Coal feeder control method and system based on DCS

Abstract

The invention discloses a coal feeder control method and system based on a DCS. The method comprises the steps that coal feeder operation data collected by the DCS are acquired and preprocessed; the preprocessed data are analyzed, and factors influencing the coal feeding rotating speed of the coal feeder are determined; constructing a coal feeding rotation speed prediction model based on the factor to predict coal feeding prediction rotation speed data; determining predicted coal supply quantity data according to the coal supply predicted rotating speed data, obtaining a coal supply quantity demand, and determining the predicted coal supply quantity data and coal supply quantity deviation data thereof; evaluating the coal feeding quantity deviation data, and determining a coal feeding optimization coefficient according to a result; the coal feeding rotating speed is optimized and adjusted according to the coal feeding optimization coefficient, and the coal feeding rotating speed is controlled according to the optimized coal feeding rotating speed. The coal feeder operation data is obtained based on the DCS, analysis and modeling are carried out, the coal feeding amount can be predicted in real time, the coal feeding optimization coefficient of the coal feeder is determined by evaluating the coal feeding amount deviation data, the coal feeder is optimized and adjusted, and the working efficiency of the coal feeder is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal feeder control, and in particular to a DCS-based coal feeder control method and system. Background Art

[0002] In industrial production, coal feeders are key equipment in the coal transportation system, and their operating status and performance play a vital role in the stability and efficiency of the entire production process. In order to achieve precise control of coal feeders and improve the stability and efficiency of coal supply, the speed control method of coal feeders based on DCS (distributed control system) has become an important technical means. The DCS system can realize remote monitoring and automatic control of various key equipment in the industrial production process. Through real-time collection and analysis of coal feeder operation data, precise control of coal feeders can be achieved.

[0003] However, traditional coal feeder control methods usually control the speed of coal feeders based on experience and simple control algorithms. Their accuracy is limited and it is difficult to meet the needs of refined control in complex production environments. At the same time, traditional methods often lack intelligent optimization means, making it difficult to dynamically adjust and optimize the operation of coal feeders based on real-time data. Operators are often required to make manual adjustments based on experience, which is easily affected by the level and experience of the supervisory operator, and also increases labor costs and the possibility of errors. In addition, traditional methods often cannot achieve remote monitoring and automated control, limiting the level of intelligent management of the production process. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a DCS-based coal feeder control method and system, comprising:

[0005] Obtain the coal feeder operation data collected by DCS and pre-process the coal feeder operation data;

[0006] Analyze the pre-processed coal feeder operation data to determine the factors affecting the coal feeder speed;

[0007] A coal feeding speed prediction model of the coal feeder is constructed based on the influencing factors, and the coal feeding speed of the coal feeder is predicted according to the coal feeding speed prediction model of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder;

[0008] Determine the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtain the coal feeding amount demand, and determine the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand;

[0009] Evaluate the coal feeding amount deviation data and determine the coal feeding optimization coefficient of the coal feeder based on the evaluation results;

[0010] The coal feeding speed of the coal feeder is optimized and adjusted according to the coal feeding optimization coefficient, and the coal feeding speed of the coal feeder is controlled according to the optimized coal feeding speed.

[0011] Furthermore, the preprocessing of the coal feeder operation data includes:

[0012] The coal feeder operation data is preprocessed, which includes removing abnormal data and overload data in the coal feeder operation data and filling in missing data, and standardizing the coal feeder operation data.

[0013] Furthermore, the pre-processed coal feeder operation data is analyzed to determine the influencing factors affecting the coal feeding speed of the coal feeder, including:

[0014] Acquire the preprocessed coal feeder operation data, and divide the coal feeder operation data into several operation parameter data groups according to parameter types;

[0015] Obtain the coal feeding speed data of the coal feeder, analyze the correlation between each set of operating parameter data and the coal feeding speed data of the coal feeder, and select the operating parameter data set whose correlation exceeds a preset threshold;

[0016] The parameter corresponding to the selected operating parameter data group is determined, and the parameter is determined as an influencing factor affecting the coal feeding speed of the coal feeder.

[0017] Furthermore, the coal feeding speed prediction model of the coal feeder is constructed based on the influencing factors, and the coal feeding speed prediction model of the coal feeder is used to predict the coal feeding speed of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder, including:

[0018] A data set is constructed based on the influencing factors, and the data set is input into a preset neural network model to construct an initial model for predicting the coal feeding speed;

[0019] The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for predicting the coal feeding speed;

[0020] The initial model for predicting the coal feeding speed is trained and tested until the initial model for predicting the coal feeding speed meets a preset convergence condition, thereby obtaining a coal feeding speed prediction model;

[0021] The coal feeding speed prediction model is used to predict the coal feeding speed data of the coal feeder in the future.

[0022] Further, the step of determining the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtaining the coal feeding amount demand, and determining the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand includes:

[0023] Obtain design parameters of the coal feeder, and determine predicted coal feeding amount data according to the design parameters of the coal feeder and the coal feeding predicted speed data;

[0024] The coal feeding quantity demand is obtained, and the difference between each predicted coal feeding quantity data and the coal feeding quantity demand is calculated, and these differences are determined as the coal feeding quantity deviation data between the predicted coal feeding quantity data and the coal feeding quantity demand.

[0025] Furthermore, the evaluation of the coal feed rate deviation data includes:

[0026] Obtain coal feed amount deviation data and calculate the average value of all coal feed amount deviation data;

[0027] The difference between each coal feeding amount deviation data and the average value is calculated to obtain the corresponding deviation difference;

[0028] Evaluate and value the deviation difference corresponding to each coal feeding amount deviation data, obtain the evaluation value of each coal feeding amount deviation data, and assign a corresponding weight to each coal feeding amount deviation data;

[0029] The evaluation value of each coal feeding amount deviation data and the weight of each coal feeding amount deviation data are weighted and added together to obtain the coal feeding difference evaluation value of the coal feeder.

[0030] Further, determining the coal feeding optimization coefficient of the coal feeder according to the evaluation result, and optimizing and adjusting the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient, includes:

[0031] A corresponding relationship between coal feeding optimization coefficient and coal feeding difference evaluation value interval is preset, and for each coal feeding difference evaluation value interval, a corresponding coal feeding optimization coefficient is associated with the corresponding coal feeding optimization coefficient;

[0032] The coal feeding difference evaluation value is obtained, and based on the mapping relationship between the coal feeding difference evaluation value interval to which the difference belongs and the corresponding relationship between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval, the coal feeding optimization coefficient corresponding to the coal feeding difference evaluation value interval is selected.

[0033] The present invention also provides a DCS-based coal feeder control system, comprising:

[0034] The acquisition module is used to acquire the coal feeder operation data collected by the DCS and pre-process the coal feeder operation data;

[0035] An analysis module is used to analyze the pre-processed coal feeder operation data and determine the factors affecting the coal feeding speed of the coal feeder;

[0036] A prediction module is used to construct a coal feeding speed prediction model of the coal feeder based on the influencing factors, and predict the coal feeding speed of the coal feeder according to the coal feeding speed prediction model of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder;

[0037] A determination module is used to determine the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtain the coal feeding amount demand, and determine the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand;

[0038] An evaluation module is used to evaluate the coal feeding amount deviation data and determine the coal feeding optimization coefficient of the coal feeder according to the evaluation result;

[0039] The control module is used to optimize and adjust the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient, and control the coal feeding speed of the coal feeder according to the optimized coal feeding speed.

[0040] Compared with the prior art, the DCS-based coal feeder control method and system of the embodiment of the present invention has the following beneficial effects:

[0041] The present invention, through the analysis and modeling of coal feeder operation data, allows decision makers to make more accurate decisions based on actual data, rather than just relying on experience or intuition;

[0042] The coal feeding speed prediction model established in the present invention can help realize the real-time prediction of the coal feeding amount, thereby making the regulation of coal supply more accurate and timely, and helping to optimize the production process;

[0043] The present invention adjusts the coal feeding speed of the coal feeder by optimizing the coefficient, thereby realizing automatic control of the coal feeder, reducing manual intervention and improving production efficiency.

[0044] The present invention can achieve more effective utilization of resources such as coal, reduce production costs, and improve resource utilization efficiency by analyzing and optimizing the coal feeder operation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 1 is a schematic diagram of the flow structure of a DCS-based coal feeder control method in an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram of the composition of a DCS-based coal feeder control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0048] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0049] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0050] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0051] like Figure 1 As shown, in an embodiment of the present application, a DCS-based coal feeder control method is provided, including: S100: acquiring coal feeder operation data collected by the DCS, and preprocessing the coal feeder operation data; S200: analyzing the preprocessed coal feeder operation data to determine the influencing factors affecting the coal feeding speed of the coal feeder; S300: constructing a coal feeding speed prediction model of the coal feeder based on the influencing factors, and predicting the coal feeding speed of the coal feeder according to the coal feeding speed prediction model of the coal feeder, to obtain the coal feeding prediction speed data of the coal feeder; S400: determining predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtaining the coal feeding amount demand, and determining the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand; S500: evaluating the coal feeding amount deviation data, and determining the coal feeding optimization coefficient of the coal feeder according to the evaluation result; S600: optimizing and adjusting the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient, and controlling the coal feeding speed of the coal feeder according to the optimized coal feeding speed.

[0052] Furthermore, the present invention, through the analysis and modeling of the coal feeder operating data, can enable decision makers to make more accurate decisions based on actual data, rather than just relying on experience or intuition; the coal feeding speed prediction model established by the present invention can help realize the real-time prediction of the coal feeding amount, so that the regulation and control of the coal supply is more accurate and timely, which helps to optimize the production process; the present invention adjusts the coal feeding speed of the coal feeder through the optimization coefficient, so as to realize the automatic control of the coal feeder, reduce manual intervention and improve production efficiency; the present invention can realize more effective utilization of resources such as coal, reduce production costs and improve resource utilization efficiency by analyzing and optimizing the coal feeder operating data.

[0053] In an embodiment of the present application, a DCS-based coal feeder control method is provided, wherein the preprocessing of the coal feeder operating data comprises: preprocessing the coal feeder operating data, the preprocessing comprising removing abnormal data and overload data in the coal feeder operating data and filling in missing data, and standardizing the coal feeder operating data.

[0054] Specifically, identify and remove abnormal data and duplicate data. Abnormal data may be caused by sensor failure or other factors, and can be identified and removed by setting thresholds or using anomaly detection algorithms. Duplicate data may be due to problems in the data acquisition system and requires deduplication. Missing data is a common problem and can be filled in through interpolation (such as linear interpolation, polynomial interpolation), mean filling, median filling, or prediction using machine learning models to ensure data integrity. Standardization can make the value ranges of different features consistent, which is beneficial to model convergence and training effects. Standardization methods include Z-score standardization and minimum-maximum standardization. Z-score standardization converts data into a distribution with a mean of 0 and a standard deviation of 1, and minimum-maximum standardization scales the data to a fixed range.

[0055] In an embodiment of the present application, a DCS-based coal feeder control method is provided, wherein the preprocessed coal feeder operating data is analyzed to determine the influencing factors affecting the coal feeding speed of the coal feeder, including: obtaining the preprocessed coal feeder operating data, and dividing the coal feeder operating data into several groups of operating parameter data groups according to parameter types; obtaining the coal feeding speed data of the coal feeder, analyzing the correlation between each group of operating parameter data groups and the coal feeding speed data of the coal feeder, and selecting the operating parameter data groups whose correlation exceeds a preset threshold; determining the parameter corresponding to the selected operating parameter data group, and determining the parameter as the influencing factor affecting the coal feeding speed of the coal feeder.

[0056] Specifically, the preprocessed coal feeder operation data is obtained from the data source, and the coal feeder operation data is a data set containing various operation parameters; the data is divided according to the parameter type to form several groups of operation parameter data groups, such as temperature-related parameters, pressure-related parameters, etc.; a correlation analysis is performed on each group of operation parameter data groups and the coal feeding speed data of the coal feeder, and the correlation between them can be measured by methods such as the Pearson correlation coefficient or the Spearman correlation coefficient; a preset correlation threshold is set, and the operation parameter data groups whose correlation exceeds the threshold are selected as candidate influencing factors; for the selected operation parameter data groups, their corresponding parameters are determined, and they are determined as influencing factors affecting the coal feeding speed of the coal feeder.

[0057] In an embodiment of the present application, a DCS-based coal feeder control method is provided, wherein a coal feeding speed prediction model of the coal feeder is constructed based on influencing factors, and the coal feeding speed of the coal feeder is predicted according to the coal feeding speed prediction model of the coal feeder to obtain predicted coal feeding speed data of the coal feeder, including: constructing a data set based on the influencing factors, and inputting the data set into a preset neural network model to construct an initial model for predicting coal feeding speed; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the initial model for predicting coal feeding speed; training and testing the initial model for predicting coal feeding speed until the initial model for predicting coal feeding speed meets the preset convergence conditions to obtain the coal feeding speed prediction model; and predicting the predicted coal feeding speed data of the coal feeder in a future period of time according to the obtained coal feeding speed prediction model.

[0058] Specifically, according to the determined influencing factors, a data set including these factors is constructed, in which each sample corresponds to the coal feeder operation data and the corresponding coal feeding speed data at a time point; appropriate neural network structure and parameters are selected to establish an initial prediction model for the coal feeding speed; the constructed data set is divided into a training set and a test set according to a certain ratio, usually 80% of the data can be used as the training set and 20% of the data can be used as the test set; the training set is input into the initial model for predicting the coal feeding speed for training, and the model parameters are optimized through continuous iteration until the model meets the preset convergence conditions; the trained coal feeding speed prediction model is used to predict the coal feeding speed data of the coal feeder in the future.

[0059] In an embodiment of the present application, a DCS-based coal feeder control method is provided, which determines predicted coal feeding amount data based on predicted coal feeding speed data of the coal feeder, obtains coal feeding amount demand, and determines coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand, including: obtaining design parameters of the coal feeder, and determining the predicted coal feeding amount data based on the design parameters of the coal feeder and the predicted coal feeding speed data; obtaining the coal feeding amount demand, and calculating the difference between each predicted coal feeding amount data and the coal feeding amount demand, and determining these differences as coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand.

[0060] Specifically, the design parameters of the coal feeder are obtained, and the predicted coal feeding speed data and design parameters can be used to determine the predicted coal feeding quantity data; the coal feeding quantity demand data is obtained, and the difference between each predicted coal feeding quantity data and the coal feeding quantity demand is calculated. These differences can be determined as the coal feeding quantity deviation data between the predicted coal feeding quantity data and the coal feeding quantity demand. These deviation data can improve the accuracy of the evaluation prediction, and whether the performance of the coal feeder meets the actual demand.

[0061] In an embodiment of the present application, a DCS-based coal feeder control method is provided, wherein the evaluation of coal feeding amount deviation data comprises: obtaining coal feeding amount deviation data and taking an average value of all coal feeding amount deviation data; performing a difference calculation between each coal feeding amount deviation data and the average value to obtain a corresponding deviation difference; evaluating and valuing the deviation difference corresponding to each coal feeding amount deviation data to obtain an evaluation value of each coal feeding amount deviation data, and assigning a corresponding weight to each coal feeding amount deviation data; performing a weighted addition calculation on the evaluation value of each coal feeding amount deviation data and the weight of each coal feeding amount deviation data to obtain a coal feeding difference evaluation value of the coal feeder.

[0062] Specifically, all coal feeding amount deviation data are summed up and then divided by the number of data points to obtain the average value; each coal feeding amount deviation data is subtracted from the average value to obtain the corresponding deviation difference, which is intended to measure the degree of deviation of each deviation data relative to the overall average value; each deviation difference is evaluated and valued, and a score of 0-10 can be used to indicate the degree of deviation; a corresponding weight is assigned to each coal feeding amount deviation data, and the weight can be determined based on the importance and impact of the deviation data; the evaluation value of each coal feeding amount deviation data is weightedly added to the corresponding weight to obtain the coal feeding difference evaluation value of the coal feeder. This process can comprehensively consider the degree of deviation, importance and impact of the coal feeding amount deviation data, thereby obtaining a comprehensive evaluation of the coal feeding difference of the coal feeder.

[0063] In an embodiment of the present application, a DCS-based coal feeder control method is provided, wherein the coal feeding optimization coefficient of the coal feeder is determined according to the evaluation result, and the coal feeding speed of the coal feeder is optimized and adjusted according to the coal feeding optimization coefficient, including: pre-setting a correspondence between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval, wherein each coal feeding difference evaluation value interval is associated with a corresponding coal feeding optimization coefficient in the correspondence between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval; obtaining the coal feeding difference evaluation value, and selecting the coal feeding optimization coefficient corresponding to the coal feeding difference evaluation value interval based on a mapping relationship between the coal feeding difference evaluation value interval to which the difference belongs and the corresponding relationship between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval.

[0064] Specifically, the correspondence between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval can be pre-set by creating a table or function, which lists different coal feeding difference evaluation value intervals and the coal feeding optimization coefficient corresponding to each interval. For example, a table can be created to list different evaluation value intervals and corresponding optimization coefficients, and the corresponding coal feeding optimization coefficient can be found in the correspondence between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval according to the interval to which it belongs. By using a table to represent the mapping relationship, the corresponding interval can be found according to the coal feeding difference evaluation value, and then the coal feeding optimization coefficient corresponding to the interval can be found in the table. This mapping relationship can determine the corresponding optimization coefficient according to different situations of the coal feeding difference evaluation value, thereby realizing dynamic adjustment and optimization of the performance of the coal feeder.

[0065] like Figure 2 As shown, in an embodiment of the present application, a DCS-based coal feeder control system is provided, including: an acquisition module, used to acquire coal feeder operation data collected by the DCS, and pre-process the coal feeder operation data; an analysis module, used to analyze the pre-processed coal feeder operation data, and determine the influencing factors that affect the coal feeding speed of the coal feeder; a prediction module, used to construct a coal feeding speed prediction model of the coal feeder based on the influencing factors, and predict the coal feeding speed of the coal feeder according to the coal feeding speed prediction model of the coal feeder, and obtain the coal feeding prediction speed data of the coal feeder; a determination module, used to determine the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtain the coal feeding amount demand, and determine the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand; an evaluation module, used to evaluate the coal feeding amount deviation data, and determine the coal feeding optimization coefficient of the coal feeder according to the evaluation result; a control module, used to optimize and adjust the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient, and control the coal feeding speed of the coal feeder according to the optimized coal feeding speed.

[0066] In summary, the embodiment of the present invention provides a DCS-based coal feeder control method and system, which includes: acquiring and preprocessing the coal feeder operation data collected by the DCS; analyzing the preprocessed data to determine the factors affecting the coal feeding speed of the coal feeder; constructing a coal feeding speed prediction model based on the factors to predict the coal feeding prediction speed data; determining the predicted coal feeding amount data according to the coal feeding prediction speed data, obtaining the coal feeding amount demand, and determining the predicted coal feeding amount data and the coal feeding amount deviation data; evaluating the coal feeding amount deviation data, and determining the coal feeding optimization coefficient according to the result; optimizing and adjusting the coal feeding speed according to the coal feeding optimization coefficient, and controlling the coal feeding speed according to the optimized coal feeding speed. The present invention acquires the coal feeder operation data based on the DCS, and performs analysis and modeling, so as to predict the coal feeding amount in real time, and determine the coal feeding optimization coefficient of the coal feeder by evaluating the coal feeding amount deviation data, so as to optimize and adjust the coal feeder and improve the working efficiency of the coal feeder.

[0067] Finally, it should be noted that: Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0068] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0069] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0070] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A DCS-based coal feeder control method, characterized in that: include: Obtain the coal feeder operation data collected by DCS and pre-process the coal feeder operation data; Analyze the pre-processed coal feeder operation data to determine the factors affecting the coal feeder's coal feeding speed; A coal feeding speed prediction model of the coal feeder is constructed based on the influencing factors, and the coal feeding speed of the coal feeder is predicted according to the coal feeding speed prediction model of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder; Determine the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtain the coal feeding amount demand, and determine the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand; Evaluate the coal feeding amount deviation data and determine the coal feeding optimization coefficient of the coal feeder based on the evaluation results; The coal feeding speed of the coal feeder is optimized and adjusted according to the coal feeding optimization coefficient, and the coal feeding speed of the coal feeder is controlled according to the optimized coal feeding speed.

2. A DCS-based coal feeder control method according to claim 1, characterized in that: The preprocessing of the coal feeder operation data includes: The coal feeder operation data is preprocessed, which includes removing abnormal data and overload data in the coal feeder operation data and filling in missing data, and standardizing the coal feeder operation data.

3. A DCS-based coal feeder control method according to claim 2, characterized in that: The analysis of the pre-processed coal feeder operation data to determine the influencing factors affecting the coal feeding speed of the coal feeder includes: Acquire the preprocessed coal feeder operation data, and divide the coal feeder operation data into several operation parameter data groups according to parameter types; Obtain the coal feeding speed data of the coal feeder, analyze the correlation between each set of operating parameter data and the coal feeding speed data of the coal feeder, and select the operating parameter data set whose correlation exceeds a preset threshold; The parameter corresponding to the selected operating parameter data group is determined, and the parameter is determined as an influencing factor affecting the coal feeding speed of the coal feeder.

4. A DCS-based coal feeder control method according to claim 3, characterized in that: The coal feeding speed prediction model of the coal feeder is constructed based on the influencing factors, and the coal feeding speed prediction model of the coal feeder is used to predict the coal feeding speed of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder, including: A data set is constructed based on the influencing factors, and the data set is input into a preset neural network model to construct an initial model for predicting the coal feeding speed; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for predicting the coal feeding speed; The initial model for predicting the coal feeding speed is trained and tested until the initial model for predicting the coal feeding speed meets a preset convergence condition, thereby obtaining a coal feeding speed prediction model; The coal feeding speed prediction model is used to predict the coal feeding speed data of the coal feeder in the future.

5. A DCS-based coal feeder control method according to claim 4, characterized in that: The step of determining the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtaining the coal feeding amount demand, and determining the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand includes: Obtain design parameters of the coal feeder, and determine predicted coal feeding amount data according to the design parameters of the coal feeder and the coal feeding predicted speed data; The coal feeding quantity demand is obtained, and the difference between each predicted coal feeding quantity data and the coal feeding quantity demand is calculated, and these differences are determined as the coal feeding quantity deviation data between the predicted coal feeding quantity data and the coal feeding quantity demand.

6. A DCS-based coal feeder control method according to claim 5, characterized in that: The evaluation of the coal feed rate deviation data includes: Obtain coal feed amount deviation data and calculate the average value of all coal feed amount deviation data; The difference between each coal feeding amount deviation data and the average value is calculated to obtain the corresponding deviation difference; Evaluate and value the deviation difference corresponding to each coal feeding amount deviation data, obtain the evaluation value of each coal feeding amount deviation data, and assign a corresponding weight to each coal feeding amount deviation data; The evaluation value of each coal feeding amount deviation data and the weight of each coal feeding amount deviation data are weighted and added together to obtain the coal feeding difference evaluation value of the coal feeder.

7. A DCS-based coal feeder control method according to claim 6, characterized in that: The step of determining the coal feeding optimization coefficient of the coal feeder according to the evaluation result, and optimizing and adjusting the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient includes: A corresponding relationship between coal feeding optimization coefficient and coal feeding difference evaluation value interval is preset, and for each coal feeding difference evaluation value interval, a corresponding coal feeding optimization coefficient is associated with the corresponding coal feeding optimization coefficient; The coal feeding difference evaluation value is obtained, and based on the mapping relationship between the coal feeding difference evaluation value interval to which the difference belongs and the corresponding relationship between the coal feeding optimization coefficient and the coal feeding difference evaluation value interval, the coal feeding optimization coefficient corresponding to the coal feeding difference evaluation value interval is selected.

8. A DCS-based coal feeder control system, characterized in that: include: The acquisition module is used to acquire the coal feeder operation data collected by the DCS and pre-process the coal feeder operation data; An analysis module is used to analyze the pre-processed coal feeder operation data and determine the factors affecting the coal feeding speed of the coal feeder; A prediction module is used to construct a coal feeding speed prediction model of the coal feeder based on the influencing factors, and predict the coal feeding speed of the coal feeder according to the coal feeding speed prediction model of the coal feeder to obtain the coal feeding speed prediction data of the coal feeder; A determination module is used to determine the predicted coal feeding amount data according to the predicted coal feeding speed data of the coal feeder, obtain the coal feeding amount demand, and determine the coal feeding amount deviation data between the predicted coal feeding amount data and the coal feeding amount demand; An evaluation module is used to evaluate the coal feeding amount deviation data and determine the coal feeding optimization coefficient of the coal feeder according to the evaluation result; The control module is used to optimize and adjust the coal feeding speed of the coal feeder according to the coal feeding optimization coefficient, and control the coal feeding speed of the coal feeder according to the optimized coal feeding speed.

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