Dify + DeepSeek-based biomass gasifier control optimization method and system

Abstract

The invention relates to the technical field of biomass gasification furnace intelligent control, in particular to a biomass gasification furnace control optimization method and system based on Dify + DeepSeek, and the method comprises the steps: taking a Dify platform as an intelligent agent for operation, biomass gasification furnace operation state data collection and biomass gasification furnace operation state control infrastructure; constructing a biomass gasification field knowledge base, and integrating the biomass gasification field knowledge base into the Dify platform; taking the De epSeek model as an intelligent agent for analyzing the running state of the biomass gasifier, fusing the De epSeek model into a Dify platform, and analyzing and processing real-time data during the running period of the biomass gasifier in combination with a biomass gasification domain knowledge base to generate decision suggestions; and converting the decision suggestion generated by the DeepSeek model into an executable control instruction, and sending the control instruction to a corresponding execution mechanism to execute related actions. The intelligent agent reasoning model can be quickly built, and accurate prediction and intelligent decision making of the operation state of the gasification furnace are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of biomass gasifiers, and more specifically, to a control optimization method and system for biomass gasifiers based on Dify + DeepSeek. Background Art

[0002] Biomass gasification technology is an effective way to convert biomass into combustible gas. As a common biomass gasification device, the updraft gasifier has the advantages of simple structure and convenient operation. However, there are many problems in the operation of existing biomass updraft gasifiers, such as low gasification efficiency, unstable gas production quality, and difficulty in adapting to different types and characteristics of biomass raw materials.

[0003] Traditional control methods mainly rely on manual experience and simple feedback control, and cannot accurately perceive the complex working conditions inside the gasifier in real time, making it difficult to dynamically adjust and optimize according to the actual situation. With the development of artificial intelligence technology, the application of artificial intelligence to the control of biomass gasifiers has become a research hotspot, but the current related technologies still have deficiencies in aspects such as data processing ability, model adaptability, and intelligence level.

[0004] Therefore, how to quickly apply large models to the control of updraft gasifiers to explore the potential laws behind the data and achieve accurate prediction and intelligent decision-making of the operating state of gasifiers is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a control optimization method and system for biomass gasifiers based on Dify + DeepSeek to solve the problems existing in the control of traditional biomass gasifiers, such as high development difficulty, insufficient reasoning ability, poor control accuracy and adaptability.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides a control optimization method for biomass gasifiers based on Dify + DeepSeek, including the following steps:

[0008] Take the Dify platform as the infrastructure for the operation of the intelligent agent, the acquisition of biomass gasifier operating state data, and the control of the biomass gasifier operating state;

[0009] Construct a knowledge base in the field of biomass gasification and integrate it into the Dify platform;

[0010] The DeepSeek model is used as an agent for analyzing the operating status of the biomass gasifier and integrated into the Dify platform. Combining with the knowledge base in the field of biomass gasification, it analyzes and processes the real-time data during the operation of the biomass gasifier to generate decision-making suggestions.

[0011] Convert the decision-making suggestions generated by the DeepSeek model into executable control instructions and send the control instructions to the corresponding executing agencies to perform relevant actions.

[0012] Furthermore, the Dify platform docks with various sensors distributed inside the biomass gasifier through a data transmission channel to obtain the temperature data, pressure data, and gas composition data collected by each sensor.

[0013] Furthermore, the knowledge base in the field of biomass gasification covers gasification reaction principles, thermodynamics, and kinetics knowledge, and stores detailed gasification characteristics data of different biomass raw materials, equipment maintenance records, past optimization cases, as well as equipment operation parameters and gas output conditions under different time periods and working conditions.

[0014] Furthermore, when storing various data in the knowledge base in the field of biomass gasification, the content is segmented and data structured. The long text is divided into multiple content segments, and after segmentation, an indexing method is configured to organize various parameters of different biomass raw materials into a table form.

[0015] Furthermore, the DeepSeek model also issues various operation prompts based on the real-time operation data of the biomass gasifier, including equipment maintenance reminders, equipment maintenance suggestions, and early warnings of abnormal situations.

[0016] Furthermore, the DeepSeek model real-time detects the gas composition and calorific value indicators in the biomass gasifier. When any abnormal situation such as gas production lower than expected, unqualified gas quality, or abnormal temperature fluctuation is found, it immediately analyzes various data. First, based on its own knowledge reserve and learning experience, it generates a preliminary solution; then it uses the internal simulation analysis mechanism to predict the potential impact of the preliminary solution on gas production, gas quality, and other operation parameters under the current working conditions. If it is found that the preliminary solution cannot solve the current abnormal situation, it will conduct reasoning attempts again. Through multiple rounds of repeated reasoning and optimization, a solution that takes into account normal temperature, normal gas production and quality, and safe operation of the equipment will be generated until the solution is obtained.

[0017] In a second aspect, the present invention provides a biomass gasifier control optimization system based on Dify + DeepSeek, adopting the method as described above, including: a basic architecture construction module, a data acquisition module, a database module, a model reasoning module, and a control instruction generation module;

[0018] The infrastructure building module integrates the data acquisition module, the database module, the model inference module, and the control instruction generation module through the Dify platform;

[0019] The data acquisition module is used to collect the operation status data of the biomass gasifier in real time;

[0020] The database module is used to build a knowledge base in the field of biomass gasification;

[0021] The model inference module uses the DeepSeek model as an intelligent agent for analyzing the operation status of the biomass gasifier. Combining with the knowledge base in the field of biomass gasification, it analyzes and processes the real-time data during the operation of the biomass gasifier to generate decision-making suggestions;

[0022] The control instruction generation module is used to convert the decision-making suggestions generated by the DeepSeek model into executable control instructions and send the control instructions to the corresponding execution agencies to perform relevant actions.

[0023] Furthermore, the infrastructure building module also has a human-computer interaction module. The human-computer interaction module is used to set various operation parameters of the biomass gasifier, display the key operation status data of the gasifier in real time, and receive various operation prompts sent by the model inference module to display maintenance suggestions and warning information.

[0024] Furthermore, the data acquisition module includes a temperature sensor, a pressure sensor, and a gas composition sensor; the temperature sensors are distributed at different positions inside the gasifier to collect the temperature field data inside the furnace; the pressure sensor is used to detect the pressure data during the gasification process in real time; the gas composition sensor is used to detect the content of various main components in the fuel gas.

[0025] Furthermore, the database module also includes a real-time database and a historical database; the real-time database is used to store the operation status data of the gasifier collected by various sensors in real time; the historical database is used to store the historical operation data of the gasifier, equipment maintenance records, and optimization case information.

[0026] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention utilizes the rich components and tools provided by the Dify platform to quickly build intelligent applications. It can rapidly construct an application framework with functions such as data acquisition, control instruction generation, and human-computer interaction through a visual interface, solving the problem of high difficulty in traditional AI development, reducing the development cost and difficulty, and improving the development efficiency. Integrating the DeepSeek inference model with the "chain of thought" mechanism, which has been pre-trained on a large amount of corpus and optimized through multiple rounds, can simulate the multi-round thinking process of humans during operation. It combines real-time collected data such as the temperature, pressure, and gas composition of the gasifier for reasoning and analysis, and simultaneously retrieves in real-time the knowledge base constructed from biomass gasification expertise, historical operation data, etc., providing a reliable basis for control instruction generation and effectively enhancing the ability to handle complex non-linear problems in the gasification process.

[0028] Based on this framework, the present invention formulates an intelligent agent control strategy. For example, it precisely adjusts the rotation speed of the feeding device and the frequency of the gasifying agent delivery pump according to the model inference results to achieve precise control of the gasifier operation. By combining the ease of use of the Dify platform and the powerful inference ability of the DeepSeek model, the present invention successfully constructs an intelligent agent for the biomass updraft gasifier, effectively solving the problems existing in the control of traditional gasifiers, improving the operation efficiency and intelligent level of the gasifier, enhancing the adaptability of the system to different raw materials and operating conditions, and promoting the intelligent development in the field of biomass energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0030] Figure 1 It is a flowchart of the control optimization method for the biomass gasifier based on Dify + DeepSeek provided by the present invention;

[0031] Figure 2 It is a schematic structural diagram of the control optimization system for the biomass gasifier based on Dify + DeepSeek provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0033] As Figure 1 shown, an embodiment of the present invention discloses a biomass gasifier control optimization method based on Dify + DeepSeek, including the following steps:

[0034] S1. Take the Dify platform as the infrastructure for the operation of the intelligent agent, the acquisition of biomass gasifier operation status data, and the control of the biomass gasifier operation status;

[0035] S2. Build a knowledge base in the field of biomass gasification and integrate it into the Dify platform;

[0036] S3. Take the DeepSeek model as the intelligent agent for analyzing the operation status of the biomass gasifier, integrate it into the Dify platform, and combine it with the knowledge base in the field of biomass gasification to analyze and process the real-time data during the operation of the biomass gasifier, and generate decision-making suggestions;

[0037] S4. Convert the decision-making suggestions generated by the DeepSeek model into executable control instructions, and send the control instructions to the corresponding execution mechanism to perform relevant actions.

[0038] The following further explains each of the above steps.

[0039] S1. Application framework construction based on the Dify platform:

[0040] An embodiment of the present invention relies on the open-source LLM application development platform of Dify, and its visual interface greatly simplifies the development process. Developers do not need to be proficient in complex programming. By simply dragging and dropping components and configuring parameters, they can quickly build an application framework suitable for the intelligent agent of the biomass updraft gasifier. This low-code development mode not only significantly shortens the development cycle but also reduces the development cost, enabling more people who focus on the field of biomass energy but lack a professional programming background to participate in the development of the intelligent agent. This framework, as the infrastructure for the operation of the intelligent agent, the acquisition of biomass gasifier operation status data, and the control of the biomass gasifier operation status, organically integrates each functional module to ensure the efficient and stable operation of the intelligent agent.

[0041] First, the Dify platform connects to various sensors and PLCs distributed inside the biomass gasifier through a data transmission channel. The various sensors include temperature sensors, pressure sensors, and gas composition sensors. The temperature sensors are distributed at different positions inside the gasifier and can accurately capture the subtle changes in the temperature field inside the furnace, providing key evidence for judging the reaction process and equipment status. The pressure sensors continuously monitor the pressure data during the gasification process. The stability of the pressure directly affects the rate and product distribution of the gasification reaction. The gas composition sensors use advanced detection technologies to accurately detect the content and proportion of main components such as carbon monoxide, hydrogen, and methane in the fuel gas. These data are important indicators for evaluating the quality of the fuel gas and adjusting the operating parameters. The various data collected are transmitted to the subsequent processing module through a high-speed data transmission channel at a speed of milliseconds, ensuring the timeliness and accuracy of the data.

[0042] Secondly, the Dify platform also has a control instruction generation function (control instruction generation module) for receiving the results obtained by the DeepSeek model through complex reasoning and generating a series of executable control instructions based on pre-set specific control algorithms and logics. When controlling the feeding device, according to the model's comprehensive analysis of multiple factors such as the current fuel gas output, the temperature inside the furnace, and the characteristics of the raw materials, the rotational speed adjustment value required for the feeding motor is accurately calculated, thereby realizing precise control of the biomass feeding speed. For the gasifying agent blower, the module accurately adjusts the frequency of the blower based on feedback information such as the fuel gas composition and calorific value, combined with model reasoning, to ensure that the supply of the gasifying agent precisely matches the requirements of the gasification reaction, keeping the gasifier always in the best operating condition and achieving efficient and stable energy conversion.

[0043] Finally, the Dify platform also has a human-machine interaction function. By providing a convenient and intuitive operation interface, operators can easily set various operating parameters of the gasifier, set the target fuel gas output according to the actual energy consumption requirements, and expect a specific fuel gas calorific value based on the equipment characteristics and usage scenarios. During operation, operators can view the key operating status data of the gasifier in real time, including temperature, pressure, feeding volume, gasifying agent flow rate, etc., to comprehensively understand the equipment operation. At the same time, various operation prompts sent by the intelligent agent can be received in a timely manner, such as equipment maintenance reminders, providing maintenance suggestions for operators in advance based on factors such as the equipment operation duration and the wear degree of key components to ensure the long-term stable operation of the equipment; abnormal situation warnings immediately issue alarms when parameters such as temperature and pressure are detected to exceed the normal range, and provide possible cause analysis and emergency handling suggestions, effectively improving the safety and reliability of the operation.

[0044] S2. Knowledge Enhancement and Real-Time Retrieval:

[0045] The present invention makes full use of the advantage of seamless integration between the Dify platform and the knowledge base to construct a comprehensive and professional knowledge base in the field of biomass gasification. The knowledge base in the field of biomass gasification covers gasification reaction principles, thermodynamics and kinetics knowledge. And it stores detailed gasification characteristic data of different biomass raw materials (such as the gasification product distribution, gasification efficiency and other characteristics of various raw materials such as wood, straw, rice husk, etc. under different temperature and pressure conditions), equipment maintenance records, past optimization cases (optimization measures and effects taken for various operation problems), and historical operation data accumulated by gasifiers over different time periods (equipment operation parameters and gas output under different working conditions).

[0046] These knowledge and data adopt an advanced structured storage method, and through reasonable classification and indexing, ensure that they can be quickly and accurately called during subsequent retrieval processes.

[0047] Specifically, when the knowledge base in the field of biomass gasification stores various data, it conducts segmentation and data structuring on the content, divides the long text into multiple content segments, and configures the indexing method after segmentation, and arranges various parameters of different biomass raw materials in tabular form so that the model can quickly retrieve during reasoning.

[0048] S3. Integration of DeepSeek Model and Application of Inference Mechanism:

[0049] Deeply integrate the DeepSeek inference model with a reflection chain mechanism into the established application framework. The DeepSeek model is based on the advanced Transformer architecture and has been pre-trained with a large amount of multi-modal data, covering rich information such as professional literature, experimental data, and actual operation cases in the field of biomass gasification. On the basis of pre-training, it has undergone multiple rounds of fine optimization, making it excellent in language understanding, logical reasoning, and complex problem-solving capabilities. The deep integration of the model and the application framework ensures that it can receive the data transmitted by the data acquisition module in real time and stably, and perform inference operations efficiently, providing a solid technical support for the generation of control instructions.

[0050] During the operation of the gasifier, the DeepSeek model real-time detects the gas composition and calorific value indicators in the biomass gasifier. When any abnormal situation such as gas production lower than expected, gas quality not meeting the standard, or abnormal temperature fluctuation is found, it immediately analyzes various data. First, based on its own knowledge reserve and learning experience, it generates a preliminary solution; then it uses the internal simulation analysis mechanism to predict the potential impact of the preliminary solution on gas production, gas quality, and other operation parameters under the current working conditions. If it is found that the preliminary solution cannot solve the current abnormal situation, it conducts inference attempts again, and through multiple rounds of repeated reasoning and optimization, until a solution that takes into account normal temperature, normal gas production and quality, and safe operation of the equipment is generated.

[0051] Taking the complex problem of abnormal increase in the gasifier temperature as an example, the model activates the reflection chain mechanism. First, based on its huge knowledge reserve and rich learning experience, the model quickly generates a preliminary solution, suggesting reducing the feeding speed to decrease the rate of biomass raw materials entering the furnace, thereby reducing the heat release of the reaction. Subsequently, the model conducts in-depth self-reflection on this solution, using the internal simulation analysis mechanism to predict the potential impacts of this solution on gas production, gas quality, and other operating parameters under the current working conditions. If it is found that this solution may not effectively solve the problem of abnormal temperature, such as reducing the feeding speed may lead to a significant decrease in gas production and cannot meet the actual energy demand, the model will re-organize the problem, comprehensively consider various factors such as raw material characteristics (such as moisture content, volatile content of the raw material), equipment operating status (such as the aging degree of the equipment, wear condition of key components), environmental factors (such as environmental temperature, humidity, etc.), and conduct reasoning attempts again. Through multiple rounds of repeated reasoning and optimization, until a practical solution that can effectively solve the problem of abnormal temperature, ensure the stability of gas production and quality, and at the same time take into account the safe operation of the equipment is generated, providing a reliable decision-making basis for the control instruction generation module.

[0052] For another example, during the operation of the intelligent agent, when the DeepSeek model conducts multiple rounds of conversations and complex reasoning, it can retrieve relevant content in the knowledge base in real-time and accurately. When the model analyzes the gasification situation of a certain specific biomass raw material under the current working conditions, it can quickly obtain detailed gasification characteristic parameters of this raw material from the knowledge base, including information such as the optimal reaction temperature range, suitable gasifying agent ratio, and product generation rules at different reaction stages. The model combines this prior knowledge in the knowledge base with the actual operating data and context of the current gasifier for comprehensive analysis and judgment. For example, when judging the reason for the low carbon monoxide content in the current gas, the model not only refers to the real-time temperature and pressure data but also retrieves the relevant knowledge about carbon monoxide generation of this raw material under similar working conditions from the knowledge base, analyzing that it may be due to insufficient supply of the gasifying agent or the reaction temperature not reaching the optimal range, etc., so as to provide more accurate and professional answers and decision-making suggestions. This knowledge enhancement and real-time retrieval mechanism significantly improve the intelligent agent's understanding and response ability to the complex operating conditions of the gasifier, making its decision-making more scientific and reasonable.

[0053] In addition, the DeepSeek model also issues various operation prompts based on the real-time operating data of the biomass gasifier, including equipment maintenance reminders, equipment maintenance suggestions, and early warnings of abnormal situations.

[0054] S4. Intelligent agent control strategy:

[0055] The present invention deeply analyzes the operating principle of an updraft biomass gasifier, including the reaction processes such as the drying, pyrolysis, oxidation, and reduction of biomass, as well as the interrelationships between these processes and their impacts on the final gas output. Combining the characteristics and advantages of the intelligent agent, such as real-time data acquisition, in-depth reasoning analysis, and knowledge retrieval capabilities, a comprehensive and accurate control strategy is formulated. The core objective of this strategy is to achieve the efficient and stable operation of the gasifier, improve the energy conversion efficiency and gas quality. It gives full play to the intelligent decision-making ability of the intelligent agent to conduct all-round and refined control over the operation of the gasifier.

[0056] Specifically, the intelligent agent obtains key data such as gas production and furnace temperature in real time through the data acquisition module. When it is found that the gas production is lower than expected or the furnace temperature fluctuates greatly, the model infers through reasoning that it may be caused by an unreasonable feeding speed. At this time, the control instruction generation module issues precise control instructions to the feeding device based on the model reasoning result. For example, when the temperature is low and the gas production is insufficient, the control instruction generation module converts the calculation result of the model into an executable control instruction and sends it to the PLC. The PLC appropriately increases the rotation speed of the feeding motor, increasing the speed of biomass raw materials entering the gasifier, enabling more raw materials to participate in the reaction, enhancing the reaction intensity, thereby increasing the gas production and furnace temperature. At the same time, the model continuously monitors the subsequent operation data and dynamically adjusts the feeding speed to ensure that the temperature and gas production are maintained within a stable and reasonable range.

[0057] The intelligent agent monitors key indicators such as gas composition and calorific value in real time. Once it is found that the gas quality does not meet the standard, such as too low carbon monoxide content or unbalanced hydrogen ratio, the DeepSeek model immediately conducts in-depth analysis of the data and judges that it may be due to abnormal gasifying agent supply. The control instruction generation module then generates control instructions based on the model reasoning result and precisely adjusts the frequency of the gasifying agent delivery pump through the PLC. For example, when the carbon monoxide content is low, the model analyzes that it may be due to insufficient oxidation reaction and needs to appropriately increase the gasifying agent supply. Accordingly, the delivery pump frequency is adjusted to allow more gasifying agent to enter the gasifier, promoting the oxidation reaction and increasing the carbon monoxide production, thereby improving the gas quality. During the adjustment process, the model continuously fine-tunes the gasifying agent supply based on the real-time feedback data to ensure that the gas quality always meets the set standard, effectively improving the gasification efficiency and gas quality of the gasifier and enhancing the system's adaptability to different raw materials and operating conditions changes. Compared with the traditional fixed gasifying agent supply mode, the intelligent agent control strategy proposed in the present invention can be dynamically adjusted according to the actual operation situation, significantly improving the energy utilization efficiency and equipment operation stability.

[0058] such as Figure 2As shown in the figure, the present invention also provides a biomass gasifier control optimization system based on Dify + DeepSeek, which adopts the method described above and includes: an infrastructure construction module, a data acquisition module, a database module, a model inference module, and a control instruction generation module.

[0059] The infrastructure construction module integrates the data acquisition module, the database module, the model inference module, and the control instruction generation module to the server through the Dify platform to form a Dify + DeepSeek server. Communicate with the upper computer through the PLC, store the data in the Dify + DeepSeek server, and the PLC controls the operating states of various devices of the gasifier according to the control instructions issued by the server. Figure 2 Among them, various execution devices of the gasifier include TT01 - TT10 temperature sensors, PT01 - PT03 pressure sensors, FT01 - FT04 gas flow meters, LT* level gauges, and so on.

[0060] The data acquisition module is used to collect the operating state data of the biomass gasifier in real time, including temperature sensors, pressure sensors, and gas composition sensors; the temperature sensors are distributed at different positions inside the gasifier to collect the temperature field data in the furnace; the pressure sensors are used to detect the pressure data during the gasification process in real time; the gas composition sensors are used to detect the contents of various main components in the fuel gas.

[0061] The database module is used to construct a knowledge base in the field of biomass gasification, and also includes a real - time database and a historical database; the real - time database is used to store the operating state data of the gasifier collected by various sensors in real time; the historical database is used to store the historical operating data of the gasifier, equipment maintenance records, and optimization case information, providing rich data resources for data analysis, model training, and optimization. At the same time, the data warehouse technology is used to integrate, clean, and pre - process the historical data for subsequent in - depth mining and analysis. In addition, the knowledge base has a knowledge update and maintenance function, and can continuously improve the knowledge system according to the latest research results and practical experience.

[0062] The model inference module is used to take the DeepSeek model as an intelligent agent for analyzing the operating state of the biomass gasifier, and combine the knowledge base in the field of biomass gasification and the historical database to analyze and process the real - time data during the operation of the biomass gasifier, and generate decision - making suggestions.

[0063] The control instruction generation module is used to convert the decision - making suggestions generated by the DeepSeek model into executable control instructions, and send the control instructions to the corresponding actuators to perform relevant actions.

[0064] In addition, the infrastructure building module also has a dedicated system monitoring software and a human-machine interaction module. The system monitoring software real-time monitors the operating status of hardware devices, network connection conditions, performance indicators of software modules, etc. When device failures, network anomalies, or software operation errors are detected, alarm messages are sent in a timely manner, and fault diagnosis and repair suggestions are provided through the human-machine interaction module. At the same time, the system is regularly inspected, maintained, and upgraded to ensure its long-term stable operation.

[0065] The human-machine interaction module can set various operating parameters of the biomass gasifier, display the key operating status data of the gasifier in real time, and receive various operation prompts sent by the model inference module to display maintenance suggestions and warning information.

[0066] In a specific embodiment, the process of building the biomass gasifier control optimization system based on Dify + DeepSeek of the present invention is as follows:

[0067] 1. Build an application framework:

[0068] Use Siemens 1200PLC to collect instrument and gasifier system equipment data, and clean the data to make the uploaded data accurate. Communicate with the upper computer through the Siemens S7 protocol, and the upper computer stores the data on the server. Install the Docker application container engine on the server, deploy the Dify platform on Docker, install the OLLAMA framework on the server, install and deploy the deepseek model through the OLLAMA framework. After completion, deploy the embedded model bge-m3 model in OLLAMA. After completion, deploy the re-ranking model bge-reranker model in OLLAMA. Obtain the model files through official channels and integrate them into the model layer of the built application framework.

[0069] 2. Agent integration:

[0070] Enter the DifyAI application engine from the server, configure the DeepSeek model through the interface provided by the Dify platform, select OLLAMA for installation in the model provider of the Dify settings parameters. After installation, select to add the Deepseek model in OLLAMA. Set the system inference model deepseek-r1 in the system model settings, set bge-m3 in the embedded model, and set the bge-reranker model in the re-ranking model. After the above configuration is completed, establish a workflow for connecting to the database in Dify and establish an analysis result workflow in Dify.

[0071] 3. Build a knowledge base:

[0072] Select the knowledge base on the Dify interface, create a new knowledge base, and then drag or click to select files to choose the text files to be uploaded. There are also many supported types, including "TXT, MARKDOWN, MDX, PDF, HTML, XLSX, XLS, DOCX, CSV, MD, HTM", and each file does not exceed 15MB. Use the knowledge base management function of the Dify platform to organize and classify these data and upload them to the knowledge base.

[0073] Collect professional literature, research reports, gasification characteristic data of different biomass raw materials, as well as historical operation data and optimization cases of gasifiers in the field of biomass gasification for uploading.

[0074] After the upload is completed, it is necessary to segment the content and clean the data. This stage is the preprocessing of the content and the data structuring process. Long texts will be divided into multiple content segments, and the indexing method will be configured after the segmentation is completed. Organize the optimal gasification temperature, pressure and other parameters of different biomass raw materials into a table form for uploading, so that the model can quickly retrieve them during inference.

[0075] 4. Run the agent and test:

[0076] Start the biomass updraft gasifier and run the agent system based on the Dify+DeepSeek architecture at the same time. The agent obtains real-time data such as the temperature, pressure, and gas composition of the gasifier through the data acquisition module, and transmits this data to the DeepSeek model. The model conducts inference and analysis according to the operating mechanism, and at the same time retrieves relevant knowledge in the knowledge base to provide a decision-making basis for the control instruction generation module. The control instruction generation module generates control instructions according to the model inference results, adjusts the feeding speed of the feeding device, the conveying volume of the gasifying agent conveying device, etc., and the PLC sends the instructions to the actuator for execution. During the operation, observe the operating status of the gasifier, gas production, gas quality and other indicators, and compare them with the operating indicators of the gasifier under the traditional control method to test the control effect of the agent. After multiple tests and optimizations, continuously adjust the model parameters and control strategies to enable the agent to better adapt to the operating requirements of the gasifier and improve the gasification efficiency and gas quality.

[0077] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for related parts.

[0078] The foregoing description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control optimization method for a biomass gasifier based on Dify + DeepSeek, characterized in that, It includes the following steps: Take the Dify platform as the infrastructure for running the intelligent agent, collecting the operation status data of the biomass gasifier, and controlling the operation status of the biomass gasifier; Build a knowledge base in the field of biomass gasification and integrate it into the Dify platform; Take the DeepSeek model as the intelligent agent for analyzing the operation status of the biomass gasifier, integrate it into the Dify platform, and combine it with the knowledge base in the field of biomass gasification to analyze and process the real-time data during the operation of the biomass gasifier, and generate decision-making suggestions; Convert the decision-making suggestions generated by the DeepSeek model into executable control instructions, and send the control instructions to the corresponding execution agencies to perform relevant actions.

2. The biomass gasifier control optimization method based on Dify + DeepSeek according to claim 1, wherein, The Dify platform docks various sensors distributed in the biomass gasifier through a data transmission channel, and is used to obtain the temperature data, pressure data, and gas component data collected by each sensor.

3. The biomass gasifier control optimization method based on Dify + DeepSeek according to claim 1, wherein, The knowledge base in the field of biomass gasification covers gasification reaction principles, thermodynamics and kinetics knowledge, and stores detailed gasification characteristic data of different biomass raw materials, equipment maintenance records, past optimization cases, as well as equipment operation parameters and gas production conditions under different time periods and working conditions.

4. The biomass gasifier control optimization method based on Dify+DeepSeek according to claim 1, characterized in that, When storing various data in the knowledge base in the field of biomass gasification, the content is segmented and data structured. The long text is divided into multiple content segments, and after the segmentation is completed, the indexing method is configured, and the various parameters of different biomass raw materials are sorted into a table form.

5. The biomass gasifier control optimization method based on Dify + DeepSeek according to claim 1, wherein The DeepSeek model also issues various operation prompts based on the real-time operation data of the biomass gasifier, including equipment maintenance reminders, equipment maintenance suggestions, and early warnings of abnormal situations.

6. The biomass gasifier control optimization method based on Dify + DeepSeek according to claim 1, wherein, The DeepSeek model real-time detects the gas component and calorific value indicators in the biomass gasifier. When any abnormal situation such as gas production lower than expected, unqualified gas quality, or abnormal temperature fluctuation is found, it immediately analyzes various data, first generates a preliminary solution based on its own knowledge reserve and learning experience; then uses the internal simulation analysis mechanism to predict the potential impact of the preliminary solution on gas production, gas quality, and other operation parameters under the current working conditions. If it is found that the preliminary solution cannot solve the current abnormal situation, it will conduct another reasoning attempt, and through multiple rounds of repeated reasoning and optimization, until a solution that takes into account normal temperature, normal gas production and quality, and safe operation of the equipment is generated.

7. A biomass gasifier control optimization system based on Dify + DeepSeek, characterized in that, Adopt the method described in any one of claims 1-6, including: an infrastructure building module, a data collection module, a database module, a model reasoning module, and a control instruction generation module; The infrastructure building module integrates the data collection module, the database module, the model reasoning module, and the control instruction generation module through the Dify platform; The data collection module is used to collect the operation status data of the biomass gasifier in real time; The database module is used to build a knowledge base in the field of biomass gasification; The model inference module is used to take the DeepSeek model as an agent for analyzing the operating state of the biomass gasifier, and combine with the knowledge base in the field of biomass gasification to analyze and process the real-time data during the operation of the biomass gasifier, and generate decision-making suggestions. The control instruction generation module is used to convert the decision-making suggestions generated by the DeepSeek model into executable control instructions, and send the control instructions to the corresponding actuators to perform relevant actions.

8. The biomass gasifier control optimization system based on Dify + DeepSeek according to claim 7, characterized in that, The infrastructure building module also has a human-computer interaction module. The human-computer interaction module is used to set various operating parameters of the biomass gasifier, display the key operating state data of the gasifier in real time, and receive various operating prompts issued by the model inference module, and display maintenance suggestions and warning information.

9. The biomass gasifier control optimization system based on Dify+DeepSeek according to claim 7, wherein, The data acquisition module includes a temperature sensor, a pressure sensor and a gas composition sensor; the temperature sensors are distributed at different positions inside the gasifier to collect the temperature field data in the furnace; the pressure sensor is used to detect the pressure data during the gasification process in real time; the gas composition sensor is used to detect the content of various main components in the fuel gas.

10. The biomass gasifier control optimization system based on Dify+DeepSeek according to claim 7, characterized in that The database module also includes a real-time database and a historical database; the real-time database is used to store the operating state data of the gasifier collected by various sensors in real time; the historical database is used to store the historical operating data of the gasifier, equipment maintenance records and optimization case information.

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