A method and device for determining the position of a coal seam in a coal bunker, and a storage medium

Abstract

The application provides a coal bunker coal seam position determination method and device and a storage medium. The method comprises the following steps: in response to a coal seam position query instruction, determining a target coal seam indicated by the coal seam position query instruction and a query time indicated by the coal seam position query instruction; determining a storage state of the target coal seam according to storage information of the target coal seam, and determining an estimated coal seam quality corresponding to the target coal seam according to the storage state of the target coal seam; calculating and outputting a height value of the target coal seam according to the estimated coal seam quality, a coal seam density correction factor corresponding to a coal type of the target coal seam at the query time, and a preset density of the target coal seam, wherein the height value is used to indicate the position of the coal seam in the coal bunker; wherein the coal seam density correction factor is used to correct the influence of environmental factors on the moisture content of the coal seam of different coal types, so as to improve the accuracy of the determined coal seam position in the coal bunker.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a coal seam position determination method and device for a coal bunker and a storage medium. BACKGROUND

[0002] In the coking process of a steel plant, the position of the coal seam in the coal bunker is invisible, and there is no accurate method for measuring the coal seam out of the bunker. The current coal seam position determination scheme is to roughly estimate by using manual experience, which results in poor real-time performance and large errors of the determined coal seam position. Therefore, a more accurate coal seam position determination method for the coal bunker is needed. SUMMARY

[0003] Therefore, the present application aims to provide a coal seam position determination method and device for a coal bunker and a storage medium to improve the accuracy of the determined coal seam position in the coal bunker.

[0004] In a first aspect, the present application provides a coal seam position determination method for a coal bunker, which comprises: in response to a coal seam position query instruction, determining a target coal seam indicated by the coal seam position query instruction and a query time indicated by the coal seam position query instruction; determining the storage state of the target coal seam according to the storage information of the target coal seam; determining the estimated coal seam quality corresponding to the target coal seam according to the storage state of the target coal seam; calculating and outputting the height value of the target coal seam according to the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam, the height value being used to indicate the position of the coal seam in the coal bunker; wherein the coal seam density correction factor is used to correct the influence of environmental factors on the moisture content of the coal seam of different coal types.

[0005] Preferably, the coal seam density correction factor corresponding to the coal type of the target coal seam is obtained by: obtaining the temperature value of the coal bunker and the humidity value of the coal bunker corresponding to the query time; inputting the temperature value of the coal bunker, the humidity value of the coal bunker, the statistical moisture index of the target coal seam, and the number of the coal type of the target coal seam into a pre-trained coal seam density correction factor deep learning model, and taking the result output by the coal seam density correction factor deep learning model as the coal seam density correction factor corresponding to the coal type of the target coal seam.

[0006] Preferably, the storage state of the target coal seam includes an un-stored coal seam, a storing-in coal seam, a storing coal seam, a storing-out coal seam and a stored-out coal seam. According to the storage state of the target coal seam, the step of determining the estimated coal seam quality corresponding to the target coal seam specifically includes: when the storage state of the target coal seam is one of the storing-in coal seam, the storing coal seam and the storing-out coal seam, the estimated coal seam quality corresponding to the target coal seam is determined based on the coal seam quality calculation model; and when the storage state of the target coal seam is one of the un-stored coal seam and the stored-out coal seam, the estimated coal seam quality corresponding to the target coal seam is determined as zero.

[0007] Preferably, the calculation formula of the coal seam quality calculation model is:

[0008]

[0009] wherein, M0 is the estimated coal seam quality, m i is the quality of the storing coal seam whose storage time is before the storage time of the target coal seam, m n is the quality of the storing-out coal seam, v is the average storing-out speed of the storing-out coal seam, a is the storing-out speed correction factor, t out is the cumulative storing-out time of the storing-out coal seam, t in is the cumulative storing-in time of the storing-in coal seam, b is the friction correction factor.

[0010] Preferably, the storing-out speed correction factor is obtained by: obtaining the coal type of the storing-out coal seam, the water separation value and the viscosity value of the storing-out coal seam; inputting the number of the coal type of the storing-out coal seam, the water separation value and the viscosity value of the storing-out coal seam into a pre-trained storing-out speed correction factor deep learning model, and taking the output result of the storing-out speed correction factor deep learning model as the storing-out speed correction factor corresponding to the storing-out coal seam.

[0011] Preferably, the friction correction factor is obtained by: obtaining the coal type of the storing-out coal seam, the friction factor corresponding between the storing-out coal bunker and the coal bunker; inputting the number of the coal type of the storing-out coal seam and the friction factor corresponding between the storing-out coal bunker and the coal bunker into a pre-trained friction correction factor deep learning model, and taking the output result of the friction correction factor deep learning model as the friction correction factor corresponding to the storing-out coal seam.

[0012] Preferably, the step of calculating and outputting the height value of the target coal seam according to the standard radius of the coal bunker, the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam and the preset density of the target coal seam specifically includes:

[0013] When the storage state of the target coal seam is the storing-in coal seam or the storing coal seam, the height value of the target coal seam is calculated by the following relationship:

[0014]

[0015] wherein, γ is a coal seam density correction factor, ρ is a standard density of the target coal seam, and r0 is a standard radius of the coal bunker;

[0016] When the storage state of the target coal seam is a coal seam in the process of discharging, the height value of the target coal seam is calculated by the following relationship:

[0017]

[0018]

[0019] r x =r0+h*tanθ;

[0020] wherein, θ is an inclination angle of the bottom of the coal bunker.

[0021] In a second aspect, the application provides a device for determining the position of a coal seam in a coal bunker, the device comprising:

[0022] a response module configured to determine a target coal seam indicated by a coal seam position query instruction and a query time indicated by the coal seam position query instruction in response to the coal seam position query instruction;

[0023] an analysis module configured to determine a storage state of the target coal seam according to storage information of the target coal seam;

[0024] a statistics module configured to determine an estimated coal seam quality corresponding to the target coal seam according to the storage state of the target coal seam;

[0025] a calculation module configured to calculate and output a height value of the target coal seam according to the estimated coal seam quality, a coal seam density correction factor corresponding to a coal type of the target coal seam at the query time, and a preset density of the target coal seam, the height value being used to indicate the position of the coal seam in the coal bunker;

[0026] wherein, the coal seam density correction factor is used to correct the influence of environmental factors on the moisture content of coal seams of different coal types.

[0027] In a third aspect, the application further provides an electronic device comprising a processor, a memory, and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory being in communication through the bus when the electronic device is running, and the machine-readable instructions being executed by the processor to perform the steps of a coal seam position determination method as described above.

[0028] In a fourth aspect, the application further provides a computer-readable storage medium storing a computer program, the computer program being executed by a processor to perform the steps of a coal seam position determination method as described above.

[0029] This application provides a method, apparatus, and storage medium for determining the location of coal seams in a coal bunker. The method includes responding to a coal seam location query command, determining the target coal seam indicated by the query command, and the query time indicated by the query command; determining the storage status of the target coal seam based on its storage information, and determining the estimated coal seam quality corresponding to the target coal seam based on its storage status; calculating and outputting the height value of the target coal seam based on the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam. The height value indicates the location of the coal seam in the coal bunker. The coal seam density correction factor is used to correct for the influence of environmental factors on the moisture content of coal seams of different coal types. By building a coal seam model in the coal bunker and considering the error caused by the environmental influence on coal seam density, the location of the coal seam is determined based on the relationship between mass, density, and volume, thus improving the accuracy of the determined coal seam location in the coal bunker.

[0030] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating a method for determining the location of a coal seam in a coal bunker, as provided in an embodiment of this application;

[0033] Figure 2 A cross-sectional view of a coal bunker in the vertical direction provided in an embodiment of this application;

[0034] Figure 3 A schematic diagram of a device for determining the location of coal seams in a coal bunker, provided in an embodiment of this application;

[0035] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0037] First, the applicable application scenarios of this application will be introduced. This application can be applied to the monitoring of coal seam location in the coal bunker of a steel plant during the coking process.

[0038] In the coking process of steel plants, the location of the coal seam in the coal bunker is invisible, and there is a lack of accurate methods for calculating the location of the coal seam upon exiting the bunker. Current methods for determining the coal seam location rely on rough estimations based on manual experience, which results in poor real-time accuracy and significant errors. Therefore, a more accurate method for determining the location of the coal seam in the coal bunker is needed.

[0039] Based on this, embodiments of this application provide a method, apparatus, and storage medium for determining the location of coal seams in a coal bunker.

[0040] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for determining the location of a coal seam in a coal bunker, as provided in an embodiment of this application. Figure 1 As shown in the illustration, an embodiment of this application provides a method for determining the location of a coal seam in a coal bunker, comprising:

[0041] S101. Respond to the coal seam location query command and determine the target coal seam indicated by the coal seam location query command and the query time indicated by the coal seam location query command.

[0042] For each coal seam, the coal storage management system will assign a unique identifier (ID) to the coal seam and associate it with the coal type, weight, entry time, and storage status of the coal seam logged in by the storage staff.

[0043] The coal storage management system also has a coal seam query function, which allows staff to query the location of coal seams by specifying a coal seam ID.

[0044] S102. Determine the storage status of the target coal seam based on its storage information.

[0045] S103. Determine the estimated coal seam quality corresponding to the target coal seam based on the storage status of the target coal seam.

[0046] Once the target coal seam is obtained, its storage status can be determined first. The storage status of the target coal seam includes: unstored coal seam, coal seam in storage, coal seam in storage, coal seam in the process of leaving storage, and coal seam already out of storage. Based on the storage status of the target coal seam, the steps for determining the estimated coal seam quality corresponding to the target coal seam specifically include:

[0047] When the target coal seam is in one of the following storage states: coal seam entering storage, coal seam in storage, or coal seam exiting storage, the estimated coal seam quality is determined based on the coal seam quality calculation model. When the target coal seam is in one of the following storage states: coal seam not yet entering storage or coal seam already exiting storage, the estimated coal seam quality is determined to be zero.

[0048] The calculation formula for the coal seam quality calculation model is as follows:

[0049] M0=∑m i +m n +αv(t on -t out )+β

[0050] Where M0 is the estimated coal seam quality, m i m represents the mass of the coal seam stored before the target coal seam's entry time. n Let t be the mass of the coal seam being discharged, v be the average discharge velocity of the coal seam being discharged, α be the discharge velocity correction factor, and t be the mass of the coal seam being discharged. out The cumulative time for coal seams to be removed from the storage bin, t in β represents the cumulative time of coal entering the coal storage bin, and β is the friction correction factor.

[0051] S104. Based on the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam, calculate and output the height value of the target coal seam. The height value is used to indicate the location of the coal seam in the coal bunker.

[0052] The coal seam density correction factor is used to correct for the impact of environmental factors on the moisture content of coal seams of different coal types. Here, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time can be obtained in the following way:

[0053] Obtain the temperature and humidity values ​​of the coal bunker corresponding to the query time. Input the temperature and humidity values ​​of the coal bunker, the statistical moisture index of the target coal seam, and the coal type number of the target coal seam into a pre-trained deep learning model for coal seam density correction factors. Use the output of the deep learning model for coal seam density correction factors as the coal seam density correction factor corresponding to the coal type of the target coal seam.

[0054] The coal seam density correction factor γ can be derived by using a neural network algorithm to transform the height calculation formula.

[0055]

[0056] Here, it is necessary to calculate the γ corresponding to different coal types, moisture content, and densities.

[0057] The factors influencing the coal seam density correction factor include the moisture content, temperature, fineness, coal type, and sulfur content of the coal seam at the time of entry into the coal storage facility. This coal seam density correction factor can be determined by establishing an influencing factor model through partial sampling, constructing a corresponding loss function, solving for the optimal model solution, and thus predicting the value of γ. The input to the deep learning model for the coal seam density correction factor can also include characteristic parameters such as moisture content, fineness, and sulfur content corresponding to the coal type of the coal seam.

[0058] It should be noted that the coal seam density correction factor here is affected by factors such as coal type, coal bunker temperature, and coal bunker humidity. Therefore, the coal bunker temperature and humidity values ​​here are real-time temperature and humidity values ​​at the time of query to ensure the accuracy and real-time performance of the coal seam density correction factor.

[0059] Specifically, the step of calculating and outputting the height value of the target coal seam based on the standard radius of the coal bunker, the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam, and the preset density of the target coal seam includes:

[0060] When the target coal seam is in the storage state of either a coal seam being put into storage or a coal seam being stored, the height of the target coal seam is calculated using the following formula:

[0061]

[0062] Where γ is the coal seam density correction factor, ρ is the standard density of the target coal seam, and r0 is the standard radius of the coal bunker;

[0063] When the target coal seam is in the middle of its storage state, the height of the target coal seam is calculated using the following formula:

[0064]

[0065]

[0066] r x =r0 + h * tanθ;

[0067] Where θ is the inclination angle of the bottom of the coal bunker.

[0068] like Figure 2 As shown.Figure 2 This is a vertical cross-sectional view of a coal bunker provided in an embodiment of this application. A, B, and C indicate different coal seams. The coal bunker model can be divided into cylindrical and conical models; the calculation methods for volume, mass, and density differ between models of different shapes. In practical applications, the bottom inclination angle θ of the coal bunker is 30°.

[0069]

[0070] The method for determining the location of coal seams in a coal bunker provided in this application estimates the quality of the target coal seam and then calculates its location based on physical and geometric relationships. It takes into account the density of different coal types affected by environmental factors. The calculated coal seam location is more accurate than that obtained by manual estimation, which helps to monitor and track coal seams in the coal bunker and judge coking quality. It provides an important theoretical basis for the overall selection of incoming coal and the scheduling of bunker locations.

[0071] In one embodiment of this application, the friction correction factor is obtained in the following manner:

[0072] Obtain the coal type of the coal seam in the outflow bin and the corresponding friction factor between the coal bins in the outflow bin;

[0073] The coal type number of the coal seam in the outgoing coal bin and the corresponding friction factor between the coal bins in the outgoing coal bin are input into a pre-trained friction correction factor deep learning model, and the output of the friction correction factor deep learning model is used as the friction correction factor corresponding to the coal seam in the outgoing coal bin.

[0074] For the friction correction factor, a linear regression approach can be used. This involves statistically analyzing the theoretical calculations and actual coal bin output to establish a linear regression equation, ultimately yielding the friction correction factor. It's important to note that this friction correction factor is influenced by factors such as coal type, coal bin temperature, and coal bin humidity. Therefore, the coal bin temperature and humidity values ​​used here are real-time values ​​at the time of the query to ensure the accuracy and timeliness of the friction correction factor.

[0075] And obtain the exit speed correction factor through the following methods:

[0076] Obtain the coal type, moisture content, and viscosity of the coal seam exiting the storage bin;

[0077] The coal type number, water content, and viscosity of the coal seam in the discharge chamber are input into a pre-trained deep learning model for discharge speed correction factor, and the output of the deep learning model for discharge speed correction factor is used as the discharge speed correction factor corresponding to the coal seam in the discharge chamber.

[0078] The determination of the exit speed correction factor α can also be achieved using a neural network algorithm, taking into account the influence of multiple factors:

[0079]

[0080] Δm, v, and Δt can be obtained by statistically analyzing the coal seams entering and leaving the actual coal bunker, thus providing multiple α values ​​as training samples. Next, Z-score is used to perform dimensionless normalization on each training sample, and the corresponding loss function is constructed as follows:

[0081]

[0082] Where x i Relevant influencing variables (including at least the moisture content of the coal seam upon entry into the storage bin, the friction angle, and the temperature inside the storage bin), viscosity, α i For the final output, the function G(x) i ) represents the predicted value, and m represents the sample size.

[0083] Using the gradient descent algorithm, the minimum value of the loss function is found; this set of parameters is α. i The prediction model.

[0084]

[0085] Where ω i For the desired α i The parameters of the prediction model, and Let ω be the activation function of each neuron. The corresponding model parameters ω are obtained by minimizing the loss function. i Then, by inputting the relevant influencing factors of the new coal type, its corresponding α is obtained.

[0086] In one embodiment of this application, the coal seam density correction factor, the discharge speed correction factor, and the friction correction factor are all predicted by a pre-trained neural network model based on the coal type of the target coal seam and real-time environmental factors, which further improves the accuracy of the calculated coal seam location.

[0087] The following explains the reasoning process for calculating the coal seam location based on the coal bunker model:

[0088] Taking coal seam exiting the silo as an example, when the coal seam is exiting the silo, that is, when the belt pulley starts exiting the silo at a fixed power:

[0089] Over a time interval Δt, the weight discharged from the warehouse is Δm. Therefore, the mass discharged in a small time interval is as follows:

[0090] dm=Qρdt=vSρdt

[0091] Where Q is the flow rate of its coal seam, ρ is the density of the coal type, v is the velocity of the coal seam, and S is the cross-sectional area of ​​the coal seam (cross-sectional area of ​​the coal bunker). Considering that different coal types have different influencing factors due to moisture, density, etc., and considering using the average velocity at the time of exiting the bunker to replace the instantaneous velocity, the weight exiting the bunker can be approximated as:

[0092] Δm=αvΔt

[0093] Where α is the influence factor of coal type, and v is the average speed during the unloading time.

[0094] Considering the frictional force exerted on the coal seam by the coal bunker walls, some coal will adhere to the bunker walls. The frictional force between the bunker walls and the coal seam is:

[0095] df=μdF n

[0096] The above formula represents the frictional force acting on a tiny coal tower. From the equilibrium condition, we have:

[0097] df = gdm = μdF n

[0098] The above formula shows that the amount of coal adhering to the coal seam is related to the friction factor of the coal bunker and the type of coal; that is, for the same type of coal and the same coal bunker, the amount of adhering is linearly related. Therefore, the relevant correction for the weight leaving the bunker is as follows:

[0099] Δm=αvΔt-β

[0100] Where β is the corrected adhesion amount, and β is also the correction amount for the cumulative error.

[0101] Similarly, the calculation of coal seam height varies depending on the moisture content and other indicators of different coal types, leading to certain discrepancies. Therefore, a variation factor is introduced into the traditional calculation formula.

[0102] Based on the same inventive concept, this application also provides a device for determining the location of coal seams in a coal bunker, which corresponds to the method for determining the location of coal seams in a coal bunker. Since the principle of the device in this application is similar to the method for determining the location of coal seams in a coal bunker as described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0103] Please see Figure 3 , Figure 3 This is a schematic diagram of a device for determining the location of a coal seam in a coal bunker, provided in an embodiment of this application. Figure 3 As shown, the device 300 for determining the location of the coal seam in the coal bunker includes:

[0104] The response module 310 is used to respond to the coal seam location query command, and determine the target coal seam indicated by the coal seam location query command and the query time indicated by the coal seam location query command.

[0105] Analysis module 320 is used to determine the storage status of the target coal seam based on the storage information of the target coal seam;

[0106] The statistics module 330 is used to determine the estimated coal seam quality corresponding to the target coal seam based on the storage status of the target coal seam.

[0107] The calculation module 340 is used to calculate and output the height value of the target coal seam based on the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam. The height value is used to indicate the location of the coal seam in the coal bunker. The coal seam density correction factor is used to correct the water content of coal seams of different coal types affected by environmental factors.

[0108] In a preferred embodiment, a deep learning module (not shown in the figure) is further included, which is used to obtain the coal seam density correction factor corresponding to the coal type of the target coal seam in the following manner: obtaining the temperature value and humidity value of the coal bunker corresponding to the query time; inputting the temperature value, humidity value, statistical moisture index of the target coal seam, and coal type number of the target coal seam into a pre-trained deep learning model for coal seam density correction factor, and using the output of the deep learning model for coal seam density correction factor as the coal seam density correction factor corresponding to the coal type of the target coal seam.

[0109] In a preferred embodiment, the storage status of the target coal seam includes non-entered coal seam, coal seam in storage, coal seam in storage, coal seam in exiting storage, and coal seam already exited storage. Specifically, the analysis module 320 is used to determine the estimated coal seam quality corresponding to the target coal seam based on the coal seam quality calculation model when the storage status of the target coal seam is one of coal seam in storage, coal seam in storage, or coal seam in exiting storage; and to determine the estimated coal seam quality corresponding to the target coal seam as zero when the storage status of the target coal seam is one of coal seam in storage or coal seam already exited storage.

[0110] In a preferred embodiment, the calculation formula for the coal seam quality calculation model is as follows:

[0111] M0=∑m i +m n +αv(t in -t out )+β

[0112] Where M0 is the estimated coal seam quality, m i m represents the mass of the coal seam stored before the target coal seam's entry time. nLet t be the mass of the coal seam being discharged, v be the average discharge velocity of the coal seam being discharged, α be the discharge velocity correction factor, and t be the mass of the coal seam being discharged. out The cumulative time for coal seams to be removed from the storage bin, t in β represents the cumulative time of coal entering the coal storage bin, and β is the friction correction factor.

[0113] In a preferred embodiment, the deep learning module is further configured to obtain the discharge speed correction factor by: obtaining the coal type, moisture content, and viscosity value of the coal seam in the discharge process; inputting the coal type number, moisture content, and viscosity value of the coal seam into a pre-trained deep learning model for the discharge speed correction factor, and using the output of the deep learning model for the discharge speed correction factor as the discharge speed correction factor corresponding to the coal seam in the discharge process.

[0114] In a preferred embodiment, the deep learning module is further configured to obtain the friction correction factor by: obtaining the coal type of the coal seam in the outgoing coal bin and the corresponding friction factor between the coal bins in the outgoing coal bin; inputting the coal type number of the coal seam in the outgoing coal bin and the corresponding friction factor between the coal bins in the outgoing coal bin into a pre-trained friction correction factor deep learning model, and using the output of the friction correction factor deep learning model as the friction correction factor corresponding to the coal seam in the outgoing coal bin.

[0115] In a preferred embodiment, the calculation module 340 is specifically used for:

[0116] When the target coal seam is in the storage state of either a coal seam being put into storage or a coal seam being stored, the height of the target coal seam is calculated using the following formula:

[0117]

[0118] Where γ is the coal seam density correction factor, ρ is the standard density of the target coal seam, and r0 is the standard radius of the coal bunker;

[0119] When the target coal seam is in the middle of its storage state, the height of the target coal seam is calculated using the following formula:

[0120]

[0121]

[0122] r x =r0 + h * tanθ;

[0123] Where θ is the inclination angle of the bottom of the coal bunker.

[0124] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0125] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, they can perform the operations described above. Figure 1 The steps of the method for determining the location of the coal seam in the coal bunker shown in the method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0126] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the method for determining the location of the coal seam in the coal bunker shown in the method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0127] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0131] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0132] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the location of a coal seam in a coal bunker, characterized in that, The method includes: In response to a coal seam location query command, determine the target coal seam indicated by the coal seam location query command and the query time indicated by the coal seam location query command; The storage status of the target coal seam is determined based on the storage information of the target coal seam. Based on the storage status of the target coal seam, the estimated coal seam quality corresponding to the target coal seam is determined; Based on the standard radius of the coal bunker, the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam, the height value of the target coal seam is calculated and output. The height value is used to indicate the location of the coal seam in the coal bunker. The coal seam density correction factor is used to correct for the influence of environmental factors on the moisture content of coal seams of different coal types. The coal seam density correction factor corresponding to the coal type of the target coal seam at the query time is obtained through the following method: Obtain the temperature and humidity values ​​of the coal bunker corresponding to the query time; The temperature value of the coal bunker, the humidity value of the coal bunker, the statistical moisture index of the target coal seam, and the coal type number of the target coal seam are input into a pre-trained deep learning model for coal seam density correction factors. The output of the deep learning model for coal seam density correction factors is then used as the coal seam density correction factor corresponding to the coal type of the target coal seam. The storage status of the target coal seam includes coal seams that have not yet been stored, coal seams that are being stored, coal seams that are being stored, coal seams that are being removed from storage, and coal seams that have been removed from storage. The step of determining the estimated coal seam quality corresponding to the target coal seam based on the storage status of the target coal seam specifically includes: When the storage status of the target coal seam is one of the following: coal seam entering the warehouse, coal seam in storage, or coal seam leaving the warehouse, the estimated coal seam quality corresponding to the target coal seam is determined based on the coal seam quality calculation model. When the storage status of the target coal seam is either an unstored coal seam or a coal seam that has been removed from storage, the estimated coal seam quality corresponding to the target coal seam is determined to be zero.

2. The method according to claim 1, characterized in that, The calculation formula for the coal seam quality calculation model is as follows: in, To predict coal seam quality, This refers to the quality of coal seams stored before the target coal seam's entry time. For the quality of the coal seam during the unloading process, The average exit velocity of the coal seam during the exit process. This is a correction factor for the outbound speed. This refers to the cumulative time for coal seams to be removed from the storage bin. This refers to the cumulative storage time of coal in the storage bins. This is the friction correction factor.

3. The method according to claim 1, characterized in that, The exit speed correction factor is obtained through the following methods: Obtain the coal type, moisture content, and viscosity of the coal seam exiting the storage bin; The coal type number, water content, and viscosity of the coal seam in the discharge chamber are input into a pre-trained deep learning model for discharge speed correction factor, and the output of the deep learning model for discharge speed correction factor is used as the discharge speed correction factor corresponding to the coal seam in the discharge chamber.

4. The method according to claim 1, characterized in that, The friction correction factor is obtained through the following methods: Obtain the coal type of the coal seam in the outflow bin and the corresponding friction factor between the coal bins in the outflow bin; The coal type number of the coal seam in the outgoing coal bin and the corresponding friction factor between the coal bins in the outgoing coal bin are input into a pre-trained friction correction factor deep learning model, and the output of the friction correction factor deep learning model is used as the friction correction factor corresponding to the coal seam in the outgoing coal bin.

5. A device for determining the location of a coal seam in a coal bunker, characterized in that, The device includes: The response module is used to respond to the coal seam location query command, and determine the target coal seam indicated by the coal seam location query command and the query time indicated by the coal seam location query command; The analysis module is used to determine the storage status of the target coal seam based on the storage information of the target coal seam. The statistics module is used to determine the estimated coal seam quality corresponding to the target coal seam based on the storage status of the target coal seam. The calculation module is used to calculate and output the height value of the target coal seam based on the estimated coal seam quality, the coal seam density correction factor corresponding to the coal type of the target coal seam at the query time, and the preset density of the target coal seam. The height value is used to indicate the location of the coal seam in the coal bunker. The coal seam density correction factor is used to correct for the influence of environmental factors on the moisture content of coal seams of different coal types. The calculation module is also used to obtain the temperature and humidity values ​​of the coal bunker corresponding to the query time; input the temperature and humidity values ​​of the coal bunker, the statistical moisture index of the target coal seam, and the coal type number of the target coal seam into a pre-trained deep learning model for coal seam density correction factors; and use the output of the deep learning model for coal seam density correction factors as the coal seam density correction factor corresponding to the coal type of the target coal seam. The storage status of the target coal seam includes coal seams that have not yet been stored, coal seams that are being stored, coal seams that are being stored, coal seams that are being removed from storage, and coal seams that have already been removed from storage. The process is based on the storage status of the target coal seam. The calculation module is also used to determine the estimated coal seam quality corresponding to the target coal seam based on the coal seam quality calculation model when the storage status of the target coal seam is one of the coal seam entering the warehouse, the coal seam in storage, or the coal seam leaving the warehouse; and to determine the estimated coal seam quality corresponding to the target coal seam as zero when the storage status of the target coal seam is one of the coal seam not entering the warehouse or the coal seam that has left the warehouse.

6. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions that the processor can execute. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for determining the location of the coal seam in the coal bunker as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for determining the location of the coal seam in a coal bunker as described in any one of claims 1 to 4.

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