Biomass gasifier control optimization method and system based on DeepSeek

Abstract

The invention discloses a DeepSeek-based biomass gasifier control optimization method and a DeepSeek-based biomass gasifier control optimization system. The DeepSeek-based biomass gasifier control optimization method comprises the following steps of: acquiring operation data of a biomass gasifier in real time and in all directions by utilizing multiple sensors and preprocessing the operation data; collecting and organizing professional knowledge related to biomass gasification, and constructing a knowledge graph in combination with the preprocessed biomass gasifier operation data; taking the preprocessed data as input, taking the constructed knowledge graph as a knowledge base, designing a training objective function of a comprehensive performance index through transfer learning, and combining reinforcement learning with a DeepSeek model to carry out model training and optimization; the DeepSeek model utilizes the trained model to predict an optimal control strategy according to real-time gasifier operation data and knowledge graph related knowledge and converts the optimal control strategy into an instruction for execution; s5, adjusting a control strategy in real time through online monitoring and feedback optimization; according to the method, the control precision and adaptability are remarkably improved, and the model generalization and adaptability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass energy utilization, and more specifically, to a method and system for optimizing the control of a biomass gasifier based on DeepSeek. Background Art

[0002] Currently, under the general trend of sustainable energy development, biomass energy, as an important representative of clean and renewable energy, is increasingly prominent in the energy system. As the core equipment for realizing the efficient conversion of biomass energy, the biomass gasifier can convert biomass raw materials into combustible gas, which is widely used in multiple fields such as distributed chemical industry, building materials, power generation, and district heating, contributing to alleviating energy shortages and reducing environmental pollution.

[0003] However, the operation control of current biomass gasifiers faces many severe challenges.

[0004] Traditional control methods are mostly based on simple empirical rules or fixed control algorithms and are difficult to cope with the complex and variable characteristics of biomass raw materials. Specifically, there are many types of raw materials, and the chemical compositions and physical properties of different types of biomass vary greatly. Biomass raw materials have significant heterogeneity characteristics, and key parameters such as their elemental composition, volatile content, and ash melting point vary significantly. When the raw material is switched, the traditional PID control algorithm cannot dynamically adjust the control parameters to adapt to the changes in reaction kinetics.

[0005] The biomass gasification process is essentially a complex process with strong coupling, nonlinearity, and large lag characteristics, which makes it extremely difficult for conventional control methods to accurately regulate key parameters such as temperature, pressure, and gas composition in the gasifier. For example, the temperature in the furnace is not only related to the gasification reaction rate and direction but also closely connected to the equipment safety and gas production quality. The multiphase reactions in the gasifier involve strong coupling effects of mass transfer, heat transfer, and chemical reaction kinetics. Traditional control methods are difficult to achieve precise control of key parameters such as temperature because they cannot perceive and process the mutual relationships between complex parameters in real time and accurately.

[0006] In recent years, with the rapid development of artificial intelligence technology, intelligent control methods have begun to be applied to the control of biomass gasifiers. However, existing intelligent control schemes generally have problems of poor model adaptability and weak generalization ability. On the one hand, the amount of data relied on for model training is limited and it is difficult to comprehensively cover the rich and diverse operating conditions in the biomass gasification process. Biomass gasification is affected by a variety of factors, and the actual operating conditions are extremely complex. Existing training data often cannot fully cover the data under these complex operating conditions, resulting in a poor fitting degree of the model to the actual operating conditions. On the other hand, when facing new operating scenarios or changes in raw material characteristics, the model lacks an effective self-adjustment and optimization mechanism.

[0007] Therefore, how to overcome the problems in the prior art such as the difficulty of the biomass gasifier control to adapt to the changes in raw material characteristics and dynamic changes in working conditions, low control accuracy, and poor model adaptability, and to achieve the efficient, stable, and intelligent operation of the biomass gasifier, so as to improve the utilization efficiency and quality of biomass energy is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a control optimization method and system for a biomass gasifier based on DeepSeek to solve some of the technical problems mentioned in the background art.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A control optimization method for a biomass gasifier based on DeepSeek, comprising the following steps:

[0011] S1. Use a variety of sensors to collect the operation data of the biomass gasifier in real time and all-round and perform preprocessing;

[0012] S2. Collect and organize the professional knowledge related to biomass gasification, and combine it with the preprocessed operation data of the biomass gasifier to construct a knowledge graph;

[0013] S3. Take the preprocessed data as the input and the constructed knowledge graph as the knowledge base, and through transfer learning, design a training objective function for comprehensive performance indicators, and combine reinforcement learning with the DeepSeek model to perform model training and optimization;

[0014] S4. The DeepSeek model, based on the real-time collected operation data of the gasifier and the relevant knowledge of the knowledge graph, uses the trained model to predict and generate the optimal control strategy under the current working conditions, and converts the generated control strategy into control instructions for execution;

[0015] S5. Through online monitoring and feedback optimization, adjust the control strategy in real time.

[0016] Preferably, in step S1, the specific content of using a variety of sensors to collect the operation data of the biomass gasifier in real time and all-round is:

[0017] Monitor the temperature changes in different reaction areas in the furnace through a thermocouple temperature sensor;

[0018] Precisely measure the pressure parameters of key parts through a pressure sensor;

[0019] Detect the main gas components through a gas composition analyzer;

[0020] Detect the characteristic parameters of raw materials, detect the type of raw materials through near-infrared spectrometer, detect humidity through moisture meter, detect particle size through laser particle size analyzer, and detect density through density meter;

[0021] As well as operating parameters, the feed rate is detected by a flow meter, and the blast volume is detected by an air volume sensor.

[0022] Preferably, in step S1, the specific contents of the preprocessing include: for the collected raw data, using a data cleaning algorithm based on statistical principles to eliminate outliers; for noise data, using a wavelet denoising algorithm to filter out high-frequency noise interference; for temperature data, according to its range, using a Z-score standardization method to perform data standardization processing to normalize the interval; for gas component concentration data, using a corresponding conversion formula to convert the percentage concentration into a numerical form suitable for model input, and converting the volume percentage concentration into a mole fraction form and normalizing it.

[0023] Preferably, in step S2, the content of constructing the knowledge graph is:

[0024] Use the named entity recognition (NER) algorithm in natural language processing technology to parse and extract the collected knowledge and identify the entities therein;

[0025] Use the relationship extraction algorithm to determine and annotate the relationships between entities;

[0026] Based on the extracted entities and the relationships between them, a knowledge graph in the field of biomass gasification is constructed using a graph database. The knowledge graph is stored and presented in the form of nodes representing entities and edges representing relationships. At the same time, attribute information is added to each node and edge to enrich the information content of the knowledge graph.

[0027] Preferably, in step S3, the specific contents of model training and optimization are:

[0028] S31. Use transfer learning technology to initialize the model parameters with the help of DeepSeek's pre-trained model parameters on large-scale general domain data, and fine-tune them for specific data in the biomass gasification field;

[0029] S32. Taking gasification efficiency, gas calorific value and gas production stability as different weight items of the objective function, determining the weight coefficient, and obtaining a training objective function that comprehensively considers multiple performance indicators of the gasifier;

[0030] In S33, a simulation of the gasifier operating environment is built by combining a reinforcement learning algorithm with a DeepSeek model. Based on the current operating state reflected by the input data and the knowledge graph, the model selects corresponding control actions, including adjusting the feed rate, air flow rate, and temperature value. The environment returns a new operating state and a reward value according to the model's control actions. By continuously optimizing the reward value, the model is guided to learn the optimal control strategy through repeated trial and error.

[0031] Preferably, the training objective function that comprehensively considers multiple performance indicators of the gasifier is:

[0032] Objective = 0.4×η_normalized + 0.3×H_g_normalized + 0.3×S_normalized

[0033] Among them, η_normalized is the normalized gasification efficiency, H_g_normalized is the normalized calorific value of the fuel gas, and S_normalized is the normalized gas production stability;

[0034] η_normalized = (η - η_min) / (η_max - η_min)

[0035] H_g_normalized = (H_g - H_g_min) / (H_g_max - H_g_min)

[0036] S_normalized = 1 - [(S - S_min) / (S_max - S_min)]

[0037] Gasification efficiency: η = (H_g × Q_g) / (m_b × H_b)

[0038] Gas production stability: S = (1 / n)×Σσ_ci + (σ_Q / μ_Q)

[0039] Calorific value of the fuel gas: Measured by a calorimeter to measure the higher or lower calorific value of the fuel gas;

[0040] Among them, H_g is the calorific value of the fuel gas, Q_g is the fuel gas flow rate, m_b is the mass flow rate of the biomass raw material, H_b is the calorific value of the raw material, σ_ci is the standard deviation of the concentration of the i-th gas component, n is the number of gas component types, σ_Q is the standard deviation of the flow rate, μ_Q is the average flow rate, η_min is the minimum value of the gasification efficiency, η_max is the maximum value of the gasification efficiency, H_g_min is the minimum value of the calorific value of the fuel gas, H_g_max is the maximum value of the calorific value of the fuel gas, S_min is the minimum value of the gas production stability, and S_max is the maximum value of the gas production stability.

[0041] Preferably, in step S4, the output control strategy includes, but is not limited to, the precise adjustment amount of the feeding rate, the accurate set value of the air blowing volume, the target value of the gasification temperature, and the optimization of other key control parameters for the reaction pressure control range;

[0042] Converting the generated control strategy into control instructions for execution specifically includes:

[0043] For the feeding motor speed controller, the PID control algorithm is adopted, and the motor speed is controlled according to the feeding rate adjustment amount output by the model to ensure that the feeding rate accurately reaches the set value;

[0044] For the blower frequency converter regulator, the air blowing volume is adjusted by controlling the output frequency of the frequency converter;

[0045] For the temperature regulating valve, an electric control valve is adopted, and the valve opening is automatically adjusted according to the target value of the gasification temperature set by the model to control the temperature inside the gasification furnace;

[0046] Meanwhile, sensors are used to continuously monitor the execution situation of the actuator and the feedback data of the gasification furnace in real time. The actual speed of the feeding motor is monitored through a motor speed sensor, the actual value of the air blowing volume is monitored through a flow sensor, and the actual temperature inside the furnace is monitored through a temperature sensor. The feedback data is compared with the set value. If a deviation is found, the control instruction is adjusted in a timely manner through a feedback control algorithm to ensure the accurate execution of the control strategy.

[0047] Preferably, the specific content of step S5 includes:

[0048] S51. With the help of a real-time data acquisition system and a data transmission network, continuously monitor the operating state of the biomass gasification furnace online in real time, collect various operating data and perform visual display;

[0049] S52. Compare and analyze the real-time collected data with the ideal state predicted by the DeepSeek model, calculate the difference between the actual gas calorific value and the target gas calorific value predicted by the model using the mean square error (MSE) algorithm, calculate the deviation degree of the actual gasification efficiency from the expected gasification efficiency through the relative deviation algorithm, and evaluate the implementation effect of the current control strategy;

[0050] S53. According to the monitoring and analysis results, when the deviation between the actual operating state and the ideal state exceeds the preset value, the deviation information and the current operating data are input into the DeepSeek model as feedback signals, triggering the model to re-optimize and adjust the control strategy.

[0051] Preferably, step S35 further includes deeply analyzing and diagnosing the cause of the deviation by using the knowledge of the knowledge graph, and locating the cause of the fault and sending out early warning information in a timely manner through the knowledge graph association query and reasoning function.

[0052] A biomass gasifier control optimization system based on DeepSeek, based on the described biomass gasifier control optimization method based on DeepSeek, includes: a data acquisition and preprocessing module based on PLC, a knowledge graph construction module, a DeepSeek model integration and training module, a control strategy generation and execution module, and an online monitoring and feedback optimization module;

[0053] The data acquisition and preprocessing module based on PLC is used to collect and preprocess the operation data of the biomass gasifier in real time and all-round by using a variety of sensors, and transmit the processed data to the knowledge graph construction module and the DeepSeek model integration and training module respectively;

[0054] The knowledge graph construction module is used to collect and organize the professional knowledge related to biomass gasification, combine the preprocessed operation data of the biomass gasifier, construct a knowledge graph, and provide it to the DeepSeek model integration and training module;

[0055] The DeepSeek model integration and training module is used to take the preprocessed data as input, the constructed knowledge graph as a knowledge base, and through transfer learning, designing a training objective function for comprehensive performance indicators, and combining reinforcement learning with the DeepSeek model, perform model training and optimization;

[0056] The control strategy generation and execution module is used to use the DeepSeek model to predict and generate the optimal control strategy under the current working conditions based on the real-time collected operation data of the gasifier and the relevant knowledge of the knowledge graph, and convert the generated control strategy into control instructions for execution, and feedback the execution results to the online monitoring and feedback optimization module;

[0057] The online monitoring and feedback optimization module is used to monitor the operation status of the gasifier in real time, compare and analyze the monitoring data with the model prediction results, and if there is a deviation, send the feedback information back to the DeepSeek model integration and training module for optimization, and adjust the control strategy in real time.

[0058] It can be seen from the above technical solutions that compared with the prior art, the present invention discloses a biomass gasifier control optimization method and system based on DeepSeek, which has the following beneficial effects:

[0059] Significantly improve control accuracy and adaptability: Through deep learning of a large amount of operating data and professional knowledge, the DeepSeek model can deeply understand the intricate relationships between various parameters in the biomass gasification process and accurately predict the optimal control strategies under different operating conditions. Whether facing significant changes in raw material characteristics or dynamic fluctuations in operating conditions, this method and system can quickly and accurately adjust control parameters. Experimental data shows that compared with traditional control methods, after adopting the control scheme of the present invention, the gasification efficiency has increased by an average of 15%-20%, and the fluctuation range of gas calorific value has decreased by 30%-40%, effectively improving the utilization efficiency and quality of biomass energy and ensuring that the gasifier always operates in a highly efficient and stable state.

[0060] Enhance the generalization ability and self-adaptability of the model: Based on the innovative training method of knowledge graph and reinforcement learning, the DeepSeek model can not only learn the laws in historical data but also use the domain knowledge in the knowledge graph to reason and analyze new emerging operating conditions and problems. When encountering raw material combinations or operating scenarios that have never been seen before, the model can quickly generate reasonable control strategies based on the relevant knowledge in the knowledge graph and the rich experience accumulated in the reinforcement learning process, greatly enhancing the generalization ability and self-adaptability of the model.

[0061] Realize intelligent fault diagnosis and early warning: The construction of the knowledge graph provides the system with rich and comprehensive domain knowledge. Combining with real-time monitoring data, the DeepSeek model can conduct comprehensive and in-depth analysis and evaluation of the operating state of the gasifier. Once abnormal fluctuations in operating data are detected, the model can use the fault diagnosis knowledge in the knowledge graph and, through technologies such as association reasoning and pattern matching, quickly locate the cause of the fault and send out early warning information in a timely manner. For example, when it is detected that the pressure in the furnace suddenly rises and the temperature drops abnormally, the model may analyze through the knowledge graph that it is caused by coking, resulting in poor gas flow, and then promptly remind the operator to take corresponding cleaning measures to avoid the further deterioration of equipment failures, improve the reliability and safety of equipment operation, and reduce the downtime and economic losses caused by equipment failures.

[0062] Reduce operating costs and improve economic benefits: The control optimization method of the present invention can effectively improve the operating efficiency of the gasifier and reduce energy waste and equipment losses caused by improper control; on the one hand, by precisely controlling the gasification process, the conversion rate of biomass raw materials is increased, and raw material consumption is reduced; on the other hand, the occurrence frequency of equipment failures is reduced, and maintenance costs are lowered. The average number of equipment maintenance times can be reduced by 30%-40%. Generally speaking, after adopting the technical solution of the present invention, the overall operating cost of the biomass gasification project has been reduced by 10%-15%, significantly improving economic benefits, contributing to the sustainable development of the biomass energy industry, and enhancing the competitiveness of biomass energy in the energy market. Brief Description of the Drawings

[0063] 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 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, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0064] Figure 1 Schematic diagram of a method for optimizing the control of a biomass gasifier based on DeepSeek provided by the present invention;

[0065] Figure 2 Schematic diagram of the sensor layout of the biomass gasifier provided by the present invention;

[0066] Figure 3 Schematic diagram of a system for optimizing the control of a biomass gasifier based on DeepSeek provided by the present invention. Detailed Embodiments

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0068] Embodiment 1

[0069] The embodiment of the present invention discloses a method for optimizing the control of a biomass gasifier based on DeepSeek, including the following steps:

[0070] S1. Use a variety of sensors to collect the operation data of the biomass gasifier in real time and in all directions and perform preprocessing;

[0071] S2. Collect and organize the professional knowledge related to biomass gasification, and combine it with the preprocessed operation data of the biomass gasifier to construct a knowledge graph;

[0072] S3. Use the preprocessed data as the input and the constructed knowledge graph as the knowledge base, and combine transfer learning, design the training objective function of the comprehensive performance index, and combine reinforcement learning with the DeepSeek model to perform model training and optimization;

[0073] S4. The DeepSeek model predicts and generates the optimal control strategy under the current working conditions based on the real-time collected operation data of the gasifier and the relevant knowledge of the knowledge graph, and converts the generated control strategy into control instructions for execution;

[0074] S5. Adjust the control strategy in real time through online monitoring and feedback optimization.

[0075] In order to further implement the above technical solution, in step S1, the specific contents of using multiple sensors to collect the operation data of the biomass gasifier in real time and in all directions are as follows:

[0076] The temperature changes in different reaction areas in the furnace are monitored by thermocouple temperature sensors;

[0077] The different reaction zones include drying zone, pyrolysis zone, reduction zone, and oxidation zone;

[0078] The pressure parameters of key parts are accurately measured by pressure sensors to ensure the stable flow of air in the gasifier. The pressure sensors are high-precision piezoresistive sensors installed in key parts such as the furnace, air inlet and outlet pipes, with an accuracy of up to ±0.05Pa.

[0079] The main gas components are detected by the gas composition analyzer. The gas composition analyzer adopts advanced gas chromatography analyzer, which can detect the main gas components such as H2, CO, CH4, CO2 in real time and accurately, with a detection accuracy of up to ±0.5%;

[0080] Detect the characteristic parameters of raw materials, detect the type of raw materials through near-infrared spectrometer, detect humidity through moisture meter, detect particle size through laser particle size analyzer, and detect density through density meter;

[0081] As well as operating parameters, the feed rate is detected by a flow meter, and the blast volume is detected by an air volume sensor.

[0082] In order to further implement the above technical solution, in step S1, the specific contents of preprocessing include: for the collected raw data, using a data cleaning algorithm based on statistical principles to eliminate outliers (by calculating the data mean and standard deviation, the data that deviates from the mean by more than 3 times the standard deviation is identified as an outlier and removed); for noise data, a wavelet denoising algorithm is used to process it, while effectively retaining the data characteristics, high-frequency noise interference is filtered out; for temperature data, according to its range, the Z-score standardization method is used to perform data standardization processing to normalize the interval; for gas component concentration data, the corresponding conversion formula is used to convert the percentage concentration into a numerical form suitable for model input, and the volume percentage concentration is converted into a mole fraction form and normalized.

[0083] In this embodiment, in step S2, relevant professional knowledge of biomass gasification is collected and sorted out. The sources cover a large number of academic literatures (retrieved and screened from academic databases such as Web of Science and CNKI), industry standards (such as national biomass energy utilization related standards, gasification equipment manufacturing and operation standards), patent materials (retrieving relevant patents at home and abroad), and summaries of actual operation experience (collected in cooperation with biomass gasification project operation enterprises); these knowledges are classified and sorted out, including chemical reaction principles (chemical reaction equations and reaction kinetic parameters of biomass pyrolysis, gasification, and combustion), equipment structure and performance (structural characteristics and performance parameters of the furnace chamber structure, feeding device, gasification agent supply system, etc. of the gasifier), operation control strategies (feeding rate, air blowing volume, temperature control strategies under different working conditions), and the influence of raw material characteristics on the gasification process (influence laws of different raw material types, humidity, particle size, etc. on the gasification reaction rate and gas composition), etc.

[0084] To further implement the above technical solution, in step S2, the content of constructing the knowledge graph is as follows:

[0085] Using the named entity recognition NER algorithm in natural language processing technology, the collected knowledge is parsed and extracted to identify the entities (biomass raw material types, gasification reaction products, equipment components, etc.);

[0086] Adopting a relation extraction algorithm, determine the relationships between entities (reaction relationships between raw materials and products, connection relationships between equipment components, correlation relationships between control parameters and gasification indexes) and label them; for example, through techniques such as dependency syntactic analysis and semantic role annotation, identify the relationship between "biomass raw material" and "gasification reaction generates" "gas composition".

[0087] Based on the extracted entities and the relationships between entities, use a graph database to construct a knowledge graph in the field of biomass gasification. The knowledge graph is stored and presented in the form of nodes representing entities and edges representing relationships. Specifically, the "biomass raw material" node is connected to the "gas composition" node through the "gasification reaction generates" edge, intuitively and clearly showing the chemical reaction relationship between the two; the "temperature sensor" node is connected to the "gasifier furnace chamber" node through the "installed in" edge, clarifying the positional relationship between equipment components; at the same time, add attribute information to each node and edge to enrich the information volume of the knowledge graph. For example, the attributes of the "biomass raw material" node include raw material type, chemical composition, physical properties, etc., and the attributes of the "gasification reaction generates" edge include reaction conditions, reaction rate, etc.

[0088] To further implement the above technical solution, in step S3, the specific content of model training and optimization is as follows:

[0089] S31. Apply transfer learning technology, initialize with the pre-trained model parameters of DeepSeek on large-scale general domain data, and fine-tune for specific data in the biomass gasification field;

[0090] In this embodiment, by setting specific fine-tuning parameters, the learning rate is set to 0.001, and the number of fine-tuning layers is set to 3 layers, which speeds up the model convergence rate and improves the model's adaptability to the data in this field, enabling the model to quickly master the key knowledge and rules in the biomass gasification field;

[0091] S32. Take gasification efficiency, gas calorific value, and gas production stability as different weight terms of the objective function, determine the weight coefficients. After multiple experiments and data analysis, the weight coefficients are determined to be 0.4, 0.3, and 0.3 respectively, and a training objective function that comprehensively considers multiple performance indicators of the gasifier is obtained;

[0092] In this embodiment, the gasification efficiency is defined as the ratio of the chemical energy of the gas to the chemical energy of the input biomass raw material, and is calculated by measuring the gas calorific value, flow rate, and the mass and calorific value of the raw material; the gas calorific value is measured by a calorimeter to measure the higher or lower calorific value of the gas; the gas production stability is characterized by the fluctuation range of gas composition and flow rate per unit time, and is quantified by calculating the standard deviation of gas composition concentration and the coefficient of variation of flow rate;

[0093] S33. Adopt a method that combines the reinforcement learning algorithm with the DeepSeek model to build a simulated gasifier operation environment. The model selects corresponding control actions according to the current operation state reflected by the input data and the knowledge graph, including adjusting the feeding rate, air blowing volume, and temperature value. The environment returns a new operation state and a reward value according to the model's control actions. By continuously optimizing the reward value, the model is guided to learn the optimal control strategy through repeated trial and error. After multiple trainings, the model learns to appropriately reduce the feeding rate and increase the air blowing volume when the raw material humidity increases to maintain the stable and efficient operation of the gasifier;

[0094] In this embodiment, the reward value setting is closely related to the gasifier performance indicators. When the gasification efficiency increases, the gas calorific value increases, and the gas production stability enhances, a positive reward is given, and the reward value is set to +1; when the performance deteriorates, a negative reward is given, and the reward value is set to -1.

[0095] The input data includes: vector temperature data (including the oxidation layer temperature of the TT01 gasifier, the reduction layer temperature of the TT02 gasifier, the pyrolysis layer temperature of the TT03, the gas layer temperature of the TT04, the temperature of the TT05 gas pipeline, the temperature inside the TT06 water jacket, the drying layer temperature of the TT07, the temperature of the TT08 circulating return water, the temperature of the TT09 circulating outlet water, the temperature of the TT10 gasifying agent inlet), pressure data including (the inlet pressure of the PT01 gasifying agent, the gas layer pressure of the PT02 gasifier, the outlet pressure of the PT03 gas pipeline), flow data including (the inlet flow rate of the FT01 gasifying agent, the inlet flow rate of the FT02 gasifying agent, the outlet flow rate of the FT03 gas, the flow rate of the FT04 circulating return water), level data including (the low level of the LT01 batching bin, the high level of the LT02 batching bin, the low level of the LT03 feed bin, the water level of the LT11 gasifier jacket, the water level of the LT12 circulating water tank), analysis data (the AI01 gas analyzer, the AI02 material humidity monitoring, the AI03 material composition calorific value monitoring), fan and water pump data (the M1 feeding belt, the M2 feeding screw, the M3 slag discharging device, the M4 gasifying blower, the M5 annular blower of the gasifier, the M6 induced draft fan of the gasifier, the M7 burner air supply fan, the M8 tar combustion air supply fan, the M9 circulating water pump #1, the M10 circulating water pump #2), valve data (the SV1 blower regulating valve, the SV2 annular blower regulating valve, the SV3 gas regulating valve, the SV11 air exhausting pneumatic valve, the SV12 gas cut-off valve, the SV13 gas cut-off valve), data of the drying bin, belt scale, etc.

[0096] The constructed knowledge graph is: A [raw material attribute] > B {humidity threshold}, B > |H≥25%| C [reduce the feeding rate], B > |H≥15%| C [reduce the feeding rate], B > |H<15%| D [standard rate], E [thermodynamic rule] > F [temperature - air volume relationship], F > ΔQ = α·ΔT

[0097] The dynamic knowledge is: when it is detected that H > 15%, trigger the humidity processing rule in the knowledge graph; according to the gasification efficiency, trigger the processing rules of temperature and air volume in the knowledge graph.

[0098] To further implement the above technical solution, the training objective function considering multiple performance indicators of the gasifier comprehensively is:

[0099] Objective = 0.4×η_normalized + 0.3×H_g_normalized + 0.3×S_normalized

[0100] Where, η_normalized is the normalized gasification efficiency, H_g_normalized is the normalized calorific value of the gas, and S_normalized is the normalized gas production stability;

[0101] η_normalized = (η - η_min) / (η_max - η_min)

[0102] H_g_normalized = (H_g - H_g_min) / (H_g_max - H_g_min)

[0103] S_normalized = 1 - [(S - S_min) / (S_max - S_min)]

[0104] Gasification efficiency: η = (H_g × Q_g) / (m_b × H_b)

[0105] Gas production stability: S = (1 / n) × Σσ_ci + (σ_Q / μ_Q)

[0106] Calorific value of fuel gas: Measured by a calorimeter to measure the higher or lower calorific value of the fuel gas;

[0107] Wherein, H_g is the calorific value of the fuel gas, Q_g is the fuel gas flow rate, m_b is the mass flow rate of the biomass raw material, H_b is the calorific value of the raw material, σ_ci is the standard deviation of the concentration of the i-th gas component, n is the number of gas component types, σ_Q is the standard deviation of the flow rate, μ_Q is the average flow rate, η_min is the minimum value of the gasification efficiency, η_max is the maximum value of the gasification efficiency, H_g_min is the minimum value of the calorific value of the fuel gas, H_g_max is the maximum value of the calorific value of the fuel gas, S_min is the minimum value of the gas production stability, and S_max is the maximum value of the gas production stability.

[0108] To further implement the above technical solution, in step S4, the output control strategy includes, but is not limited to, the accurate adjustment amount of the feeding rate, the accurate setting value of the air blowing amount, the target value of the gasification temperature, and the optimization of other key control parameters for the reaction pressure control range;

[0109] For the accurate adjustment amount of the feeding rate, according to the raw material characteristics and the operating state of the gasifier, calculate the specific value of the increase or decrease of the feeding rate, accurate to kg / h; for the accurate setting value of the air blowing amount, calculate the air blowing amount that meets the requirements of the gasification reaction through model calculation, with the unit of m 3 / h; for the target value of the gasification temperature, determine the appropriate gasification temperature according to different raw materials and working conditions, accurate to °C;

[0110] Convert the generated control strategy into a control command and send it to the gasifier actuator through the industrial control network Modbus TCP. Specifically:

[0111] For the feeding motor speed controller, adopt the PID control algorithm to control the motor speed according to the feeding rate adjustment amount output by the model to ensure that the feeding rate accurately reaches the set value;

[0112] For the blower frequency converter regulator, the air volume of the blower is adjusted by controlling the output frequency of the frequency converter;

[0113] For the temperature regulating valve, an electric regulating valve is adopted. According to the target gasification temperature value set by the model, the valve opening is automatically adjusted to control the temperature inside the gasifier;

[0114] Meanwhile, sensors are used to monitor the execution of the actuator and the feedback data of the gasifier in real time. The actual rotation speed of the feeding motor is monitored by a motor speed sensor, the actual value of the blower air volume is monitored by a flow sensor, and the actual temperature inside the furnace is monitored by a temperature sensor. The feedback data is compared with the set value. If a deviation is found, the control instruction is adjusted in time through a feedback control algorithm to ensure the accurate execution of the control strategy.

[0115] To further implement the above technical solution, the specific content of step S5 includes:

[0116] S51. With the help of a real-time data acquisition system and a data transmission network, continuously monitor the operating state of the biomass gasifier online in real time, collect various operating data and perform visual display;

[0117] S52. Compare and analyze the real-time collected data with the ideal state predicted by the DeepSeek model, calculate the difference between the actual gas calorific value and the target gas calorific value predicted by the model using the mean square error (MSE) algorithm, and calculate the deviation degree of the actual gasification efficiency from the expected gasification efficiency through the relative deviation algorithm to evaluate the implementation effect of the current control strategy.

[0118] S53. According to the monitoring and analysis results, when the deviation between the actual operating state and the ideal state exceeds the preset value (such as the deviation of the actual gas calorific value from the target value exceeds 5%, and the deviation of the actual gasification efficiency from the expected value exceeds 10%), the deviation information and the current operating data are input into the DeepSeek model as feedback signals to trigger the model to re-optimize and adjust the control strategy.

[0119] To further implement the above technical solution, step S35 also includes using the knowledge in the knowledge graph to deeply analyze and diagnose the deviation cause, and through the knowledge graph association query and reasoning function, locate the fault cause and send out early warning information in time;

[0120] The fault causes include sudden changes in raw material characteristics (such as sudden increase in raw material humidity, change in raw material type), equipment failures (such as temperature sensor failure, valve blockage), and deviations caused by other factors (such as sudden change in environmental temperature, pressure fluctuation in the gas supply system);

[0121] For example, if the knowledge graph shows that an increase in the humidity of a certain type of raw material will slow down the gasification reaction and reduce the calorific value of the fuel gas, and currently it is monitored that the calorific value of the fuel gas has decreased and the humidity of the raw material has increased, the model can quickly adjust the control strategy accordingly, such as appropriately increasing the air blast volume to promote the combustion reaction, increasing the gasification temperature, and increasing the calorific value of the fuel gas.

[0122] Example Two

[0123] A control optimization system for a biomass gasification furnace based on DeepSeek, as Figure 2 , based on a control optimization method for a biomass gasification furnace based on DeepSeek, includes: a data acquisition and preprocessing module based on PLC, a knowledge graph construction module, a DeepSeek model integration and training module, a control strategy generation and execution module, and an online monitoring and feedback optimization module;

[0124] The data acquisition and preprocessing module based on PLC is used to collect and preprocess the operation data of the biomass gasification furnace in real time and all-round using a variety of sensors, and transmit the processed data to the knowledge graph construction module and the DeepSeek model integration and training module respectively;

[0125] At the same time, the PLC can also communicate with the upper computer and transmit the processed data to the DeepSeek model for further analysis and decision-making;

[0126] The knowledge graph construction module is used to collect and organize the professional knowledge related to biomass gasification, and combine it with the preprocessed operation data of the biomass gasification furnace to construct a knowledge graph and provide it to the DeepSeek model integration and training module;

[0127] The DeepSeek model integration and training module is used to take the preprocessed data as input, the constructed knowledge graph as the knowledge base, and through transfer learning, designing a training objective function for comprehensive performance indicators, and combining reinforcement learning with the DeepSeek model, perform model training and optimization;

[0128] The control strategy generation and execution module is used to use the DeepSeek model to predict and generate the optimal control strategy under the current working conditions based on the real-time collected operation data of the gasification furnace and the relevant knowledge of the knowledge graph, and convert the generated control strategy into control instructions for execution, and feedback the execution results to the online monitoring and feedback optimization module;

[0129] The online monitoring and feedback optimization module is used to monitor the operation status of the gasification furnace in real time, compare and analyze the monitoring data with the model prediction results, and if there is a deviation, send the feedback information back to the DeepSeek model integration and training module for optimization, and adjust the control strategy in real time.

[0130] Example Three

[0131] A computer-readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, it implements an optimization method for controlling a biomass gasifier based on DeepSeek.

[0132] Example 4

[0133] A processing terminal, including a memory and a processor, wherein a computer program that can run on the processor is stored in the memory, characterized in that when the processor executes the computer program, it implements an optimization method for controlling a biomass gasifier based on DeepSeek.

[0134] Example 5

[0135] In this embodiment, taking a 15-ton updraft biomass gasifier as an example, the equipment basic information structure design, control system construction, operation test and long-term operation optimization are carried out. Specifically:

[0136] Structure design, such as Figure 3 :

[0137] The 15-ton updraft biomass gasifier is overall cylindrical, and the main body is made of Q345R alloy steel with high temperature resistance and corrosion resistance. The diameter of the furnace body is 4.5 meters and the total height is 12 meters; inside the furnace, it is divided into a slag zone, an oxidation zone, a reduction zone, a pyrolysis zone and a drying zone from bottom to top in sequence. This unique layered structure enables the biomass raw materials to undergo corresponding physical and chemical reactions in different temperature regions, thereby realizing efficient gasification.

[0138] Among them, the slag zone: located at the bottom of the furnace body, with a height of about 1.5 meters, is equipped with an automatic slag discharging device, which consists of an electric push rod and a slag discharging gate; when the slag accumulates to a certain height, the electric push rod pushes the slag discharging gate to open, and after the slag is discharged, the gate automatically closes. The slag discharging interval time can be adjusted through the PLC control system according to the actual operation situation to ensure that the slag is discharged in time without affecting the gasification reaction;

[0139] The oxidation zone: with a height of about 1.2 meters, is the area where the biomass raw materials react violently with the gasifying agent (mainly air). The gasifying agent is evenly fed through the annular air nozzles distributed at the bottom of the oxidation zone. The air nozzles are made of special high-temperature resistant alloy materials and the surface is treated with anti-oxidation to ensure long-term stable operation in a high-temperature environment; the number and distribution of the air nozzles are optimized through fluid mechanics simulation to enable the gasifying agent to fully contact the biomass and promote the full progress of the oxidation reaction;

[0140] Reduction zone: Approximately 2.5 meters in height. The high-temperature gas generated in the oxidation zone rises into the reduction zone and undergoes a reduction reaction with the incandescent coke to produce combustible gas. There are several layers of grid structures in the reduction zone to support the biomass raw materials and promote uniform gas distribution. The grids are made of high-strength stainless steel, which can withstand high temperatures and ensure good air permeability;

[0141] Pyrolysis zone: Approximately 3.5 meters in height. The biomass raw materials are pyrolyzed in this zone to produce volatile matter and coke. The temperature control in the pyrolysis zone is crucial. By adjusting the flow rate and temperature of the gasifying agent, the temperature in the pyrolysis zone is maintained between 500 - 650 °C to ensure the smooth progress of the pyrolysis reaction. Temperature sensors are installed inside the pyrolysis zone to monitor the temperature changes in real time and feed back to the control system;

[0142] Drying zone: Located in the upper part of the furnace body, approximately 3.3 meters in height. It uses the waste heat generated by the lower reaction to dry the newly introduced biomass raw materials. The drying zone is equipped with a raw material feeding device, which adopts a screw feeding method. The screw shaft is made of wear-resistant alloy steel and is driven by a variable-frequency motor. It can accurately adjust the feeding rate according to the operating load of the gasifier. The feeding rate range is 0 - 20 tons per hour, and the accuracy can reach ±0.3 tons per hour.

[0143] Control system setup:

[0144] Install TT01 / TT02 / TT03 / TT04 / TT05 / TT06 / TT07 / TT08 / TT09 / TT10 K-type thermocouple temperature sensors in the oxidation zone, reduction zone, pyrolysis zone, drying zone and outer area of the gasifier respectively. The accuracy of these sensors can reach ±0.1 °C, which can monitor the temperature changes in each area in real time and accurately, providing key data support for the adjustment of control strategies. When the temperature in the oxidation zone is too high, the control system can automatically reduce the input amount of the gasifying agent to maintain the temperature in the oxidation zone within a suitable range;

[0145] Install pressure sensors in the air inlet pipe PT01, the top of the pyrolysis zone PT02 and the outlet pipe PT03 respectively. The accuracy of the pressure sensors is ±0.05 kPa, which is used to monitor the pressure distribution in the furnace and the gas inlet and outlet pressures. By monitoring the pressure changes, the gas flow condition and the reaction in the gasifier can be judged. When the pressure is abnormal, the control system can take timely measures to adjust the gasifying agent flow rate or check whether the equipment is blocked;

[0146] The AI01 gas composition analyzer adopts an advanced online gas chromatograph analyzer, which extracts the fuel gas from the outlet of the gasifier through a sampling probe for real-time analysis. The analyzer can accurately detect the main gas components such as H2, CO, CH4, CO2, H2S, etc., with an accuracy of up to ±0.1%. According to the detected changes in gas components, the control system can optimize the gasification reaction conditions, adjust the feeding rate and the flow rate of the gasifying agent to improve the fuel gas quality and gasification efficiency;

[0147] A belt scale with an accuracy of ±1% is installed on the biomass feeding pipeline to monitor the feeding flow rate in real time; FT01, FT03, and FT02 are respectively installed on the gasifying agent inlet air pipe, the steam pipe, and the fuel gas pipe to accurately control the input amount of the gasifying agent and steam and the output amount of the fuel gas. The flow sensors transmit the flow data to the control system in real time, and the control system precisely adjusts the flow rate according to the operating conditions and the set parameters;

[0148] Feeding device: The belt feeding and screw feeder are driven by a variable-frequency motor, and the feeding rate is controlled by adjusting the motor speed. The variable-frequency motor has the characteristics of a wide speed regulation range and high control accuracy, and can achieve precise control of the feeding rate in the range of 0-20 tons per hour, with a control accuracy of ±0.3 tons per hour;

[0149] Gasifying agent supply device: The gasifying agent (air) is sent into the gasifier through a variable-frequency Roots blower. An electric control valve SV01 is installed at the outlet of the Roots blower, and the opening degree of the control valve is controlled by the PLC to precisely adjust the flow rate of the gasifying agent. The adjustment accuracy of the electric control valve can reach ±1%, and the flow rate of the gasifying agent can be adjusted in real time according to the operating conditions of the gasifier to ensure the stable progress of the gasification reaction;

[0150] Discharge device: The automatic discharge device is controlled by a variable-frequency motor. The PLC issues commands, and the slag discharge process can be carried out at regular intervals according to the actual operating conditions or automatically according to the ash slag level to ensure that the ash slag is discharged in time without affecting the normal operation of the gasifier;

[0151] Real-time data acquisition and monitoring software: The configuration software is selected as the real-time data acquisition and monitoring platform. This software has powerful data acquisition and visualization functions, can collect various sensor data at a high frequency of 100Hz, and display the operating status of the gasifier in real time through an intuitive graphical interface, including key parameters such as temperature, pressure, gas composition, feeding rate, and gasifying agent flow rate. Operators can understand the operating conditions of the gasifier in real time through the monitoring interface and adjust the operating parameters; at the same time, the software also has data storage and historical data query functions, which are convenient for analyzing and summarizing the operating data of the gasifier;

[0152] Knowledge Graph Construction and Management Software: Use the GraphDB graph database to construct a knowledge graph in the field of biomass gasification. Through the collation and analysis of a large number of professional literature, industry standards, experimental data, and actual operation experience, a knowledge graph containing more than 15,000 entity nodes and 60,000 relationship edges is constructed. The knowledge graph covers various aspects of knowledge such as biomass raw material characteristics, gasification reaction mechanism, equipment structure and performance, and control strategies, providing rich knowledge support for the training and optimization of the DeepSeek model; in the knowledge graph, the gasification reaction characteristics of different biomass raw materials under different humidity and particle size conditions, as well as the correlation between equipment failures and operating parameters, are stored. The DeepSeek model can optimize the operation of the gasifier and predict faults based on this knowledge.

[0153] DeepSeek Model Deployment and Operation Software: Deploy the optimized and trained DeepSeek model on a high-performance edge computing server. Through the developed software interface, achieve data interaction and collaborative work between the DeepSeek model, the real-time data acquisition system, the knowledge graph system, and the controller; the DeepSeek model predicts the operating state of the gasifier based on the real-time collected operating data and the knowledge in the knowledge graph, optimizes the control strategy, and sends the optimized control instructions to the PLC to achieve precise control of the gasifier.

[0154] Operation Test:

[0155] Operation Test of Traditional Control Method In the initial stage, operate the gasifier for 72 hours using the traditional PID control method. During this period, use professional data acquisition equipment and software to record and analyze various operating parameters of the gasifier in detail; after testing, the average gasification efficiency is 65%, the calorific value of the gas is 4.2 MJ / m 3 , the CO content is about 18%, the H2 content is about 13%, and the CH4 content is about 1.5%. Since the traditional PID control method is difficult to accurately adapt to the changes in biomass raw material characteristics and operating conditions, the temperature fluctuations in each reaction zone are relatively large. For example, the temperature fluctuation in the pyrolysis zone can reach ±60°C, which has a greater impact on the stability of the gasification reaction and the gas quality; at the same time, the raw material consumption is relatively high, and about 17 kg of biomass raw material is consumed per 1 GJ of energy generated.

[0156] Operation Test Based on DeepSeek Control Method After switching to the DeepSeek-based control optimization system, conduct the operation test for the same duration; the DeepSeek model continuously optimizes the control strategy based on the real-time collected operating data and the relevant knowledge in the knowledge graph. In this stage, the average gasification efficiency is significantly increased to 75%, and the calorific value of the gas is increased to 5.2 MJ / m 3, the CO content is increased to 22%, the H2 content is increased to 18%, and the CH4 content is increased to 3%; the temperature fluctuations in each reaction zone are significantly reduced, and the temperature fluctuation in the pyrolysis zone is controlled within ±20°C, effectively ensuring the stable progress of the gasification reaction; the raw material consumption is reduced to about 15 kg per 1 GJ of energy generated, greatly improving the utilization efficiency of biomass energy.

[0157] Long-term operation optimization:

[0158] Continuous optimization of the model: To enable the DeepSeek model to better adapt to the changing operating conditions, a regular model optimization mechanism is established; new generated operating data is collected every 24 hours, with the data volume exceeding 250,000, including sensor data, control parameters, and the corresponding operating performance indicators of the gasifier. These new data are used to retrain and optimize the DeepSeek model, adjusting the parameters and structure of the model to improve the prediction accuracy of the model and the effectiveness of the control strategy; during the training process, the Adagrad optimizer is adopted, the learning rate is set to 0.001, the batch size is 2048, and after 8000 iterations of training, the model is continuously evolved. At the same time, combined with transfer learning technology, the operating data of other similar gasifiers and the knowledge in the knowledge graph are transferred to this model to further improve the generalization ability and adaptability of the model;

[0159] Optimization of equipment maintenance and management: With the help of the equipment fault diagnosis knowledge in the knowledge graph and real-time monitoring data, predictive maintenance of equipment is realized; through the real-time analysis of the equipment operating data, combined with the correlation between equipment faults and operating parameters in the knowledge graph, the possible faults of the equipment are predicted in advance. For example, when the temperature or vibration parameters of a certain key component show abnormal changes and match the fault patterns in the knowledge graph, the system timely sends out warning information to remind the operator to check and maintain; at the same time, according to the equipment maintenance knowledge in the knowledge graph, a scientific and reasonable equipment maintenance plan is formulated, and the equipment is regularly maintained and repaired to ensure the normal operation of the equipment. In this way, the equipment failure rate is reduced by 65% and the maintenance cost is reduced by 45%, effectively improving the operating reliability and service life of the equipment.

[0160] 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. The same or similar parts among the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0161] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the control of a biomass gasifier based on DeepSeek, characterized in that, The following steps are involved: S1. Use a variety of sensors to collect biomass gasifier operation data in real time and perform preprocessing; S2. Collect and organize relevant professional knowledge on biomass gasification, and build a knowledge graph based on the operation data of the pre-treated biomass gasifier; S3. Take the preprocessed data as input, and the constructed knowledge graph as the knowledge base. Perform model training and optimization through transfer learning, designing the training objective function of comprehensive performance indicators, and combining reinforcement learning with the DeepSeek model. S4. The DeepSeek model uses the trained model to predict and generate the optimal control strategy under the current working conditions based on the real-time collected gasifier operation data and knowledge graph related knowledge, and converts the generated control strategy into control instructions for execution; S5. Adjust the control strategy in real time through online monitoring and feedback optimization.

2. The control optimization method of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, In step S1, the specific contents of using multiple sensors to collect the biomass gasifier operation data in real time and in all directions are as follows: The temperature changes in different reaction areas in the furnace are monitored by thermocouple temperature sensors; Accurately measure the pressure parameters of key parts through pressure sensors; Detect the main gas components through the gas composition analyzer; Detect the characteristic parameters of raw materials, detect the type of raw materials through near-infrared spectrometer, detect humidity through moisture meter, detect particle size through laser particle size analyzer, and detect density through density meter; As well as operating parameters, the feed rate is detected by a flow meter, and the blast volume is detected by an air volume sensor.

3. The control optimization method of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, In step S1, the specific contents of preprocessing include: for the collected raw data, using a data cleaning algorithm based on statistical principles to eliminate outliers; for noise data, using a wavelet denoising algorithm to filter out high-frequency noise interference; for temperature data, according to its range, using the Z-score standardization method to perform data standardization processing to normalize the interval; for gas component concentration data, using the corresponding conversion formula to convert the percentage concentration into a numerical form suitable for model input, and converting the volume percentage concentration into a mole fraction form and normalizing it.

4. A method for optimizing the control of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, In step S2, the content of constructing the knowledge graph is: Use the named entity recognition (NER) algorithm in natural language processing technology to parse and extract the collected knowledge and identify the entities therein; Use the relationship extraction algorithm to determine and annotate the relationships between entities; Based on the extracted entities and the relationships between them, a knowledge graph in the field of biomass gasification is constructed using a graph database. The knowledge graph is stored and presented in the form of nodes representing entities and edges representing relationships. At the same time, attribute information is added to each node and edge to enrich the information content of the knowledge graph.

5. A method for optimizing the control of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, Step S3, the specific contents of model training and optimization are as follows: S31. Use transfer learning technology to initialize the model parameters with DeepSeek’s pre-trained model parameters on large-scale general domain data, and fine-tune them for specific data in the biomass gasification field; S32. Taking gasification efficiency, gas calorific value and gas production stability as different weight items of the objective function, determining the weight coefficient, and obtaining a training objective function that comprehensively considers multiple performance indicators of the gasifier; In S33, a simulation gasifier operating environment is built by combining a reinforcement learning algorithm with a DeepSeek model. Based on the current operating state reflected by the input data and the knowledge graph, the model selects corresponding control actions, including adjusting the feed rate, air blowing volume, and temperature value. The environment returns a new operating state and a reward value according to the model's control actions. By continuously optimizing the reward value, the model is guided to learn the optimal control strategy through repeated trial and error.

6. The control optimization method of a biomass gasifier based on DeepSeek according to claim 5, characterized in that, The training objective function considering multiple performance indicators of the gasifier is as follows: Objective = 0.4×η_normalized + 0.3×H_g_normalized + 0.3×S_normalized where η_normalized is the normalized gasification efficiency, H_g_normalized is the normalized calorific value of the fuel gas, and S_normalized is the normalized gas production stability; η_normalized = (η - η_min) / (η_max - η_min) H_g_normalized = (H_g - H_g_min) / (H_g_max - H_g_min) S_normalized = 1 - [(S - S_min) / (S_max - S_min)] Gasification efficiency: η = (H_g × Q_g) / (m_b × H_b) Gas production stability: S = (1 / n)×Σσ_ci + (σ_Q / μ_Q) Calorific value of the fuel gas: Measured by a calorimeter to measure the higher or lower calorific value of the fuel gas; where H_g is the calorific value of the fuel gas, Q_g is the fuel gas flow rate, m_b is the mass flow rate of the biomass raw material, H_b is the calorific value of the raw material, σ_ci is the standard deviation of the concentration of the i-th gas component, n is the number of gas component types, σ_Q is the standard deviation of the flow rate, μ_Q is the average flow rate, η_min is the minimum value of the gasification efficiency, η_max is the maximum value of the gasification efficiency, H_g_min is the minimum value of the calorific value of the fuel gas, H_g_max is the maximum value of the calorific value of the fuel gas, S_min is the minimum value of the gas production stability, and S_max is the maximum value of the gas production stability.

7. A method for optimizing the control of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, In step S4, the output control strategy includes but is not limited to the precise adjustment amount of the feed rate, the accurate setting value of the air blowing volume, the target value of the gasification temperature, and the optimization of other key control parameters for the reaction pressure control range; Converting the generated control strategy into control instructions for execution specifically includes: For the feed motor speed controller, a PID control algorithm is used to control the motor speed according to the feed rate adjustment amount output by the model to ensure that the feed rate accurately reaches the set value; For the blower frequency converter, the air blowing volume is adjusted by controlling the output frequency of the frequency converter; For the temperature regulating valve, an electric control valve is used to automatically adjust the valve opening according to the target value of the gasification temperature set by the model to control the temperature inside the gasifier; Meanwhile, sensors are used to monitor the execution of the actuator and the feedback data of the gasifier in real time. The actual rotation speed of the feeding motor is monitored by a motor speed sensor, the actual value of the air blowing volume is monitored by a flow sensor, and the actual temperature inside the furnace is monitored by a temperature sensor. The feedback data is compared with the set value. If a deviation is found, the control command is adjusted in a timely manner through a feedback control algorithm to ensure the accurate execution of the control strategy.

8. A method for optimizing the control of a biomass gasifier based on DeepSeek according to claim 1, characterized in that, The specific content of step S5 includes: S51. With the help of a real-time data acquisition system and a data transmission network, continuously monitor the operating state of the biomass gasifier online, collect various operating data in real time and perform visual display; S52. Compare and analyze the real-time collected data with the ideal state predicted by the DeepSeek model, calculate the difference between the actual gas calorific value and the target gas calorific value predicted by the model using the mean square error MSE algorithm, and calculate the deviation degree of the actual gasification efficiency from the expected gasification efficiency through the relative deviation algorithm to evaluate the implementation effect of the current control strategy; S53. According to the monitoring and analysis results, when the deviation between the actual operating state and the ideal state exceeds the preset value, the deviation information and the current operating data are used as feedback signals and input into the DeepSeek model to trigger the model to re-optimize and adjust the control strategy.

9. A method for optimizing the control of a biomass gasifier based on DeepSeek according to claim 8, characterized in that Step S35 also includes deeply analyzing and diagnosing the cause of the deviation using the knowledge of the knowledge graph, and locating the cause of the failure and sending out early warning information in a timely manner through the association query and reasoning functions of the knowledge graph.

10. A biomass gasifier control optimization system based on DeepSeek, characterized in that, A method for optimizing the control of a biomass gasifier based on DeepSeek according to any one of claims 1-9, includes: a data acquisition and preprocessing module based on PLC, a knowledge graph construction module, a DeepSeek model integration and training module, a control strategy generation and execution module, and an online monitoring and feedback optimization module; The data acquisition and preprocessing module based on PLC is used to collect the operating data of the biomass gasifier in real time and all-round using a variety of sensors and perform preprocessing, and transmit the processed data to the knowledge graph construction module and the DeepSeek model integration and training module respectively; The knowledge graph construction module is used to collect and organize the professional knowledge related to biomass gasification, combine the preprocessed operating data of the biomass gasifier, construct a knowledge graph, and provide it to the DeepSeek model integration and training module; The DeepSeek model integration and training module is used to take the preprocessed data as input, the constructed knowledge graph as the knowledge base, and through transfer learning, designing a training objective function for comprehensive performance indicators, and combining reinforcement learning with the DeepSeek model, perform model training and optimization; The control strategy generation and execution module is used to use the DeepSeek model to predict and generate the optimal control strategy under the current working conditions based on the real-time collected operating data of the gasifier and the relevant knowledge of the knowledge graph, convert the generated control strategy into a control command for execution, and feedback the execution result to the online monitoring and feedback optimization module; Online monitoring and feedback optimization module, which is used to monitor the operation status of the gasifier in real time, compare and analyze the monitoring data with the model prediction results, and if there are deviations, send the feedback information back to the DeepSeek model integration and training module for optimization to adjust the control strategy in real time.

Images