Coal Mill Maintenance Cycle Detection and Early Warning System and Method

Abstract

The present invention discloses a detection and early warning system and method for the overhaul period of a coal mill. The system includes: an acquisition module, which is used to determine the overhaul time according to the overhaul records and obtain the working condition parameters of the coal mill within a period of time after the overhaul through the SIS; a modeling module, which is used to perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain current values; a correction module, which is used to correct the current values according to the coal mill influence parameters and the coal material influence parameters; an overhaul module, which is used to perform time series prediction on the corrected current values according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the overhaul time and issue an early warning according to the performance degradation trend of the coal mill. Adjusting the overhaul period and issuing corresponding early warnings improve the accuracy of detection and strengthen the safety management of the coal mill.

Description

Technical Field

[0001] This application relates to the technical field of coal mill detection, and more specifically, to a detection and early warning system and method for the overhaul cycle of a coal mill. Background Art

[0002] A coal mill is a machine that crushes coal blocks and grinds them into pulverized coal. It is an important auxiliary equipment for a pulverized coal boiler. The coal grinding process is a process in which coal is crushed and its surface area continuously increases. To increase the new surface area, the binding force between solid molecules must be overcome, so energy consumption is required. Coal is ground into pulverized coal in a coal mill mainly through three methods: crushing, smashing, and grinding. Among them, the crushing process consumes the least energy. The grinding process consumes the most energy. All kinds of coal mills have two or three of the above methods in the coal pulverization process, but which one is the main depends on the type of coal mill.

[0003] In the prior art, it is very difficult to determine the overhaul cycle of a coal mill because many wearings are the wearings of internal parts of the coal mill, which are difficult to measure, resulting in the inability to accurately represent the performance change of the coal mill. Often, when a fault occurs, the overhaul is carried out, but it is already too late. Or, overhauling every month or every quarter is time-consuming and laborious, wasting a lot of manpower and material resources.

[0004] Therefore, how to improve the accuracy of the overhaul cycle is a technical problem to be solved at present. Summary of the Invention

[0005] The present invention provides a detection and early warning system for the overhaul cycle of a coal mill to solve the technical problem of low accuracy of the overhaul cycle of a coal mill in the prior art. The system includes:

[0006] An acquisition module, configured to determine the overhaul time according to the overhaul record, and obtain the working condition parameters of the coal mill within a period of time after the overhaul through SIS;

[0007] A modeling module, configured to perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain a current value;

[0008] A correction module, configured to correct the current value according to the coal mill influence parameter and the coal material influence parameter;

[0009] An overhaul module, configured to perform time series prediction on the corrected current value according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the overhaul time and issue an early warning according to the performance degradation trend of the coal mill.

[0010] In some embodiments of the present application, the correction module is specifically configured to:

[0011] The coal mill influence parameters include the outlet temperature and the deflector door opening;

[0012] Modify the current value according to the outlet temperature, the opening degree of the deflecting door and the first preset weight value to obtain the current value after correction of the mill influence parameter.

[0013] In some embodiments of the present application, the correction module is specifically configured to:

[0014] The coal material influence parameters include coal moisture content, coal quality performance and coal impurity content;

[0015] Modify the current value according to the coal moisture content, coal quality performance, coal impurity content and the second preset weight value to obtain the current value after correction of the coal material influence parameter.

[0016] In some embodiments of the present application, the correction module is further specifically configured to:

[0017] Modify the current value according to the current value after correction of the mill influence parameter, the current value after correction of the coal material influence parameter and the third preset weight value to obtain the corrected current value.

[0018] In some embodiments of the present application, the maintenance module is specifically configured to:

[0019] If the performance degradation of the mill is lower than the threshold, a danger warning is issued, and the future time when the performance degradation of the mill is lower than the threshold is included in the danger warning.

[0020] Correspondingly, the present application also provides a method for detecting and warning the maintenance cycle of a mill, and the method includes:

[0021] Determine the maintenance time according to the maintenance record, and obtain the mill condition parameters within a period of time after maintenance through the SIS;

[0022] Perform steady-state screening on the mill condition parameters, establish a current model according to the screened condition parameters, input typical working conditions into the current model, and obtain the current value;

[0023] Modify the current value according to the mill influence parameter and the coal material influence parameter;

[0024] Perform time series prediction on the corrected current value according to the preset neural network to obtain the performance degradation trend of the mill, and adjust the maintenance time and issue a warning according to the performance degradation trend of the mill.

[0025] In some embodiments of the present application, modifying the current value according to the mill influence parameter and the coal material influence parameter includes:

[0026] The mill influence parameters include the outlet temperature and the opening degree of the deflecting door;

[0027] Modify the current value according to the outlet temperature, the opening degree of the deflecting door and the first preset weight value to obtain the current value after correction of the mill influence parameter.

[0028] In some embodiments of the present application, correcting the current value according to the mill influence parameter and the coal material influence parameter includes:

[0029] The coal material influence parameter includes coal moisture content, coal quality performance, and coal impurity content;

[0030] Correcting the current value according to the coal moisture content, coal quality performance, coal impurity content, and the second preset weight value to obtain the current value after being corrected by the coal material influence parameter.

[0031] In some embodiments of the present application, correcting the current value according to the mill influence parameter and the coal material influence parameter includes:

[0032] Correcting the current value according to the current value after being corrected by the mill influence parameter, the current value after being corrected by the coal material influence parameter, and the third preset weight value to obtain the corrected current value.

[0033] In some embodiments of the present application, adjusting the maintenance time and issuing a warning according to the degradation trend of the mill performance includes:

[0034] If the degradation of the mill performance is lower than the threshold, a danger warning is issued, and the future time when the degradation of the mill performance is lower than the threshold is included in the danger warning.

[0035] By applying the above technical solution, the system includes: an acquisition module for determining the maintenance time according to the maintenance record and acquiring the mill condition parameters within a period of time after maintenance through the SIS; a modeling module for performing steady-state screening on the mill condition parameters, establishing a current model according to the screened condition parameters, inputting typical working conditions into the current model to obtain the current value; a correction module for correcting the current value according to the mill influence parameter and the coal material influence parameter; a maintenance module for performing time-series prediction on the corrected current value according to a preset neural network to obtain the degradation trend of the mill performance, and adjusting the maintenance time and issuing a warning according to the degradation trend of the mill performance. Through the current model in the present application, the current value is obtained, and the current value is corrected by the influence parameter. The corrected current value is substituted into the neural network model to predict the future current change, so as to determine the performance change of the mill, adjust the maintenance cycle and issue the corresponding warning, improving the detection accuracy and strengthening the safety management of the mill. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1The structural schematic diagram of the detection and early warning system for the maintenance cycle of the coal mill proposed by the embodiment of the present invention is shown;

[0038] Figure 2 The flowchart of the detection and early warning method for the maintenance cycle of the coal mill proposed by the embodiment of the present invention is shown. Specific embodiments

[0039] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0040] The embodiment of the present application provides a detection and early warning system for the maintenance cycle of a coal mill. As Figure 1 shown, the system includes:

[0041] An acquisition module 201, configured to determine the maintenance time according to the maintenance record, and obtain the working condition parameters of the coal mill within a period of time after maintenance through the SIS.

[0042] In this embodiment, the maintenance record is the record made by on-site staff, and the time point of maintenance is determined accordingly. The working condition parameters of the coal mill within a period of time after maintenance are obtained through the SIS (plant-level monitoring information system). The working condition parameters of the coal mill include the inlet coal feeding amount, oil pressure, primary air mass flow rate, inlet air pressure, primary air temperature, current, etc.

[0043] A modeling module 202, configured to perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, and input typical working conditions into the current model to obtain a current value.

[0044] In this embodiment, steady-state screening is performed on the acquired data. The specific steady-state screening method will not be elaborated here and belongs to the conventional technology in the art. A current model is established according to the screened working condition parameters. The inlet coal feeding amount, oil pressure, primary air mass flow rate, inlet air pressure, and primary air temperature are used as the model input items to obtain the output item current. Typical working conditions (working condition parameters) are input into the current model to obtain a current value. The model is not specifically limited here as long as it can achieve the above functions.

[0045] It should be noted that a large number of experiments or studies have shown that the current of the coal mill can characterize the performance change of the coal mill to a certain extent, and the process of performance degradation of the coal mill can be captured by monitoring the time-series change of the current. Therefore, the output item of the model is the current. The type of input item parameters should not be too many, otherwise the output item current will be inaccurate. The working condition parameters of the coal mill within a period of time after the above-mentioned maintenance are segmented and divided according to a fixed time period, and one time period corresponds to one model.

[0046] A correction module 203, configured to correct the current value according to the mill influence parameter and the coal material influence parameter.

[0047] In this embodiment, the current value obtained through the model can only characterize the influence of the input items on the current. Some key parameters affecting the current are missing, such as the mill operation parameters and the coal material property parameters. Therefore, it is necessary to correct it according to the influence parameters so that the subsequent neural network can make accurate predictions.

[0048] In some embodiments of the present application, in order to improve the accuracy of the current, the correction module 203 is specifically configured to: the mill influence parameters include the outlet temperature and the deflector opening; correct the current value according to the outlet temperature, the deflector opening, and the first preset weight value to obtain the current value after correction by the mill influence parameter.

[0049] In this embodiment, the mill influence parameters include the outlet temperature and the deflector opening, and the time periods of the two parameters correspond to the above time periods. The specific correction process is as follows:

[0050] Set the outlet temperature as A, and the preset outlet temperature array A0(A1, A2, A3, A4), where the first preset outlet temperature is A1, the second preset outlet temperature is A2, the third preset outlet temperature is A3, and the fourth preset outlet temperature is A4, and A1 < A2 < A3 < A4;

[0051] Preset the first correction coefficient array Q01(Q11, Q12, Q13, Q14), where the first preset first correction coefficient is Q11, the second preset first correction coefficient is Q12, the third preset first correction coefficient is Q13, and the fourth preset first correction coefficient is Q14, and 1.2 > Q11 > Q12 > Q13 > Q14 > 0.8; set the current output by the model as S.

[0052] Determine the first correction coefficient according to the relationship between the outlet temperature and each preset outlet temperature, and correct the current according to the first correction coefficient;

[0053] If A < A1, determine the first preset first correction coefficient Q11 as the first correction coefficient, and the corrected current is Q11*S;

[0054] If A1 ≤ A < A2, determine the second preset first correction coefficient Q12 as the first correction coefficient, and the corrected current is Q12*S;

[0055] If A2 ≤ A < A3, determine the third preset first correction coefficient Q13 as the first correction coefficient, and the corrected current is Q13*S;

[0056] If A3 ≤ A < A4, determine the fourth preset first correction coefficient Q14 as the first correction coefficient, and the corrected current is Q14 * S.

[0057] Here, the lower the outlet temperature, the generally larger the current, and the two change in a negative correlation.

[0058] Set the folding door opening to B, and the preset folding door opening array B0 (B1, B2, B3, B4), where the first preset folding door opening is B1, the second preset folding door opening is B2, the third preset folding door opening is B3, and the fourth preset folding door opening is B4, and B1 < B2 < B3 < B4;

[0059] Preset the second correction coefficient array Q02 (Q21, Q22, Q23, Q24), where the first preset second correction coefficient is Q21, the second preset second correction coefficient is Q22, the third preset second correction coefficient is Q23, and the fourth preset second correction coefficient is Q24, and 1.2 > Q21 > Q22 > Q23 > Q24 > 0.8;

[0060] Determine the second correction coefficient according to the relationship between the folding door opening and each preset opening, and correct the current;

[0061] If B < B1, determine the first preset second correction coefficient Q21 as the second correction coefficient, and the corrected current is S * Q21;

[0062] If B1 ≤ B < B2, determine the second preset second correction coefficient Q22 as the second correction coefficient, and the corrected current is S * Q22;

[0063] If B2 ≤ B < B3, determine the third preset second correction coefficient Q23 as the second correction coefficient, and the corrected current is S * Q23;

[0064] If B3 ≤ B < B4, determine the fourth preset second correction coefficient Q24 as the second correction coefficient, and the corrected current is S * Q24.

[0065] Here, the lower the folding door opening, the larger the current, and the two are negatively correlated.

[0066] Correct the current value according to the outlet temperature, the folding door opening, and the first preset weight value to obtain the corrected current value of the mill influence parameter, specifically:

[0067] The first preset weight value is the corresponding weight of the outlet temperature and the folding door opening respectively. Set the weights to f1 and f2 respectively. The corrected current value of the mill influence parameter is f1 * Q01 * S + f2 * Q02 * S.

[0068] In order to improve the accuracy of the current, in some embodiments of the present application, the correction module 203 is specifically configured to: the coal material influence parameters include coal moisture content, coal quality performance, and coal impurity content; correct the current value according to the coal moisture content, coal quality performance, coal impurity content, and the second preset weight value to obtain the current value after correction by the coal material influence parameters.

[0069] In this embodiment, the self-attributes of the coal material can also affect the current value, and the influence of the coal material factors needs to be considered.

[0070] Set the coal moisture content as C, and the preset coal moisture content array C0(C1, C2, C3, C4), where the first preset coal moisture content is C1, the second preset coal moisture content is C2, the third preset coal moisture content is C3, the fourth preset coal moisture content is C4, and C1 < C2 < C3 < C4;

[0071] Preset the third correction coefficient array Q03(Q31, Q32, Q33, Q34), where the first preset third correction coefficient is Q31, the second preset third correction coefficient is Q32, the third preset third correction coefficient is Q33, the fourth preset third correction coefficient is Q34, and 0.8 < Q31 < Q32 < Q33 < Q34 < 1.2;

[0072] Determine the third correction coefficient according to the relationship between the coal moisture content and each preset coal moisture content, and correct the current.

[0073] If C < C1, determine the first preset third correction coefficient Q31 as the third correction coefficient, and the corrected current is S * Q31;

[0074] If C1 ≤ C < C2, determine the second preset third correction coefficient Q32 as the third correction coefficient, and the corrected current is S * Q32;

[0075] If C2 ≤ C < C3, determine the third preset third correction coefficient Q33 as the third correction coefficient, and the corrected current is S * Q33;

[0076] If C3 ≤ C < C4, determine the fourth preset third correction coefficient Q34 as the third correction coefficient, and the corrected current is S * Q34.

[0077] Here, the more the coal moisture content, the greater the current, and the two are positively correlated.

[0078] Set the coal quality performance as D. The greater the coal quality performance, the better the coal quality. The preset coal quality performance array is D0(D1, D2, D3, D4), where the first preset coal quality performance is D1, the second preset coal quality performance is D2, the third preset coal quality performance is D3, the fourth preset coal quality performance is D4, and D1 < D2 < D3 < D4;

[0079] Preset the fourth correction coefficient array Q04 (Q41, Q42, Q43, Q44), where the first preset fourth correction coefficient is Q41, the second preset fourth correction coefficient is Q42, the third preset fourth correction coefficient is Q43, the fourth preset fourth correction coefficient is Q44, and 1.2 > Q41 > Q42 > Q43 > Q44 < 0.8;

[0080] Determine the fourth correction coefficient according to the relationship between the coal quality performance and each preset coal quality performance, and correct the current;

[0081] If D < D1, determine the first preset fourth correction coefficient Q41 as the fourth correction coefficient, and the corrected current is S * Q41;

[0082] If D1 ≤ D < D2, determine the second preset fourth correction coefficient Q42 as the fourth correction coefficient, and the corrected current is S * Q42;

[0083] If D2 ≤ D < D3, determine the third preset fourth correction coefficient Q43 as the fourth correction coefficient, and the corrected current is S * Q3;

[0084] If D3 ≤ D < D4, determine the fourth preset fourth correction coefficient Q44 as the fourth correction coefficient, and the corrected current is S * Q44.

[0085] Here, the better the coal quality performance, the smaller the current, and the two are negatively correlated.

[0086] Set the impurity content of the coal as E, and the preset impurity content array of the coal E0 (E1, E2, E3, E4), where the first preset impurity content of the coal is E1, the second preset impurity content of the coal is E2, the third preset impurity content of the coal is E3, the fourth preset impurity content of the coal is E4, and E1 < E2 < E3 < E4;

[0087] Preset the fifth correction coefficient array Q05 (Q51, Q52, Q53, Q54), where the first preset fifth correction coefficient is Q51, the second preset fifth correction coefficient is Q52, the third preset fifth correction coefficient is Q53, the fourth preset fifth correction coefficient is Q54, and 0.8 < Q51 < Q52 < Q53 < Q54 < 1.2;

[0088] Determine the fifth correction coefficient according to the relationship between the impurity content of the coal and each preset impurity content of the coal, and correct the current;

[0089] If E < E1, determine the first preset fifth correction coefficient Q51 as the fifth correction coefficient, and the corrected current is S * Q51;

[0090] If E1 ≤ E < E2, determine the second preset fifth correction coefficient Q52 as the fifth correction coefficient, and the corrected current is S * Q51;

[0091] If E2 ≤ E < E3, determine the third preset fifth correction coefficient as Q53 as the fifth correction coefficient, and the corrected current is S * Q53;

[0092] If E3 ≤ E < E4, determine the fourth preset fifth correction coefficient as Q54 as the fifth correction coefficient, and the corrected current is S * Q54.

[0093] Here, the greater the impurity content in the coal, the greater the current, showing a positive correlation.

[0094] Correct the current value according to the water content of the coal, the coal quality performance, the impurity content in the coal, and the second preset weight value. Specifically:

[0095] The second preset weight values are the weight values corresponding to the water content of the coal, the coal quality performance, and the impurity content in the coal, denoted as f3, f4, and f5 respectively. The corrected current value of the coal material influence parameter is f3 * Q03 * S + f4 * Q04 * S + f5 * Q05 * S.

[0096] In order to further improve the accuracy of the current, in some embodiments of the present application, the correction module 203 is further specifically configured to: correct the current value according to the corrected current value of the mill influence parameter, the corrected current value of the coal material influence parameter, and the third preset weight value to obtain the corrected current value.

[0097] In this embodiment, the third preset weight values are the weight values corresponding to the mill influence parameter and the coal material influence parameter respectively, denoted as F1 and F2. The corrected current value = F1 * the corrected current value of the mill influence parameter + F2 * the corrected current value of the coal material influence parameter.

[0098] It can be understood that the above-mentioned various preset values or weight values can be changed or adjusted according to the actual situation. The above-mentioned influence parameters can be increased or decreased according to different actual situations, which all belong to the protection idea of the present application.

[0099] The maintenance module 204 is used to perform time series prediction on the corrected current value according to a preset neural network to obtain the degradation trend of the mill performance, and adjust the maintenance time and issue a warning according to the degradation trend of the mill performance.

[0100] In this embodiment, the preset neural network is a pre-trained prediction neural network of long short-term memory, which is a conventional technology in this field and will not be elaborated here. It should be noted that any other neural network capable of performing this function can be used for replacement.

[0101] In some embodiments of the present application, the maintenance module 204 is specifically configured to: if the degradation of the mill performance is lower than the threshold, issue a danger warning, and the danger warning includes the future time when the degradation of the mill performance is lower than the threshold.

[0102] By applying the above technical solution, the system includes: an acquisition module, configured to determine the maintenance time according to the maintenance record, and obtain the working condition parameters of the coal mill within a period of time after maintenance through SIS; a modeling module, configured to perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain a current value; a correction module, configured to correct the current value according to the coal mill influence parameters and the coal material influence parameters; a maintenance module, configured to perform time series prediction on the corrected current value according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the maintenance time and issue a warning according to the performance degradation trend of the coal mill. In this application, a current value is obtained through a current model, and the current value is corrected by influence parameters. The corrected current value is substituted into a neural network model to predict future current changes, so as to determine the performance change of the coal mill, adjust the maintenance cycle and issue corresponding warnings, improving the detection accuracy and strengthening the safety management of the coal mill.

[0103] Those skilled in the art can understand that the modules in the system in the implementation scenario can be distributed in the system in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more systems different from this implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0104] To further elaborate on the technical idea of the present invention, the technical solution of the present invention will be described below in combination with specific application scenarios.

[0105] Correspondingly, this application also provides a method for detecting and warning the maintenance cycle of a coal mill, as Figure 2 shown, the method includes:

[0106] Step S101, determine the maintenance time according to the maintenance record, and obtain the working condition parameters of the coal mill within a period of time after maintenance through SIS;

[0107] Step S102, perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain a current value;

[0108] Step S103, correct the current value according to the coal mill influence parameters and the coal material influence parameters;

[0109] Step S104, perform time series prediction on the corrected current value according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the maintenance time and issue a warning according to the performance degradation trend of the coal mill.

[0110] In some embodiments of this application, correcting the current value according to the coal mill influence parameters and the coal material influence parameters includes:

[0111] The influencing parameters of the coal mill include the outlet temperature and the opening degree of the deflection door;

[0112] According to the outlet temperature, the opening degree of the deflection door, and the first preset weight value, correct the current value to obtain the corrected current value of the influencing parameters of the coal mill.

[0113] In some embodiments of the present application, correcting the current value according to the influencing parameters of the coal mill and the influencing parameters of the coal material includes:

[0114] The influencing parameters of the coal material include the coal moisture content, the coal quality performance, and the coal impurity content;

[0115] According to the coal moisture content, the coal quality performance, the coal impurity content, and the second preset weight value, correct the current value to obtain the corrected current value of the influencing parameters of the coal material.

[0116] In some embodiments of the present application, correcting the current value according to the influencing parameters of the coal mill and the influencing parameters of the coal material includes:

[0117] According to the corrected current value of the influencing parameters of the coal mill, the corrected current value of the influencing parameters of the coal material, and the third preset weight value, correct the current value to obtain the corrected current value.

[0118] In some embodiments of the present application, adjusting the maintenance time and issuing a warning according to the degradation trend of the coal mill performance includes:

[0119] If the degradation of the coal mill performance is lower than the threshold, a danger warning is issued, and the future time when the degradation of the coal mill performance is lower than the threshold is included in the danger warning.

[0120] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0121] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. Coal mill maintenance cycle detection and early warning system, Characterized in that, The system includes: An acquisition module, configured to determine the maintenance time according to the maintenance record, and obtain the working condition parameters of the coal mill within a period of time after maintenance through SIS; A modeling module, configured to perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain a current value; A correction module, configured to correct the current value according to the coal mill influence parameters and the coal material influence parameters; A maintenance module, configured to perform time series prediction on the corrected current value according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the maintenance time and issue an early warning according to the performance degradation trend of the coal mill; The correction module, specifically configured to: The coal mill influence parameters include the outlet temperature and the deflector door opening; Correct the current value according to the outlet temperature, the deflector door opening, and the first preset weight value to obtain the current value corrected by the coal mill influence parameters; The correction module, specifically configured to: The coal material influence parameters include the coal moisture content, the coal quality performance, and the coal impurity content; Correct the current value according to the coal moisture content, the coal quality performance, the coal impurity content, and the second preset weight value to obtain the current value corrected by the coal material influence parameters; The correction module, further specifically configured to: Correct the current value according to the current value corrected by the coal mill influence parameters, the current value corrected by the coal material influence parameters, and the third preset weight value to obtain the corrected current value.

2. The system according to claim 1, Characterized in that, The maintenance module, specifically configured to: If the performance degradation of the coal mill is lower than the threshold, a danger early warning is issued, and the future time when the performance degradation of the coal mill is lower than the threshold is included in the danger early warning.

3. Coal mill maintenance cycle detection and early warning method, Characterized in that, The method includes: Determine the maintenance time according to the maintenance record, and obtain the working condition parameters of the coal mill within a period of time after maintenance through SIS; Perform steady-state screening on the working condition parameters of the coal mill, establish a current model according to the screened working condition parameters, input typical working conditions into the current model, and obtain a current value; Correct the current value according to the coal mill influence parameters and the coal material influence parameters; Perform time series prediction on the corrected current value according to a preset neural network, obtain the performance degradation trend of the coal mill, and adjust the maintenance time and issue an early warning according to the performance degradation trend of the coal mill; Correcting the current value according to the coal mill influence parameters and the coal material influence parameters includes: The coal mill influence parameters include the outlet temperature and the deflector door opening; Correct the current value according to the outlet temperature, the deflector door opening, and the first preset weight value to obtain the current value corrected by the coal mill influence parameters; Correcting the current value according to the coal mill influence parameters and the coal material influence parameters includes: The coal material influence parameters include the coal moisture content, the coal quality performance, and the coal impurity content; Correct the current value according to the coal moisture content, the coal quality performance, the coal impurity content, and the second preset weight value to obtain the current value corrected by the coal material influence parameters; Correcting the current value according to the coal mill influence parameters and the coal material influence parameters includes: Modify the current value according to the current value corrected by the mill influence parameter, the current value corrected by the coal material influence parameter, and the third preset weight value to obtain the corrected current value.

4. The method according to claim 3, wherein, adjusting the maintenance time and issuing a warning according to the degradation trend of the mill performance, including: if the degradation of the mill performance is lower than the threshold, a danger warning is issued, and the future time when the degradation of the mill performance is lower than the threshold is included in the danger warning.

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