Thermochemical energy regeneration and energy storage coupled intelligent allocation system

Abstract

The invention relates to a thermochemical energy regeneration and energy storage coupled intelligent blending system, which comprises a data acquisition unit and a data processing unit, the data acquisition unit comprises an energy quality characteristic acquisition unit and an impurity risk characteristic acquisition unit, effective components comprise H2, CO and CH4, and the proportion parameter of the effective components is the volume or molar ratio of H2 / CO / CH4; the special organic components are esters and / or aldehydes doped in pyrolysis gas in the pyrolysis process of the Luzhou-flavor distiller's grains, and the data processing unit can take monitoring data acquired by different acquisition units as two-dimensional input feature vectors respectively, and gradiently convert the feature vectors into a plurality of abnormal grades respectively to form a plurality of abnormal combinations; a look-up table built in the data processing unit is a pre-programmed two-dimensional mapping matrix, the transverse dimension of the look-up table is an energy quality feature anomaly level, the longitudinal dimension of the look-up table is an impurity risk feature anomaly level, and matrix cross points correspond to a group of collaborative regulation and control instructions.

Description

Technical Field

[0001] This invention relates to the field of thermochemical energy conversion and energy storage coupling technology, and in particular to an intelligent dispatching system for thermochemical energy conversion and energy storage coupling. Background Technology

[0002] As a major waste product of the brewing industry, the thermochemical conversion technology of baijiu lees has significant value in the field of energy utilization.

[0003] CN117025234A discloses a method for producing combustible gas from distiller's grains, belonging to the field of solid waste resource utilization technology. The method provided by this invention includes: using distiller's grains as raw material, subjecting dried distiller's grains to thermal pyrolysis under limited oxygen conditions, simultaneously producing three products: pyrolysis gas, pyrolysis liquid, and biochar; controlling the pyrolysis temperature to maximize the yield of combustible gas.

[0004] Due to its unique mixed-distillation and continuous-distillation process, strong-aroma baijiu mash contains a high proportion of hemicellulose and residual small-molecule alcohols, as well as special components such as humic acid from the fermentation pits. During pyrolysis, hemicellulose has a low pyrolysis activation energy in the 300-500℃ range, leading to a concentrated and rapid release of volatile components, forming a transient, strong exothermic peak. Furthermore, variations in fermentation pit depth, fermentation cycle fluctuations, and distillation rounds result in fluctuations in the lignin-to-hemicellulose ratio in the mash, reaching ±15%, which in turn causes irregular and drastic fluctuations in the pyrolysis gas components (H2, CO, CH4, CO2). In addition, hexanoic acid and ethanol produced by microbial metabolism in the fermentation pits form ester aroma components such as ethyl hexanoate and ethyl butyrate during the mixed-distillation and continuous-distillation process. These special organic components are difficult to completely decompose during pyrolysis, becoming characteristic components in the pyrolysis gas, which differs significantly from conventional biomass pyrolysis gas (mainly CO, H2, CH4).

[0005] The current coupling system for energy conversion and energy storage of pyrolysis residues from strong-aroma baijiu faces three major challenges: instantaneous strong exothermic peaks (intensity reaching 40~60kW / m³). 2The dynamic mismatch between the continuous and gradual charging and discharging design of the energy storage system (with a duration of only 1 / 3 to 1 / 2 of that of conventional biomass) and the reactor overheating or the need for external energy replenishment leads to the inability of the energy storage system's heat exchange efficiency to match the instantaneous heat release intensity, resulting in low energy storage capacity utilization. Irregular and drastic fluctuations in the composition of the pyrolysis gas cause large fluctuations in the CHP power generation, while the air-fuel ratio adjustment response time of the CHP burner is slower than the composition change cycle, resulting in incomplete combustion and a significant reduction in power generation efficiency. The energy storage system's charging and discharging triggers rely solely on the power threshold and lack a component fluctuation prediction mechanism, shortening the battery cycle life. High concentrations of esters and aldehydes such as ethyl hexanoate and ethyl butyrate in the pyrolysis gas cause poisoning of the fuel cell electrode catalyst and electrolyte reactions in the energy storage battery. Conventional gas purification technologies cannot efficiently remove these special components, and the energy storage system design does not consider the impact of special organic components, making it difficult to predict the impact of fluctuations.

[0006] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides an intelligent allocation system that couples thermochemical energy conversion and energy storage to solve at least some of the above-mentioned technical problems.

[0008] This invention discloses an intelligent allocation system coupling thermochemical energy conversion and energy storage, comprising: a data acquisition unit for acquiring monitoring data related to pyrolysis gas; and a data processing unit for analyzing and processing the received monitoring data, generating control instructions corresponding to the current abnormal combination based on a built-in lookup table. The data acquisition unit includes an energy quality characteristic acquisition unit for acquiring calorific value fluctuation parameters and effective component ratio parameters of the pyrolysis gas, and an impurity risk characteristic acquisition unit for acquiring the total concentration of incompletely decomposed special organic components in the pyrolysis gas. The effective components include H2, CO, and CH4. The proportional parameter is the volume or molar ratio of H2 / CO / CH4; the special organic component is one or more organic compounds, such as esters or aldehydes, that are mixed in with the pyrolysis gas during the pyrolysis of strong-aroma baijiu lees; the data processing unit can take the monitoring data obtained by different acquisition units as two-dimensional input feature vectors and gradient the feature vectors into multiple anomaly levels to form several anomaly combinations; the lookup table built into the data processing unit is a pre-programmed two-dimensional mapping matrix, with its horizontal dimension representing the energy quality characteristic anomaly level and its vertical dimension representing the impurity risk characteristic anomaly level, and the matrix intersection points corresponding to a set of coordinated control instructions.

[0009] This invention achieves synergistic quantitative analysis of pyrolysis gas component fluctuations and the concentration of special organic components by constructing a dual-dimensional input feature vector (energy quality characteristic anomaly level and impurity risk characteristic anomaly level) and establishing a pre-programmed two-dimensional mapping matrix. This feature enables the data processing unit to accurately identify the dynamic mismatch scenario between short-pulse strong exothermic and slow-response energy storage, as well as the combined problem of special organic components interfering with energy storage compatibility. This allows for the generation of synergistic control commands for feed rate and reaction temperature. The dynamic mismatch scenario originates from the instantaneous exothermic peak caused by the low activation energy of hemicellulose pyrolysis. This multi-dimensional mapping mechanism based on abnormal combinations overcomes the limitations of traditional single-parameter control, enabling the energy storage system to dynamically adapt to the instantaneous and fluctuating nature of the pyrolysis process, effectively avoiding reactor overheating and low energy storage capacity utilization.

[0010] According to a preferred embodiment, the energy quality characteristic acquisition unit includes a calorific value fluctuation acquisition module. This module can send the acquired real-time calorific value data to a data processing unit. The calorific value fluctuation parameter is obtained by the data processing unit by calculating the difference between the maximum and minimum values ​​of the real-time calorific value data within a set time window. The calorific value fluctuation acquisition module is configured as a combustion-type online calorific value analyzer or a physical property-type online calorific value analyzer. The combustion-type online calorific value analyzer includes a miniature combustion chamber, an ignition device, and a heat detector, used to calculate the calorific value per unit volume by completely burning pyrolysis gas and detecting the released heat. The physical property-type online calorific value analyzer calculates the calorific value by detecting the density or viscosity of the pyrolysis gas and combining it with a pre-programmed component-physical property-calorific value correlation model.

[0011] Combustion-based online calorimeters directly obtain calorific value by detecting the heat released from the complete combustion of pyrolysis gas, while physical property-based online calorimeters indirectly calculate calorific value based on a correlation model between density / viscosity and composition. These two complementary approaches improve the measurement accuracy and real-time performance of calorific value fluctuation parameters. This feature enables the system to promptly capture dynamic changes in the calorific value of the pyrolysis gas, providing accurate energy quality input to the data processing unit and thus identifying the impact of compositional fluctuations on CHP combustion in advance. This highly responsive calorific value monitoring mechanism resolves the technical contradiction that the burner's air-fuel ratio adjustment response time is slower than the compositional change cycle, avoiding incomplete combustion caused by calorific value measurement lag and ensuring the stability of CHP power generation.

[0012] According to a preferred embodiment, the energy quality characteristic acquisition unit includes an effective component ratio acquisition module. The effective component ratio acquisition module can output an electrical signal based on the detected difference in thermal conductivity of each separated component. After processing by a signal amplification and filtering module, the signal is transmitted to a data processing unit. The data processing unit calculates the volume or mole fraction of H2, CO, and CH4 by comparing the peak area integral with the calibration curve and determines their proportional relationship. The effective component ratio acquisition module is configured as a miniature gas chromatograph, which includes an automatic injection valve, a chromatographic column system, a column oven, and a detector.

[0013] The miniature gas chromatograph achieves high-precision real-time analysis of the volume / molar ratio of H2 / CO / CH4 through thermal conductivity difference detection and peak area integration technology. This feature enables the system to accurately analyze the dynamic changes in the pyrolysis gas component ratio, providing effective component ratio parameters for the data processing unit. This precise quantification mechanism of component ratio solves the problem of traditional technologies being unable to respond to component fluctuations in a timely manner. It allows the CHP burner to dynamically adjust the air-fuel ratio according to the real-time component ratio, avoiding power generation fluctuations caused by drastic fluctuations in components such as H2 and CO, and significantly improving the operational stability of the cogeneration system.

[0014] According to a preferred embodiment, the impurity risk characteristic acquisition unit is configured as a Fourier transform infrared spectrometer, which can identify the characteristic absorption peaks of each organic compound contained in the special organic component after background correction of the obtained infrared absorption spectrum, calculate its concentration by combining Lambert-Beer law and pre-calibrated absorption coefficient, and then sum to obtain the total concentration parameter of the special organic component, and send it to the data processing unit.

[0015] Fourier transform infrared spectroscopy (FTIR) enables rapid quantitative detection of special organic components such as esters and aldehydes through characteristic absorption peak identification and Lambert-Beer law calculations. This feature allows the system to quantify the concentration risk of special organic components in pyrolysis gas in real time, providing impurity risk characteristic input to the data processing unit. This highly sensitive impurity detection mechanism addresses the difficulty in decomposing esters / aldehydes in the pyrolysis gas of strong-aroma baijiu (Chinese liquor) residues, avoiding the failure of conventional purification technologies to remove special components. It effectively prevents poisoning of fuel cell electrode catalysts and electrolyte reactions in energy storage batteries, improving the compatibility of energy storage systems with special organic components.

[0016] According to a preferred embodiment, the coordinated control command includes a feed rate adjustment amount, a reaction temperature adjustment amount, and an adjustment priority, wherein the adjustment range of the feed rate adjustment amount is positively correlated with the abnormal level of energy quality characteristics; the adjustment range of the reaction temperature adjustment amount is positively correlated with the abnormal level of impurity risk characteristics; and the adjustment priority is set according to the system risk level and parameter sensitivity.

[0017] The feed rate adjustment is positively correlated with the level of energy quality anomalies, and the reaction temperature adjustment is positively correlated with the level of impurity risk anomalies. Furthermore, the adjustment priority is dynamically set based on the system risk level. This feature enables the system to precisely control the pyrolysis reaction rate based on the fluctuation of the pyrolysis gas composition to mitigate instantaneous strong exothermic peaks, while simultaneously enhancing the pyrolysis temperature based on the risk of specific organic component concentrations to promote the decomposition of these components. This tiered control mechanism avoids the mechanical response of traditional constant power regulation, allowing the energy storage system to dynamically match the short-pulse characteristics of the pyrolysis process and the interference of specific components, significantly improving heat exchange efficiency and energy storage capacity utilization.

[0018] According to a preferred embodiment, the data processing unit is further configured to call the traceability data stored in the data storage unit and generate a traceability data correction factor to dynamically correct the original control instructions in the lookup table. The traceability data includes real-time hemicellulose content and caproic acid bacteria concentration. The hemicellulose content is used to characterize the release rate of volatile components during the pyrolysis of the waste material; the data is acquired by a near-infrared spectroscopy detector, and after baseline correction and quantitative analysis by a spectral data preprocessing module, it is uploaded to the data storage unit. The caproic acid bacteria concentration is used to characterize the potential for ester precursor formation; the data is acquired by a quantitative real-time PCR detector, and after sample sampling, nucleic acid extraction, and amplification, it is uploaded to the data storage unit.

[0019] By utilizing traceability data on hemicellulose content and caproic acid bacteria concentration to generate correction factors, the control commands can dynamically adapt to batch-to-batch differences in the pyrolysis feedstock. Hemicellulose content reflects the release rate of volatile components during pyrolysis, while caproic acid bacteria concentration characterizes the potential for ester precursor formation. These correction factors enable the system to optimize control strategies in real time based on process fluctuations such as pit depth and fermentation cycle. This feature solves the control inaccuracy problem caused by fluctuations in feedstock characteristics in conventional technologies, allowing the energy storage system to adapt to process differences in hemicellulose enrichment and ester formation, effectively improving the stability and energy storage adaptability of the pyrolysis process.

[0020] According to a preferred embodiment, the data processing unit matches a corresponding hemicellulose content correction factor based on the real-time hemicellulose content. The hemicellulose content correction factor is used to amplify the original feed rate adjustment amount to achieve control over the exothermic peak intensity and component stability. The range of values ​​for the hemicellulose content correction factor is determined based on the content range of the hemicellulose content, and a value corresponding to the abnormal energy quality characteristic level is determined within the range of values.

[0021] The hemicellulose content correction factor amplifies the initial feed rate adjustment, and its value range is dynamically determined based on the hemicellulose content range. When the hemicellulose content is high, the system automatically increases the feed rate adjustment amplitude, reducing the pyrolysis reaction intensity and thus mitigating the instantaneous strong exothermic peak caused by the low activation energy of hemicellulose pyrolysis. This feature, by precisely controlling the pyrolysis reaction rate, avoids the problems of reactor overheating and insufficient heat exchange efficiency of the energy storage system, enabling dynamic matching between the heat exchange efficiency and instantaneous exothermic intensity of the molten salt energy storage system, and significantly improving the utilization rate of energy storage capacity.

[0022] According to a preferred embodiment, the data processing unit matches a corresponding hexanoic acid bacteria concentration correction factor based on the real-time hexanoic acid bacteria concentration. The hexanoic acid bacteria concentration correction factor is used to increase the original reaction temperature adjustment amount and enhance the pyrolysis removal of special organic components. The value range of the hexanoic acid bacteria concentration correction factor is determined based on the concentration range of the hexanoic acid bacteria concentration, and a value corresponding to the abnormal level of impurity risk characteristics is determined within the value range.

[0023] The caproic acid bacteria concentration correction factor is used to increase the initial reaction temperature adjustment, and its value range is dynamically determined based on the caproic acid bacteria concentration range. When the caproic acid bacteria concentration is high, the system automatically increases the reaction temperature adjustment range, enhances the pyrolysis decomposition of esters such as ethyl hexanoate, and reduces their residue in the pyrolysis gas. This feature directly targets ester precursor substances produced by the microbial metabolism in the fermentation pit, effectively avoiding catalyst poisoning in fuel cells and electrolyte reactions in energy storage batteries caused by special organic components, and improving the compatibility of the energy storage system with the pyrolysis gas of strong-aroma baijiu lees.

[0024] According to a preferred embodiment, the system further includes an execution unit communicatively connected to the data processing unit to receive control commands sent by the data processing unit and to perform corresponding control actions according to the control commands. The execution unit includes an electro-hydraulic proportional valve for adjusting the feed rate of the slag entering the pyrolysis reactor and a heating rod for adjusting the reaction temperature inside the pyrolysis reactor.

[0025] The electro-hydraulic proportional valve and heating rod, acting as actuators, can rapidly respond to control commands to precisely regulate the feed rate and reaction temperature. The electro-hydraulic proportional valve provides high-precision material input control, while the heating rod ensures rapid and uniform temperature regulation. Together, they enable the system to dynamically adjust the feed rate and reaction temperature in a timely manner. This feature solves the problems of overheating and power generation fluctuations caused by the lag in traditional systems, allowing the energy storage system to effectively cope with instantaneous strong heat release and composition fluctuations during pyrolysis, significantly improving dynamic response capabilities and energy storage adaptability.

[0026] According to a preferred embodiment, the data processing unit can achieve closed-loop verification of the control effect by calculating the comprehensive energy storage adaptability index. The comprehensive energy storage adaptability index is calculated by weighting the molten salt charging rate score, the CHP power generation volatility score, and the fuel cell voltage decay score according to preset weights. In the weight allocation, the CHP power generation volatility score has the highest weight, followed by the molten salt charging rate score and the fuel cell voltage decay score. After the drive execution unit executes the control action according to the control command, the data processing unit acquires the comprehensive energy storage adaptability index in real time and compares it with a preset target threshold to determine the control effect.

[0027] The comprehensive energy storage adaptability index achieves closed-loop verification of the control effect through a weighted calculation of molten salt charging rate, CHP power generation volatility, and fuel cell voltage decay. CHP power generation volatility has the highest weight, ensuring priority is given to power generation stability. This feature enables the system to evaluate the control effect in real time, automatically optimize subsequent strategies after comparing with preset thresholds, and form a dynamic feedback mechanism. This closed-loop verification mechanism avoids the blind control relying on experience in traditional systems, continuously improves the adaptability of the energy storage system, and effectively extends the cycle life of energy storage devices such as batteries. Attached Figure Description

[0028] Figure 1 This is a hardware connection diagram of an intelligent dispatching system according to a preferred embodiment of the present invention; Figure 2 This is a schematic diagram of data transmission between a data acquisition unit and a data processing unit according to a preferred embodiment of the present invention; Figure 3 This is a schematic diagram of data transmission between a data storage unit and a data processing unit according to a preferred embodiment of the present invention.

[0029] List of reference numerals 100: Data acquisition unit; 110: Energy quality characteristic acquisition unit; 111: Calorific value fluctuation acquisition module; 112: Effective component ratio acquisition module; 120: Impurity risk characteristic acquisition unit; 200: Data processing unit; 300: Execution unit; 310: Electro-hydraulic proportional valve; 320: Heating rod; 400: Data storage unit; 500: Pyrolysis reactor. Detailed Implementation

[0030] The following is a detailed explanation with reference to the accompanying drawings.

[0031] like Figure 1 As shown, this invention discloses an intelligent dispatching system that couples thermochemical energy conversion and energy storage, comprising: Data acquisition unit 100 is used to acquire monitoring data related to pyrolysis gas; The data processing unit 200 is used to analyze and process the monitoring data acquired by the data acquisition unit 100, so as to generate control instructions corresponding to the current combination of abnormality levels using the built-in lookup table. The execution unit 300 is used to receive the control instructions sent by the data processing unit 200 in order to execute the corresponding control actions.

[0032] Preferably, such as Figure 1 and Figure 2 As shown, the data acquisition unit 100 may include an energy quality characteristic acquisition unit 110 for capturing the calorific value fluctuation and effective component ratio parameters of the pyrolysis gas, and an impurity risk characteristic acquisition unit 120 for capturing the total concentration of special organic components that have not been completely decomposed in the pyrolysis gas. In this invention, the effective components of the pyrolysis gas may include H2, CO, and CH4, and the effective component ratio parameter may be H2 / CO / CH4. Due to differences in pit depth, fermentation cycle, and distillation rounds, the lignin / hemicellulose ratio of strong-aroma baijiu mash fluctuates greatly, causing irregular and violent fluctuations in the composition of the pyrolysis gas. This leads to significant fluctuations in the power output of combined heat and power (CHP), causing the energy storage system to fall into a cycle of "frequent charging and discharging - start-stop losses." The special organic components that are not completely decomposed in the pyrolysis gas may include one or more organic compounds, such as (high concentration) ethyl hexanoate, ethyl butyrate, and aldehydes. These organic compounds are special components that exist when strong-aroma baijiu lees are pyrolyzed to produce gas, which is significantly different from conventional biomass pyrolysis gas. These components can cause poisoning of fuel cell electrode catalysts and electrolyte reactions in energy storage batteries. Moreover, the gas composition fluctuates drastically with the fermentation pit / process, exacerbating the instability of the input quality of the energy storage system.

[0033] Preferably, such as Figure 1 As shown, the energy quality characteristic acquisition unit 110 may include a calorific value fluctuation acquisition module 111 and an effective component ratio acquisition module 112.

[0034] Preferably, the calorific value fluctuation acquisition module 111 can be configured as an online calorimeter. Depending on the compositional characteristics of the pyrolysis gas and the detection requirements, a combustion-type online calorimeter or a physical property-type online calorimeter (such as a vibrating tube densitometer combined with a component correlation algorithm) can be selected. The combustion-type online calorimeter is equipped with a miniature combustion chamber, an ignition device, and a heat detector. After pretreatment, the pyrolysis gas enters the miniature combustion chamber and is ignited by the ignition device for complete combustion. The heat detector (such as a thermocouple or heat flow meter) detects the heat released during combustion in real time and calculates the calorific value per unit volume of pyrolysis gas by combining the gas flow data. The physical property-type online calorimeter, on the other hand, calculates the calorific value data by detecting physical parameters such as the density and viscosity of the pyrolysis gas and using a pre-programmed component-physical property-calorific value correlation model. The detection frequency of the online calorimeter can be set according to the system control requirements. The real-time calorific value data obtained is sent to the data processing unit 200 through the data synchronization transmission module. The data processing unit 200 calculates the difference between the maximum and minimum calorific values ​​within the set time window to obtain the calorific value fluctuation parameters. To ensure testing accuracy, the online calorimeter can be equipped with a periodic calibration module, which can automatically calibrate by connecting a standard gas. The calibration cycle can be set according to the instrument's stability.

[0035] Preferably, the effective component proportion acquisition module 112 can be configured as a miniature gas chromatograph, which is adaptable to the dynamic changes in pyrolysis gas components. Its core components include an automatic injection valve, a column system, a column oven, and a detector. The automatic injection valve adopts a six-way valve structure, using electromagnetic drive to achieve quantitative sample extraction and injection. The injection volume can be set according to the column capacity, and the injection cycle matches the detection frequency of the online calorimeter, ensuring time synchronization of the two sets of data. The column system selects appropriate columns according to the separation requirements of H2, CO, and CH4. It can use a single porous open-layer column or a multi-column tandem structure, with the column length and inner diameter set according to the separation efficiency requirements. The column oven adopts a programmed temperature control mode, achieving linear or stepwise temperature increases from the initial temperature to the target temperature through closed-loop control of the heating module and temperature sensor, ensuring effective separation of different components within the column. The detector can be a thermal conductivity detector (TCD), which has good response characteristics to inorganic gases and small-molecule organic gases. It outputs an electrical signal by detecting the difference in thermal conductivity of the separated components. This signal is processed by the built-in signal amplification and filtering module of the miniature gas chromatograph and then transmitted to the data processing unit 200. The data processing unit 200 calculates the volume fraction or mole fraction of H2, CO, and CH4 by integrating the peak area and comparing it with the calibration curve, thus obtaining the proportional relationship between the three. The miniature gas chromatograph can also be equipped with a calibration module, which updates the calibration curve by periodically injecting a mixed standard gas of known concentration (H2-CO-CH4) to ensure detection accuracy.

[0036] Preferably, the impurity risk characteristic acquisition unit 120 can be configured as an infrared spectrometer, and more preferably a Fourier transform infrared spectrometer, to meet the detection requirements of low-concentration impurities. Furthermore, ethyl hexanoate and ethyl butyrate will be used as examples of specific organic components in the following description.

[0037] Preferably, the Fourier transform infrared spectrometer may include an infrared light source, an interferometer, a gas flow cell, a detector, and a spectral data processing module. The infrared light source is a mid-infrared source (such as a silicon carbide rod), capable of emitting wavelengths covering 4000~4000 cm⁻¹. -1 The infrared radiation in this band includes the characteristic absorption peaks of ethyl hexanoate and ethyl butyrate. The interferometer adopts a Michelson interferometer structure, converting the continuous light emitted by the infrared source into interference light through the relative motion of the moving and fixed mirrors. After passing through the gas flow cell, the interference light undergoes selective absorption with impurities in the pyrolysis gas. The gas flow cell is made of a highly transparent material (such as quartz glass), equipped with calcium fluoride or potassium bromide windows at both ends. The length of the cell is set according to the required detection sensitivity, and the interior of the cell can be gold-plated to reduce the reflection loss of infrared light. The pretreated pyrolysis gas flows continuously through the gas flow cell at a constant flow rate to ensure that the gas composition in the cell is consistent with that in the pipeline. The detector uses a high-sensitivity mercury cadmium telluride detector or indium antimonide detector, which can convert the interference light signal passing through the gas flow cell into an electrical signal. This electrical signal is then transmitted to the spectral data processing module after analog-to-digital conversion.

[0038] Furthermore, the spectral data processing module incorporates spectral analysis and concentration calculation algorithms. First, it performs a Fourier transform on the original interferogram data to obtain the infrared absorption spectrum of the pyrolysis gas. Then, through spectral background correction, it subtracts the background spectrum of blank nitrogen (or inert gas) and instrument noise interference, extracting the pure characteristic spectrum of the pyrolysis gas. By identifying the characteristic absorption peak positions and intensities of ethyl hexanoate and ethyl butyrate in the spectrum, and combining this with Lambert-Beer's law and pre-calibrated absorption coefficients, the concentration data of the two substances are calculated. Finally, the concentration data of the two substances are summed to obtain the total concentration parameter of the special organic component, which is sent to the data processing unit 200 via the data synchronization transmission module. To ensure detection accuracy, the Fourier transform infrared spectrometer can be calibrated periodically by injecting a mixed standard gas of ethyl hexanoate-ethyl butyrate-nitrogen at a known concentration to update the absorption coefficient and calibration curve.

[0039] Preferably, such as Figure 2As shown, the data processing unit 200 can use the collected data transmitted by the energy quality feature acquisition unit 110 and the impurity risk feature acquisition unit 120 as input dual-dimensional feature vectors, namely energy quality features and impurity risk features, and gradient-divide the energy quality features and impurity risk features into multiple anomaly levels, forming several anomaly combinations, providing a clear and unique index dimension for the lookup table. For example, when both the energy quality features and impurity risk features are gradient-divide into 3 anomaly levels, 9 anomaly combinations of "3×3" can be formed, as shown in Table 1.

[0040] Table 1. Two-dimensional mapping matrix in a preferred embodiment

[0041] Preferably, the classification of energy quality characteristic anomaly levels is based on the "calorific value fluctuation" and "H2 / CO / CH4 ratio fluctuation" acquired by the energy quality characteristic acquisition unit 110. For example, mild quality anomaly (X1) corresponds to a state where the parameter fluctuation amplitude is small and the impact on the system is minor. For example, the calorific value fluctuation within the set time window is in the first calorific value fluctuation range, or the instantaneous fluctuation of the H2 / CO / CH4 ratio is in the first ratio fluctuation range. At this time, the system only shows a slight initial appearance of the exothermic peak or a decrease in component stability. Moderate quality anomaly (X2) corresponds to a state where the parameter fluctuation amplitude is moderate and has a significant impact on the system operation. For example, the calorific value fluctuation is in the second calorific value fluctuation range, or the component ratio fluctuation is in the second ratio fluctuation range. At this time, the exothermic peak intensity increases and the CHP power generation shows perceptible fluctuations. Severe quality anomaly (X3) corresponds to a state where the parameter fluctuation amplitude is extremely large and may cause system failure. For example, the calorific value fluctuation is in the third calorific value fluctuation range, or the component ratio fluctuation is in the third ratio fluctuation range. At this time, a violent exothermic peak is likely to occur, causing the reactor to overheat and the CHP power generation fluctuation rate to exceed the safe range.

[0042] Preferably, the classification of impurity risk characteristic anomaly levels is based on the "total concentration of special organic components" obtained by the impurity risk characteristic acquisition unit 120. For example, a slight impurity anomaly (Y1) corresponds to a special organic component concentration in the first concentration range, where the fuel cell only experiences a very slight voltage drop and no significant performance degradation; a moderate impurity anomaly (Y2) corresponds to a concentration in the second concentration range, where the fuel cell voltage drop rate accelerates and the energy storage battery efficiency decreases to some extent; a severe impurity anomaly (Y3) corresponds to a concentration in the third concentration range, where the fuel cell catalyst is at risk of poisoning and the energy storage device performance degrades significantly.

[0043] Preferably, as shown in Table 1, the data processing unit 200 may have a built-in lookup table, which can be configured as a pre-programmed two-dimensional mapping matrix. The horizontal dimension represents the energy quality characteristic anomaly level (e.g., X1~X3), and the vertical dimension represents the impurity risk characteristic anomaly level (e.g., Y1~Y3). The intersection of the matrix corresponds to a set of coordinated control instructions. The adjustment amount set by the control instructions in this lookup table is not based solely on a fixed value set by experience, but rather generates a corresponding traceability data correction factor by calling the traceability data stored in the data storage unit 400, so as to achieve dynamic correction of the adjustment amount, such as... Figure 3 As shown.

[0044] Furthermore, the "feed rate adjustment amount" in the control command is designed with the stability of the pyrolysis process as the core objective. The adjustment direction is divided into reduction, stabilization and increase (in actual application, reduction and stabilization are the main ones, because strong-aroma baijiu lees are prone to sudden increases in pyrolysis rate). The adjustment range is directly related to the abnormal level of energy quality characteristics - the higher the abnormal level of energy quality characteristics, the larger the adjustment range of feed rate is usually. For example, the adjustment range for severe quality abnormality (X3) is greater than that for mild quality abnormality (X1). This is because: the feed rate directly affects the volatile release rate in the pyrolysis reactor 500. Reducing the feed rate can slow down the concentrated release of volatiles, thereby simultaneously suppressing the exothermic peak and stabilizing the proportion of components.

[0045] Furthermore, the "reaction temperature adjustment amount" in the control command aims at both impurity degradation efficiency and component stability. The adjustment direction is divided into reduction, stabilization, and increase (in practical applications, increase is the primary approach, as special organic components such as esters require higher temperatures for degradation). The adjustment magnitude is positively correlated with the impurity risk characteristic anomaly level—the higher the impurity risk characteristic anomaly level, the larger the reaction temperature adjustment magnitude is typically; for example, the temperature increase for severe impurity anomaly (Y3) is greater than for mild impurity anomaly (Y1). Simultaneously, adjusting the reaction temperature also indirectly optimizes the H2 / CO / CH4 formation ratio, further improving energy quality stability.

[0046] Furthermore, setting the "adjustment priority" is key to resolving "regulation conflicts when multiple scenarios are simultaneously abnormal." Its logic is based on "system risk level" and "parameter sensitivity": when the impurity risk is severe (Y3), the reaction temperature is adjusted first (because catalyst poisoning risk is irreversible damage); when the energy quality is severe (X3), the feed rate is adjusted first (because drastic fluctuations in composition will directly affect the safe operation of the CHP system); when both are moderate, a balanced adjustment strategy is adopted. For example, when the combination of "X1 (mild quality abnormality) + Y3 (severe impurity abnormality)" occurs, the lookup table outputs the instruction "reduce feed rate + significantly increase reaction temperature," and the priority is set to "temperature priority"—significantly increasing the temperature prioritizes the degradation of impurities, moderately reducing the feed rate smooths out the initial exothermic peak, and the synergistic effect of increasing the temperature and decreasing the feed rate stabilizes the component ratio, achieving simultaneous improvement in the three scenarios.

[0047] Preferably, such as Figure 3 As shown, the traceability data correction factor is a set of quantification coefficients or incremental values ​​pre-programmed by the data processing unit 200 to adapt to the specificity of different batches of strong-aroma baijiu lees. Based on the traceability data such as real-time hemicellulose content and hexanoic acid bacteria concentration data stored in the data storage unit 400, it is used to dynamically adjust the original control instructions (feed rate adjustment amount, reaction temperature adjustment amount) of the lookup table. It is used to convert fixed instructions into adaptive instructions, solve the problem of different adjustment effects of the same level caused by batch differences of lees, provide underlying support for the scientific nature of lookup table instructions, avoid setting control instructions based solely on experience, and ensure that the adjustment amount is accurately matched with the actual characteristics of the lees.

[0048] Preferably, hemicellulose content is a core parameter for predicting the intensity of the exothermic peak. The higher the hemicellulose content, the faster the volatile components are released during pyrolysis, resulting in a stronger exothermic peak. Under abnormal energy quality characteristics of the same grade, a greater reduction in the feed rate is required to smooth the exothermic peak and stabilize the components. Based on this principle, this invention designs a "hemicellulose content correction factor" to dynamically adjust the feed rate adjustment range.

[0049] Preferably, the hemicellulose content stored in the data storage unit 400 can be uploaded by the hemicellulose content monitoring unit, which can be configured as a near-infrared spectrometer, equipped with an online sampling device and a spectral data preprocessing module. The online sampling device uses a flow-through sampling pool connected to the waste material conveying pipeline to ensure that the waste material sample maintains its original characteristics during the detection process. The near-infrared spectrometer's spectral detection range covers the characteristic absorption bands of common organic compounds, enabling the acquisition of spectral data by detecting the absorption characteristics of near-infrared light in the waste material sample. The spectral data preprocessing module can perform baseline correction, smoothing, and noise reduction on the raw spectral data to eliminate the influence of interference factors. Then, the hemicellulose content data is obtained through analysis using a built-in quantitative analysis model. After the detection is completed, the data is sent to the data storage unit 400 via the data transmission module.

[0050] Preferably, the data processing unit 200 can retrieve real-time hemicellulose content data from the data storage unit 400 to determine the range of the traceability parameter, thereby automatically matching the corresponding hemicellulose content correction factor. Specifically, the hemicellulose content correction factor is a dimensionless coefficient, with a value range of 1.0 to 2.0. The data processing unit 200 can pre-program a normal range of hemicellulose content values, which can be set based on the typical component range of strong-aroma baijiu lees. Then, the real-time collected hemicellulose content is compared with the normal range, and divided into multiple levels such as normal value, slightly high, moderately high, and severely high, with each level corresponding to a unique correction coefficient. For example, the specific rules are as follows: When the hemicellulose content is in the first content range (i.e., the normal value level): the correction factor is 1.0, that is, the original feed rate adjustment range of the lookup table remains unchanged; When the hemicellulose content is in the second content range (i.e., slightly high level, which exceeds the normal value but does not reach a high level): the correction coefficient is set to a value in the range of 1.2 to 1.4, that is, the feed rate adjustment range is increased by 20% to 40% based on the original value; When the hemicellulose content is in the third content range (i.e., moderately high level, which is a relatively high level): the correction factor is set to a value in the range of 1.5 to 1.7, and the adjustment range is increased by 50% to 70%; When the hemicellulose content is in the fourth content range (i.e., severely high level, which is significantly higher than the normal value): the correction factor is set to a value in the range of 1.8 to 2.0, and the adjustment range is increased by 80% to 100%.

[0051] Furthermore, the hemicellulose content correction factor can be linked to the energy quality characteristic anomaly level: the higher the energy quality characteristic anomaly level (e.g., X3 severe anomaly compared to X1 mild anomaly), the lower the upper limit of the adjustment range of the correction coefficient (e.g., when the severe anomaly is too high, the correction coefficient for X1 level can reach 2.0, while the upper limit for X3 level correction coefficient is 1.8), avoiding a sharp drop in pyrolysis capacity due to excessive reduction in the feed rate. For example, when "X3 severe quality characteristic anomaly + moderately high hemicellulose content" occurs, the original lookup table instruction is "reduce feed rate by 4%", the correction coefficient is 1.6, and the final output is "reduce feed rate by 6.4%"—both stabilizing the pyrolysis rate by increasing the rate reduction (smoothing the exothermic peak + stabilizing components) and avoiding excessive rate reduction from affecting system capacity.

[0052] Preferably, the concentration of caproic acid bacteria is a core parameter for predicting the potential for impurity formation. The higher the concentration of caproic acid bacteria, the higher the content of ester precursors in the residue, and the higher the concentration of special organic components (such as ethyl hexanoate and ethyl butyrate) after pyrolysis. Under the same level of impurity risk characteristics, a higher reaction temperature is required to promote ester degradation. Based on this principle, the present invention designs a "caproic acid bacteria concentration correction factor" to dynamically adjust the reaction temperature adjustment range.

[0053] Preferably, the concentration of caproic acid bacteria stored in the data storage unit 400 can be uploaded by the caproic acid bacteria concentration monitoring unit in the fermentation pit. This monitoring unit can be configured with a real-time quantitative PCR (qPCR) instrument as the core detection hardware, and is equipped with a sample collection device and a sample pretreatment module. The sampling device uses a columnar sampler that can penetrate to different depths in the fermentation pit to obtain representative samples. The sample pretreatment module integrates sample grinding, nucleic acid extraction, and purification functions, converting the samples into nucleic acid templates that meet the detection requirements. The qPCR instrument has a built-in temperature cycling module and a fluorescence signal acquisition module. The temperature cycling module can precisely control the denaturation, annealing, and extension temperatures during nucleic acid amplification, while the fluorescence signal acquisition module can capture the signal intensity emitted by the fluorescent probe during amplification in real time and transmit the detection data to the data storage unit 400, enabling quantitative detection of the caproic acid bacteria concentration in the fermentation pit.

[0054] Preferably, the data processing unit 200 can retrieve real-time caproic acid bacteria concentration data from the data storage unit 400 to determine the range of the traceability parameter, thereby automatically matching the corresponding caproic acid bacteria concentration correction factor. Specifically, the caproic acid bacteria concentration correction factor is a dimensional incremental value (unit: °C), and its value range can be set to 0~15 °C. The data processing unit 200 can set a normal value range for caproic acid bacteria concentration based on the typical bacterial community concentration of the strong-aroma baijiu cellar, and divide the real-time collected caproic acid bacteria concentration into multiple levels such as normal value, slightly high, moderately high, and severely high, with each level corresponding to a unique temperature increment value. For example, the specific rules are as follows: When the concentration of caproic acid bacteria is in the first concentration range (i.e., the normal value range): the correction increment is 0℃, and the original reaction temperature adjustment range remains unchanged by looking up the table; When the concentration of caproic acid bacteria is in the second concentration range (i.e., slightly high): the correction increment value is set to a value within the range of 3~5℃, that is, the reaction temperature adjustment range is increased by 3~5℃ based on the original value; When the concentration of caproic acid bacteria is in the third concentration range (i.e., moderately high): the correction increment value is set to a value within the range of 6~10℃, and the adjustment range is increased by 6~10℃; When the concentration of caproic acid bacteria is in the third concentration range (i.e., severely high): the correction increment value is set to a value within the range of 11~15℃, and the adjustment range is increased by 11~15℃.

[0055] Furthermore, the caproic acid bacteria concentration correction factor can be linked to the impurity risk characteristic anomaly level: the higher the impurity risk characteristic anomaly level (e.g., severe anomaly of Y3 compared to mild anomaly of Y1), the slightly higher the lower limit of the adjustment range of the correction increment value (e.g., when it is mildly high, the correction increment value of Y3 level is not less than 5℃, while that of Y1 level can be as low as 3℃), prioritizing the degradation effect under high impurity risk scenarios. For example, when "Y2 moderate impurity anomaly + severe high caproic acid bacteria concentration" occurs, the original lookup table instruction is "increase the reaction temperature by 10℃", the correction increment value is 12℃, and the final output is "increase the reaction temperature by 22℃"—by increasing the temperature to enhance ester degradation (reduce impurity concentration), while the temperature increase action simultaneously stabilizes the component ratio.

[0056] Preferably, the data processing unit 200 can send the control command determined from the lookup table to the corresponding execution unit 300 to execute the corresponding control action, wherein, for example... Figure 1 As shown, the execution unit 300 may include an electro-hydraulic proportional valve 310 for adjusting the feed rate and a heating rod 320 for adjusting the reaction temperature.

[0057] Preferably, the electro-hydraulic proportional valve 310 may include a proportional electromagnet, a valve core, a valve body, and a position feedback sensor. The analog control signal output by the data processing unit 200 acts on the proportional electromagnet, driving the valve core to move axially to change the valve opening, thereby regulating the flow rate of the slag entering the pyrolysis reactor 500. Preferably, the heating rods 320 may be installed in multiple distributed arrangements on the sidewall or bottom of the pyrolysis reactor 500. Each group of heating rods 320 has a built-in resistance heating element. The data processing unit 200 controls the on / off duration of the heating element through a PWM (pulse width modulation) signal to achieve continuous adjustment of the heating power.

[0058] Preferably, the data processing unit 200 can achieve closed-loop verification through a built-in comprehensive energy storage adaptability index calculation formula. The comprehensive energy storage adaptability index is a comprehensive quantitative representation of the energy storage system's operating status by the data processing unit 200. It is calculated by weighting the molten salt charging rate score, the CHP power generation volatility score, and the fuel cell voltage decay score according to preset weights, with a maximum score of 100. The calculation logic for the molten salt charging rate score is as follows: the data processing unit 200 obtains the actual molten salt charging rate detected by the flow sensor and compares it with the theoretical charging rate corresponding to the reactor's rated heat load. The ratio of the two is converted into a score of 0-100; for example, a higher ratio results in a higher score. The calculation logic for the CHP power generation volatility score is as follows: the ratio of the standard deviation to the average value of the real-time power generation of the CHP system within a set time window is calculated, i.e., the power generation volatility. This is converted into a score based on the inverse proportion of volatility; for example, a lower volatility results in a higher score. The calculation logic for fuel cell voltage decay score is as follows: record the initial output voltage and current output voltage within a set continuous operating time of the fuel cell, calculate the voltage decay rate, and convert it into a score inversely proportional to the decay rate; for example, the lower the decay rate, the higher the score. The weighting of the three types of scores can be based on the priority of system risk. For example, the CHP power generation volatility score has the highest weight (related to power supply security), followed by the molten salt charging rate score and the fuel cell voltage decay score. The specific weight ratio can be set according to actual control requirements.

[0059] Furthermore, after the execution unit 300 completes the control action of the control command, the data processing unit 200 acquires the comprehensive energy storage adaptation index in real time and compares the monitored value with the preset compliance threshold. This compliance threshold can be set based on the system's safe operating range and energy utilization efficiency target. If the index reaches or exceeds the threshold, it is determined that the current control action has effectively resolved the abnormal problem, and the data processing unit 200 outputs a stop control signal, while the execution unit 300 maintains the current operating parameters. If the index is still below the compliance threshold, it is determined that there is insufficient control, and the data processing unit 200 immediately starts a new round of control process to re-output the next set of coordinated commands through a lookup table. The time interval between the two command outputs can be set according to the response cycle of the pyrolysis reaction to ensure the timeliness of the control action and prevent the abnormal state from continuing to expand.

[0060] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; phrases such as "preferred" or "according to a preferred embodiment" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the feature introduced by "preferred" is only an optional mode and should not be construed as mandatory. Therefore, the applicant reserves the right to abandon or delete relevant preferred features at any time.

Claims

1. A smart dispatching system coupling thermochemical energy conversion and energy storage, characterized in that, It includes: Data acquisition unit (100) is used to acquire monitoring data related to pyrolysis gas; The data processing unit (200) is used to analyze and process the received monitoring data, and generate control instructions corresponding to the current abnormal combination based on the built-in lookup table. The data acquisition unit (100) includes an energy quality characteristic acquisition unit (110) for acquiring calorific value fluctuation parameters and effective component ratio parameters of the pyrolysis gas, and an impurity risk characteristic acquisition unit (120) for acquiring the total concentration of special organic components that were not completely decomposed in the pyrolysis gas. The effective components include H2, CO, and CH4, and the effective component ratio parameter is the volume or molar ratio of H2 / CO / CH4. The special organic components are one or more organic compounds, such as esters or aldehydes, that are mixed into the pyrolysis gas during the pyrolysis of strong-aroma baijiu lees. The data processing unit (200) can take the monitoring data obtained by different acquisition units as two-dimensional input feature vectors and gradient the feature vectors into multiple abnormal levels to form several abnormal combinations. The built-in lookup table of the data processing unit (200) is a pre-programmed two-dimensional mapping matrix. Its horizontal dimension is the abnormal level of energy quality characteristics, and its vertical dimension is the abnormal level of impurity risk characteristics. The intersection of the matrix corresponds to a set of coordinated control instructions.

2. The system according to claim 1, characterized in that, The energy quality characteristic acquisition unit (110) includes a calorific value fluctuation acquisition module (111), which can send the acquired real-time calorific value data to the data processing unit (200). The calorific value fluctuation parameter is obtained by the data processing unit (200) by calculating the difference between the maximum and minimum values ​​of the real-time calorific value data within a set time window. The calorific value fluctuation acquisition module (111) is configured as a combustion-type online calorimeter or a physical property-type online calorimeter. The combustion-type online calorimeter includes a miniature combustion chamber, an ignition device, and a heat detector, used to calculate the calorific value per unit volume by completely burning the pyrolysis gas and detecting the released heat. The physical property-type online calorimeter calculates the calorific value by detecting the density or viscosity of the pyrolysis gas and combining it with a pre-programmed component-physical property-calorific value correlation model.

3. The system according to claim 1 or 2, characterized in that, The energy quality characteristic acquisition unit (110) includes an effective component ratio acquisition module (112). The effective component ratio acquisition module (112) can output an electrical signal based on the detected difference in thermal conductivity of each separated component. After processing by the signal amplification and filtering module, the signal is transmitted to the data processing unit (200). The data processing unit (200) then calculates the volume or mole fraction of H2, CO, and CH4 by comparing the peak area integral with the calibration curve, and determines their proportional relationship. The effective component ratio acquisition module (112) is configured as a miniature gas chromatograph, which includes an autosampler, a column system, a column oven and a detector.

4. The system according to any one of claims 1 to 3, characterized in that, The impurity risk characteristic acquisition unit (120) is configured as a Fourier transform infrared spectrometer, which can identify the characteristic absorption peaks of each organic compound contained in the special organic component after background correction of the obtained infrared absorption spectrum, calculate its concentration by combining Lambert-Beer law and pre-calibrated absorption coefficient, and then sum to obtain the total concentration parameter of the special organic component, and send it to the data processing unit (200).

5. The system according to any one of claims 1 to 4, characterized in that, The coordinated control commands include feed rate adjustment, reaction temperature adjustment, and adjustment priority. The adjustment range of the feed rate adjustment is positively correlated with the abnormal level of energy quality characteristics; the adjustment range of the reaction temperature adjustment is positively correlated with the abnormal level of impurity risk characteristics; and the adjustment priority is set according to the system risk level and parameter sensitivity.

6. The system according to any one of claims 1 to 5, characterized in that, The data processing unit (200) is further configured to call the traceability data stored in the data storage unit (400) to generate a traceability data correction factor to dynamically correct the original control instructions in the lookup table. The traceability data includes real-time hemicellulose content and caproic acid bacteria concentration. The hemicellulose content is used to characterize the release rate of volatile components during the pyrolysis of the waste material. The data is acquired by a near-infrared spectrometer and uploaded to the data storage unit (400) after baseline correction and quantitative analysis by the spectral data preprocessing module. The caproic acid bacteria concentration is used to characterize the potential for the formation of ester precursor substances. The data is acquired by a fluorescence quantitative PCR detector and uploaded to the data storage unit (400) after sample sampling, nucleic acid extraction and amplification.

7. The system according to any one of claims 1 to 6, characterized in that, The data processing unit (200) matches the corresponding hemicellulose content correction factor according to the real-time hemicellulose content. The hemicellulose content correction factor is used to amplify the original feed rate adjustment amount to achieve the control of the exothermic peak intensity and component stability. The range of the hemicellulose content correction factor is determined based on the content range of the hemicellulose content, and the value corresponding to the abnormal level of energy quality characteristics is determined within the range of the value.

8. The system according to any one of claims 1 to 7, characterized in that, The data processing unit (200) matches the corresponding hexanoic acid bacteria concentration correction factor according to the real-time hexanoic acid bacteria concentration. The hexanoic acid bacteria concentration correction factor is used to increase the original reaction temperature adjustment amount and enhance the pyrolysis removal of special organic components. The range of the hexanoic acid bacteria concentration correction factor is determined based on the concentration range of the hexanoic acid bacteria concentration, and the value corresponding to the abnormal level of impurity risk characteristics is determined within the range of the value.

9. The system according to any one of claims 1 to 8, characterized in that, It also includes an execution unit (300) that is communicatively connected to the data processing unit (200) to receive control commands sent by the data processing unit (200) and perform corresponding control actions according to the control commands. The execution unit (300) includes an electro-hydraulic proportional valve (310) for adjusting the feed rate of the dregs into the pyrolysis reactor (500) and a heating rod (320) for adjusting the reaction temperature inside the pyrolysis reactor (500).

10. The system according to any one of claims 1 to 9, characterized in that, The data processing unit (200) can calculate the comprehensive energy storage adaptability index to achieve closed-loop verification of the control effect, wherein, The comprehensive energy storage compatibility index is calculated by weighting the molten salt charging rate score, the CHP power generation volatility score, and the fuel cell voltage decay score according to preset weights. In the weight allocation, the CHP power generation volatility score has the highest weight, followed by the molten salt charging rate score and the fuel cell voltage decay score. The data processing unit (200) acquires the comprehensive energy storage adaptation index in real time after the drive execution unit (300) executes the control action according to the control instruction, and compares it with the preset target threshold to judge the control effect.

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