Clean coal ash content control method and device and computer equipment

Abstract

The invention relates to a clean coal ash content control method and device and computer equipment. The method comprises the steps that clean coal ash content prediction data at the current moment, historical floating coal slime data in a first preset time period and flotation agent feeding amount stacking data of each flotation agent feeding amount scheme at the current moment are input into a pre-constructed transfer model, and clean coal ash content prediction data at the next moment are obtained; updating the clean coal ash content prediction data at the next moment as the clean coal ash content prediction data at the current moment, updating the flotation agent adding amount stacking data of each flotation agent adding amount scheme at the next moment as the flotation agent adding amount stacking data of each flotation agent adding amount scheme at the current moment, and repeating the clean coal ash content prediction step. Until a clean coal ash content prediction track corresponding to each flotation agent dosage scheme is obtained; and selecting an optimal flotation agent dosage scheme according to the plurality of clean coal ash content prediction trajectories, and carrying out clean coal ash content control. By adopting the method, the ash content of the clean coal can be accurately controlled.

Description

Technical Field

[0001] The present application relates to the technical field of coal slime flotation, and in particular to a method, device and computer equipment for controlling ash content in clean coal. Background Art

[0002] Coal slime flotation is a coal preparation method that uses the difference in surface properties of mineral particles to separate coal particles and gangue. During the coal slime flotation process, flotation agents need to be added to the flotation machine, and the ash content of clean coal is controlled by adjusting the amount of flotation agents added. The ash content of clean coal refers to the gangue content in the clean coal.

[0003] In traditional technology, the ash content of clean coal in the process of coal slime flotation is controlled by manual dosing. However, the manual dosing method has lag and inaccuracy, which makes it impossible to accurately control the ash content of clean coal. Summary of the invention

[0004] Based on this, it is necessary to provide a clean coal ash control method, device and computer equipment that can accurately control the clean coal ash content in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for controlling ash content in clean coal, comprising:

[0006] Inputting the clean coal ash content prediction data at the current moment, the historical flotation coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment;

[0007] The clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment is updated to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and the process returns to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and the clean coal ash content is continuously predicted until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained;

[0008] According to the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes, the optimal flotation agent dosage scheme is selected to control the clean coal ash content.

[0009] In one embodiment, before inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment, the method further includes:

[0010] According to the flotation agent dosage stacking time and the clean coal ash content prediction step at the current moment, the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is determined;

[0011] According to the stacking time of the floating coal slime data and the clean coal discharging time, the historical floating coal slime data for the first preset time period is determined.

[0012] In one embodiment, the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into a pre-built transfer model to obtain the clean coal ash content prediction data at the next moment, including:

[0013] The clean coal ash content prediction data at the current moment is used as the observed quantity prediction value at the current moment, the historical floating coal slime data of the first preset time period is used as the external variable of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action quantity stacking data of each flotation agent dosage scheme at the current moment;

[0014] The observed quantity prediction value at the current moment, the external variables of the first preset time period and the action quantity stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the observed quantity prediction value at the next moment.

[0015] In one embodiment, the method further comprises:

[0016] When the current moment is the first moment, the actual data of clean coal ash content at the current moment is obtained;

[0017] The actual clean coal ash content data at the current moment, the historical flotation slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment.

[0018] In one embodiment, before inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stack data of each flotation agent dosage scheme at the current moment into a pre-constructed transfer model to obtain the clean coal ash content prediction data at the next moment, the method further includes a transfer model construction step, including:

[0019] Acquire the clean coal ash content prediction data at the current training moment, and acquire the historical floating coal slime data and flotation agent dosage stacking data matching the current training moment; input the clean coal ash content prediction data at the current training moment, the historical floating coal slime data and flotation agent dosage stacking data matching the current training moment into the transfer model to be trained, and obtain the clean coal ash content prediction data at the next training moment;

[0020] The clean coal ash content prediction data at the next training moment is updated to the clean coal ash content prediction data at the current training moment, and the process returns to the step of obtaining the historical floating coal slime data and the flotation agent dosage stacking data matching the current training moment, and continues to predict the clean coal ash content until a prediction trajectory of a preset length is obtained;

[0021] The transfer model to be trained is updated according to the predicted trajectory of the preset length until the preset conditions are met to obtain the pre-built transfer model.

[0022] In one embodiment, the method further comprises:

[0023] When the current training moment is the initial training moment, actual clean coal ash content data at the current training moment is obtained;

[0024] The actual clean coal ash content data at the current training moment, the historical flotation slime data matching the current training moment and the flotation agent dosage stacked data are input into the transfer model to be trained to obtain the clean coal ash content prediction data at the next training moment.

[0025] In one embodiment, updating the transfer model to be trained according to the predicted trajectory of the preset length includes:

[0026] Determine the error between the predicted trajectory of a preset length and the actual trajectory through a discriminant network;

[0027] The transfer model to be trained is updated according to the error between the predicted trajectory of the preset length and the true trajectory.

[0028] In one embodiment, updating the transfer model to be trained according to the predicted trajectory of the preset length until the preset conditions are met to obtain the pre-built transfer model includes:

[0029] The transfer model to be trained is updated according to the predicted trajectory of the preset length until the training conditions are met, thereby obtaining the trained transfer model;

[0030] The trained transfer model is subjected to model testing. When the model testing passes, the pre-built transfer model is obtained. The model testing includes deduction testing, response curve testing and data distribution testing.

[0031] In a second aspect, the present application also provides a clean coal ash control device, comprising:

[0032] The ash content prediction module is used to input the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into a pre-built transfer model to obtain the clean coal ash content prediction data at the next moment;

[0033] A cyclic prediction module, used for updating the clean coal ash content prediction data at the next moment to the clean coal ash content prediction data at the current moment, updating the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, returning to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and continuing to predict the clean coal ash content until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained;

[0034] The scheme selection module is used to select the optimal flotation agent dosage scheme according to the clean coal ash content prediction trajectory corresponding to multiple flotation agent dosage schemes to control the clean coal ash content.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Inputting the clean coal ash content prediction data at the current moment, the historical flotation coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment;

[0037] The clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment is updated to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and the process returns to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and the clean coal ash content is continuously predicted until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained;

[0038] According to the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes, the optimal flotation agent dosage scheme is selected to control the clean coal ash content.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0040] Inputting the clean coal ash content prediction data at the current moment, the historical flotation coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment;

[0041] The clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment is updated to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and the process returns to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and the clean coal ash content is continuously predicted until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained;

[0042] According to the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes, the optimal flotation agent dosage scheme is selected to control the clean coal ash content.

[0043] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0044] Inputting the clean coal ash content prediction data at the current moment, the historical flotation coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment;

[0045] The clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment is updated to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and the process returns to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and the clean coal ash content is continuously predicted until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained;

[0046] According to the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes, the optimal flotation agent dosage scheme is selected to control the clean coal ash content.

[0047] The above-mentioned clean coal ash control method, device, computer equipment, storage medium and computer program product, by introducing a transfer model, predicts the clean coal ash prediction data at the next moment based on the clean coal ash prediction data at the current moment, the historical floating coal slime data in the first preset time period and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, until the clean coal ash prediction trajectory corresponding to each flotation agent dosage scheme is predicted, that is, the clean coal ash data for a period of time in the future is predicted, which can accurately capture the large inertia and large time lag characteristics of the coal slime flotation process, and also fully consider the impact of the fluctuation of key parameters such as the floating coal slime data and the flotation agent dosage data on the clean coal ash, and realize the accurate, efficient and comprehensive simulation of the actual coal slime flotation process. By combining the model control strategy algorithm, the optimal flotation agent dosage scheme can be calculated in real time based on the predicted trajectory of the transfer model. This dynamic adjustment strategy effectively overcomes the lag and inaccuracy caused by human factors in the traditional manual dosing method, and realizes the precise control of the clean coal ash. In addition, this method improves the quality of coal products and enhances market competitiveness by accurately controlling the ash content of clean coal. On the other hand, it reduces the waste of resources and cost increase caused by excessive fluctuations in the ash content of clean coal. At the same time, the automated and intelligent dosing control also reduces the labor intensity of operators and improves the production efficiency of clean coal. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] FIG1 is a schematic flow diagram of a method for controlling ash content in clean coal in one embodiment;

[0050] 2 is a schematic diagram of a process for obtaining flotation agent dosage stacking data and historical flotation coal slime data for a first preset time period in one embodiment;

[0051] FIG3 is a schematic diagram of a process for constructing a transfer model in one embodiment;

[0052] FIG4 is a structural block diagram of a clean coal ash content control device in one embodiment;

[0053] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] In one embodiment, as shown in FIG1 , a clean coal ash control method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] Step 102, inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into a pre-built transfer model to obtain the clean coal ash content prediction data at the next moment.

[0057] Among them, the clean coal ash content prediction data at the current moment refers to the clean coal ash content prediction data at the current moment obtained according to the prediction. The historical floating coal slime data of the first preset time period refers to the historical floating coal slime data of the known recent time period, and the historical floating coal slime data includes the ash content and concentration of the floating coal slime. Each flotation agent dosage scheme refers to the flotation agent dosage, and the flotation agent includes a collector and a frother. The flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment refers to the flotation agent dosage data of multiple continuous moments that match the current moment when each flotation agent dosage scheme is adopted to control the clean coal ash content. Multiple continuous moments include the current moment.

[0058] Since the flotation of coal slime needs a long period of time from feeding to discharging, this time is taken as the clean coal discharging time, recorded as T. The clean coal ash content discharged at time t is not only determined by the floating coal slime data at time tT and the flotation agent dosage at time t, but is comprehensively determined by all the floating coal slime data and flotation agent dosage within a period of time. Specifically, the clean coal ash content discharged at time t is affected by the floating coal slime data for a period of time before and after time tT, and the flotation agent dosage for a period of time before time t. At the same time, in the actual ash control process, the future floating coal slime data is unknown. Therefore, the historical floating coal slime data of the known recent time period, that is, the historical floating coal slime data of the first preset time period is obtained as the future floating coal slime data. The future floating coal slime data remains unchanged in the process of adopting each flotation agent dosage scheme to control the clean coal ash content, and the flotation agent dosage remains unchanged in the process of adopting each flotation agent dosage scheme to control the clean coal ash content.

[0059] Optionally, after obtaining the clean coal ash content prediction data at the current moment, the historical coal slime data for floating in the first preset time period, and the flotation agent dosage stacked data of each flotation agent dosage scheme at the current moment, the terminal inputs the clean coal ash content prediction data at the current moment, the historical coal slime data for floating in the first preset time period, and the flotation agent dosage stacked data of each flotation agent dosage scheme at the current moment into a pre-constructed transfer model, and predicts the clean coal ash content data at the next moment through the pre-constructed transfer model to obtain the clean coal ash content prediction data at the next moment.

[0060] Step 104, update the clean coal ash content prediction data at the next moment to the clean coal ash content prediction data at the current moment, update the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, return to the step of inputting the clean coal ash content prediction data at the current moment, the historical floatation coal slime data of the first preset time period and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and continue to predict the clean coal ash content until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained.

[0061] Optionally, after obtaining the clean coal ash content prediction data at the next moment, the clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment. Obtain the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment, update the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and return to step 102, that is, input the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-constructed transfer model, obtain the clean coal ash content prediction data at the next moment, continue to predict the clean coal ash content, until the clean coal ash content prediction data of the preset length is obtained, thereby obtaining the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme.

[0062] When each flotation agent dosage scheme is adopted to control the clean coal ash content, the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained according to the above-mentioned clean coal ash content control method, thereby obtaining the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes.

[0063] Step 106 , selecting the optimal flotation agent dosage scheme according to the clean coal ash content prediction trajectories corresponding to the multiple flotation agent dosage schemes, and controlling the clean coal ash content.

[0064] Optionally, the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes are compared with the preset clean coal ash content trajectory, the error between the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme and the preset clean coal ash content trajectory is determined, and the flotation agent dosage scheme of the clean coal ash content prediction trajectory with the smallest error is selected as the optimal flotation agent dosage scheme, that is, the optimal clean coal ash content control scheme. Clean coal ash content is controlled by the optimal clean coal ash content control scheme.

[0065] Furthermore, the method of determining the error between the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme and the preset clean coal ash content trajectory may include error calculation methods such as mean absolute error and root mean square error.

[0066] Furthermore, a plurality of flotation agent dosage schemes may be selected by a model control strategy algorithm through uniform sampling.

[0067] Since the coal slime flotation process requires a long processing time from the flotation of coal slime to the final clean coal output, it has the characteristics of large inertia and large time lag. In addition, factors such as the flotation coal slime data fluctuate greatly, which has a great impact on the ash content of the clean coal. As a result, manual dosing is still widely used in traditional technologies, making it difficult to achieve precise control of the ash content of the clean coal.

[0068] In the above-mentioned clean coal ash control method, by introducing a transfer model, the clean coal ash prediction data at the next moment is predicted based on the clean coal ash prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, until the clean coal ash prediction trajectory corresponding to each flotation agent dosage scheme is predicted, that is, the clean coal ash data for a period of time in the future is predicted, which can accurately capture the large inertia and large time lag characteristics of the coal slime flotation process, and fully consider the impact of the fluctuation of key parameters such as the floating coal slime data and the flotation agent dosage data on the clean coal ash, and realize the accurate, efficient and comprehensive simulation of the actual coal slime flotation process. By combining the model control strategy algorithm, the optimal flotation agent dosage scheme can be calculated in real time based on the predicted trajectory of the transfer model. This dynamic adjustment strategy effectively overcomes the lag and inaccuracy caused by human factors in the traditional manual dosing method, and realizes the precise control of the clean coal ash. In addition, this method improves the quality of coal products and enhances market competitiveness by accurately controlling the ash content of clean coal. On the other hand, it reduces the waste of resources and cost increase caused by excessive fluctuations in the ash content of clean coal. At the same time, the automated and intelligent dosing control also reduces the labor intensity of operators and improves the production efficiency of clean coal.

[0069] In an exemplary embodiment, as shown in FIG2 , before inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment, the method further includes: a step of acquiring the flotation agent dosage stacking data and the historical floating coal slime data of the first preset time period, including:

[0070] Step 202, determining the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment according to the flotation agent dosage stacking time and the clean coal ash content prediction step at the current moment.

[0071] Step 204, determining historical floating coal slime data in a first preset time period according to the stacking time of the floating coal slime data and the clean coal discharge time.

[0072] The stacking time of flotation agent dosage refers to the time span of flotation agent dosage before time t that will affect the clean coal ash content discharged at time t. The stacking time of floating coal slime data refers to the time span of floating coal slime data before and after time tT that will affect the clean coal ash content discharged at time t.

[0073] Specifically, the ash content of the clean coal discharged at time t is affected by the floating coal slime data for a period of time before and after time tT, and the flotation agent dosage for a period of time before time tT. The spans of the two periods of time are respectively referred to as the first floating coal slime data stacking time and the second floating coal slime data stacking time, and the floating coal slime data stacking time is obtained according to the first floating coal slime data stacking time and the second floating coal slime data stacking time. According to the floating coal slime data stacking time and the clean coal discharge time, the historical floating coal slime data for the first preset time period is determined. The span of a period of time before time t is referred to as the flotation agent dosage stacking time. According to the flotation agent dosage stacking time and the clean coal ash prediction step size at the current moment, the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is determined.

[0074] For example, the current time is recorded as , the flotation agent dosage stacking time is recorded as , ( >0) the prediction step length of clean coal ash content is i, that is, The ash content of clean coal at the time is affected by Time has come +i The influence of the flotation agent dosage during this period of time, the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment includes Time has come The flotation agent dosage at time +i. The first floating coal slime data stacking time and the second floating coal slime data stacking time are recorded as and , ( ), that is, the ash content of the clean coal at time t1 is affected by Time has come The historical floating coal slime data of the first preset time period includes Time has come Therefore, the historical floating coal slime data and flotation agent dosage input into the transfer model are respectively + The stacking and of stacking.

[0075] In this embodiment, the influence of fluctuations in key parameters such as flotation agent dosage and floated coal slime data on the clean coal ash content is fully considered, thereby achieving a comprehensive simulation of the actual clean coal production process.

[0076] In an exemplary embodiment, since the input of the transfer model includes observations, action quantities and external variables, the observations refer to the quantities that need to be predicted, the action quantities refer to the quantities that control the observations, and the external variables refer to other quantities that have an impact on the observations. , the amount of flotation agent added is the action amount , the floating coal slime data is an external variable , predict the clean coal ash content at the next moment. Therefore, step 102, inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and obtaining the clean coal ash content prediction data at the next moment, including: using the clean coal ash content prediction data at the current moment as the observed quantity prediction value at the current moment, using the historical floating coal slime data of the first preset time period as the external variable of the first preset time period, and using the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment as the action quantity stacking data of each flotation agent dosage scheme at the current moment; inputting the observed quantity prediction value at the current moment, the external variable of the first preset time period, and the action quantity stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and obtaining the observed quantity prediction value at the next moment.

[0077] Specifically, the clean coal ash content prediction data at the current moment is used as the observation quantity prediction value at the current moment, the historical flotation slime data of the first preset time period is used as the external variable of the first preset time period, the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action quantity stacking data of each flotation agent dosage scheme at the current moment, and the observation quantity prediction value at the current moment, the external variable of the first preset time period and the action quantity stacking data of each flotation agent dosage scheme at the current moment are input into a pre-constructed transfer model, and the observation quantity at the next moment is predicted by the transfer model to obtain the observation quantity prediction value at the next moment.

[0078] In this embodiment, the clean coal ash content prediction data at the current moment is used as the observed quantity prediction value at the current moment, the historical flotation coal slime data of the first preset time period is used as the external variable of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action quantity stacking data of each flotation agent dosage scheme at the current moment, which are input into the pre-constructed transfer model. The input data that meets the model requirements can be input into the transfer model, thereby enabling the coal slime flotation process to be accurately and efficiently simulated in a virtual environment.

[0079] In an exemplary embodiment, the above method also includes: when the current moment is the first moment, obtaining the actual data of clean coal ash content at the current moment; inputting the actual data of clean coal ash content at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into a pre-built transfer model to obtain the predicted data of clean coal ash content at the next moment.

[0080] Among them, the actual data of clean coal ash content refers to the actual output of clean coal ash content data.

[0081] Optionally, when the current moment is the first moment, since there is no clean coal ash prediction data, the actual clean coal ash data at the current moment can be directly used as the observed value at the current moment. The historical floating coal slime data of the first preset time period is used as the external variable of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action amount stacking data of each flotation agent dosage scheme at the current moment. The observed value at the current moment, the external variable of the first preset time period, and the action amount stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the observed value prediction value at the next moment, that is, the clean coal ash prediction data at the next moment. When the current moment is the first moment, the clean coal ash content is predicted according to the following formula:

[0082]

[0083] in, Represents the predicted value of the observation at time t1+1; represents a pre-built transfer model; Represents the observed value at the current time t1; Indicates the nth flotation agent dosage scheme corresponding to the current time t1 Time has come The action amount stacking data at each moment; N represents the number of samples of the flotation agent dosage scheme. The larger N is, the more flotation agent dosage schemes there are; express Time has come Moment, that is, the external variable of the first preset time period. It should be noted that when the current moment is the first moment, the clean coal ash content prediction step i is 0, so the above formula does not need to be calculated based on the clean coal ash content prediction step i.

[0084] When the current moment is not the first moment, the clean coal ash content prediction data at the current moment is used as the observed quantity prediction value at the current moment, the historical floating coal slime data of the first preset time period is used as the external variable of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action quantity stacking data of each flotation agent dosage scheme at the current moment; the observed quantity prediction value at the current moment, the external variable of the first preset time period, and the action quantity stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the observed quantity prediction value at the next moment. When the current moment is not the first moment, the clean coal ash content prediction is performed according to the following formula:

[0085]

[0086]

[0087]

[0088] Where i represents the prediction step length of clean coal ash content, the maximum value is L-1, and L represents the preset length, that is, the predicted trajectory length; express The predicted value of the observed quantity at time; represents the predicted value of the observation at time t1+i, Indicates the nth flotation agent dosage scheme in Corresponding Time has come The amount of action at each moment is stacked.

[0089] In this embodiment, when the current moment is the first moment, the actual clean coal ash content data at the current moment is input into the transfer model as an observation quantity, so that the clean coal ash content in the future period can be more accurately predicted based on the actual clean coal ash content.

[0090] In an exemplary embodiment, before the clean coal ash prediction data at the current moment, the historical flotation coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into a pre-built transfer model to obtain the clean coal ash prediction data at the next moment, the above method also includes a transfer model construction step and a model control strategy algorithm construction step, wherein the transfer model is a simulation of the coal slime flotation process, and the transfer model is driven by historical data to complete virtual modeling. The model control strategy algorithm construction step refers to the construction process of a method for selecting the optimal flotation agent dosage scheme based on the clean coal ash prediction trajectory of the pre-built transfer model. Further, the transfer model construction step is shown in Figure 3, including:

[0091] Step 302, obtain the clean coal ash content prediction data at the current training moment, and obtain the historical floating coal slime data and flotation agent dosage stacked data that match the current training moment; input the clean coal ash content prediction data at the current training moment, the historical floating coal slime data and flotation agent dosage stacked data that match the current training moment into the transfer model to be trained, and obtain the clean coal ash content prediction data at the next training moment.

[0092] Step 304, update the clean coal ash content prediction data of the next training moment to the clean coal ash content prediction data of the current training moment, return to the step of obtaining the historical floating coal slime data and flotation agent dosage stacking data matching the current training moment, and continue to predict the clean coal ash content until a prediction trajectory of a preset length is obtained.

[0093] Step 306 , updating the transfer model to be trained according to the predicted trajectory of the preset length until the preset conditions are met, thereby obtaining a pre-constructed transfer model.

[0094] Since the flotation agent added to the flotation machine requires a certain reaction time, simply predicting the clean coal ash content at the next moment and controlling based on the result may not necessarily achieve the best control effect. In order to improve the control effect, the transfer model is required to have a certain deduction ability, that is, it can deduce the clean coal ash value for a period of time in the future. If the transfer model trained by the traditional supervised learning algorithm is used for deduction, once an error occurs, the error will continue to increase with the deduction process, and control based on wrong results will inevitably lead to poor control results. Therefore, when training the transfer model in this embodiment, the transfer model is first allowed to generate a prediction trajectory.

[0095] Specifically, the clean coal ash at a certain moment is selected from the historical clean coal ash data as the clean coal ash prediction data at the current training moment, and the historical coal slime data and flotation agent dosage stacking data matching the current training moment are obtained. Among them, the historical coal slime data and flotation agent dosage stacking data matching the current training moment refer to the historical coal slime data and flotation agent dosage that can be correctly matched and stacked with the current training moment. In other words, the historical coal slime data and flotation agent dosage stacking data matching each training moment must be determined according to the clean coal ash prediction step size at each training moment.

[0096] The clean coal ash content prediction data at the current training moment, the historical floating coal slime data and the flotation agent dosage stacked data matching the current training moment are input into the transfer model to be trained to obtain the clean coal ash content prediction data at the next training moment. The clean coal ash content prediction data at the next training moment is updated to the clean coal ash content prediction data at the current training moment, and the step of obtaining the historical floating coal slime data and the flotation agent dosage stacked data matching the current training moment is returned to continue predicting the clean coal ash content until a prediction trajectory of a preset length is obtained. The preset length refers to the total prediction step length of the clean coal ash content, which can be the same as the total prediction step length of the clean coal ash content in the actual application process.

[0097] After obtaining the prediction trajectory of the preset length, the transfer model to be trained is updated according to the prediction trajectory of the preset length until the preset condition is met to obtain the pre-constructed transfer model. Further, the preset condition may be a training stop condition, specifically, a Nash equilibrium may be reached, at which time the transfer model has approximately simulated the coal slime flotation process. When the training stop condition is reached, the transfer model at this time is used as the pre-constructed transfer model.

[0098] In this embodiment, the transfer model is trained based on the clean coal ash prediction data at the current training moment, the historical flotation coal slime data and the flotation agent dosage stack data matching the current training moment, which can fully consider the impact of the fluctuation of key parameters such as the flotation coal slime data and the flotation agent dosage data on the clean coal ash, so that the pre-built transfer model can accurately, efficiently and comprehensively simulate the actual coal slime flotation process. By predicting a prediction trajectory of a preset length and training the transfer model, the pre-built transfer model can accurately capture the large inertia and large time lag characteristics in the coal slime flotation process.

[0099] In an optional manner of the above embodiment, the above method also includes: when the current training moment is the initial training moment, obtaining the actual data of clean coal ash content at the current training moment; inputting the actual data of clean coal ash content at the current training moment, the historical floating coal slime data matching the current training moment, and the flotation agent dosage stacked data into the transfer model to be trained to obtain the predicted data of clean coal ash content at the next training moment.

[0100] Specifically, when the current training moment is the first training moment, since there is no clean coal ash prediction data, the actual clean coal ash data at the current training moment can be directly used as the observed value at the current training moment. The historical floating coal slime data matching the current training moment is used as the external variable matching the current training moment, and the flotation agent dosage stacking data matching the current training moment is used as the action stacking data matching the current training moment. The observed value at the current training moment, the external variables and the action matching the current training moment are input into the transfer model to be trained to obtain the predicted value of the observed value at the next training moment, that is, the clean coal ash prediction data at the next training moment. When the current training moment is the first training moment, the clean coal ash content is predicted according to the following formula:

[0101]

[0102] in, Represents the predicted value of the observation at training time t2+1; represents the transfer model to be trained; Represents the observation at the current training time t2; Indicates that it matches the current training time t2 Time has come The amount of action at each moment is stacked; Indicates that it matches the current training time t2 Time has come It should be noted that when the current training moment is the first training moment, the clean coal ash content prediction step i is 0, so the above formula does not need to be calculated based on the clean coal ash content prediction step i.

[0103] When the current training moment is not the first training moment, the clean coal ash content prediction data at the current training moment is used as the observed value prediction value at the current training moment, the historical floating coal slime data matching the current training moment is used as the external variable matching the current training moment, and the flotation agent dosage stacking data matching the current training moment is used as the action amount stacking data matching the current training moment. The observed value at the current training moment, the external variables matching the current training moment, and the action amount are input into the transfer model to be trained to obtain the observed value prediction value at the next training moment. When the current training moment is not the first training moment, the clean coal ash content prediction is performed according to the following formula:

[0104]

[0105]

[0106] Where i represents the prediction step length of clean coal ash content, the maximum value is L-1, and L represents the preset length, that is, the predicted trajectory length; express The predicted value of the observed quantity at the training time; express The predicted value of the observed quantity at the training time; Representation and Training time matches Time has come The amount of action at each moment is stacked; Representation and Training time matches Time has come External variables at the moment.

[0107] In this embodiment, when the current training moment is the first training moment, the actual data of the clean coal ash content at the current training moment is input as an observation quantity into the transfer model to be trained, so that the transfer model can make a more accurate prediction of the clean coal ash content in the future based on the actual clean coal ash content.

[0108] In an optional manner of the above embodiment, in step 306, updating the transfer model to be trained according to the predicted trajectory of the preset length includes: determining the error between the predicted trajectory of the preset length and the true trajectory through a discriminant network; and updating the transfer model to be trained according to the error between the predicted trajectory of the preset length and the true trajectory.

[0109] Optionally, the transfer model is trained by generating an adversarial strategy. Specifically, after obtaining a predicted trajectory of a preset length, the error between the predicted trajectory of the preset length and the true trajectory is determined by a discriminant network. Among them, the discriminant network can be a discriminant network well known to those skilled in the art, such as a fully connected neural network, a convolutional neural network, a recursive neural network, a conditional discriminant network, a self-attention network, etc. According to the error between the predicted trajectory of the preset length and the true trajectory, the transfer model to be trained is updated. Specifically, the discriminant network is used to distinguish between the predicted trajectory of the preset length and the true trajectory, that is, to correctly identify whether the input trajectory is real or generated by the transfer model. The transfer model is used to achieve as accurate a prediction as possible, that is, to generate a predicted trajectory as close to the true trajectory as possible. During the training process, the discriminant network and the transfer model confront each other and constantly compete until a Nash equilibrium is reached. At this time, the discriminant network cannot distinguish whether the predicted trajectory generated by the transfer model is real or generated, indicating that the transfer model has approximately achieved the simulation of the coal slime flotation process, and the transfer model at this time is used as a pre-built transfer model.

[0110] In this embodiment, the error between the predicted trajectory of a preset length and the actual trajectory is determined by the discriminant network, and the transfer model to be trained is updated according to the error, so as to construct the transfer model based on the generative adversarial strategy, so that the coal slime flotation process can be accurately and efficiently simulated in a virtual environment, and a transfer model that can accurately predict the trajectory of clean coal ash is obtained.

[0111] In an optional manner of the above embodiment, step 306, updating the transfer model to be trained according to the prediction trajectory of a preset length until the preset conditions are met to obtain a pre-constructed transfer model, includes: updating the transfer model to be trained according to the prediction trajectory of a preset length until the training conditions are met to obtain a trained transfer model; performing a model test on the trained transfer model, and obtaining the pre-constructed transfer model after passing the model test; the model test includes a deduction test, a response curve test, and a data distribution test.

[0112] Optionally, during the construction of the transfer model, after the model training is completed, a model test may be performed on the trained transfer model. Only through the model test can the correctness of the constructed transfer model be fully demonstrated.

[0113] Specifically, model testing can include deduction testing, response curve testing and data distribution testing. Passing the deduction test means that the generated prediction trajectory is as close to the real trajectory as possible. Passing the response curve test means that when the action amount or external variable changes, the predicted value of the observed quantity should also change correctly. Passing the data distribution test means that when the transfer model is accurate enough to simulate the real coal slime flotation process, the data distribution of the output generated by the model based on historical data should be similar to the data distribution of the real output. For example, the data distribution can be the clean coal ash prediction data, that is, the probability distribution of the observed quantity.

[0114] Furthermore, the trained model can be tested in the order of deduction test, response curve test and data distribution test.

[0115] In this embodiment, by performing deduction test, response curve test and data distribution test on the trained transfer model, the correctness of the transfer model can be further ensured.

[0116] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0117] Based on the same inventive concept, the embodiment of the present application also provides a clean coal ash control device for implementing the clean coal ash control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the clean coal ash control device provided below can refer to the limitations of the clean coal ash control method above, and will not be repeated here.

[0118] In an exemplary embodiment, as shown in FIG4 , a clean coal ash control device is provided, comprising: an ash prediction module 402 , a cycle prediction module 404 and a scheme selection module 406 , wherein:

[0119] The ash prediction module 402 is used to input the clean coal ash prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into a pre-built transfer model to obtain the clean coal ash prediction data at the next moment.

[0120] The cyclic prediction module 404 is used to update the clean coal ash content prediction data at the next moment to the clean coal ash content prediction data at the current moment, update the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, return to the step of inputting the clean coal ash content prediction data at the current moment, the historical flotation slime data of the first preset time period and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and continue to predict the clean coal ash content until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained.

[0121] The scheme selection module 406 is used to select the optimal flotation agent dosage scheme according to the clean coal ash content prediction trajectory corresponding to multiple flotation agent dosage schemes to control the clean coal ash content.

[0122] In an exemplary embodiment, the above device further includes:

[0123] The data acquisition module is used to determine the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment according to the flotation agent dosage stacking time and the clean coal ash content prediction step at the current moment; and determine the historical flotation coal slime data for the first preset time period according to the flotation coal slime data stacking time and the clean coal discharge time.

[0124] In an exemplary embodiment, the ash prediction module 402 is also used to use the clean coal ash content prediction data at the current moment as the observed quantity prediction value at the current moment, use the historical floating coal slime data of the first preset time period as the external variable of the first preset time period, and use the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment as the action quantity stacking data of each flotation agent dosage scheme at the current moment; the observed quantity prediction value at the current moment, the external variable of the first preset time period and the action quantity stacking data of each flotation agent dosage scheme at the current moment are input into the pre-constructed transfer model to obtain the observed quantity prediction value at the next moment.

[0125] In an exemplary embodiment, the ash content prediction module 402 is also used to obtain the actual clean coal ash content data at the current moment when the current moment is the first moment; the actual clean coal ash content data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into a pre-built transfer model to obtain the clean coal ash content prediction data at the next moment.

[0126] In an exemplary embodiment, the above device further includes:

[0127] A model building module is used to obtain the clean coal ash content prediction data at the current training moment, and obtain the historical floating coal slime data and flotation agent dosage stacked data that match the current training moment; input the clean coal ash content prediction data at the current training moment, the historical floating coal slime data and flotation agent dosage stacked data that match the current training moment into the transfer model to be trained, and obtain the clean coal ash content prediction data at the next training moment; update the clean coal ash content prediction data at the next training moment to the clean coal ash content prediction data at the current training moment, return to the step of obtaining the historical floating coal slime data and flotation agent dosage stacked data that match the current training moment, and continue to predict the clean coal ash content until a prediction trajectory of a preset length is obtained; update the transfer model to be trained according to the prediction trajectory of the preset length until the preset conditions are met, and a pre-constructed transfer model is obtained.

[0128] In an exemplary embodiment, the model building module is also used to obtain the actual data of clean coal ash content at the current training moment when the current training moment is the initial training moment; the actual data of clean coal ash content at the current training moment, the historical floating coal slime data matching the current training moment, and the flotation agent dosage stacked data are input into the transfer model to be trained to obtain the predicted data of clean coal ash content at the next training moment.

[0129] In an exemplary embodiment, the model building module is further used to determine the error between the predicted trajectory of a preset length and the actual trajectory through a discriminant network; and update the transfer model to be trained according to the error between the predicted trajectory of the preset length and the actual trajectory.

[0130] In an exemplary embodiment, the model building module is also used to update the transfer model to be trained according to the prediction trajectory of a preset length until the training conditions are met to obtain the trained transfer model; perform model testing on the trained transfer model, and when the model test passes, obtain the pre-built transfer model; the model test includes deduction test, response curve test and data distribution test.

[0131] Each module in the above-mentioned clean coal ash content control device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0132] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a clean coal ash control method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0133] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0136] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling ash content in clean coal, characterized in that: The method comprises: Inputting the clean coal ash content prediction data at the current moment, the historical flotation coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment; The clean coal ash content prediction data at the next moment is updated to the clean coal ash content prediction data at the current moment, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment is updated to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, and the process returns to the step of inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and continues to predict the clean coal ash content until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained; According to the clean coal ash content prediction trajectories corresponding to multiple flotation agent dosage schemes, the optimal flotation agent dosage scheme is selected to control the clean coal ash content.

2. The method according to claim 1, characterized in that Before inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment, the method further includes: According to the flotation agent dosage stacking time and the clean coal ash content prediction step at the current moment, the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is determined; According to the stacking time of the floating coal slime data and the clean coal discharging time, the historical floating coal slime data for the first preset time period is determined.

3. The method according to claim 1, characterized in that The clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment, including: The clean coal ash content prediction data at the current moment is used as the observed quantity prediction value at the current moment, the historical floating coal slime data of the first preset time period is used as the external variable of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment is used as the action quantity stacking data of each flotation agent dosage scheme at the current moment; The predicted value of the observed quantity at the current moment, the external variables of the first preset time period, and the stacked data of the action amount of each flotation agent dosage scheme at the current moment are input into a pre-built transfer model to obtain the predicted value of the observed quantity at the next moment.

4. The method according to claim 1, characterized in that The method further comprises: When the current moment is the first moment, obtaining actual clean coal ash content data at the current moment; The actual clean coal ash content data at the current moment, the historical flotation slime data in the first preset time period and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment are input into the pre-built transfer model to obtain the clean coal ash content prediction data at the next moment.

5. The method according to any one of claims 1 to 4, characterized in that: Before inputting the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-constructed transfer model to obtain the clean coal ash content prediction data at the next moment, the method further includes a transfer model construction step, including: Acquire the clean coal ash content prediction data at the current training moment, and acquire the historical floating coal slime data and flotation agent dosage stacking data matching the current training moment; input the clean coal ash content prediction data at the current training moment, the historical floating coal slime data and flotation agent dosage stacking data matching the current training moment into the transfer model to be trained, and obtain the clean coal ash content prediction data at the next training moment; The clean coal ash content prediction data at the next training moment is updated to the clean coal ash content prediction data at the current training moment, and the process returns to the step of obtaining the historical floating coal slime data and the flotation agent dosage stacking data matching the current training moment, and continues to predict the clean coal ash content until a prediction trajectory of a preset length is obtained; The transfer model to be trained is updated according to the predicted trajectory of the preset length until a preset condition is met, thereby obtaining a pre-constructed transfer model.

6. The method according to claim 5, characterized in that The method further comprises: When the current training moment is the initial training moment, obtaining actual clean coal ash content data at the current training moment; The actual clean coal ash content data at the current training moment, the historical floating coal slime data and the flotation agent dosage stacking data matching the current training moment are input into the transfer model to be trained to obtain the clean coal ash content prediction data at the next training moment.

7. The method according to claim 5, characterized in that The updating of the transfer model to be trained according to the predicted trajectory of the preset length includes: Determine the error between the predicted trajectory of the preset length and the actual trajectory through a discriminant network; The transfer model to be trained is updated according to the error between the predicted trajectory of the preset length and the actual trajectory.

8. The method according to claim 5, characterized in that The step of updating the transfer model to be trained according to the predicted trajectory of the preset length until a preset condition is met to obtain a pre-built transfer model comprises: updating the transfer model to be trained according to the predicted trajectory of the preset length until the training condition is met, thereby obtaining a trained transfer model; The trained transfer model is subjected to a model test, and when the model test passes, a pre-built transfer model is obtained; the model test includes a deduction test, a response curve test, and a data distribution test.

9. A clean coal ash content control device, characterized in that: The device comprises: The ash content prediction module is used to input the clean coal ash content prediction data at the current moment, the historical floating coal slime data in the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into a pre-built transfer model to obtain the clean coal ash content prediction data at the next moment; a cyclic prediction module, for updating the clean coal ash content prediction data at the next moment to the clean coal ash content prediction data at the current moment, updating the flotation agent dosage stacking data of each flotation agent dosage scheme at the next moment to the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment, returning to the step of inputting the clean coal ash content prediction data at the current moment, the historical floatation coal slime data of the first preset time period, and the flotation agent dosage stacking data of each flotation agent dosage scheme at the current moment into the pre-built transfer model, and continuing to predict the clean coal ash content until the clean coal ash content prediction trajectory corresponding to each flotation agent dosage scheme is obtained; The scheme selection module is used to select the optimal flotation agent dosage scheme according to the clean coal ash content prediction trajectory corresponding to multiple flotation agent dosage schemes to control the clean coal ash content.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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