monitoring report: Provides the current pump vibration RMS value and historical trend chart.

[0187] Risk warning: If the vibration approaches or exceeds the warning line, a risk warning message will be generated.

[0188] Operational suggestions: Make specific operational suggestions, such as adjusting operating parameters, arranging maintenance, etc.

[0189] The role of the agent:

[0190] Result integration: Receive the analysis results of the small models and summarize them to generate a complete monitoring report.

[0191] Data verification: Combined with other operating data, such as flow rate, pressure, etc., verify whether the abnormal vibration is caused by other factors.

[0192] (4) Generation of optimization suggestions

[0193] Large model integration analysis results:

[0194] Combine the output of the small model with other information about the system, such as equipment operating conditions and production needs, for a comprehensive evaluation.

[0195] Generate action suggestions:

[0196] Provides specific recommendations to operators in natural language, such as:

[0197] The vibration value of the pump is close to the warning line. The current RMS value is 3.8mm / s. It is recommended to reduce the flow rate by 10% to reduce vibration and extend the life of the equipment. At the same time, it is recommended to arrange for technicians to inspect the equipment and eliminate potential faults.

[0198] Explain the cause: Explain the possible reasons for the increased vibration, such as overload operation, bearing wear, etc.

[0199] (5) Implement optimization measures and continuously monitor user operations:

[0200] According to the suggestions provided by the system, adjust the operating parameters of the pump, such as reducing the flow or pressure.

[0201] Schedule equipment inspections and maintenance to prevent breakdowns.

[0202] The role of the agent:

[0203] Continuous monitoring: Monitor pump vibration in real time to evaluate the effectiveness of adjustment measures.

[0204] Feedback loop: If the vibration value does not decrease or continues to increase, the agent will call the small model again for in-depth analysis and provide further suggestions.

[0205] Notification and alarm: If the vibration exceeds the safety threshold, the intelligent body will immediately issue an alarm and notify relevant personnel to take emergency measures.

[0206] Advantages of the above solution:

[0207] (1) Ensure equipment safety

[0208] Prevent failures from happening: Through real-time monitoring and early warning, abnormal vibrations can be discovered and handled in a timely manner to prevent sudden failures of the pump.

[0209] Protect personnel safety: avoid safety accidents caused by equipment failure and ensure the safety of operators.

[0210] (2) Extend equipment life

[0211] Reduce wear: By adjusting operating parameters, vibration can be reduced and wear of mechanical parts can be reduced.

[0212] Optimize maintenance: According to the vibration trend, arrange the maintenance plan reasonably to extend the service life of the equipment.

[0213] (3) Reduce operating costs

[0214] Reduced maintenance costs: Preventive maintenance is cheaper than emergency repairs, reducing maintenance costs and spare parts consumption.

[0215] Avoid downtime losses: Prevent unplanned downtime, ensure production continuity, and avoid economic losses.

[0216] (4) Improve production efficiency

[0217] Stable operation: The equipment is kept running in the best condition, improving production efficiency and product quality.

[0218] Optimize scheduling: Rationally arrange production tasks according to equipment status to improve overall operational efficiency.

[0219] (5) Data-driven decision making

[0220] Scientific management: Use real-time data and intelligent analysis to support management in making wise operation and maintenance decisions.

[0221] Reduce human errors: Automated monitoring and early warning reduce reliance on human judgment, and reduce misjudgments and omissions.

[0222] (6) Improving employee capabilities

[0223] Assisted operation: Provides clear guidance and suggestions to operators to improve their operation level and safety awareness.

[0224] Training value: Help employees understand the operating principles and maintenance methods of equipment through system feedback.

[0225] (7) Environmental protection and compliance

[0226] Reduce energy consumption: optimize equipment operation, reduce energy consumption, and save energy costs.

[0227] Comply with standards: Meet industry and regulatory requirements for equipment safety and operational quality, and avoid fines and legal risks.

[0228] 3. Outlet temperature and equipment load optimization:

[0229] 1. Process description

[0230] (1) Natural language input and demand understanding User operation: The operator sends instructions to the system through natural language: Please optimize the outlet temperature of the heating furnace to reduce the load on subsequent equipment.

[0231] The role of the generative large model:

[0232] Semantic analysis: Understand the natural language input by the user and extract keywords and core intentions, such as "optimize the outlet temperature of the heating furnace" and "reduce the load on subsequent equipment."

[0233] Intention identification: Determine that the user wants to optimize the operating load of subsequent equipment such as separators and heat exchangers by adjusting the outlet temperature of the heating furnace.

[0234] Task decomposition: Decompose the overall task into specific subtasks and identify the small identification model that needs to be called: outlet temperature time series prediction small model.

[0235] (2) Task allocation and small model calling

[0236] The role of the agent:

[0237] Task scheduling: Based on the task decomposition of the large model, it is decided to call the small model for outlet temperature timing prediction.

[0238] Data collection: Collect historical and real-time outlet temperature data of the heating furnace and the operating parameters of subsequent equipment through sensors and data interfaces.

[0239] The role of the identification model:

[0240] The outlet temperature time series prediction model can be based on patent CN117889561A: An intelligent control method and system for gathering and transportation based on the external temperature control of upstream and downstream heating furnaces in oil fields to achieve the following functions:

[0241] Data analysis: Analyze the outlet temperature trend of the heating furnace and identify the relationship between temperature fluctuations and subsequent equipment load.

[0242] Predict future temperature: Based on current operating conditions, predict outlet temperature changes within a certain period of time in the future.

[0243] Optimization suggestion: Calculate the optimal outlet temperature setting value to achieve balance and optimization of subsequent equipment loads.

[0244] (3) Data analysis and result feedback

[0245] Small model output:

[0246] Prediction results: Provides time series prediction charts and data of outlet temperature.

[0247] Optimization plan: Make specific suggestions for temperature adjustments, such as lowering the outlet temperature to a specific value.

[0248] The role of the agent:

[0249] Result integration: Receive the analysis results of the small model and conduct preliminary verification and integration.

[0250] Risk Assessment: Evaluate the impact of adjusting outlet temperature on the overall process flow to ensure that no new problems are introduced.

[0251] (4) Optimization suggestion generation large model integration analysis results:

[0252] Combine the output of the small model with other relevant information, such as production goals and equipment status.

[0253] Generate optimization report:

[0254] Provide optimization suggestions to operators in natural language, such as:

[0255] It is recommended to reduce the outlet temperature of the heating furnace to 85°C, which will reduce the load on subsequent separation equipment, improve its operating efficiency, and extend the service life of the equipment.

[0256] Justification: Explain the reasons and expected effects of the adjustment to help operators understand and adopt the recommendations.

[0257] (5) Implement optimization plans and continuously monitor user operations:

[0258] Manually adjust the outlet temperature setting of the heating furnace according to the optimization suggestions provided by the system.

[0259] The role of the agent:

[0260] Automatic Control: If the system has automation capabilities, the agent can adjust the temperature settings directly through the control system.

[0261] Continuous monitoring: Real-time monitoring of the outlet temperature of the heating furnace and the operating status of subsequent equipment to ensure that adjustments achieve the expected results.

[0262] Feedback loop: If new deviations or problems are found, the intelligent body will call the small model again for analysis and provide further optimization suggestions.

[0263] Advantages of the above solution:

[0264] (1) Improve production efficiency

[0265] Optimize equipment operation: By adjusting the outlet temperature, subsequent equipment can operate under optimal conditions to improve overall production efficiency.

[0266] (2) Reduce operating costs

[0267] Energy saving: Accurately control the temperature of the heating furnace to avoid overheating and reduce energy consumption.

[0268] Reduce maintenance costs: reduce equipment load, reduce wear and failure rate, extend equipment life, and reduce repair and replacement costs.

[0269] (3) Improve product quality

[0270] Stabilize process parameters: Maintain the stability of outlet temperature and equipment load to improve the consistency and reliability of product quality.

[0271] (4) Enhanced security

[0272] Prevent risks: Avoid potential safety hazards caused by equipment overload and ensure the safety of the production process.

[0273] (5) Improve decision-making efficiency

[0274] Data-driven decision making: Through analysis and suggestions from intelligent systems, operators can make optimized decisions quickly and reduce reliance on personal experience.

[0275] (6) Environmentally friendly

[0276] Reduce emissions: Optimize energy use, reduce exhaust and greenhouse gas emissions, and comply with environmental protection requirements.

[0277] (7) Employee training and satisfaction

[0278] Simplify operations: Provide a user-friendly interface and clear suggestions to reduce operational complexity and improve employee satisfaction.

[0279] 4. Multi-area PLC equipment linkage optimization

[0280] 1. Process description

[0281] (1) Natural language input and demand understanding

[0282] User operation: The operator issues instructions through natural language, such as:

[0283] Please optimize the equipment linkage of multiple PLC areas to improve production efficiency.

[0284] The role of the generative large model:

[0285] Semantic analysis: Understand user input and extract keywords and core intent, such as "optimize multiple PLC areas", "equipment linkage", and "improve production efficiency".

[0286] Intent recognition: Determines if the user wants to optimize the collaboration between devices in multiple PLC-controlled areas to ensure efficient operation of the process.

[0287] Task decomposition: decompose the overall requirements into specific subtasks, and identify the small identification models that need to be called: partition block PLC timing prediction small model, full station multi-PLC timing prediction small model;

[0288] (2) Task allocation and small model calling

[0289] The role of the agent:

[0290] Task scheduling: Based on the task decomposition of the large model, the intelligent agent decides to call the relevant small identification model.

[0291] Data collection: Through sensors and PLC systems in each area, the operating data of the equipment is collected, such as start-up time, operating status, load conditions, etc.

[0292] The role of the identification model:

[0293] Small model for partition block PLC timing prediction:

[0294] Regional analysis: Analyze the equipment operation sequence in each PLC-controlled area to identify potential bottlenecks and optimization space in the area.

[0295] Timing prediction: Predict the operation of the device in the future and find possible resource conflicts or idleness.

[0296] Small model for predicting the timing of multiple PLCs in the entire station:

[0297] Global analysis: Analyze the equipment linkage status of multiple PLC areas within the entire site to identify cross-area timing conflicts, resource competition or energy waste.

[0298] Collaborative optimization: Propose global optimization strategies to ensure efficient collaboration of equipment in each region.

[0299] (3) Data analysis and result feedback

[0300] Small model output:

[0301] Analysis Report: Provides timing analysis results for each area and the entire site, including potential bottlenecks, conflicts, and optimization opportunities.

[0302] Optimization suggestions: Propose preliminary optimization plans based on the problems found.

[0303] The role of the agent:

[0304] Result integration: Summarize the analysis results of each small model to form a global view.

[0305] Conflict coordination: If there are conflicts in the suggestions of different small models, the agent is responsible for coordination and seeking the best balance solution.

[0306] (4) Generation of optimization suggestions

[0307] Generative large model integration analysis results:

[0308] Comprehensive evaluation: Combine the analysis of the small model and the coordination results of the intelligent agent to evaluate the feasibility and expected effect of the overall optimization plan.

[0309] Generate natural language feedback:

[0310] Optimization suggestions:

[0311] It is recommended to delay the start of the pump equipment in the first block for 5 seconds and adjust the temperature of the heating furnace in the second block to 85°C to achieve efficient linkage of equipment and improve production efficiency. In addition, optimize the operation sequence of equipment in the third block to avoid resource conflicts with other areas.

[0312] Explanation: Provides the reasons and expected benefits of the optimization measures to help operators understand and adopt the recommendations.

[0313] (5) Execution and Monitoring

[0314] User Action:

[0315] Adjust the operating parameters and timing settings of equipment in each area based on the optimization suggestions provided by the system.

[0316] The role of the agent:

[0317] Continuous monitoring: monitor the operating status and performance indicators of the equipment in real time to evaluate the actual effect of optimization measures.

[0318] Feedback loop: If the expected effect is not achieved, the intelligent body will call the small model again for analysis and provide further optimization suggestions.

[0319] Advantages of the above solution:

[0320] (1) Improve production efficiency

[0321] Process optimization: The coordination between equipment is smoother, reducing waiting time and resource waste, and improving overall production efficiency.

[0322] Bottleneck Elimination: Improve the smoothness of production processes by identifying and resolving potential bottlenecks.

[0323] (2) Reduce operating costs

[0324] Save energy: Optimize the operating sequence and parameters of equipment, reduce unnecessary energy consumption, and reduce energy costs.

[0325] Reduce maintenance costs: Reduce excessive operation and frequent start and stop of equipment, extend equipment life, and reduce maintenance and replacement costs.

[0326] (3) Improve product quality

[0327] Enhanced stability: Equipment operation is more stable, process parameters are more consistent, and product quality consistency and reliability are improved.

[0328] (4) Enhance decision-making efficiency

[0329] Data-driven: Based on accurate data analysis and prediction, it supports management to make scientific decisions and improve operational management.

[0330] Quick response: The system provides timely optimization suggestions to help operators take quick action.

[0331] Improved safety

[0332] Risk warning: By monitoring the operating status of equipment, possible failures and safety hazards can be warned in advance.

[0333] Reduce human errors: Automated optimization and adjustment reduce the risk of errors caused by human operations.

[0334] (5) Improve employee work efficiency

[0335] Simplified operations: Provide clear optimization guidance to reduce operational complexity and reduce the burden on employees.

[0336] Skill improvement: Employees accumulate knowledge and experience and improve their professional capabilities when using intelligent systems.

[0337] (6) Environmentally friendly

[0338] Reduce emissions: save energy and reduce consumption, and reduce carbon emissions.

[0339] Sustainable development: improving resource utilization efficiency.

[0340] Through the collaborative work of generative large models, identifiable small models and intelligent agents, enterprises can achieve efficient linkage optimization of multi-region PLC equipment, bringing significant economic and social benefits. This intelligent system not only improves production efficiency and product quality, but also reduces operating costs and environmental impact, reflecting the huge potential and value of intelligent technology in the industrial field.

[0341] 5. Comprehensive performance optimization of multiple devices:

[0342] 1. Process description

[0343] (1) Natural language input and demand understanding

[0344] User Action: Operator makes a request:

[0345] Please optimize the operating efficiency of all site equipment and reduce energy consumption.

[0346] The role of the generative large model:

[0347] Semantic analysis: Through natural language processing technology, we understand the core needs of users and extract keywords such as "optimize all site equipment", "operation efficiency", and "reduce energy consumption".

[0348] Intent identification: Identify that the user wants to optimize the comprehensive performance of all equipment in the well station to improve production efficiency and reduce energy consumption.

[0349] Task decomposition: Decompose the overall requirements into specific subtasks and identify the small identification models that need to be called, such as:

[0350] Small model for predicting the timing of multiple PLCs in the entire station

[0351] Small model for predicting outlet temperature time series

[0352] Small model for pump fault diagnosis

[0353] Small model for predicting furnace temperature time series

[0354] (2) Task allocation and small model calling

[0355] The role of the agent:

[0356] Task scheduling: Based on the task decomposition of the large model, the intelligent agent decides to call the relevant small identification models and coordinate their execution order and data interaction.

[0357] Data collection: Collect real-time operating data from various devices and sensors, such as temperature, pressure, flow, energy consumption, equipment status, etc.

[0358] The role of the identification model:

[0359] Small model for predicting the timing of multiple PLCs in the entire station:

[0360] Analyze equipment operation data: Perform timing analysis on PLC equipment throughout the site to identify the interactions and collaborative relationships between devices.

[0361] Identify energy inefficiencies: Discover potential energy waste or redundant equipment operation.

[0362] Small model for outlet temperature time series prediction:

[0363] Predict outlet temperature changes: Analyze the outlet temperature of equipment such as heating furnaces and predict its impact on subsequent equipment.

[0364] Small model for pump fault diagnosis:

[0365] Assess pump health: Detect pump operating efficiency and potential failures to ensure pumps operate at optimal conditions.

[0366] Small model for furnace temperature time series prediction:

[0367] Monitor furnace temperature: Ensure furnaces are operating within safe and efficient temperature ranges.

[0368] (3) Data analysis and result feedback

[0369] Small model output:

[0370] Comprehensive performance data: Each small model provides analysis results such as the equipment's operating efficiency, energy consumption level, and potential problems.

[0371] Optimization suggestions: Propose preliminary optimization measures for respective fields.

[0372] The role of the agent:

[0373] Result integration: Summarize the analysis results of each small model to form a global view.

[0374] Conflict detection and coordination: If there is a conflict between the suggestions of different small models, the agent is responsible for coordination and seeking the best balance point.

[0375] (4) Generation of optimization suggestions

[0376] Large model integration analysis results:

[0377] Comprehensive evaluation: Combine the output of each small model and the coordination results of the intelligent agent to conduct global performance optimization analysis.

[0378] Generate natural language feedback:

[0379] Provides comprehensive performance optimization reports to operators, such as:

[0380] It is recommended to adjust the temperature of the heating furnace to 90°C to improve combustion efficiency; at the same time, increase the flow rate of the pump to 250L / min to optimize the production process. It is expected that energy efficiency will increase by 10% and equipment load will decrease by 15%, thereby reducing energy consumption and equipment wear.

[0381] Explain the rationale: Detail the rationale and expected effects of each adjustment to help operators understand and adopt the recommendations.

[0382] (5) Execution and Monitoring

[0383] Operator Action:

[0384] Adjust the operating parameters of related equipment according to the suggestions.

[0385] Agent execution:

[0386] Automatic control: If the system has automation capabilities, the agent can directly adjust the equipment parameters.

[0387] Continuous monitoring: Real-time monitoring of the adjusted equipment operating status and energy consumption indicators to evaluate the optimization effect.

[0388] Feedback loop: If the expected effect is not achieved, the intelligent body will call the small model again for analysis and provide further optimization suggestions.

[0389] Advantages of the above solution:

[0390] (1) Improve overall production efficiency

[0391] Optimize resource allocation: Through comprehensive analysis, ensure that all devices work together to avoid bottlenecks and waste of resources.

[0392] Improve production capacity: The equipment runs at the best condition, improving production speed and quality.

[0393] (2) Reduce energy consumption

[0394] Save energy costs: Reduce unnecessary energy consumption by optimizing equipment parameters and operating methods.

[0395] Improve energy efficiency: improve energy utilization and reduce energy cost per unit product.

[0396] (3) Extend equipment life

[0397] Reduce equipment load: Reduce excessive operation and wear of equipment through reasonable adjustments.

[0398] Reduce maintenance costs: Reduce the number of failures and downtime, save repair and replacement costs.

[0399] (4) Improve operational decision-making efficiency

[0400] Data-driven decision making: Leverage real-time data and intelligent analysis to support management in making fast and accurate operational decisions.

[0401] Reduced human error: Automated analysis and recommendations reduce the risk of human judgement errors.

[0402] (5) Environmental protection and compliance

[0403] Reduce emissions: Reduce energy consumption, reduce carbon emissions, and comply with environmental regulations.

[0404] Enhance corporate image: Demonstrate the company's commitment to green production and sustainable development.

[0405] (6) Flexible production scheduling

[0406] Quickly respond to market changes: Flexibly adjust equipment operation according to production needs to improve market response speed.

[0407] Optimize inventory management: Efficient production planning reduces inventory backlog and capital occupation.

[0408] (7) Improve employee work efficiency

[0409] Assisted Operation: Provides clear optimization suggestions to reduce the burden on operators.

[0410] Skill improvement: Help employees understand complex production processes and optimization principles through systematic feedback and explanation.

[0411] 6. Equipment response and operation optimization in emergency situations:

[0412] 1. Process description

[0413] (1) Natural language input and demand understanding

[0414] User operation: The operator issues emergency instructions through natural language:

[0415] The current heating furnace temperature is too high. Please provide an emergency solution.

[0416] The role of the generative large model:

[0417] Semantic analysis: Use natural language processing technology to understand users' urgent requests and extract key information, such as "the heating furnace temperature is too high" and "emergency treatment plan".

[0418] Intent recognition: Determines that the user needs to immediately obtain an emergency response plan for furnace overheating to ensure safety and minimize equipment damage.

[0419] Task decomposition: Decompose the requirements into specific subtasks and identify the small recognition models that need to be called:

[0420] Small model for predicting furnace temperature time series

[0421] Small model for pump fault diagnosis

[0422] (2) Task allocation and small model calling

[0423] The role of the agent:

[0424] Task scheduling: Based on the task decomposition of the large model, the intelligent agent quickly decides to call related small identification models in parallel to obtain analysis results as quickly as possible.

[0425] Data collection: Obtain key data such as furnace temperature and pump operating status in real time from sensors and equipment interfaces.

[0426] The role of the identification model:

[0427] Small model for furnace temperature time series prediction:

[0428] Data analysis: Analyze the current furnace temperature data and predict future temperature change trends.

[0429] Risk assessment: Assess whether the temperature will continue to rise and identify the cause of overheating, such as excessive fuel supply and cooling system failure.

[0430] Small model for pump fault diagnosis:

[0431] Status detection: Analyze the real-time operation data of the pump to determine whether there is a fault or abnormality.

[0432] Impact Assessment: Evaluate the effect of the pump's condition on the entire system, especially in emergency situations.

[0433] (3) Data analysis and result feedback

[0434] Small model output:

[0435] Furnace temperature prediction results: Provides a temperature trend graph to indicate the time point and possible reasons when the temperature exceeds the safety threshold.

[0436] Pump Fault Diagnostic Results: Reports the health status of the pump, indicating any anomalies, faults and their potential impact on the system.

[0437] The role of the agent:

[0438] Result integration: Rapidly integrate the analysis results of small models to provide comprehensive information support for the generation of large models.

[0439] Priority determination: Determine the priority of emergency response based on the urgency of the problem.

[0440] (4) Generation of emergency response plans

[0441] Large model integration analysis results:

[0442] Comprehensive assessment: Develop a comprehensive emergency response plan based on the furnace overheating and pump operating status.

[0443] Generate natural language feedback:

[0444] Provide clear emergency advice to operators, such as:

[0445] It is recommended to immediately reduce the fuel supply of the heating furnace to reduce the temperature rise rate; at the same time, start the backup cooling system to quickly reduce the furnace temperature. If the pump is operating abnormally, it is recommended to immediately reduce the pump load or stop running, and arrange for technicians to perform maintenance to prevent equipment damage and production interruption.

[0446] Explain the reasons: explain the necessity and expected effects of the above measures to help operators understand and implement them quickly.

[0447] (5) Execution and Monitoring

[0448] Operator Action:

[0449] According to the emergency recommendations, immediately adjust the fuel supply, start the cooling system, load adjust or shut down the pump, and notify maintenance personnel to carry out maintenance.

[0450] The role of the agent:

[0451] Automatic control: If the system has automation capabilities, the agent can directly perform some emergency operations, such as adjusting fuel valves and starting the cooling system.

[0452] Continuous Monitoring: Monitor furnace temperature, pump operating status and overall system status in real time to evaluate the effectiveness of emergency measures.

[0453] Feedback loop: If the problem is not effectively alleviated, the intelligent body will call the small model again for analysis and provide further countermeasures or issue an alarm.

[0454] Advantages of the above solution:

[0455] (1) Ensure the safety of personnel and equipment

[0456] Quick response: In an emergency, the system can quickly provide accurate emergency plans to reduce the possibility of accidents and protect the safety of operators and equipment.

[0457] (2) Reduce economic losses

[0458] Prevent equipment damage: Take emergency measures in time to prevent serious damage to equipment due to overheating or malfunction, and reduce repair and replacement costs.

[0459] Avoid production stagnation: Effective emergency response can prevent the complete shutdown of the production line and reduce the economic losses caused by the shutdown.

[0460] (3) Improve emergency response efficiency

[0461] Decision support: The data analysis and professional suggestions provided by the system help operators make the best decisions in high-pressure situations and avoid misjudgments caused by nervousness or lack of experience.

[0462] Automated execution: When conditions permit, the agent can automatically perform some emergency operations to further speed up the response.

[0463] (4) Enhance system reliability

[0464] Prevent secondary failures: Through comprehensive analysis of related equipment, prevent failures of other equipment during emergency handling.

[0465] Continuous improvement: After the event is over, the system can summarize and analyze the emergency situation and optimize future emergency plans.

[0466] (5) Improve enterprise safety management level

[0467] Data recording and tracing: The system automatically records the entire emergency response process, providing valuable data support for future training and improvements.

[0468] Compliance and Certification: Complete security management and emergency response capabilities help pass relevant security certifications and comply with industry regulations and standards.

[0469] (6) Improve employee skills and confidence

[0470] Knowledge accumulation: Operators handle emergencies with the assistance of the system, accumulate valuable practical experience, and improve their emergency response capabilities.

[0471] Psychological support: The system's professional advice and support helps reduce operator stress and boost confidence in emergency situations.

[0472] 7. Furnace temperature regulation and safety management:

[0473] 1. Process description

[0474] (1) Natural language input and demand understanding

[0475] User Action: Operator asks question:

[0476] How to ensure that the furnace temperature of the heating furnace operates within a safe range;

[0477] The role of the generative large model:

[0478] Semantic parsing: Understand the user's natural language input and extract key requirements, such as "make sure the furnace temperature of the heating furnace is within a safe range."

[0479] Intent recognition: Identify that the user wants to monitor the furnace temperature of the heating furnace to prevent the equipment from overheating and ensure safe operation.

[0480] Task decomposition: Decompose the overall requirements into specific tasks and determine the small identification model that needs to be called: the furnace temperature time series prediction small model.

[0481] (2) Task allocation and small model calling

[0482] The role of the agent:

[0483] Task scheduling: Based on the task decomposition of the large model, the intelligent agent decides to call the small model for furnace temperature timing prediction.

[0484] Data collection: Obtain real-time and historical data of the heating furnace body temperature through sensors and data interfaces.

[0485] The role of the identification model:

[0486] Small model for furnace temperature time series prediction:

[0487] Data analysis: Analyze the current furnace temperature and combine it with historical data to identify the temperature change pattern.

[0488] Time series prediction: predict the changing trend of furnace temperature in the future and identify possible overheating risks.

[0489] Risk assessment: Determine whether the furnace temperature is close to the safety upper limit and evaluate the possibility and severity of overheating.

[0490] (3) Data analysis and result feedback

[0491] Small model output:

[0492] Prediction results: Provide future trend charts and data of furnace temperature.

[0493] Risk Warning: Indicates the time and reasons why the temperature may exceed the safe range.

[0494] The role of the agent:

[0495] Result integration: Receive the analysis results of the small model and conduct summary and preliminary evaluation.

[0496] Data validation: Verify the accuracy of the prediction results and cross-validate with other relevant data such as fuel supply and cooling system status.

[0497] (4) Generation of safety management suggestions

[0498] Large model integration analysis results:

[0499] Combine the predictions of the small model and the verification results of the intelligent agent to comprehensively evaluate the operating status of the heating furnace.

[0500] Generate security management recommendations:

[0501] Provides specific action suggestions to operators in natural language, such as:

[0502] The current furnace temperature is close to the safety upper limit. It is recommended to immediately reduce the fuel supply by 10% and start the enhanced mode of the cooling system to ensure that the temperature is stable within a safe range.

[0503] Explain the cause: Detail the cause of the temperature increase, the possible consequences, and how the proposed action will effectively prevent overheating.

[0504] (5) Implement security measures and continuous monitoring

[0505] User Action:

[0506] Adjust the fuel supply, start or adjust the cooling system according to the recommendations provided by the system.

[0507] The role of the agent:

[0508] Automatic Control: If the system has automation capabilities, the agent can directly control the fuel supply valves and operating parameters of the cooling system.

[0509] Continuous monitoring: Real-time monitoring of furnace temperature and related parameters to ensure that adjustment measures are effective.

[0510] Feedback loop: If the temperature still does not drop to a safe range, the intelligent body will call the small model again for analysis and provide further suggestions.

[0511] Advantages of the above solution:

[0512] (1) Ensure production safety

[0513] Prevent equipment from overheating: Monitor and predict furnace temperature in real time, and take timely measures to prevent safety accidents caused by equipment overheating.

[0514] Reduce safety risks: Reduce downtime and personal safety risks caused by equipment failure.

[0515] (2) Extend equipment life

[0516] Avoid high temperature loss: Keep the furnace temperature within a reasonable range, reduce the loss of equipment materials caused by high temperature, and extend the service life of the equipment.

[0517] Reduce maintenance costs: Reduce equipment failures caused by overheating and reduce the cost of repairs and replacement parts.

[0518] (3) Improve production efficiency

[0519] Stable operation: Through effective temperature management, the heating furnace can be ensured to operate stably and production interruptions caused by abnormal temperature can be avoided.

[0520] Optimize resource utilization: Rationally control the use of fuel and cooling systems to improve energy efficiency.

[0521] (4) Reduce operating costs

[0522] Save energy: avoid fuel waste caused by over-burning, optimize fuel supply and reduce energy costs.

[0523] Reduce downtime losses: Through preventive management, reduce the number of unexpected downtimes and reduce production losses.

[0524] (5) Compliance and Environmental Protection

[0525] Comply with safety standards: Ensure that equipment operation complies with industry safety standards and regulatory requirements to avoid fines or production suspension due to violations.

[0526] Reduce emissions: Optimize the combustion process, reduce the emission of harmful gases, and improve environmental benefits.

[0527] (6) Data-driven decision making

[0528] Intelligent analysis: Utilize advanced predictive models to provide data-supported decision-making recommendations and improve management capabilities.

[0529] Quick response: Get system warnings and suggestions in a timely manner, take quick actions, and enhance emergency response capabilities.

[0530] (7) Improving employee capabilities

[0531] Decision support: Provide professional analysis and suggestions to operators, reduce reliance on personal experience, and improve overall operational level.

[0532] Training value: Help employees understand the operating principles of equipment and safety management methods through prompts and explanations from the intelligent system.

[0533] 8. Exhaust temperature and energy efficiency optimization:

[0534] 1. Process description

[0535] (1) Natural language input and demand understanding

[0536] User Action: The operator made a request: "How to optimize exhaust gas temperature to improve energy efficiency".

[0537] The role of the generative large model:

[0538] Perform semantic analysis on the user's natural language input to extract keywords and core intent, such as "optimize exhaust temperature" and "improve energy efficiency".

[0539] Understanding user needs is to optimize the exhaust temperature of the heating system to reduce energy waste and improve efficiency.

[0540] Decompose the task and determine the small identification model that needs to be called: the smoke exhaust temperature time series prediction small model.

[0541] (2) Task decomposition and small model calling

[0542] Agent Scheduling:

[0543] According to the task decomposition of the large model, the intelligent agent decides to call the small model for exhaust temperature time series prediction.

[0544] The role of the identification model:

[0545] Small model for exhaust temperature time series prediction:

[0546] Collect current exhaust temperature data.

[0547] Conduct time series analysis to predict future trends in exhaust gas temperature.

[0548] Identify unreasonable temperature fluctuations and potential energy waste.

[0549] (3) Data analysis and result feedback

[0550] The small model can provide detailed exhaust temperature time series prediction results based on patent CN116108359A: A method for judging ash accumulation in the convection section of the flue of an oilfield heating furnace. It can also point out the specific time period and possible reasons for excessively high or abnormal temperature fluctuations.

[0551] The role of the agent:

[0552] Receive analysis results for the small model.

[0553] Integrate data to provide decision support for generative large models.

[0554] (4) Generation of optimization suggestions

[0555] Large model integration analysis results:

[0556] Combined with the predictions from the small model, different adjustment scenarios and their impact on energy efficiency are evaluated.

[0557] Generate optimization suggestions:

[0558] For example: "It is recommended to reduce the fuel input of the burner by 5% and control the exhaust temperature below 200°C, which is expected to increase energy efficiency by 8%."

[0559] (5) Implementation of optimization plan and continuous monitoring

[0560] User Action:

[0561] According to the optimization suggestions, adjust the fuel input of the burner.

[0562] The role of the agent:

[0563] Continue to monitor exhaust temperature and energy efficiency indicators.

[0564] If the system supports automatic control, the agent can adjust the burner parameters directly through the API interface.

[0565] Continuous Feedback:

[0566] Collect adjusted data in real time and evaluate the optimization effect.

[0567] If necessary, repeat the above process for further optimization.

[0568] Advantages of the above solution:

[0569] (1) Improve energy efficiency and reduce costs:

[0570] By optimizing the exhaust gas temperature, energy consumption is reduced, which directly reduces fuel costs.

[0571] (2) Real-time decision support:

[0572] The system provides instant optimization suggestions to help operators make decisions quickly and improve work efficiency.

[0573] (3) Extend equipment life:

[0574] Control the exhaust temperature within a reasonable range to reduce high-temperature losses of equipment, extend equipment service life, and reduce maintenance costs.

[0575] (4) Environmental compliance:

[0576] Lower emission temperature, reduce greenhouse gas and pollutant emissions, comply with environmental regulations, and enhance corporate image.

[0577] (5) Safety in production:

[0578] Avoid potential safety hazards caused by excessively high exhaust temperatures and ensure the safety of the production process.

[0579] (6) Data-driven optimization:

[0580] Utilize advanced data analysis and predictive models to achieve intelligent and refined management of the production process.

[0581] (7) Improve competitiveness:

[0582] Enhance the company's competitive advantage in the market by reducing operating costs and improving production efficiency.

[0583] (8) Reduce human errors:

[0584] Automated monitoring and optimization reduce reliance on manual experience and lower the risk of human operational errors.

[0585] 9. Equipment scaling prediction and cleaning arrangements:

[0586] 1. Process description

[0587] (1) Natural language input and demand understanding

[0588] User Action:

[0589] The operator makes a request:

[0590] Please predict scaling in the pipes and recommend cleaning times.

[0591] The role of the generative large model:

[0592] Semantic parsing: Understand the user's natural language input and extract key requirements, such as "predict pipeline scaling" and "recommend cleaning time".

[0593] Intent recognition: Identify that the user wants to understand the scaling trend of equipment such as pipes and heaters and get a recommended time for cleaning.

[0594] Task decomposition: Decompose the overall requirements into specific tasks and determine the small identification model that needs to be called: scaling prediction small model.

[0595] (2) Task allocation and small model calling

[0596] The role of the agent:

[0597] Task scheduling: Based on the task decomposition of the large model, decide to call the small scaling prediction model.

[0598] Data collection: Obtain relevant data through sensors and data interfaces, including current water quality parameters such as hardness, pH value, dissolved solids, temperature, flow, pressure, historical scaling conditions, etc.

[0599] The role of the identification model:

[0600] The small scaling prediction model can be based on patent CN114529106B: A scaling prediction algorithm for oilfield heating furnace coils to achieve the following functions:

[0601] Data analysis: Analyze current process parameters and water quality characteristics to assess scaling tendency.

[0602] Trend prediction: Based on historical data and current operating conditions, predict the growth trend of scaling in equipment such as pipes and heaters.

[0603] Risk assessment: Determine the impact of scaling on equipment performance and energy efficiency, and predict when scaling will reach a critical value requiring cleaning.

[0604] (3) Data analysis and result feedback

[0605] Small model output:

[0606] Prediction results: Provide prediction curves and data on the degree of equipment scaling, and estimate the time when scaling reaches the critical value.

[0607] Risk warning: Point out the risks that scaling may cause, such as reduced energy efficiency, equipment damage, blockage, etc.

[0608] The role of the agent:

[0609] Result integration: Receive the analysis results of the small model, summarize and verify them.

[0610] Scheduling planning: Consider production plans, equipment operating conditions, and make preliminary recommendations for cleaning arrangements.

[0611] (4) Generation of cleaning suggestions

[0612] Large model integration analysis results:

[0613] Combine the prediction results of the small model with the company's production plan, maintenance cycle and other information.

[0614] Generate cleaning schedule suggestions:

[0615] Provides specific recommendations to operators in natural language, such as:

[0616] According to predictions, scaling in the pipeline will reach a critical level after one month, which may lead to reduced energy efficiency and equipment damage. It is recommended to schedule cleaning after two weeks to avoid affecting production. Other maintenance work can be combined during the cleaning period to reduce downtime.

[0617] Explanation: Explain the impact of scaling and the necessity of early cleaning, and provide optimization suggestions.

[0618] (5) Implementation of cleaning plan and continuous monitoring

[0619] User Action:

[0620] According to system recommendations, coordinate with relevant departments to develop and arrange cleaning plans.

[0621] The role of the agent:

[0622] Plan tracking: record cleaning plans and set reminders to ensure cleaning work is carried out on time.

[0623] Continuous monitoring: Before and after cleaning, continuously monitor the scaling and operating efficiency of the equipment to evaluate the cleaning effect.

[0624] Feedback loop: If abnormal scaling growth is found, the intelligent body will call the small model again for analysis and provide new suggestions.

[0625] Advantages of the above solution:

[0626] (1) Improve equipment energy efficiency

[0627] Save energy: Scaling will reduce heat transfer efficiency and lead to energy waste. Timely cleaning can improve energy efficiency and reduce energy costs.

[0628] (2) Extend equipment life

[0629] Prevent equipment damage: Scaling can cause overheating, corrosion and other problems. Timely cleaning can avoid premature equipment damage and extend its service life.

[0630] (3) Reduce operating costs

[0631] Reduced maintenance costs: Preventive cleaning costs less than emergency repairs, avoiding expensive repairs due to equipment failure.

[0632] Optimize downtime arrangements: Plan cleaning time in advance so that it can be carried out simultaneously with other maintenance work, reducing downtime and the impact on production.

[0633] (4) Ensure production efficiency

[0634] Avoid production interruptions: Prevent equipment failure and downtime caused by scaling, and ensure the continuity and stability of the production process.

[0635] (5) Improve product quality

[0636] Stabilize process parameters: Scaling may affect product quality. Through timely cleaning, the process parameters can be kept stable and the product quality can be improved.

[0637] (6) Data-driven decision making

[0638] Scientific Management: Use predictive models and data analysis to support management in making informed maintenance and operational decisions.

[0639] Reduce human error: Reduce reliance on experience and subjective judgment, and reduce risks caused by negligence or misjudgment.

[0640] (7) Environmentally friendly

[0641] Reduce emissions: Improve energy efficiency, reduce energy consumption, lower carbon emissions, and comply with environmental protection requirements.

[0642] Avoid pollution: Prevent leakage or pollution accidents caused by equipment failure.

[0643] 10. Real-time adjustment of oxygen content and combustion efficiency:

[0644] 1. Process description

[0645] (1) Natural language input and demand understanding

[0646] User operation: The operator uses natural language instructions, such as: "Monitor and optimize the oxygen content of the combustion system in real time."

[0647] The role of the generative large model:

[0648] Semantic parsing: Parse user input and extract key requirements, namely "real-time monitoring" and "optimization" of oxygen content to improve combustion efficiency.

[0649] Task decomposition: Break down the requirements into multiple subtasks:

[0650] Monitor oxygen content data.

[0651] Predict future trends in oxygen content.

[0652] Optimize oxygen supply to improve combustion efficiency.

[0653] Task scheduling: Determine the small identification model that needs to be called and generate a task scheduling request.

[0654] (2) Task allocation and small model calling

[0655] The role of the agent:

[0656] Task scheduling: According to the task decomposition of the generative large model, call the corresponding identification small model.

[0657] Small model call:

[0658] Small model for time series prediction of oxygen content: Analyze current oxygen content and predict future changes.

[0659] The combustion efficiency analysis model can evaluate the combustion efficiency under different oxygen supply amounts based on patent CN114646075A: A method for automatically adjusting and updating the air-fuel ratio of an oil field gas heating furnace.

[0660] The role of the identification model:

[0661] Small model for predicting oxygen content time series:

[0662] Data Analysis: Receive real-time sensor data and analyze the current status of oxygen content.

[0663] Trend prediction: Use historical data and algorithms to predict future changes in oxygen content.

[0664] Optimization recommendations: Identify the optimal oxygen supply.

[0665] (3) Data analysis and result feedback

[0666] Data collection: Obtain real-time oxygen content data of the combustion system through sensors and data interfaces.

[0667] Results feedback:

[0668] The small model will feed back the analysis results, such as the oxygen content prediction curve and the optimal oxygen supply, to the intelligent agent.

[0669] The agent summarizes the information and prepares to provide optimization solutions.

[0670] (4) Generate optimization suggestions and execute them

[0671] The role of the generative large model:

[0672] Integrate analysis results: Generate natural language optimization suggestions based on the feedback from the small model.

[0673] Example advice: "It is recommended to reduce the oxygen supply by 5% to achieve optimal combustion efficiency while reducing fuel consumption."

[0674] Agent execution:

[0675] If the system supports automatic control: directly adjust the parameters of the oxygen supply equipment.

[0676] Manual Prompt: Optimization suggestions are presented to operators to assist them in making manual adjustments.

[0677] (5) Continuous monitoring and dynamic adjustment

[0678] Continuous monitoring:

[0679] The agent continuously obtains data on oxygen content and combustion efficiency and monitors the system status.

[0680] Dynamic Adjustment:

[0681] If new changes are detected, the agent calls the small model again for analysis.

[0682] Generate large model update optimization suggestions to ensure that the system always runs in the best condition.

[0683] Advantages of the above method:

[0684] (1) Improve combustion efficiency:

[0685] Optimize the combustion process: Improve the combustion efficiency of fuel by precisely controlling the amount of oxygen supplied.

[0686] Reduce energy consumption: Reduce energy loss caused by excess oxygen.

[0687] (2) Reduce operating costs:

[0688] Save fuel costs: Improved combustion efficiency directly reduces fuel consumption.

[0689] Reduce maintenance costs: Stable combustion process reduces equipment wear and extends equipment life.

[0690] (3) Improve production safety:

[0691] Prevent abnormal combustion: monitor oxygen content in real time to prevent safety hazards caused by excessive or insufficient oxygen.

[0692] Timely warning: Intelligent agents can provide early warnings before abnormal situations occur to prevent accidents.

[0693] (4) Environmental protection and compliance:

[0694] Reduce emissions: Optimize combustion to reduce emissions of harmful gases such as CO, NOx, etc.

[0695] Meet regulatory requirements: Comply with environmental protection regulations and avoid penalties for exceeding emission standards.

[0696] (5) Ease of operation:

[0697] Intelligent assisted decision-making: Operators can make optimization adjustments based on system recommendations without the need for deep professional knowledge.

[0698] Reduce work intensity: Automated monitoring and adjustment reduce the workload of manual monitoring.

[0699] (6) Data-driven continuous improvement:

[0700] Data accumulation and analysis: The system continuously collects and analyzes operating data to provide a basis for long-term optimization.

[0701] Decision support: Data insights provided by generative big models.

[0702] (7)Flexible adaptation to production changes:

[0703] Real-time response: The system can dynamically adjust oxygen supply according to changes in production load to achieve flexible production.

[0704] Preventive maintenance: Through data analysis, potential equipment problems can be identified in advance and maintenance plans can be arranged.

[0705] In summary, the digital agent oilfield intelligent optimization system based on a large model of the present invention has the following advantages:

[0706] Intelligent agent architecture design: The intelligent agent architecture design that combines the generative large model and the discriminative small model can achieve automatic scheduling and execution of complex tasks through the natural language understanding of the large model and the precise data analysis of the small model.

[0707] Small models and large models work together: the large model is responsible for macro-task decomposition and decision-making recommendations, while the small model is responsible for real-time analysis and prediction in specific areas, such as equipment health monitoring and energy efficiency optimization.

[0708] Application of digital twin system: Use digital twin technology to map the operating status of oilfield equipment in real time, and combine it with the intelligent system to optimize operations and generate maintenance suggestions.

[0709] Real-time data processing and optimization: The real-time data of the oil field, such as pump vibration, temperature, flow, etc., is processed through a small identification model to generate an optimization plan. The large model integrates the information and outputs decision-making recommendations.

[0710] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A digital agent oilfield intelligent optimization system based on a large model, characterized in that: include: The digital twin layer is used to monitor, optimize and predict the operating status of oilfield equipment in real time; The agent system is used to decompose tasks according to user needs, call small models, and interact with users; Small identification model, used to process real-time data of equipment operation and provide predictive analysis results; The generative large model is used to process user instructions through natural language processing and generate operational guidance, optimization suggestions and reports based on the results of the discriminative small model.

2. The digital agent oilfield intelligent optimization system based on a large model as claimed in claim 1, characterized in that: The digital twin layer includes: Maintenance monitoring unit, used to continuously collect operating data of oilfield equipment and transmit the data to the analysis and detection unit in real time; An analysis and detection unit, used to receive data from the maintenance and monitoring unit, process the data using a data analysis algorithm, and identify abnormal data; The load optimization unit is used to calculate the optimal operating load of the equipment by applying the load optimization algorithm according to the oilfield production plan and the actual operation data of the equipment, and generate load adjustment instructions; The linkage optimization unit is used to collect the operating data of each device, analyze the mutual influence relationship between devices, apply the linkage optimization algorithm to calculate the optimal coordination strategy between devices, and generate linkage adjustment instructions; The performance optimization unit is used to regularly collect the performance data of the equipment, apply the performance evaluation algorithm to evaluate the performance of the equipment, and generate performance optimization suggestions or adjust parameters based on the evaluation results; Response optimization unit, which is used to monitor equipment failures or abnormal conditions and immediately trigger emergency plans once an abnormality is detected; The adjustment management unit is used to calculate the optimal operating parameters of the equipment and generate adjustment instructions based on changes in production demand and environmental changes using intelligent control algorithms; Energy efficiency optimization unit, used to collect energy consumption data of equipment, apply energy efficiency evaluation algorithm to evaluate the energy efficiency of equipment, generate energy efficiency optimization suggestions or adjust the working mode of equipment to reduce energy consumption according to the evaluation results; The prediction and scheduling unit is used to predict the future operating status of the equipment based on historical data and real-time data using prediction algorithms, and generate maintenance and overhaul plans based on the prediction results; The real-time adjustment unit is used to collect the equipment's operating data in real time according to changes in production demand and environmental changes, apply the real-time adjustment algorithm to calculate the equipment's optimal operating strategy, and generate real-time adjustment instructions to ensure that the equipment is always in the best operating state.

3. The oilfield intelligent optimization system based on a digital agent based on a large model as claimed in claim 1, characterized in that: The intelligent agent system comprises: Action unit, used to perform operations; The planning and tool unit is used to generate task plans according to user needs; through the combination of small models and large models, data analysis, fault diagnosis and optimization suggestions are completed to generate memory agents; Memory unit, used to interact with users based on memory agents.

4. The oilfield intelligent optimization system based on a large model of digital agent according to claim 1, characterized in that: The identification model is used for the preventive maintenance and real-time monitoring unit of the pump. The preventive maintenance and real-time monitoring workflow of the pump is as follows: Natural language input and demand understanding: The operator asks the system whether the pump needs preventive maintenance. The generative large model understands the user's natural language input and extracts key requirements. It determines that the user wants to know the health status of the pump equipment and determines whether preventive maintenance is needed. Decompose the requirements into specific tasks and identify the small identification models that need to be called; Task allocation and small model calling: The intelligent agent decides to call the pump fault diagnosis small model based on the task decomposition of the generative large model; obtains the real-time operation data and historical data of the pump through sensors and data interfaces; the pump fault diagnosis small model analyzes the real-time and historical operation data of the pump, identifies abnormal patterns and fault signs; evaluates the current health status of the pump, and calculates the health index or fault risk level; Predict the time frame when a pump may fail and provide recommended time points for preventive maintenance; Data analysis and result feedback: The small model provides health status assessment and failure risk level of the pump; Predict the time period in the future when the pump may fail; the agent receives the analysis results of the small model, summarizes and verifies them; Verify the analysis results to ensure accuracy and reliability; Preventive maintenance recommendation generation: The large model combines the diagnosis and prediction results of the small model to evaluate the necessity and urgency of preventive maintenance; Provide specific suggestions to operators in natural language and provide analysis basis to help operators understand the importance and necessity of the suggestions; Execution of maintenance plan and continuous monitoring: Users arrange preventive maintenance plans based on recommendations and notify the maintenance team to carry out related work; The agent records the maintenance plan and sets reminders to ensure that maintenance work is carried out on time. Before and after maintenance, it continuously monitors the operating status of the pump to verify the maintenance effect. If anomalies still exist after maintenance, the agent will call the small model again for in-depth analysis and provide further suggestions. The identification model is used for vibration analysis and RMS detection of the pump. The vibration analysis and RMS detection workflow of the pump is as follows: Natural language input and demand understanding: The operator issues instructions to the system through natural language: Please monitor the vibration of the pump to ensure safe operation; the generative large model understands the natural language input by the user and extracts keywords and core intent; Determine that the user wants to monitor the vibration of the pump equipment in real time, evaluate its safety, and take necessary measures to prevent failures; break down the requirements into specific tasks and identify the small identification models that need to be called; Task allocation and small model calling: The intelligent agent decides to call the pump effective value RMS detection small model based on the task decomposition of the large model; obtains the real-time vibration data, historical vibration records and related operating parameters of the pump through sensors and data interfaces; the pump effective value RMS detection small model collects and processes the vibration signal of the pump in real time and calculates the effective value of the vibration; compares the real-time RMS value with the preset safety threshold to evaluate whether the vibration exceeds the safety range; analyzes historical data, predicts vibration trends, determines whether there is a trend of gradual increase, and predicts potential wear or failure; Data analysis and result feedback: The small model provides the current pump vibration RMS value and historical trend chart; if the vibration approaches or exceeds the warning line, it generates risk warning information and puts forward specific operational suggestions; the intelligent agent receives the analysis results of the small model and summarizes and generates a complete monitoring report; Combined with other operating data, verify whether the abnormal vibration is caused by other factors; Optimization suggestion generation: The generative large model combines the output of the small model with other information of the system for comprehensive evaluation; it provides specific suggestions to the operator in natural language and recommends that technicians conduct equipment inspections to eliminate potential faults; Explain possible causes of increased vibration; Implementation of optimization measures and continuous monitoring: Users adjust the operating parameters of the pump according to the suggestions provided by the system; arrange equipment inspection and maintenance to prevent failures; the intelligent agent monitors the vibration of the pump in real time and evaluates the effectiveness of the adjustment measures; if the vibration value does not decrease or continues to increase, the intelligent agent will call the small model again for in-depth analysis and provide further suggestions; if the vibration exceeds the safety threshold, the intelligent agent will immediately issue an alarm and notify relevant personnel to take emergency measures.

5. The digital agent oilfield intelligent optimization system based on a large model as claimed in claim 1, characterized in that: The identification model is used for outlet temperature and equipment load optimization. The outlet temperature and equipment load optimization workflow is as follows: Natural language input and demand understanding: The operator issues instructions to the system through natural language: Please optimize the outlet temperature of the heating furnace to reduce the load of subsequent equipment; the generative large model understands the natural language input by the user and extracts keywords and core intentions; Determine that the user wants to optimize the operating load of subsequent equipment by adjusting the outlet temperature of the heating furnace; decompose the overall task into specific subtasks and identify the small identification model that needs to be called; Task allocation and small model calling: The intelligent agent decides to call the outlet temperature time series prediction small model according to the task decomposition of the large model; collects the historical and real-time outlet temperature data of the heating furnace and the operating parameters of the subsequent equipment through sensors and data interfaces; the outlet temperature time series prediction small model analyzes the outlet temperature change trend of the heating furnace and identifies the relationship between temperature fluctuations and subsequent equipment loads; predicts outlet temperature changes in the future based on current working conditions; calculates the optimal outlet temperature setting value to achieve balance and optimization of subsequent equipment loads; Data analysis and result feedback: The small model provides time-series forecast charts and data of the outlet temperature; proposes specific suggestions for temperature adjustment; the intelligent agent receives the analysis results of the small model, conducts preliminary verification and integration; evaluates the impact of adjusting the outlet temperature on the entire process flow to ensure that no new problems are introduced; Optimization suggestion generation: The large model combines the output of the small model with other relevant information; provides optimization suggestions to the operator in natural language; explains the reasons and expected effects of the adjustments to help the operator understand and adopt the suggestions; Execution of optimization plan and continuous monitoring: The user manually adjusts the outlet temperature setting of the heating furnace according to the optimization suggestions provided by the system; the intelligent agent automatically controls the temperature setting through the control system; the outlet temperature of the heating furnace and the operating status of subsequent equipment are monitored in real time to ensure that the adjustment achieves the expected effect; if new deviations or problems are found, the intelligent agent will re-call the small model for analysis and provide further optimization suggestions; The identification model is used for multi-region PLC equipment linkage optimization. The multi-region PLC equipment linkage optimization workflow is as follows: Natural language input and demand understanding: The operator issues instructions through natural language: Please optimize the equipment linkage of multiple PLC areas to improve production efficiency; the generative large model understands the user's input and extracts keywords and core intentions; Determine that the user wants to optimize the collaboration between devices in multiple PLC-controlled areas to ensure efficient operation of the process; decompose the overall demand into specific subtasks and identify the small identification models that need to be called: Task allocation and small model calling: The intelligent agent decomposes the tasks of the large model and decides to call the relevant identification small model; collects the operation data of the equipment through the sensors and PLC systems in each area; the block PLC timing prediction small model analyzes the equipment operation timing in each PLC-controlled area, identifies the potential bottlenecks and optimization space in the area; predicts the operation of the equipment in the future and finds possible resource conflicts or idleness; the full-site multi-PLC timing prediction small model analyzes the equipment linkage in multiple PLC areas across the entire site, identifies cross-regional timing conflicts, resource competition or energy waste; proposes global optimization strategies to ensure efficient coordination of equipment in each area; Data analysis and result feedback: The small model provides timing analysis results for each area and the entire site, including potential bottlenecks, conflicts, and optimization opportunities; Propose preliminary optimization solutions for the problems found; The agent aggregates the analysis results of each small model to form a global view; If there are conflicts in the recommendations of different small models, the agent is responsible for coordination and seeking the best balance; Optimization suggestion generation: The generative large model combines the analysis of the small model and the coordination results of the intelligent agent to evaluate the feasibility and expected effect of the overall optimization plan; generate optimization suggestions and explanations; Execution and monitoring: Users adjust the operating parameters and timing settings of equipment in each area based on the optimization suggestions provided by the system; the intelligent agent monitors the operating status and performance indicators of the equipment in real time and evaluates the actual effect of the optimization measures; if the expected effect is not achieved, the intelligent agent will call the small model again for analysis and provide further optimization suggestions.

6. The digital agent oilfield intelligent optimization system based on a large model as claimed in claim 1, characterized in that: The identification model is used for multi-device comprehensive performance optimization, and the multi-device comprehensive performance optimization workflow is as follows: Natural language input and demand understanding: Operators make requests to optimize the operating efficiency of all station equipment and reduce energy consumption; The generative large model uses natural language processing technology to understand the user's core needs and extract keywords; determine that the user wants to optimize the comprehensive performance of all equipment in the well station to improve production efficiency and reduce energy consumption; decompose the overall needs into specific subtasks and identify the small identification models that need to be called; Task allocation and small model calling: The intelligent agent decomposes the tasks of the large model and decides to call the relevant small identification models, coordinates their execution order and data interaction, and collects real-time operation data from various devices and sensors; The small model for predicting the timing of multiple PLCs in the entire site performs timing analysis on the PLC devices in the entire site, identifies the interaction and coordination relationship between the devices, and discovers possible energy waste or redundant equipment operation. The outlet temperature time series prediction model analyzes the outlet temperature of equipment such as heating furnaces and predicts its impact on subsequent equipment; The pump fault diagnosis model detects the pump's operating efficiency and potential faults to ensure that the pump operates in the best condition; The furnace temperature time series prediction model ensures that the heating furnace operates within a safe and efficient temperature range; Data analysis and result feedback: Each small model provides equipment operating efficiency, energy consumption level, and potential problem analysis results; Propose preliminary optimization measures for their respective fields; the intelligent agent summarizes the analysis results of each small model to form a global view; If there is a conflict between the suggestions of different small models, the agent is responsible for coordination and seeking the best balance; Optimization suggestion generation: The large model combines the output of each small model and the coordination results of the intelligent agent to perform global performance optimization analysis; Provide comprehensive performance optimization reports to operators, detailing the basis and expected effects of each adjustment, helping operators understand and adopt recommendations; Execution and monitoring: The operator adjusts the operating parameters of the relevant equipment according to the suggestions; the intelligent agent adjusts the equipment parameters; the operating status and energy consumption indicators of the adjusted equipment are monitored in real time to evaluate the optimization effect; if the expected effect is not achieved, the intelligent agent will call the small model again for analysis and provide further optimization suggestions; The identification model is used for equipment response and operation optimization in emergency situations. The equipment response and operation optimization workflow in emergency situations is as follows: Natural language input and demand understanding: The operator issues an emergency command through natural language: The current heating furnace temperature is too high, please provide an emergency treatment plan; The generative large model uses natural language processing technology to understand users' urgent requests and extract key information; Determine that users need to immediately obtain emergency treatment plans for overheating of the heating furnace to ensure safety and minimize equipment damage; decompose the requirements into specific subtasks and identify the small identification models that need to be called; Task allocation and small model calling: According to the task decomposition of the large model, the intelligent agent quickly decides to call the relevant identification small model in parallel to obtain the analysis results; obtains data from sensors and equipment interfaces in real time; the furnace temperature time series prediction small model analyzes the current furnace temperature data and predicts the future temperature change trend; evaluates whether the temperature will continue to rise and identifies the cause of overheating; the pump fault diagnosis small model analyzes the real-time operation data of the pump to determine whether there is a fault or abnormality; evaluates the impact of the pump status on the entire system, especially in emergency situations; Data analysis and result feedback: The small model provides a temperature trend chart, indicating the time point and possible reasons when the temperature exceeds the safety threshold; Report the health status of the pump, point out any anomalies, faults and their potential impact on the system; the intelligent agent quickly integrates the analysis results of the small model to provide comprehensive information support for the generative large model; determine the priority of emergency treatment according to the urgency of the problem; Generation of emergency treatment plan: The large model combines the overheating of the heating furnace and the operating status of the pump to formulate a comprehensive emergency treatment plan; provide clear emergency suggestions to the operator, explain the necessity and expected effect of the above measures, and help the operator understand and quickly implement them; Execution and monitoring: Based on the emergency recommendations, the operator immediately adjusts the fuel supply, starts the cooling system, load-adjusts or shuts down the pump, and notifies maintenance personnel to perform maintenance. The intelligent agent performs some emergency operations. It monitors the furnace temperature, pump operating status, and overall system status in real time to evaluate the effectiveness of emergency measures. If the problem is not effectively alleviated, the intelligent agent will call the small model again for analysis and provide further response measures or issue an alarm.

7. The oilfield intelligent optimization system based on a digital agent based on a large model as claimed in claim 1, characterized in that: The identification model is used for furnace temperature regulation and safety management. The furnace temperature regulation and safety management workflow is as follows: Natural language input and demand understanding: The operator asks a question: How to ensure that the furnace body temperature of the heating furnace operates within a safe range? The generative large model understands the user's natural language input and extracts key requirements. It recognizes that the user wants to monitor the furnace body temperature of the heating furnace to prevent the equipment from overheating and ensure safe operation. It decomposes the overall requirements into specific tasks and determines the small identification model that needs to be called. Task allocation and small model calling: The intelligent agent disassembles the tasks of the large model and decides to call the small model for furnace temperature time series prediction. Through sensors and data interfaces, the real-time and historical data of the heating furnace temperature are obtained. The small model for furnace temperature time series prediction analyzes the current furnace temperature and identifies the temperature variation pattern based on historical data. It predicts the temperature variation trend of the furnace in the future and identifies possible overheating risks. It determines whether the furnace temperature is close to the safety upper limit and assesses the possibility and severity of overheating. Data analysis and result feedback: The small model provides future trend charts and data of furnace temperature; Indicate when and why temperatures may exceed safe ranges; The agent receives the analysis results of the small model, summarizes and preliminarily evaluates them; verifies the accuracy of the prediction results and cross-validates them with other relevant data; Safety management suggestion generation: The large model combines the predictions of the small model and the verification results of the intelligent agent to comprehensively evaluate the operating status of the heating furnace; Provide specific operational suggestions to operators in natural language; Detail the causes of the temperature increase, the possible consequences, and how the proposed measures will effectively prevent overheating; Execution of safety measures and continuous monitoring: The user adjusts the fuel supply and starts or adjusts the cooling system according to the suggestions provided by the system; the agent controls the fuel supply valve and the operating parameters of the cooling system; monitors the furnace temperature and related parameters in real time to ensure that the adjustment measures are effective; if the temperature still does not fall within the safe range, the agent will call the small model again for analysis and provide further suggestions; The identification model is used for exhaust temperature and energy efficiency optimization. The exhaust temperature and energy efficiency optimization workflow is as follows: Natural language input and demand understanding: The operator makes a request: how to optimize the exhaust temperature to improve energy efficiency; the generative large model performs semantic analysis on the user's natural language input to extract keywords and core intent; Understand that user needs are to optimize the exhaust temperature of the heating system to reduce energy waste and improve efficiency; decompose the task and determine the small identification model that needs to be called; Task decomposition and small model calling: Based on the task decomposition of the large model, the intelligent agent decides to call the small model for exhaust temperature time series prediction; the small model for exhaust temperature time series prediction collects the current exhaust temperature data; Conduct time series analysis to predict future exhaust temperature trends; identify unreasonable temperature fluctuations and potential energy waste factors; Data analysis and result feedback: The small model provides detailed exhaust temperature time series prediction results; points out the specific time period and possible reasons for excessively high temperature or abnormal fluctuations; the intelligent agent receives the analysis results of the small model; integrates data to provide decision support for the generative large model; Optimization suggestion generation: The large model combines the predictions of the small model to evaluate different adjustment plans and their impact on energy efficiency; generate optimization suggestions; Execution of optimization plan and continuous monitoring: The user adjusts the fuel input of the burner according to the optimization suggestions; the intelligent agent continues to monitor the exhaust temperature and energy efficiency indicators; if the system supports automatic control, the intelligent agent can directly adjust the burner parameters through the API interface; the adjusted data is collected in real time to evaluate the optimization effect.

8. The digital agent oilfield intelligent optimization system based on a large model as claimed in claim 1, characterized in that: The identification model is used for equipment scaling prediction and cleaning arrangement. The equipment scaling prediction and cleaning arrangement workflow is as follows: Natural language input and demand understanding: The operator makes a request: Please predict the scaling in the pipeline and recommend a cleaning time. The generative large model understands the user's natural language input and extracts key requirements. It determines that the user wants to understand the scaling trend of equipment such as pipelines and heaters, and get a recommended cleaning time. The overall demand is broken down into specific tasks, and the identification small model that needs to be called is determined. Task allocation and small model calling: The intelligent agent decides to call the small scaling prediction model based on the task decomposition of the large model; obtains relevant data, including current water quality parameters, through sensors and data interfaces; the small scaling prediction model analyzes the current process parameters and water quality characteristics to evaluate scaling tendency; predicts the growth trend of scaling in the equipment based on historical data and current operating conditions; determines the impact of scaling on equipment performance and energy efficiency, and predicts when scaling will reach a critical value that requires cleaning; Data analysis and result feedback: The small model provides the predicted curve and data of the equipment scaling degree, estimates the time when scaling reaches the critical value, and points out the risks that may be caused by scaling. The intelligent agent receives the analysis results of the small model, summarizes and verifies them; Consider production schedule and equipment operation status, and make preliminary suggestions for cleaning arrangements; Cleaning suggestion generation: The large model combines the prediction results of the small model with the company's production plan, maintenance cycle and other information; it provides specific suggestions to the operator in natural language, explains the impact of scaling and the necessity of early cleaning, and provides optimization suggestions; Execute cleaning plan and continuous monitoring: Users coordinate with relevant departments to develop and arrange cleaning plans based on system recommendations; the intelligent agent records the cleaning plan and sets reminders to ensure that cleaning work is carried out on time; before and after cleaning, it continuously monitors the scaling and operating efficiency of the equipment to evaluate the cleaning effect; If abnormal scaling growth is found, the intelligent body will call the small model again for analysis and provide new suggestions; The identification model is used for real-time adjustment of oxygen content and combustion efficiency. The real-time adjustment process of oxygen content and combustion efficiency is as follows: Natural language input and demand understanding: The operator uses natural language instructions, and the generative large model parses the user input and extracts key requirements; breaks down the requirements into multiple subtasks; determines the identification small model that needs to be called, and generates a task scheduling request; Task allocation and small model calling: The intelligent agent calls the corresponding identification small model according to the task decomposition of the generative large model; the oxygen content time series prediction small model analyzes the current oxygen content and predicts future changes; the combustion efficiency analysis model evaluates the combustion efficiency under different oxygen supply levels; the oxygen content time series prediction small model receives real-time sensor data and analyzes the current state of oxygen content; uses historical data and algorithms to predict future changes in oxygen content; and identifies the optimal oxygen supply; Data analysis and result feedback: The real-time oxygen content data of the combustion system is obtained through sensors and data interfaces; the small model feeds back the analysis results to the intelligent agent; the intelligent agent summarizes the information and prepares to provide optimization solutions; Generate optimization suggestions and execute them: The generative large model generates optimization suggestions in natural language based on the feedback from the small model; the agent adjusts the parameters of the oxygen supply equipment; and displays the optimization suggestions to the operator to assist him in making manual adjustments; Continuous monitoring and dynamic adjustment: The intelligent agent continuously obtains data on oxygen content and combustion efficiency to monitor the system status. If new changes are detected, the intelligent agent calls the small model again for analysis. The generative large model updates optimization suggestions to ensure that the system always runs in the best state.

Images