Intelligent fuel evaluation method and system based on energy data feedback

Abstract

The invention discloses an intelligent fuel evaluation method and system based on energy data feedback, and relates to the technical field of fuel analysis and evaluation, and the method comprises the steps: obtaining combustion system information, obtaining historical combustion data according to the combustion system information, obtaining a combustion temperature model according to the historical combustion data, and obtaining a combustion temperature model based on the combustion temperature model; and according to the historical combustion data, a fuel evaluation model is obtained, combustion of the to-be-evaluated fuel is monitored, and combustion monitoring data is obtained. According to the method, the relation between the fuel temperature and the fuel combustion time is accurately analyzed through the fuel historical data standard subset, a data basis is provided for subsequent fuel evaluation, the fuel evaluation model is obtained based on the combustion temperature model according to the historical combustion data, and the fuel evaluation efficiency is improved. The fuel is accurately evaluated through the fuel evaluation model, the accuracy and reliability of the evaluation result are ensured, and the fuel evaluation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of fuel analysis and evaluation technology, specifically to a fuel intelligent evaluation method and system based on energy data feedback. Background Technology

[0002] In recent years, fuel has played an important and indispensable role in industrial and agricultural production, transportation, and people's lives, bringing huge benefits and convenience to society. With the rapid development of science and technology, the demand for fuel has also increased. Existing technologies often maximize energy efficiency and economy by dynamically adjusting fuel selection, ratio, and combustion strategies. In order to better dynamically adjust fuel, fuel evaluation has become a crucial step. Furthermore, the evaluation of fuel calorific value has gradually become the top priority. Calorific value, simply put, refers to the heat released when a unit mass or volume of fuel is completely burned, and it is an important indicator for measuring the energy content of fuel.

[0003] Currently, fuel assessment suffers from several limitations. It lacks the ability to accurately analyze historical combustion data, establish suitable assessment models based on historical data, and accurately evaluate fuel performance. Existing technologies often employ conventional methods such as the oxygen bomb calorimetry for calorific value analysis. However, these methods require specific experimental conditions, have low detection efficiency, and a limited detection range. Furthermore, fuel loses heat during combustion, leading to errors in the final results. While assessing fuel calorific value using data after combustion can provide a more accurate analysis, the assessment is delayed after combustion, hindering timely evaluation and reducing efficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a fuel intelligent assessment method and system based on energy data feedback. This technical solution solves the problems mentioned in the background art, such as the inability to accurately analyze historical combustion data of fuel, the inability to establish a suitable assessment model based on historical data, and the inability to accurately assess fuel performance. Existing technologies often use conventional methods such as oxygen bomb calorimetry for calorific value analysis, but this method requires certain experimental conditions, has low detection efficiency, and a small detection range. Furthermore, fuel loses some heat during combustion, leading to certain errors in the final detection results. While assessing the fuel calorific value based on the data after final combustion can more accurately analyze the fuel calorific value, the assessment results are delayed after combustion is complete, making it impossible to assess the fuel in a timely manner and reducing the efficiency of fuel assessment.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A fuel intelligent assessment method based on energy data feedback includes:

[0007] Acquire combustion system information, wherein the combustion system is used to burn fuel and monitor data during combustion;

[0008] Based on the combustion system information, historical combustion data is obtained, which represents historical data on the combustion of fuel in the combustion system;

[0009] Based on historical combustion data, obtain a combustion temperature model;

[0010] Based on the combustion temperature model, a fuel assessment model is obtained using historical combustion data;

[0011] The combustion of the fuel to be evaluated is monitored to obtain combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information.

[0012] The combustion monitoring data is input into the combustion temperature model and fitted to obtain the predicted maximum combustion temperature.

[0013] The estimated calorific value of the fuel is obtained based on the expected maximum combustion temperature and the fuel assessment model.

[0014] Preferably, the step of obtaining historical fuel information based on historical combustion data includes historical fuel composition information;

[0015] Based on historical fuel information, the historical combustion data corresponding to the same type of fuel are divided into the same group to obtain the historical fuel data group;

[0016] Based on historical fuel data sets, obtain combustion count information corresponding to each historical fuel data set;

[0017] Based on the combustion frequency information, the historical combustion data in the historical fuel data group is divided to obtain several historical fuel data subsets. The historical fuel data subsets represent the historical fuel energy data and historical fuel product information corresponding to each fuel combustion.

[0018] Based on the historical fuel composition information corresponding to each historical fuel data group, and based on the incomplete combustion chemical reaction formula, the minimum calorific value information of the historical fuel corresponding to each historical fuel data group is obtained.

[0019] Based on historical fuel composition information, the historical fuel reference calorific value information is obtained by elemental analysis. The historical fuel reference calorific value represents the theoretical calorific value of historical fuel when it is completely burned under ideal conditions.

[0020] Based on the fuel historical data subset, historical fuel minimum calorific value information, and historical fuel benchmark calorific value information, obtain the fuel historical data standard subset;

[0021] A combustion temperature model is obtained based on a standard subset of historical fuel data.

[0022] Preferably, the step of obtaining a standard subset of historical fuel data based on a subset of historical fuel data, historical minimum calorific value information, and historical reference calorific value information specifically includes:

[0023] The ratio of the historical minimum calorific value of fuel to the historical reference calorific value of fuel is used as the calorific value reference deviation coefficient.

[0024] Based on historical fuel composition information, obtain historical fuel moisture mass information and historical fuel total mass information;

[0025] Based on historical combustion product information, obtain information on the types of historical combustion products and the corresponding quality information for each historical combustion product;

[0026] The ratio of historical fuel moisture content to historical fuel total mass is used as the moisture deviation coefficient.

[0027] The ratio of the total mass of historical combustion products to the total mass of historical fuel is used as the ash content deviation coefficient.

[0028] The sum of the moisture deviation coefficient and the ash deviation coefficient corresponding to each subset of fuel history data is taken as the calorific value deviation coefficient of that subset of fuel history data.

[0029] The difference between 1 and the maximum value of the calorific value deviation coefficient is taken as the first calorific value deviation coefficient, and the difference between 1 and the minimum value of the calorific value deviation coefficient is taken as the second calorific value deviation coefficient.

[0030] The ratio of the first calorific value deviation coefficient to the second calorific value deviation coefficient is taken as the actual calorific value deviation coefficient.

[0031] Based on the calorific value benchmark deviation coefficient and the actual calorific value deviation coefficient, a subset of historical fuel data is filtered to obtain a standard subset of historical fuel data.

[0032] If the actual calorific value deviation coefficient is less than the calorific value reference deviation coefficient, the fuel historical data subset corresponding to the first calorific value deviation coefficient is removed and the first calorific value deviation coefficient is re-obtained until the actual calorific value deviation coefficient is greater than the calorific value reference deviation coefficient.

[0033] Preferably, obtaining the combustion temperature model based on a standard subset of historical fuel data specifically includes:

[0034] Based on the standard subset of fuel historical data, obtain the historical fuel energy data corresponding to each standard subset of fuel historical data;

[0035] Based on historical fuel energy data, obtain combustion temperature and combustion time information;

[0036] Establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis;

[0037] Substitute the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each standard subset of historical fuel data.

[0038] The combustion temperature-time curves were filtered to obtain a combustion temperature-time baseline curve;

[0039] Based on the combustion temperature time baseline curve, historical fuel energy data are fitted to obtain a combustion temperature model;

[0040] Specifically, the combustion temperature model is as follows:

[0041] T(t) = T max (1-e -k·t )+w;

[0042] In the formula, T(t) represents the combustion temperature at time t. max represents the maximum combustion temperature, k represents the combustion coefficient, and w is a correction term.

[0043] Preferably, the step of filtering the combustion temperature-time curve to obtain a combustion temperature-time reference curve specifically includes:

[0044] Based on the combustion temperature-time curve, and using integral calculations, the area of ​​the graph formed by the combustion temperature-time curve and the horizontal axis is used as fuel heat information.

[0045] The ratio of the fuel calorific value to the total historical fuel mass corresponding to the combustion temperature-time curve is used as the fuel calibrated calorific value.

[0046] The average of the historical fuel minimum calorific value and the historical fuel benchmark calorific value corresponding to each historical fuel data group is used as the characteristic calorific value of the fuel.

[0047] The difference between the historical fuel reference calorific value and the characteristic calorific value of the fuel is used as the fuel calorific value deviation threshold;

[0048] Based on the standard subset of historical fuel data, obtain the first calorific value deviation coefficient and the second calorific value deviation coefficient corresponding to each historical fuel data group;

[0049] The difference between 1 and the calorific value benchmark deviation coefficient corresponding to each historical fuel data group is taken as the calorific value benchmark deviation value, and the difference between the second calorific value deviation coefficient and the first calorific value deviation coefficient corresponding to each historical fuel data group is taken as the actual calorific value deviation value.

[0050] The ratio of the actual deviation of calorific value to the reference deviation of calorific value is used as the calorific value mapping coefficient;

[0051] The product of the fuel calorific value deviation threshold and the calorific value mapping coefficient is used as the fuel calorific value deviation mapping threshold;

[0052] Using any two combustion temperature-time curves as a reference, the combustion temperature-time curves are filtered to obtain the combustion temperature-time reference curve.

[0053] If the difference between the calibrated calorific values ​​of the fuel corresponding to two combustion temperature-time curves exceeds the fuel calorific value deviation mapping threshold, then the combustion temperature-time curve corresponding to the smaller calibrated calorific value of the fuel in the two combustion temperature-time curves will be removed until the difference between the calibrated calorific values ​​of the fuel corresponding to any two combustion temperature-time curves is less than the fuel calorific value deviation mapping threshold.

[0054] Preferably, the fuel evaluation model, based on the combustion temperature model and historical combustion data, specifically includes:

[0055] Obtain the fuel calorific value and historical combustion product information corresponding to the combustion temperature-time baseline curve;

[0056] Based on historical combustion product information, the average specific heat capacity is obtained using the specific heat capacity corresponding to each historical combustion product.

[0057] The baseline ambient temperature is obtained based on the combustion temperature-time reference curve.

[0058] The difference between the maximum combustion temperature and the base ambient temperature is taken as the heat temperature change value;

[0059] Based on the heat temperature change value, average specific heat capacity and fuel calibrated calorific value, the data is fitted to obtain a fuel evaluation model;

[0060] Specifically, the fuel assessment model is as follows:

[0061]

[0062] In the formula, q is the calorific value of the fuel, and c p The average specific heat capacity is given by L, the correction term is given by q0, the reference calorific value of the fuel is given by G, and the calorific value deviation coefficient is given by T. h Based on ambient temperature, y i c represents the molar mass percentage of the i-th combustion product. p，i This represents the specific heat capacity of the i-th combustion product.

[0063] Furthermore, a fuel intelligent assessment system based on energy data feedback is proposed to implement the assessment method described above, including:

[0064] The main control module is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain a combustion temperature model; to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain a fuel evaluation model; to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information to obtain historical fuel data groups; to obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups; to divide the historical combustion data in the historical fuel data groups according to the combustion number information to obtain several fuel historical data subsets; to input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature; and to obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model.

[0065] The information acquisition module is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, acquire historical fuel information based on the historical combustion data, the historical fuel information including historical fuel composition information, monitor the combustion of the fuel to be evaluated, and acquire combustion monitoring data, the combustion monitoring data including combustion temperature data, ambient temperature data, combustion time data, and combustion product information;

[0066] The data processing module is used to obtain the minimum calorific value information of the historical fuel corresponding to each historical fuel data group based on the historical fuel composition information and the incomplete combustion chemical reaction formula. Based on the historical fuel composition information, the module obtains the historical fuel reference calorific value information by elemental analysis. Based on the historical fuel data subset, the historical fuel minimum calorific value information, and the historical fuel reference calorific value information, the module obtains the fuel historical data standard subset. The module establishes a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis. The module substitutes the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset. The module filters the combustion temperature-time curves to obtain the combustion temperature-time reference curve.

[0067] The display module interacts with the main control module and is used to output and display the combustion temperature model, fuel assessment model, combustion monitoring data, expected maximum combustion temperature, and fuel assessment calorific value.

[0068] Optionally, the control unit is used to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information, obtain historical fuel data groups, obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups, divide the historical combustion data in the historical fuel data groups based on the combustion number information, obtain several fuel historical data subsets, input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature, and obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model.

[0069] An information receiving unit interacts with an information acquisition module and a data processing module to receive data and transmit it to a model training unit.

[0070] The model training unit is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain the combustion temperature model, and to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain the fuel evaluation model.

[0071] Optionally, the information acquisition module specifically includes:

[0072] The first acquisition unit is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, and acquire historical fuel information based on the historical combustion data, wherein the historical fuel information includes historical fuel composition information.

[0073] The second acquisition unit is used to monitor the combustion of the fuel to be evaluated and acquire combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information.

[0074] Optionally, the data processing module specifically includes:

[0075] The first data processing unit is configured to obtain the historical fuel minimum calorific value information corresponding to each historical fuel data group based on the historical fuel composition information corresponding to each historical fuel data group and the incomplete combustion chemical reaction formula; obtain the historical fuel reference calorific value information based on the historical fuel composition information and the elemental analysis method; and obtain the fuel historical data standard subset based on the fuel historical data subset, the historical fuel minimum calorific value information and the historical fuel reference calorific value information.

[0076] The second data processing unit is used to establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis, substitute combustion temperature information and combustion time information into the combustion temperature-time coordinate system, obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset, filter the combustion temperature-time curve, and obtain the combustion temperature-time reference curve.

[0077] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0078] This invention proposes a fuel intelligent assessment method and system based on energy data feedback. By obtaining a standard subset of historical fuel data, historical minimum calorific value information, and historical benchmark calorific value information, the relationship between fuel temperature and fuel combustion time is accurately analyzed using this standard subset, providing a data foundation for subsequent fuel assessment. Based on a combustion temperature model and historical combustion data, a fuel assessment model is obtained. The fuel is then accurately assessed using this model, ensuring the accuracy and reliability of the assessment results and improving fuel assessment efficiency. Attached Figure Description

[0079] Figure 1 This is a flowchart of a fuel intelligent assessment method based on energy data feedback proposed in this invention.

[0080] Figure 2 This is a flowchart of the combustion temperature model acquisition process in this invention;

[0081] Figure 3 This is a flowchart illustrating the process of acquiring a standard subset of historical fuel data in this invention.

[0082] Figure 4 This is a flowchart of the process for obtaining the combustion temperature-time reference curve in this invention;

[0083] Figure 5 This is a block diagram of a fuel intelligent assessment system based on energy data feedback proposed in this invention. Detailed Implementation

[0084] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0085] Reference Figure 1 - Figure 4 As shown in the figure, an embodiment of the present invention provides a fuel intelligent assessment method based on energy data feedback, comprising:

[0086] Acquire combustion system information, wherein the combustion system is used to burn fuel and monitor data during combustion;

[0087] Based on the combustion system information, historical combustion data is obtained, which represents historical data on the combustion of fuel in the combustion system;

[0088] Based on historical combustion data, obtain a combustion temperature model;

[0089] Specifically, a combustion temperature model is obtained based on historical combustion data, including:

[0090] Historical fuel information is obtained based on historical combustion data, including historical fuel composition information;

[0091] Based on historical fuel information, the historical combustion data corresponding to the same type of fuel are divided into the same group to obtain the historical fuel data group;

[0092] Based on historical fuel data sets, obtain combustion count information corresponding to each historical fuel data set;

[0093] Based on the combustion frequency information, the historical combustion data in the historical fuel data group is divided to obtain several historical fuel data subsets. The historical fuel data subsets represent the historical fuel energy data and historical fuel product information corresponding to each fuel combustion.

[0094] Based on the historical fuel composition information corresponding to each historical fuel data group, and based on the incomplete combustion chemical reaction formula, the minimum calorific value information of the historical fuel corresponding to each historical fuel data group is obtained.

[0095] Based on historical fuel composition information, the historical fuel reference calorific value information is obtained by elemental analysis. The historical fuel reference calorific value represents the theoretical calorific value of historical fuel when it is completely burned under ideal conditions.

[0096] Based on the fuel historical data subset, historical fuel minimum calorific value information, and historical fuel benchmark calorific value information, obtain the fuel historical data standard subset;

[0097] A combustion temperature model is obtained based on a standard subset of historical fuel data.

[0098] This solution structures fragmented historical fuel data by categorizing it by type and number of combustion cycles. Grouping data of the same type of fuel avoids interference from different fuel characteristics. Subdividing data subsets by number of combustion cycles accurately reconstructs each combustion process, eliminating the impact of single abnormal data. A calorific value assessment boundary is constructed by combining the minimum calorific value (based on incomplete combustion) and the baseline calorific value (ideal complete combustion). The minimum calorific value reflects the lower limit of actual combustion, while the baseline calorific value provides the theoretical upper limit. The combination of these two values ​​accurately determines the fuel combustion efficiency range, providing a data foundation for subsequent historical data screening. A standard subset generated based on historical data, minimum calorific value, and baseline calorific value eliminates invalid or abnormal data, retaining data from typical combustion conditions. This data serves as input to the combustion temperature model, ensuring that the model accurately reflects the true combustion characteristics of the fuel.

[0099] It should be noted that obtaining the minimum calorific value of fuel through incomplete combustion chemical reaction formulas and obtaining the theoretical maximum calorific value of fuel through elemental analysis are both well-known technical means in the field, and therefore no further explanation is required in this solution.

[0100] Specifically, based on a subset of historical fuel data, historical minimum calorific value information, and historical benchmark calorific value information, a standard subset of historical fuel data is obtained, which includes:

[0101] The ratio of the historical minimum calorific value of fuel to the historical reference calorific value of fuel is used as the calorific value reference deviation coefficient.

[0102] Based on historical fuel composition information, obtain historical fuel moisture mass information and historical fuel total mass information;

[0103] Based on historical combustion product information, obtain information on the types of historical combustion products and the corresponding quality information for each historical combustion product;

[0104] The ratio of historical fuel moisture content to historical fuel total mass is used as the moisture deviation coefficient.

[0105] The ratio of the total mass of historical combustion products to the total mass of historical fuel is used as the ash content deviation coefficient.

[0106] The sum of the moisture deviation coefficient and the ash deviation coefficient corresponding to each subset of fuel history data is taken as the calorific value deviation coefficient of that subset of fuel history data.

[0107] The difference between 1 and the maximum value of the calorific value deviation coefficient is taken as the first calorific value deviation coefficient, and the difference between 1 and the minimum value of the calorific value deviation coefficient is taken as the second calorific value deviation coefficient.

[0108] The ratio of the first calorific value deviation coefficient to the second calorific value deviation coefficient is taken as the actual calorific value deviation coefficient.

[0109] Based on the calorific value benchmark deviation coefficient and the actual calorific value deviation coefficient, a subset of historical fuel data is filtered to obtain a standard subset of historical fuel data.

[0110] If the actual calorific value deviation coefficient is less than the calorific value reference deviation coefficient, the fuel historical data subset corresponding to the first calorific value deviation coefficient is removed and the first calorific value deviation coefficient is re-obtained until the actual calorific value deviation coefficient is greater than the calorific value reference deviation coefficient.

[0111] This solution establishes a precise data foundation for intelligent fuel assessment through multi-dimensional deviation coefficient calculation and data filtering mechanisms. A benchmark for comparing theoretical and actual calorific values ​​is established using the baseline deviation coefficient and the actual calorific value deviation coefficient. The impact of fuel impurities (moisture and ash) on combustion efficiency is quantified using moisture and ash deviation coefficients, making data filtering more closely aligned with actual fuel combustion scenarios. The calorific value deviation coefficient, obtained by summing these two coefficients, comprehensively reflects the impact of fuel composition fluctuations on calorific value. The calculation of the first and second calorific value deviation coefficients, along with the actual deviation coefficient, dynamically defines the data validity range, determining the fluctuation range of the actual fuel calorific value. Iterative filtering eliminates abnormal data with excessive deviations, ensuring the reliability of the standard subset of historical fuel data. This filtering mechanism considers both the objective influence of fuel composition (such as moisture and ash) and ensures consistency between the data and the theoretical combustion model through threshold control of the calorific value deviation coefficient. This provides high-quality data support for subsequent combustion temperature model construction, enabling the fuel assessment model to more accurately reflect fuel combustion characteristics, improving the accuracy of energy data feedback, and providing a scientific basis for fuel selection and combustion optimization.

[0112] It is understandable that the calorific value reference deviation coefficient represents the ratio of the theoretical minimum calorific value to the maximum calorific value of a fuel, while the actual calorific value deviation coefficient represents the ratio of the actual minimum calorific value to the maximum calorific value of a fuel. In actual fuel combustion, under normal combustion conditions, the difference between the actual minimum calorific value and the maximum calorific value of the fuel is always less than the difference between the theoretical minimum calorific value and the maximum calorific value. Therefore, the actual calorific value deviation coefficient is always less than the calorific value reference deviation coefficient. By using the actual calorific value deviation coefficient and the calorific value reference deviation coefficient to filter data on abnormal fuel combustion, the accuracy and reliability of the data are ensured, providing a data foundation for subsequent analysis of combustion temperature.

[0113] Specifically, a combustion temperature model is obtained based on a standard subset of historical fuel data, including:

[0114] Based on the standard subset of fuel historical data, obtain the historical fuel energy data corresponding to each standard subset of fuel historical data;

[0115] Based on historical fuel energy data, obtain combustion temperature and combustion time information;

[0116] Establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis;

[0117] Substitute the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each standard subset of historical fuel data.

[0118] The combustion temperature-time curves were filtered to obtain a combustion temperature-time baseline curve;

[0119] Based on the combustion temperature time baseline curve, historical fuel energy data are fitted to obtain a combustion temperature model;

[0120] Specifically, the combustion temperature model is as follows:

[0121] T(t) = T max (1-e -k·t )+w;

[0122] In the formula, T(t) represents the combustion temperature at time t. max represents the maximum combustion temperature, k represents the combustion coefficient, and w is a correction term.

[0123] In this approach, historical fuel energy data is extracted from a standard subset of historical fuel data (high-quality data filtered through multi-dimensional deviations) to ensure the reliability of the input data and guarantee model accuracy from the source. A coordinate system is constructed and curves are plotted using combustion temperature and time information, enabling a visual representation of the combustion process and facilitating intuitive analysis of dynamic temperature changes. The process of filtering the combustion temperature-time baseline curve is essentially a process of eliminating outlier data and retaining typical combustion characteristics, further improving the model's stability and representativeness.

[0124] Understandably, in this scheme, the data is fitted into the combustion temperature model, T(t), T max Both t and t are known data. Inputting this data into the model yields the combustion coefficient k and the correction term w. The combustion rate of the fuel on the surface of the combustion system varies depending on the fuel and the catalyst used. Therefore, the combustion coefficient k is taken as a particular solution term and the correction term w is taken as a general solution term. The correction term w is then determined.

[0125] Specifically, the combustion temperature-time curves are screened to obtain a combustion temperature-time baseline curve, which includes:

[0126] Based on the combustion temperature-time curve, and using integral calculations, the area of ​​the graph formed by the combustion temperature-time curve and the horizontal axis is used as fuel heat information.

[0127] The ratio of the fuel calorific value to the total historical fuel mass corresponding to the combustion temperature-time curve is used as the fuel calibrated calorific value.

[0128] The average of the historical fuel minimum calorific value and the historical fuel benchmark calorific value corresponding to each historical fuel data group is used as the characteristic calorific value of the fuel.

[0129] The difference between the historical fuel reference calorific value and the characteristic calorific value of the fuel is used as the fuel calorific value deviation threshold;

[0130] Based on the standard subset of historical fuel data, obtain the first calorific value deviation coefficient and the second calorific value deviation coefficient corresponding to each historical fuel data group;

[0131] The difference between 1 and the calorific value benchmark deviation coefficient corresponding to each historical fuel data group is taken as the calorific value benchmark deviation value, and the difference between the second calorific value deviation coefficient and the first calorific value deviation coefficient corresponding to each historical fuel data group is taken as the actual calorific value deviation value.

[0132] The ratio of the actual deviation of calorific value to the reference deviation of calorific value is used as the calorific value mapping coefficient;

[0133] The product of the fuel calorific value deviation threshold and the calorific value mapping coefficient is used as the fuel calorific value deviation mapping threshold;

[0134] Using any two combustion temperature-time curves as a reference, the combustion temperature-time curves are filtered to obtain the combustion temperature-time reference curve.

[0135] If the difference between the calibrated calorific values ​​of the fuel corresponding to two combustion temperature-time curves exceeds the fuel calorific value deviation mapping threshold, then the combustion temperature-time curve corresponding to the smaller calibrated calorific value of the fuel in the two combustion temperature-time curves will be removed until the difference between the calibrated calorific values ​​of the fuel corresponding to any two combustion temperature-time curves is less than the fuel calorific value deviation mapping threshold.

[0136] In this scheme, the combustion temperature curve is converted into fuel heat information through integral calculation, and the calibrated calorific value of the fuel is calculated in combination with mass parameters. The temperature change characteristics are quantified into energy indicators. The fuel heat represents the heat after removing the heat loss during fuel combustion. The calorific value mapping coefficient represents the proportional relationship between the difference between the theoretical maximum and minimum calorific values ​​of the fuel and the difference between the actual maximum and minimum calorific values ​​of the fuel, which facilitates the subsequent conversion of theoretical standards into actual standards.

[0137] Based on the combustion temperature model, a fuel assessment model is obtained using historical combustion data;

[0138] Specifically, based on the combustion temperature model and historical combustion data, a fuel assessment model is obtained, which includes:

[0139] Obtain the fuel calorific value and historical combustion product information corresponding to the combustion temperature-time baseline curve;

[0140] Based on historical combustion product information, the average specific heat capacity is obtained using the specific heat capacity corresponding to each historical combustion product.

[0141] The baseline ambient temperature is obtained based on the combustion temperature-time reference curve.

[0142] The difference between the maximum combustion temperature and the base ambient temperature is taken as the heat temperature change value;

[0143] Based on the heat temperature change value, average specific heat capacity and fuel calibrated calorific value, the data is fitted to obtain a fuel evaluation model;

[0144] Specifically, the fuel assessment model is as follows:

[0145]

[0146] In the formula, q is the calorific value of the fuel, and c p The average specific heat capacity is given by L, the correction term is given by q0, the reference calorific value of the fuel is given by G, and the calorific value deviation coefficient is given by T. h Based on ambient temperature, y i c represents the molar mass percentage of the i-th combustion product. p，i This represents the specific heat capacity of the i-th combustion product.

[0147] In this solution, through multi-dimensional energy data fusion, dynamic correction modeling, and combustion product characteristic analysis, the precise quantification of energy release and the scientific prediction of fuel performance are achieved in fuel intelligent assessment. By fitting the data with heat temperature change value, average specific heat capacity, and fuel calibrated calorific value, a fuel assessment model is obtained. The fuel assessment model is used to accurately assess the fuel calorific value, ensuring the accuracy and reliability of the assessment results.

[0148] Understandably, conventional methods for evaluating fuel calorific value include oxygen bomb calorimetry or differential scanning calorimetry. However, these are laboratory testing methods. While they offer high accuracy, they often only allow testing on a portion of the fuel, making it impossible to evaluate the entire fuel. Furthermore, they require stringent experimental conditions, impacting the efficiency of fuel evaluation. On the other hand, evaluating fuel calorific value through combustion products requires waiting until combustion is complete. This not only results in a time delay, making it difficult to obtain timely assessment results, but also hinders timely detection and adjustment of fuel anomalies, leading to resource waste.

[0149] In this scheme, the calorific value of the fuel is evaluated using a fuel evaluation model. It should be noted that the initial model for the fuel evaluation model is as follows:

[0150] Q = c p ·m 产物 ·(T max -Th );

[0151] In the formula, Q is the total heat of the fuel, and c p m is the average specific heat capacity of the combustion products. 产物 This represents the total mass of the combustion products.

[0152] Under ideal adiabatic conditions, the calorific value of fuel is positively correlated with its combustion temperature, and can be estimated using theoretical combustion temperatures (such as adiabatic flame temperatures). However, in actual combustion temperature monitoring, if the fuel is not completely combusted (e.g., carbon is converted to CO instead of CO2), the heat release is reduced, leading to (T... max -T h Factors such as excessively small values ​​will reduce the accuracy of the formula's calculation results. Therefore, a correction term needs to be added to the formula.

[0153] Q = c p ·m 产物 ·(T max -T h )+D 修正项 ;

[0154] In the formula, D 修正项 This is a heat correction term, representing heat loss;

[0155] However, at this point, it is still necessary to obtain the total mass m of the combustion products. 产物 Only then can the calorific value of the fuel be evaluated. However, obtaining the total mass of the combustion products requires the fuel to be fully combusted. Therefore, in this scheme, the total mass of the combustion products is converted into the total mass of the fuel, i.e.:

[0156]

[0157] In the formula, m 燃料 m is the total mass of the fuel. 理论空气量 α is the theoretical amount of air required for fuel combustion, and α is the excess air coefficient.

[0158] Therefore, the initial model of the fuel assessment model is simplified to:

[0159]

[0160] Right now:

[0161] Q = c p ·m 燃料 ·(T max -T h )+(m 理论空气量 ×α)·m 燃料 +D 修正项 ;

[0162] Understandably, the correction term D 修正项This is often caused by incomplete fuel combustion, indicating a loss of heat. Therefore, the correction term is positively correlated with fuel quality, i.e.:

[0163] D 修正项 ∝m 燃料 ;

[0164] The excess air coefficient α requires real-time monitoring of O2 concentration, which is complex and prone to error. Therefore, the excess air coefficient is incorporated into the correction term, i.e.:

[0165] Q = c p ·m 燃料 ·(T max -T h )+D 修正项 ;

[0166] Understandably, the correction term D 修正项 To ensure a positive correlation with fuel quality, the correction term is transformed into a fuel quality-related term, i.e., let...

[0167] D 修正项 =L·m 燃料 ;

[0168] In the formula, L is the correction coefficient;

[0169] In summary, the initial model of the fuel assessment model can be simplified as follows:

[0170] Q = c p ·m 燃料 ·(T max -T h )+L·m 燃料 ;

[0171] The calorific value of fuel is equal to the total heat of combustion divided by the total mass of fuel. Therefore, by further simplifying the model, we obtain the fuel evaluation model:

[0172] q = c p ·(T max -T h )+L;

[0173] The combustion of the fuel to be evaluated is monitored to obtain combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information.

[0174] The combustion monitoring data is input into the combustion temperature model and fitted to obtain the expected maximum combustion temperature;

[0175] The estimated calorific value of the fuel is obtained based on the expected maximum combustion temperature and the fuel assessment model.

[0176] In this scheme, the predicted maximum combustion temperature is obtained by inputting combustion monitoring data into the combustion temperature model and fitting it. The minimum number of combustion monitoring data sets is 5. The combustion monitoring data is input into the combustion temperature model. With the correction term w determined, the time t and the combustion temperature T(t) at time t are used as known quantities. The maximum combustion temperature and the combustion coefficient are fitted to obtain the maximum combustion temperature. Then, the maximum combustion temperature is substituted into the fuel evaluation model to obtain the fuel evaluation calorific value.

[0177] Reference Figure 5 As shown, further, combining the above-mentioned fuel intelligent assessment method based on energy data feedback, a fuel intelligent assessment system based on energy data feedback is proposed, including:

[0178] The main control module is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain a combustion temperature model; to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain a fuel evaluation model; to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information to obtain historical fuel data groups; to obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups; to divide the historical combustion data in the historical fuel data groups according to the combustion number information to obtain several fuel historical data subsets; to input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature; and to obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model.

[0179] The information acquisition module is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, acquire historical fuel information based on the historical combustion data, the historical fuel information including historical fuel composition information, monitor the combustion of the fuel to be evaluated, and acquire combustion monitoring data, the combustion monitoring data including combustion temperature data, ambient temperature data, combustion time data, and combustion product information;

[0180] The data processing module is used to obtain the minimum calorific value information of the historical fuel corresponding to each historical fuel data group based on the historical fuel composition information and the incomplete combustion chemical reaction formula. Based on the historical fuel composition information, the module obtains the historical fuel reference calorific value information by elemental analysis. Based on the historical fuel data subset, the historical fuel minimum calorific value information, and the historical fuel reference calorific value information, the module obtains the fuel historical data standard subset. The module establishes a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis. The module substitutes the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset. The module filters the combustion temperature-time curves to obtain the combustion temperature-time reference curve.

[0181] The display module interacts with the main control module and is used to output and display the combustion temperature model, fuel assessment model, combustion monitoring data, expected maximum combustion temperature, and fuel assessment calorific value.

[0182] The main control module specifically includes:

[0183] The control unit is used to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information, obtain historical fuel data groups, obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups, divide the historical combustion data in the historical fuel data groups based on the combustion number information, obtain several fuel historical data subsets, input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature, and obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model.

[0184] An information receiving unit interacts with an information acquisition module and a data processing module to receive data and transmit it to a model training unit.

[0185] The model training unit is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain the combustion temperature model, and to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain the fuel evaluation model.

[0186] The information acquisition module specifically includes:

[0187] The first acquisition unit is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, and acquire historical fuel information based on the historical combustion data, wherein the historical fuel information includes historical fuel composition information.

[0188] The second acquisition unit is used to monitor the combustion of the fuel to be evaluated and acquire combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information.

[0189] The data processing module specifically includes:

[0190] The first data processing unit is configured to obtain the historical fuel minimum calorific value information corresponding to each historical fuel data group based on the historical fuel composition information corresponding to each historical fuel data group and the incomplete combustion chemical reaction formula; obtain the historical fuel reference calorific value information based on the historical fuel composition information and the elemental analysis method; and obtain the fuel historical data standard subset based on the fuel historical data subset, the historical fuel minimum calorific value information and the historical fuel reference calorific value information.

[0191] The second data processing unit is used to establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis, substitute combustion temperature information and combustion time information into the combustion temperature-time coordinate system, obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset, filter the combustion temperature-time curve, and obtain the combustion temperature-time reference curve.

[0192] In summary, the advantages of this invention are as follows: by obtaining a standard subset of historical fuel data based on a subset of historical fuel data, historical minimum calorific value information, and historical benchmark calorific value information, the relationship between fuel temperature and fuel combustion time can be accurately analyzed using this standard subset of historical fuel data, providing a data foundation for subsequent fuel assessment. By obtaining a fuel assessment model based on a combustion temperature model and historical combustion data, and by accurately assessing the fuel using this fuel assessment model, the accuracy and reliability of the assessment results are ensured, and the efficiency of fuel assessment is improved.

[0193] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A fuel intelligent assessment method based on energy data feedback, characterized in that, include: Acquire combustion system information, wherein the combustion system is used to burn fuel and monitor data during combustion; Based on the combustion system information, historical combustion data is obtained, which represents historical data on the combustion of fuel in the combustion system; Based on historical combustion data, obtain a combustion temperature model; Based on the combustion temperature model, a fuel assessment model is obtained using historical combustion data; The combustion of the fuel to be evaluated is monitored to obtain combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information. The combustion monitoring data is input into the combustion temperature model and fitted to obtain the expected maximum combustion temperature; The estimated calorific value of the fuel is obtained based on the expected maximum combustion temperature and the fuel assessment model.

2. The fuel intelligent assessment method based on energy data feedback according to claim 1, characterized in that, The process of obtaining the combustion temperature model based on historical combustion data specifically includes: Historical fuel information is obtained based on historical combustion data, including historical fuel composition information; Based on historical fuel information, the historical combustion data corresponding to the same type of fuel are divided into the same group to obtain the historical fuel data group; Based on historical fuel data sets, obtain combustion count information corresponding to each historical fuel data set; Based on the combustion frequency information, the historical combustion data in the historical fuel data group is divided to obtain several historical fuel data subsets. The historical fuel data subsets represent the historical fuel energy data and historical fuel product information corresponding to each fuel combustion. Based on the historical fuel composition information corresponding to each historical fuel data group, and based on the incomplete combustion chemical reaction formula, the minimum calorific value information of the historical fuel corresponding to each historical fuel data group is obtained. Based on historical fuel composition information, the historical fuel reference calorific value information is obtained by elemental analysis. The historical fuel reference calorific value represents the theoretical calorific value of historical fuel when it is completely burned under ideal conditions. Based on the fuel historical data subset, historical fuel minimum calorific value information, and historical fuel benchmark calorific value information, obtain the fuel historical data standard subset; A combustion temperature model is obtained based on a standard subset of historical fuel data.

3. The fuel intelligent assessment method based on energy data feedback according to claim 2, characterized in that, The process of obtaining a standard subset of historical fuel data based on a subset of historical fuel data, historical minimum calorific value information, and historical benchmark calorific value information specifically includes: The ratio of the historical minimum calorific value of fuel to the historical reference calorific value of fuel is used as the calorific value reference deviation coefficient. Based on historical fuel composition information, obtain historical fuel moisture mass information and historical fuel total mass information; Based on historical combustion product information, obtain information on the types of historical combustion products and the corresponding quality information for each historical combustion product; The ratio of historical fuel moisture content to historical fuel total mass is used as the moisture deviation coefficient. The ratio of the total mass of historical combustion products to the total mass of historical fuel is used as the ash content deviation coefficient. The sum of the moisture deviation coefficient and the ash deviation coefficient corresponding to each subset of fuel history data is taken as the calorific value deviation coefficient of that subset of fuel history data. The difference between 1 and the maximum value of the calorific value deviation coefficient is taken as the first calorific value deviation coefficient, and the difference between 1 and the minimum value of the calorific value deviation coefficient is taken as the second calorific value deviation coefficient. The ratio of the first calorific value deviation coefficient to the second calorific value deviation coefficient is taken as the actual calorific value deviation coefficient. Based on the calorific value benchmark deviation coefficient and the actual calorific value deviation coefficient, a subset of historical fuel data is filtered to obtain a standard subset of historical fuel data. If the actual calorific value deviation coefficient is less than the calorific value reference deviation coefficient, the fuel historical data subset corresponding to the first calorific value deviation coefficient is removed and the first calorific value deviation coefficient is re-obtained until the actual calorific value deviation coefficient is greater than the calorific value reference deviation coefficient.

4. The fuel intelligent assessment method based on energy data feedback according to claim 3, characterized in that, The step of obtaining the combustion temperature model based on a standard subset of historical fuel data specifically includes: Based on the standard subset of fuel historical data, obtain the historical fuel energy data corresponding to each standard subset of fuel historical data; Based on historical fuel energy data, obtain combustion temperature and combustion time information; Establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis; Substitute the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each standard subset of historical fuel data. The combustion temperature-time curves were filtered to obtain a combustion temperature-time baseline curve; Based on the combustion temperature time baseline curve, historical fuel energy data are fitted to obtain a combustion temperature model; Specifically, the combustion temperature model is as follows: T(t)=T max (1-e -k·t )+w; In the formula, T(t) represents the combustion temperature at time t. max represents the maximum combustion temperature, k represents the combustion coefficient, and w is a correction term.

5. The fuel intelligent assessment method based on energy data feedback according to claim 4, characterized in that, The process of filtering the combustion temperature-time curve to obtain a combustion temperature-time baseline curve specifically includes: Based on the combustion temperature-time curve, and using integral calculations, the area of ​​the graph formed by the combustion temperature-time curve and the horizontal axis is used as fuel heat information. The ratio of the fuel calorific value to the total historical fuel mass corresponding to the combustion temperature-time curve is used as the fuel calibrated calorific value. The average of the historical fuel minimum calorific value and the historical fuel benchmark calorific value corresponding to each historical fuel data group is used as the characteristic calorific value of the fuel. The difference between the historical fuel reference calorific value and the characteristic calorific value of the fuel is used as the fuel calorific value deviation threshold; Based on the standard subset of historical fuel data, obtain the first calorific value deviation coefficient and the second calorific value deviation coefficient corresponding to each historical fuel data group; The difference between 1 and the calorific value benchmark deviation coefficient corresponding to each historical fuel data group is taken as the calorific value benchmark deviation value, and the difference between the second calorific value deviation coefficient and the first calorific value deviation coefficient corresponding to each historical fuel data group is taken as the actual calorific value deviation value. The ratio of the actual deviation of calorific value to the reference deviation of calorific value is used as the calorific value mapping coefficient; The product of the fuel calorific value deviation threshold and the calorific value mapping coefficient is used as the fuel calorific value deviation mapping threshold; Using any two combustion temperature-time curves as a reference, the combustion temperature-time curves are filtered to obtain the combustion temperature-time reference curve. If the difference between the calibrated calorific values ​​of the fuel corresponding to two combustion temperature-time curves exceeds the fuel calorific value deviation mapping threshold, then the combustion temperature-time curve corresponding to the smaller calibrated calorific value of the fuel in the two combustion temperature-time curves will be removed until the difference between the calibrated calorific values ​​of the fuel corresponding to any two combustion temperature-time curves is less than the fuel calorific value deviation mapping threshold.

6. The fuel intelligent assessment method based on energy data feedback according to claim 5, characterized in that, The fuel evaluation model, based on a combustion temperature model and historical combustion data, specifically includes: Obtain the fuel calorific value and historical combustion product information corresponding to the combustion temperature-time baseline curve; Based on historical combustion product information, the average specific heat capacity is obtained using the specific heat capacity corresponding to each historical combustion product. The baseline ambient temperature is obtained based on the combustion temperature-time reference curve. The difference between the maximum combustion temperature and the base ambient temperature is taken as the heat temperature change value; Based on the heat temperature change value, average specific heat capacity and fuel calibrated calorific value, the data is fitted to obtain a fuel evaluation model; Specifically, the fuel assessment model is as follows: In the formula, q is the calorific value of the fuel, and c p The average specific heat capacity is given by L, the correction term is given by q0, the reference calorific value of the fuel is given by G, and the calorific value deviation coefficient is given by T. h Based on ambient temperature, y i c represents the molar mass percentage of the i-th combustion product. p，i This represents the specific heat capacity of the i-th combustion product.

7. A fuel intelligent assessment system based on energy data feedback, used to implement the assessment method as described in any one of claims 1-6, characterized in that, include: The main control module is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain a combustion temperature model; to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain a fuel evaluation model; to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information to obtain historical fuel data groups; to obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups; to divide the historical combustion data in the historical fuel data groups according to the combustion number information to obtain several fuel historical data subsets; to input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature; and to obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model. The information acquisition module is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, acquire historical fuel information based on the historical combustion data, the historical fuel information including historical fuel composition information, monitor the combustion of the fuel to be evaluated, and acquire combustion monitoring data, the combustion monitoring data including combustion temperature data, ambient temperature data, combustion time data, and combustion product information; The data processing module is used to obtain the minimum calorific value information of the historical fuel corresponding to each historical fuel data group based on the historical fuel composition information and the incomplete combustion chemical reaction formula. Based on the historical fuel composition information, the module obtains the historical fuel reference calorific value information by elemental analysis. Based on the historical fuel data subset, the historical fuel minimum calorific value information, and the historical fuel reference calorific value information, the module obtains the fuel historical data standard subset. The module establishes a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis. The module substitutes the combustion temperature information and combustion time information into the combustion temperature-time coordinate system to obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset. The module filters the combustion temperature-time curves to obtain the combustion temperature-time reference curve. The display module interacts with the main control module and is used to output and display the combustion temperature model, fuel assessment model, combustion monitoring data, expected maximum combustion temperature, and fuel assessment calorific value.

8. The fuel intelligent assessment system based on energy data feedback according to claim 7, characterized in that, The main control module specifically includes: The control unit is used to divide the historical combustion data corresponding to the same type of fuel into the same group according to historical fuel information, obtain historical fuel data groups, obtain combustion number information corresponding to each historical fuel data group based on the historical fuel data groups, divide the historical combustion data in the historical fuel data groups based on the combustion number information, obtain several fuel historical data subsets, input the combustion monitoring data into the combustion temperature model and fit it to obtain the expected maximum combustion temperature, and obtain the fuel evaluation calorific value according to the expected maximum combustion temperature and the fuel evaluation model. An information receiving unit interacts with an information acquisition module and a data processing module to receive data and transmit it to a model training unit. The model training unit is used to fit the combustion temperature function according to the combustion temperature time reference curve to obtain the combustion temperature model, and to fit the data according to the heat temperature change value, average specific heat capacity and fuel calibrated calorific value to obtain the fuel evaluation model.

9. A fuel intelligent assessment system based on energy data feedback according to claim 7, characterized in that, The information acquisition module specifically includes: The first acquisition unit is used to acquire combustion system information, acquire historical combustion data based on the combustion system information, and acquire historical fuel information based on the historical combustion data, wherein the historical fuel information includes historical fuel composition information. The second acquisition unit is used to monitor the combustion of the fuel to be evaluated and acquire combustion monitoring data, which includes combustion temperature data, ambient temperature data, combustion time data, and combustion product information.

10. A fuel intelligent assessment system based on energy data feedback according to claim 7, characterized in that, The data processing module specifically includes: The first data processing unit is configured to obtain the historical fuel minimum calorific value information corresponding to each historical fuel data group based on the historical fuel composition information corresponding to each historical fuel data group and the incomplete combustion chemical reaction formula; obtain the historical fuel reference calorific value information based on the historical fuel composition information and the elemental analysis method; and obtain the fuel historical data standard subset based on the fuel historical data subset, the historical fuel minimum calorific value information and the historical fuel reference calorific value information. The second data processing unit is used to establish a combustion temperature-time coordinate system with combustion time as the horizontal axis and combustion temperature as the vertical axis, substitute combustion temperature information and combustion time information into the combustion temperature-time coordinate system, obtain the combustion temperature-time curve corresponding to each fuel historical data standard subset, filter the combustion temperature-time curve, and obtain the combustion temperature-time reference curve.

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