Intelligent control method for coal conveying

Abstract

The embodiment of the present disclosure provides a kind of intelligent control method of coal material transport warehouse, the method comprises: determining the coal bunker for placing coal material to be used;Determine the coal bunker evaluation attribute;According to the coal bunker evaluation attribute, determine the target coal bunker for placing target coal material;Periodically, the material level information of target coal bunker, shallow groove separator separation efficiency, the speed of the conveyor belt corresponding to target coal bunker and current coal flow speed are input into material level prediction model, and the predicted material level information of target coal bunker in prediction length is obtained;According to the predicted material level information of target coal bunker and preset material level information, determine the target control information of shallow groove separation and conveyor belt associated with target coal bunker.The technical scheme of the embodiment of the present disclosure, after determining the target coal bunker for placing target coal material according to the coal bunker evaluation attribute, the target control information of shallow groove separation and conveyor belt is determined according to the predicted material level information, which can efficiently transport target coal material into target coal bunker, reduce error and achieve the effect of accurate warehouse entry.

Description

Technical Field

[0001] This disclosure relates to the field of silo control technology, and in particular to an intelligent control method for coal transportation silos. Background Technology

[0002] In modern coal washing plants, intelligent silo technology is a key component of intelligent upgrading. By accurately allocating different types of coal to different silos, it can significantly improve production line efficiency, stabilize product quality, and reduce operational risks.

[0003] Currently, the main method used by coal washing plants for coal storage is manual allocation. However, manual allocation requires operators to continuously monitor the material level and manually operate the unloading equipment, which results in high labor intensity, increased workload for operators, significant safety hazards due to human negligence, and difficulty in accurately controlling the amount of coal entering the storage bins. Summary of the Invention

[0004] This disclosure provides an intelligent control method for coal transportation and storage, which enables efficient delivery of target coal to the target coal storage, reduces errors, improves the safety and accuracy of storage allocation, and achieves precise storage.

[0005] In a first aspect, embodiments of this disclosure provide an intelligent control method for coal conveying and storage, the method comprising:

[0006] Based on the coal information and the material level information of the coal bins to be selected for placing the coal, at least one coal bin to be used for placing the coal is determined.

[0007] For the at least one coal bunker to be used, the coal bunker evaluation attributes are determined based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and the equipment health information; wherein, the conveyor belt information includes at least the conveyor belt load information and the operating speed.

[0008] Based on the coal bunker evaluation attributes of the at least one coal bunker to be used, at least one target coal bunker is determined for placing the target coal material corresponding to the coal material information.

[0009] For the at least one target coal bunker, during the process of transporting coal to the target coal bunker based on the conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time.

[0010] Based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined.

[0011] Secondly, embodiments of the present invention also provide an intelligent control device for coal conveying bins, the device comprising:

[0012] The coal bunker determination module is used to determine at least one coal bunker to be used for placing the coal based on the coal information and the material level information of the coal bunker to be selected for placing the coal.

[0013] The coal bunker assessment attribute determination module is used to determine the coal bunker assessment attribute of the at least one coal bunker to be used based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and the equipment health information; wherein, the conveyor belt information includes at least the conveyor belt load information and the operating speed.

[0014] The target coal bunker determination module is used to determine at least one target coal bunker for placing target coal corresponding to the coal information, based on the coal bunker evaluation attributes of the at least one coal bunker to be used.

[0015] The predicted material level information determination module is used to periodically input the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the speed of the conveyor belt corresponding to the target coal bunker, and the current coal flow speed into a pre-trained material level prediction model for the at least one target coal bunker during the process of transporting coal to the target coal bunker based on the conveyor belt, so as to obtain the predicted material level information of the target coal bunker within the prediction time.

[0016] The target control information determination module is used to determine the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker based on the predicted material level information and the preset material level information of the target coal bunker.

[0017] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0018] One or more processors;

[0019] Storage device for storing one or more programs.

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent control method for coal transport warehouses as described in any embodiment of the present invention.

[0021] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the intelligent control method for coal transport silos as described in any of the embodiments of the present invention.

[0022] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program, characterized in that, when executed by a processor, the computer program implements the intelligent control method for coal transport warehouses as described in any embodiment of the present invention.

[0023] The technical solution of this disclosure embodiment determines at least one unused coal bin for placing coal based on coal material information and the material level information of the unused coal bins. Then, for each unused coal bin, a coal bin evaluation attribute is determined based on the material level information corresponding to the unused coal bin, the conveyor belt information associated with the unused coal bin, and equipment health information. Then, based on the coal bin evaluation attributes of the at least one unused coal bin, at least one target coal bin is determined for placing the target coal material corresponding to the coal material information. Further, for each target coal bin, during the process of transporting coal to the target coal bin via conveyor belt, the material level information of the target coal bin, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bin, and the current coal flow speed are periodically input into a pre-trained material level prediction model to obtain the predicted material level information of the target coal bin within the prediction time period. Finally, based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined. This solves the problems of high labor intensity, increased workload for operators, significant safety hazards due to human negligence, and difficulty in accurately controlling the amount of coal entering the bunker in the existing manual coal distribution method. This embodiment of the present disclosure achieves the following: after determining the target coal bunker for placing the target coal based on the coal bunker's evaluation attributes, the target control information for the shallow trough sorting and conveyor belt is determined based on the predicted material level information. This enables efficient transportation of the target coal to the target coal bunker, reduces errors, improves the safety and accuracy of distribution, and achieves precise bunker entry. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of exemplary embodiments of the present invention, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the drawings of the embodiments to be described in this invention, and not all of the drawings. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0025] Figure 1 This is a flowchart illustrating an intelligent control method for coal transport bins provided in this embodiment of the present disclosure.

[0026] Figure 2 This is a schematic diagram illustrating the determination of material level information provided in an embodiment of this disclosure;

[0027] Figure 3 This is a flowchart illustrating an intelligent control method for coal transport bins provided in this embodiment of the present disclosure.

[0028] Figure 4 This is a schematic diagram illustrating the determination of usage priority provided in an embodiment of this disclosure;

[0029] Figure 5 This is a schematic diagram of a path conflict provided by an embodiment of this disclosure;

[0030] Figure 6 This is a flowchart illustrating an intelligent control method for coal transport bins provided in this embodiment of the present disclosure.

[0031] Figure 7 This is a flowchart illustrating an intelligent control method for coal transport bins provided in this embodiment of the present disclosure.

[0032] Figure 8 This is a schematic diagram of a shallow trough sorting machine and conveyor belt provided in an embodiment of this disclosure;

[0033] Figure 9 A schematic diagram of the structure of an intelligent control device for a coal transport warehouse provided in an embodiment of this disclosure;

[0034] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0035] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0036] Before introducing the technical solutions provided by the embodiments of this disclosure, the application scenarios can be illustrated first. The technical solutions provided by the embodiments of this disclosure can be applied to the scenario of intelligent coal allocation. For example, it can be applied to the scenario of intelligent allocation of clean coal, middlings, or gangue across multiple coal bins. Based on the technical solutions of the embodiments of this disclosure, after determining the target coal bin for placing the target coal according to the coal bin evaluation attributes, the target control information for the shallow trough sorting and conveyor belt is determined according to the predicted material level information, so as to achieve efficient transportation of the target coal to the target coal bin, reduce errors, improve the safety and accuracy of allocation, and achieve the effect of precise bin entry.

[0037] Example 1

[0038] Figure 1 This is a flowchart illustrating an intelligent control method for coal transportation and storage provided in this embodiment. This embodiment is applicable to the scenario of intelligent coal allocation. The method can be executed by an intelligent control device for coal transportation and storage. This device can be implemented in the form of software and / or hardware. The hardware can be a mobile electronic device. This electronic device can execute the intelligent control method for coal transportation and storage provided in this technical solution.

[0039] like Figure 1 As shown, the method includes:

[0040] S110. Based on the coal information and the material level information of the coal bins to be selected for placing the coal, determine at least one coal bin to be used for placing the coal.

[0041] The coal information describes the basic information of the coal to be stored in this batch. This information includes at least the coal quality type, which typically includes clean coal, middlings, and gangue. The "selectable coal bins" refers to the set of coal bins currently available for storage in this batch. The "level information" refers to the current level of the selected coal bins. The "used coal bins" refers to one or more specific coal bins ultimately selected for storing this batch of coal. The "coal storage type" of the selected coal bins refers to the intended use or category label assigned to each bin, indicating the type of coal stored there. This type can be clean coal, middlings, gangue, or mixed coal.

[0042] It should be noted that determining the coal silo to be used corresponding to the coal quality type means placing the coal to be stored in a silo whose storage type is consistent with or compatible with its coal quality type. For example, placing clean coal in a clean coal silo, middlings in a middlings silo, gangue in a gangue silo, or placing coal of different quality types in a mixed coal silo according to a certain proportion, etc.

[0043] Specifically, based on the coal quality type, the coal storage type of the coal bunker to be selected, and the material level information of the coal bunker to be selected, the coal bunker to be used corresponding to the coal quality type is determined.

[0044] It can be understood that the coal bunker to be used in the embodiments of the present invention can be determined in the following way: based on the coal quality type in the coal information and the coal storage type of at least one coal bunker to be selected, the coal bunker to be used corresponding to the coal quality type is determined.

[0045] It should be noted that, based on the correspondence between coal quality type and coal storage type, coal bunkers that meet the correspondence are selected from the candidate coal bunker set, and the selection results are determined as coal bunkers to be used.

[0046] In this embodiment, the material level information of the coal bunker to be selected is determined in the following way: the coal bunker is scanned by a 3D level scanner deployed at a preset location in the coal bunker to obtain the current point cloud data corresponding to the coal bunker; if the material level height change information corresponding to the current point cloud data is greater than a preset change threshold, the median of the point cloud data is determined based on the current point cloud data and the historical point cloud data of a preset number of frames before the point cloud data in the sliding window; the point cloud data values ​​in the sliding window that exceed the preset range of the point cloud data median value are replaced based on the median of the point cloud data to obtain the updated point cloud data; the material level estimator based on Kalman filtering processes the updated point cloud data to obtain the material level height value corresponding to the updated point cloud data in the sliding window, and the material level height value is used as the material level information of the coal bunker to be selected and the coal bunker to be used.

[0047] It should be noted that the preset location in a coal bunker typically refers to a specific location, either inside or outside the bunker, for placing a 3D level scanner. The preset location can be the top of the coal bunker. Placing the 3D level scanner on top allows for a comprehensive view of the entire bunker from above, providing complete level information. A 3D level scanner is a device that uses technologies such as LiDAR for distance measurement. LiDAR calculates the distance to an object by emitting laser pulses towards a specific area and measuring the time it takes for the pulses to reflect back, generating high-precision 3D point cloud data for rapid acquisition of material level information. The scanning angle of a 3D level scanner can be 30°-45°. Based on the 3D level scanner data, the level of coal within the coal bunker can be determined.

[0048] It should be noted that, see Figure 2 A preset threshold for change can be determined based on the scanning angle of the 3D level scanner and the flow rate of the conveyor belt scale. For example, when the scanning angle of the 3D level scanner is... The flow rate of the conveyor belt scale is When this happens, the formula for determining the preset change threshold can be:

[0049] ;

[0050] Among them, Within the time range, the preset change threshold is The current point cloud data corresponds to the material level height change information as follows: When the change in material level corresponding to the current point cloud data is not greater than the preset change threshold, the material level information in the coal bunker can be determined directly based on the 3D level scanner.

[0051] It should be noted that if the change in material level height corresponding to the current point cloud data exceeds the preset change threshold, the reliability of the current point cloud data needs to be determined, and outliers in the current point cloud data should be replaced. The sliding window is used to select the most recent segment or multiple segments of point cloud data for analysis within the time series data. The preset frame count can be 5 frames. Historical point cloud data refers to the point cloud data corresponding to the preset number of frames preceding the current point cloud data. The median of the point cloud data refers to the median value corresponding to the current point cloud data in the sliding window and the historical point cloud data corresponding to the preset number of frames preceding the current point cloud data. For the current point cloud data in the sliding window and the historical point cloud data corresponding to the preset number of frames preceding the current point cloud data, the mean value corresponding to these point cloud data is determined. and variance . will be Point cloud data outside the specified range is defined as point cloud data values ​​exceeding the preset range of the median value of point cloud data. This will be... After replacing the point cloud data values ​​outside the specified range with the median point cloud data, the updated point cloud data is obtained.

[0052] It should also be noted that, after Kalman filtering, the updated point cloud data can be recursively determined using physical laws and observation data to produce a cleaner and more reliable material level height value.

[0053] S120. For at least one coal bunker to be used, determine the coal bunker assessment attributes based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and the equipment health information.

[0054] The conveyor belt information includes at least the conveyor belt load information and the operating speed.

[0055] It should be noted that conveyor belt load information is used to characterize the relationship between the current load on the conveyor belt and its maximum load-bearing capacity. Conveyor belt load information helps determine whether the conveyor belt's operating status is within a safe range. Conveyor belt operating speed is used to characterize the speed information of the conveyor belt when transporting coal. Equipment health information refers to the assessment of the operating status of the conveyor belt and its related equipment, such as motors and drive systems. The coal bunker assessment attributes refer to an assessment of the overall performance and usage status of the coal bunker after comprehensively considering the material level information, conveyor belt information, and equipment health information.

[0056] Specifically, after identifying at least one coal bunker to be used for storing coal, for each coal bunker, the coal bunker evaluation attributes are determined based on the material level information corresponding to the bunker, the load information of the conveyor belt that transports the coal to the bunker, the conveyor belt running speed, and the health information of the conveyor belt and motor, etc., that is, the overall performance of the coal bunker is determined.

[0057] S130. Based on the coal bunker evaluation attributes of at least one coal bunker to be used, determine at least one target coal bunker for placing the target coal material corresponding to the coal material information.

[0058] Here, "target coal bunker" refers to the coal bunker identified from at least one coal bunker to be used, based on the values ​​of its evaluation attributes. "Target coal" refers to coal of at least one coal quality type to be transported to the target coal bunker, based on its coal storage type.

[0059] Optionally, for coal bunkers of the same coal quality type, the usage priority of the coal bunkers is determined based on their coal bunker evaluation attributes; the coal bunker with the highest usage priority is selected as the target coal bunker, so that when the material level of the target coal bunker reaches the height capacity threshold, the next coal bunker is selected as the target coal bunker based on its usage priority.

[0060] For coal bunkers of the same coal type, the target coal bunker refers to the one with the highest value in the coal bunker evaluation attribute. The height capacity threshold is a preset maximum allowable material level height value for the target coal bunker.

[0061] Specifically, ranking the coal bunker assessment attributes of coal bunkers of the same coal quality type determines their usage priority. The coal bunker with the highest assessment attribute value is designated as the highest priority target coal bunker. When the material level in the target coal bunker reaches the maximum allowable level, the next highest-ranked coal bunker in the assessment attribute ranking is designated as the next target coal bunker. This process is repeated to determine the target coal bunkers sequentially, and coal is transported to them according to priority.

[0062] S140. For at least one target coal bunker, during the process of transporting coal to the target coal bunker via conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the speed of the conveyor belt corresponding to the target coal bunker, and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time.

[0063] The shallow trough separator's sorting efficiency refers to the coal sorting efficiency of the shallow trough separator within a cycle, usually expressed as a percentage. The sorting efficiency affects the quality of the coal and the speed at which it flows into the target coal bunker. The conveyor belt speed corresponding to the target coal bunker refers to the speed at which the conveyor belt transports the coal to the target coal bunker. The conveyor belt speed corresponding to the target coal bunker directly affects the coal conveying capacity. The current coal flow velocity refers to the actual flow speed of the coal on the conveyor belt during the coal conveying process. The current coal flow velocity can vary due to factors such as load and friction.

[0064] It should be noted that the material level prediction model is a pre-trained deep learning model, typically trained using historical data. By inputting the material level information of the target coal bunker within the current cycle, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity into the material level prediction model, the predicted material level information of the target coal bunker at each prediction time within the prediction period can be predicted.

[0065] Optionally, after obtaining the predicted material level information, the prediction error is determined based on the predicted material level information corresponding to at least one prediction time within the prediction period and the actual material level information corresponding to at least one prediction time; when the prediction error exceeds the preset error threshold, the model parameters of the material level prediction model are retrained.

[0066] It should be noted that actual material level information refers to the actual, real material level height corresponding to the target coal bunker, determined by a 3D level scanner at each prediction time within the prediction period. Prediction error refers to the error value determined based on at least one predicted material level and at least one actual material level. The preset error threshold is a pre-defined standard used to judge the accuracy of the material level prediction model. The preset error threshold is usually set based on business requirements and system tolerance. If the absolute value of the prediction error exceeds the preset error threshold, the prediction performance of the material level prediction model is considered poor, and the model parameters need to be retrained.

[0067] For example, the level prediction model can be inferred every 5 seconds, predicting the predicted level information for each moment within the next minute based on historical data from the 10 minutes prior to the current moment. After obtaining the actual level information for each moment within that minute, the error of the most recent prediction is calculated based on the predicted level information and the actual level information for each moment within that minute. If the error exceeds 5%, the model parameters of the level prediction model are retrained and adjusted. Simultaneously, an exponentially weighted moving average can be used to smooth the predicted level information, reducing the impact of instantaneous fluctuations and preventing frequent PLC operation.

[0068] In this embodiment, if the detected coal flow density fluctuation information meets the preset fluctuation conditions, the prediction duration of the material level prediction model is adjusted from the first duration to the second duration.

[0069] The first duration is longer than the second duration.

[0070] It should be noted that coal flow density fluctuation information refers to the change in the density of the coal flow, i.e., the mass per unit volume of coal, over time during coal transportation. Coal flow density fluctuates due to factors such as coal type, transportation equipment, and environmental conditions. If the detected coal flow density fluctuation meets preset fluctuation conditions, it indicates increased instability in the coal flow, which may affect the accuracy of level prediction. In this case, reducing the prediction time of the level prediction model can help it better adapt to current dynamic changes, thereby improving the accuracy and reliability of the level prediction model.

[0071] Specifically, for each target coal bunker, during the process of the conveyor belt transporting coal to the bunker, in each cycle, the material level information of the target coal bunker in the current cycle, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity are input into a pre-trained material level prediction model. Based on the material level prediction model, the predicted material level information of the target coal bunker within a certain prediction time period can be determined. Furthermore, when the prediction error exceeds a preset error threshold, the model parameters of the material level prediction model are retrained and corrected. While predicting the predicted material level information of the target coal bunker within the prediction time period based on the material level prediction model, the coal flow density is detected in real time. When the fluctuation information of the coal flow density exceeds a preset fluctuation condition, the prediction time of the material level prediction model is reduced.

[0072] S150. Based on the predicted and preset material level information of the target coal bunker, determine the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker.

[0073] The preset material level information refers to the ideal coal height pre-set in the coal bunker management system. This preset material level information can be set based on historical data, operational experience, or business needs, and can serve as the system's target to ensure the operational efficiency and safety of the target coal bunker. The target control information is generated based on a comparison between the predicted material level information and the preset material level information, issuing instructions to the shallow trough sorting and conveyor belts associated with the target coal bunker.

[0074] Optionally, forced braking may be triggered when any of the following conditions are met: the material level information of the target coal bunker is greater than the height capacity threshold; the material level prediction model fails to converge within multiple prediction periods, and / or the number of prediction errors greater than the preset error threshold is greater than the preset number threshold; the duration of the conveyor belt running speed being less than the preset speed threshold meets the preset condition; or a fault signal of the shallow trough separator is detected.

[0075] The forced braking includes: adjusting the belt speed of the transmission belt from a first speed to a second speed; cutting off the power supply to the shallow trough separator until the coal on the conveyor belt is emptied.

[0076] It should be noted that forced braking, as a safety protection mechanism, aims to prevent potential equipment failures or operational anomalies from causing greater impact on the system. When the actual material level inside the target coal bunker exceeds the preset height capacity threshold, it may lead to coal overflow or equipment damage, thus requiring immediate forced braking to prevent accidents. If the material level prediction model fails to converge within multiple prediction periods, it indicates that the model's prediction effect on the current material level is poor, potentially leading to incorrect operational decisions. If the prediction error exceeds the set preset threshold too many times, it indicates inaccurate prediction, which may affect subsequent operations and control. Therefore, forced braking needs to be triggered to avoid erroneous operations. Excessively slow conveyor belt speed may cause coal accumulation, blockage, or poor conveying, affecting overall production efficiency and safety. If the conveyor belt speed is below the preset speed threshold for a duration exceeding the set conditions, the system will trigger forced braking. Failure of the shallow trough separator may lead to inaccurate coal sorting, affecting the safety and effectiveness of subsequent operations. Therefore, once a fault signal is detected in the shallow trough separator, the system needs to immediately take forced braking measures to prevent further losses.

[0077] It should also be noted that adjusting the belt speed of the control conveyor from the first speed to the second speed typically means reducing the conveyor belt speed to a safe level to minimize coal inflow and prevent further accumulation or overflow. Disconnecting the power supply to the shallow trough separator until the coal on the conveyor belt is emptied prevents the separator from continuing to operate, avoiding adverse effects on coal processing. By cutting off the power, it ensures that no new coal enters the shallow trough separator before it is emptied, thus protecting the equipment and the safety of operators. The forced braking mechanism is a crucial safety measure in the coal handling control system, capable of reacting quickly to abnormal situations to protect equipment, ensure operational safety, and reduce potential economic losses. By setting these forced braking trigger conditions, the system can effectively monitor the operating status and take appropriate control measures when necessary.

[0078] Specifically, based on the material level prediction model, after obtaining the predicted material level information of the target coal bunker within the prediction period, the predicted material level information of the target coal bunker is compared with the preset material level information. When the conditions are met, the shallow trough sorting and conveyor belt associated with the target coal bunker are controlled.

[0079] The technical solution of this disclosure embodiment determines at least one unused coal bin for placing coal based on coal material information and the material level information of the unused coal bins. Then, for each unused coal bin, a coal bin evaluation attribute is determined based on the material level information corresponding to the unused coal bin, the conveyor belt information associated with the unused coal bin, and equipment health information. Then, based on the coal bin evaluation attributes of the at least one unused coal bin, at least one target coal bin is determined for placing the target coal material corresponding to the coal material information. Further, for each target coal bin, during the process of transporting coal to the target coal bin via conveyor belt, the material level information of the target coal bin, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bin, and the current coal flow speed are periodically input into a pre-trained material level prediction model to obtain the predicted material level information of the target coal bin within the prediction time period. Finally, based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined. This solves the problems of high labor intensity, increased workload for operators, significant safety hazards due to human negligence, and difficulty in accurately controlling the amount of coal entering the bunker in the existing manual coal distribution method. This embodiment of the present disclosure achieves the following: after determining the target coal bunker for placing the target coal based on the coal bunker's evaluation attributes, the target control information for the shallow trough sorting and conveyor belt is determined based on the predicted material level information. This enables efficient transportation of the target coal to the target coal bunker, reduces errors, improves the safety and accuracy of distribution, and achieves precise bunker entry.

[0080] Example 2

[0081] Figure 3 This is a flowchart illustrating the intelligent control method for coal storage bins provided in this embodiment of the invention. Based on the aforementioned embodiments, the evaluation attributes of the coal bins to be used are explained in detail. Specific implementation methods can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0082] like Figure 3 As shown, the method specifically includes the following steps:

[0083] S210. Based on the coal information and the material level information of the coal bins to be selected for placing the coal, determine at least one coal bin to be used for placing the coal.

[0084] S220. Determine the belt congestion attribute based on the conveyor belt load information, maximum belt capacity, and the first function in the conveyor belt information.

[0085] In coal transportation, belt congestion is an important indicator used to assess the load on the conveyor belt during operation. The maximum load capacity of the belt refers to the maximum weight the conveyor belt can bear under safe conditions. The first function is the formula used to calculate the belt congestion attribute. The first function for determining the belt congestion attribute can be:

[0086] ;

[0087] in, This refers to the belt congestion attribute at the current moment; This refers to the conveyor belt load information in the conveyor belt information at the current moment; This refers to the maximum load capacity of the belt; It refers to the constant coefficient that controls the rate of change of compressibility.

[0088] Specifically, by inputting the conveyor belt load information and the maximum load capacity of the belt from the current conveyor belt information into the first function, the belt congestion attribute at the current moment can be determined.

[0089] S230. Determine the equipment health attributes based on the equipment failure probability, average repair time, and the second function in the equipment health information.

[0090] Among these, equipment failure probability refers to the likelihood of equipment failure, and its value can be directly obtained from the system. Average repair time refers to the average time required for equipment to return to normal operation after a failure, usually measured in hours or minutes. Average repair time reflects the equipment's maintenance efficiency and its value can also be directly obtained from the system. The second function for determining equipment health attributes can be:

[0091] ;

[0092] in, This refers to the device's health status at the current moment; This refers to the probability of equipment failure in the equipment health information; This refers to the average repair time; This refers to the constant coefficient.

[0093] Specifically, by inputting the device failure probability and average repair time from the current device health information into the second function, the device health attributes can be determined.

[0094] S240. Determine the coal bunker capacity attribute based on the material level height value in the material level information and the height capacity threshold of the coal bunker to be used.

[0095] In coal transportation, the capacity attribute of the coal bunker serves as a crucial indicator for effectively monitoring and managing its usage. The height capacity threshold of a coal bunker refers to the maximum height capacity specified in its design, representing the maximum height of coal the bunker can safely store. Exceeding this height may lead to overflow or safety hazards. The formula for determining the coal bunker's capacity attribute is as follows:

[0096] ;

[0097] in, This refers to the coal bunker capacity attribute at the current moment; This refers to the material level height value in the material level information at the current moment; This refers to the height capacity threshold of the coal bunker to be used.

[0098] Specifically, by inputting the material level height value and the height capacity threshold of the coal bunker to be used from the current material level information into the formula, the capacity attribute of the coal bunker at the current moment can be determined.

[0099] S250. Determine the path cost attribute based on the conveyor belt speed, the distance from the starting point of the conveyor belt to the coal bunker to be used, and the turning angle of the conveyor belt.

[0100] In a coal transportation system, path cost attributes can be used to characterize the efficiency of coal transportation. The distance from the conveyor belt's starting point to the coal bunker to be used refers to the actual distance between the starting point of the conveyor belt and the coal bunker to be used, usually expressed in meters. The conveyor belt's turning angle refers to the angle formed by the conveyor belt during transportation, usually expressed in degrees or radians. The conveyor belt's turning angle can affect its operating efficiency. The formula for determining path cost attributes can be:

[0101] ;

[0102] in, This refers to the path cost attribute at the current moment; This refers to the distance information from the starting point of the conveyor belt to the coal bunker to be used; This refers to the conveyor belt speed; This refers to the turning angle of the conveyor belt.

[0103] Specifically, by inputting the distance information from the current conveyor belt start point to the coal bunker to be used, the conveyor belt speed, and the conveyor belt turning angle into the formula for calculating the path cost attribute, the path cost attribute can be obtained.

[0104] S260. Determine the coal bunker assessment attributes based on the belt congestion attribute, equipment health attribute, coal bunker capacity attribute, path cost attribute, and corresponding weighting coefficients.

[0105] The weighting coefficients are determined based on the target algorithm.

[0106] It should be noted that the formula for determining the assessment attributes of a coal bunker can be:

[0107] ;

[0108] in, This refers to the assessment attributes of the coal bunker; , , as well as This refers to the weighting coefficient, which reflects the importance of each attribute in determining the evaluation attributes of the coal bunker.

[0109] It should also be noted that, see Figure 4 The target algorithm can be the A* algorithm. In the coal bunker management system, the heuristic function in the A* algorithm can be trained using historical data to determine the belt congestion attribute, equipment health attribute, coal bunker capacity attribute, and their impact and contribution on the coal bunker evaluation attribute, thereby assigning weights to each attribute.

[0110] Specifically, the A* algorithm can effectively determine the weight coefficients of each attribute. Based on the calculated belt congestion attribute, equipment health attribute, coal bunker capacity attribute, path cost attribute, and the weight coefficients determined by the A* algorithm, the coal bunker evaluation attributes can be determined. This not only improves the accuracy of the evaluation but also enables flexible handling of multiple variables in complex operating environments, achieving more efficient coal management and transportation.

[0111] Optionally, since each coal bunker has different storage capacity, some bunkers may already be full. Even if the optimal path still leads to that bunker, coal should not be added again. In this case, a coal bunker path conflict resolution mechanism needs to be designed. For conflict scenarios where the optimal path points to a full bunker, a tiered processing strategy is designed. First, real-time material level information is detected, i.e., the remaining capacity of the target bunker is checked before path planning. If the capacity is insufficient, the node (i.e., that bunker) is temporarily blocked. Then, suboptimal path backtracking is performed; when a conflict is triggered, the weight coefficients are recalculated based on the remaining feasible paths, and the suboptimal but usable bunker is selected. Finally, pre-allocation locking is implemented, reducing the weight coefficient of bunkers that are about to reach full capacity in advance, achieving a smooth switch to an alternative path. See also... Figure 5When a path conflict occurs, the system first determines whether the conflict is due to equipment occupancy or path congestion. If it's temporary occupancy and the waiting time is less than 1 minute, the system continues to wait; otherwise, it checks whether the conflict is caused by equipment failure or full capacity. If either condition is met, the second path is selected, and the waiting time determination process is repeated. If the second path is still unavailable, such as due to equipment failure or full capacity, the third path is tried. If all backup paths are unavailable due to equipment failure or full capacity, a shutdown operation is ultimately triggered. This process, through multi-level path switching and waiting mechanisms, ensures that the system can continue operating or safely shut down when a conflict occurs.

[0112] S270. Based on the coal bunker evaluation attributes of at least one coal bunker to be used, determine at least one target coal bunker for placing the target coal material corresponding to the coal material information.

[0113] S280. For at least one target coal bunker, during the process of transporting coal to the target coal bunker via conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time.

[0114] S290. Based on the predicted and preset material level information of the target coal bunker, determine the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker.

[0115] The technical solution of this disclosure embodiment determines at least one coal bin for placing coal based on coal material information and the material level information of the coal bins to be selected. Then, a belt congestion attribute is determined based on conveyor belt load information, maximum belt capacity, and a first function in the conveyor belt information; an equipment health attribute is determined based on equipment failure probability, average repair time, and a second function in the equipment health information; a coal bin capacity attribute is determined based on the material level height value and the height capacity threshold of the coal bin to be used in the material level information; and a path cost attribute is determined based on the conveyor belt speed, the distance from the conveyor belt starting point to the coal bin to be used, and the conveyor belt turning angle. Then, a coal bin evaluation attribute is determined based on the belt congestion attribute, equipment health attribute, coal bin capacity attribute, path cost attribute, and corresponding weighting coefficients. Further, based on the coal bin evaluation attribute of at least one coal bin to be used, at least one target coal bin is determined for placing the target coal material corresponding to the coal material information. Furthermore, for at least one target coal bunker, after obtaining the predicted material level information of the target coal bunker within the predicted time period, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined based on the predicted material level information and the preset material level information. By acquiring and analyzing data such as material level information, conveyor belt information, and equipment health information in real time, it can be ensured that the selected coal bunker can optimally carry and transport coal under the current conditions, thereby optimizing resource utilization and more effectively selecting the coal bunker to be used. Determining the coal bunker evaluation attributes through various parameters and formulas can not only improve the efficiency and safety of coal management, but also optimize resource utilization and transportation processes, support smarter decision-making, and promote the overall performance improvement of the coal transportation system.

[0116] Example 3

[0117] Figure 6 This is a flowchart illustrating the intelligent control method for coal storage bins provided in this embodiment of the invention. Based on the aforementioned embodiments, it provides a more detailed explanation of obtaining the predicted material level information of the target coal bin within the predicted time period. For specific implementation methods, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0118] like Figure 6 As shown, the method specifically includes the following steps:

[0119] S310. Based on the coal information and the material level information of the coal bins to be selected for placing the coal, determine at least one coal bin to be used for placing the coal.

[0120] S320. For at least one coal bunker to be used, determine the coal bunker assessment attributes based on the material level information corresponding to the coal bunker to be used, the conveyor belt information associated with the coal bunker to be used, and the equipment health information.

[0121] S330. Based on the coal bunker evaluation attributes of at least one coal bunker to be used, determine at least one target coal bunker for placing the target coal material corresponding to the coal material information.

[0122] S340. Input the material level information of the target coal bunker at the current time and within the preset time before the current time, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker and the current coal flow speed into the pre-trained material level prediction model to obtain the predicted material level information corresponding to each prediction time within the prediction time.

[0123] It should be noted that the inputs to the material level prediction model are the material level information of the target coal bunker at the current time and within a preset time period prior to the current time, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity. The output of the material level prediction model is the predicted material level information corresponding to each prediction moment within the prediction time period. For example, data from the current time and five minutes prior to the current time can be input into the material level prediction model to obtain the predicted material information for each moment within one minute after the current time.

[0124] It should be noted that, during the training process of the material level prediction model, in order to improve the accuracy of the training, a large amount of material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity can be obtained. The sum of all the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity constitutes the training sample set.

[0125] For each training sample, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow speed are input into the material level prediction model to be trained, so as to obtain the predicted material level information corresponding to the current training sample.

[0126] It should be noted that this method can be used to train each training sample to obtain the required material level prediction model.

[0127] The material level prediction model to be trained is a model whose model parameters are either initial parameters or default parameters. The predicted material level information is the material level information output by inputting the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow speed into the material level prediction model to be trained.

[0128] It should be noted that the model parameters in the material level prediction model to be trained do not meet the expected requirements. Therefore, the predicted material level information output based on the model parameters at this time is different from the theoretical material level information. Therefore, the corresponding error loss value can be determined based on the material level information of each target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the predicted material level information and theoretical material level information corresponding to the current coal flow speed.

[0129] In this embodiment, the material level prediction model to be trained can be a ResNet network model. It should be noted that the main information that can be obtained is the material level information corresponding to the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow speed; that is, the specific model type is not specifically limited.

[0130] The predicted and theoretical level information of the current training samples are processed based on the first preset loss function in the level prediction model to be trained, so as to correct the model parameters in the level prediction model to be trained according to the obtained loss value.

[0131] It should be noted that the training parameters can be set to default values ​​before training the level prediction model. During training, the training parameters can be adjusted based on the model's output; that is, the level prediction model can be obtained by modifying the loss function within the model. For each set of target coal bunker level information, shallow trough separator efficiency, corresponding conveyor belt speed, and current coal flow velocity, there exists a corresponding loss value. This loss value is determined based on the predicted and theoretical level information.

[0132] Specifically, after inputting the target coal bunker's level information, the shallow trough separator's separation efficiency, the corresponding conveyor belt speed, and the current coal flow velocity from the training samples into the training level prediction model, the model can obtain predicted level information corresponding to these parameters. Based on this predicted level information and the theoretical level information, the loss values ​​corresponding to the target coal bunker's level information, the shallow trough separator's separation efficiency, the corresponding conveyor belt speed, and the current coal flow velocity can be determined. The backpropagation method can then be used to correct the model parameters in the training level prediction model.

[0133] The convergence of the first preset loss function is used as the training objective to obtain the material level prediction model. This material level prediction model is the final trained model used to determine the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the material level information of the current coal flow velocity.

[0134] Specifically, the training error of the loss function, i.e., the loss parameter, can be used as a condition to detect whether the loss function has reached convergence. For example, whether the training error is less than a preset error, whether the error trend is stable, or whether the current number of iterations equals a preset number. If the convergence condition is met, such as the training error of the loss function being less than the preset error or the error trend being stable, it indicates that the training of the material level prediction model is complete, and iterative training can be stopped. If the convergence condition is not met, the first training sample can be obtained to train the material level prediction model until the training error of the loss function is within a preset range. When the training error of the loss function converges, the material level prediction model can be used as the material level prediction model.

[0135] S350. Based on the predicted and preset material level information of the target coal bunker, determine the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker.

[0136] The technical solution of this disclosure involves determining at least one target coal bin for placing target coal corresponding to the coal material information. Then, the material level information of the target coal bin at the current time and within a preset time period prior to the current time, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bin, and the current coal flow speed are input into a pre-trained material level prediction model to obtain the predicted material level information for each prediction time within the prediction time period. Finally, based on the predicted material level information and the preset material level information of the target coal bin, the target control information for the shallow trough separator and conveyor belt associated with the target coal bin is determined. By using a pre-trained material level prediction model to obtain the predicted material level information for each prediction time within the prediction time period, the accuracy of future material level predictions can be significantly improved, providing reliable data support for subsequent decision-making and helping to improve the overall performance of the coal material management system.

[0137] Example 4

[0138] Figure 7 This is a flowchart illustrating the intelligent control method for coal storage bins provided in this embodiment of the invention. Based on the aforementioned embodiments, it provides a more detailed explanation of determining the target control information for the shallow trough sorting and conveyor belt associated with the target coal bin. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0139] like Figure 7 As shown, the method specifically includes the following steps:

[0140] S410. Based on the coal information and the material level information of the coal bins to be selected for placing the coal, determine at least one coal bin to be used for placing the coal.

[0141] S420. For at least one coal bunker to be used, determine the coal bunker assessment attributes based on the material level information corresponding to the coal bunker to be used, the conveyor belt information associated with the coal bunker to be used, and the equipment health information.

[0142] S430. Based on the coal bunker evaluation attributes of at least one coal bunker to be used, determine at least one target coal bunker for placing the target coal material corresponding to the coal material information.

[0143] S440. For at least one target coal bunker, during the process of transporting coal to the target coal bunker via conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the speed of the conveyor belt corresponding to the target coal bunker, and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time.

[0144] S450. Based on the predicted material level information corresponding to the predicted time, the shutdown delay time of the shallow trough separator, and the empty belt travel time of the conveyor belt, determine the continuous working time of the shallow trough separator and the conveyor belt associated with the target coal bunker.

[0145] The shutdown delay time of the shallow trough separator refers to the time elapsed from the moment a shutdown command is issued to the shallow trough separator until the shallow trough separator stops continuously feeding material onto the conveyor belt. The conveyor belt emptying time refers to the continuous operating time required for the material currently on the conveyor belt to completely reach the target coal bunker after the upstream material feeding stops. The continuous operating time of the shallow trough separator associated with the target coal bunker refers to the duration from now on that the shallow trough separator is allowed to remain in a "feeding state" before a shutdown command is issued. The conveyor belt will stop later than the shallow trough separator; it first receives material for the shallow trough separator's shutdown delay time, and then completes its emptying process without new material feeding.

[0146] It should be noted that the formula for determining the continuous operating time of the shallow trough separator and conveyor belt associated with the target coal bunker is as follows:

[0147] ;

[0148] in, This refers to the downtime of the shallow trough sorting machine; This refers to the time the conveyor belt travels empty. This refers to the continuous working time of the shallow trough separator associated with the target coal bunker; This refers to the estimated full-load duration of the shallow trough separator. The estimated full-load duration of the shallow trough separator can be determined based on the predicted material level information, coal flow velocity, and preset material level information at the predicted time.

[0149] Specifically, based on the predicted material level information, coal flow velocity, and preset material level information corresponding to the predicted time, the estimated full-load duration of the shallow trough separator is determined. Subtracting the shutdown delay of the shallow trough separator and the empty belt travel time of the conveyor belt from the estimated full-load duration corresponding to the predicted time, the continuous working time of the shallow trough separator associated with the target coal bunker can be obtained.

[0150] S460: When the actual working time of the shallow trough separator reaches the continuous working time, control the shallow trough separator to stop working.

[0151] For details, see Figure 8 Starting from the predicted time, once the current working time of the shallow trough separator reaches the continuous working time of the shallow trough separator, that is, when the predicted full trough time is equal to the sum of the shutdown delay time of the shallow trough separator and the empty belt time of the conveyor belt, a stop command is sent to the shallow trough separator to control the shallow trough separator to stop working. And when the conveyor belt is empty, a stop command is sent to the conveyor belt to control the conveyor belt to stop.

[0152] The technical solution of this disclosure, for at least one target coal bunker, obtains the predicted material level information of the target coal bunker within a predicted time period. Then, based on the predicted material level information corresponding to the predicted time, the shutdown delay time of the shallow trough separator, and the empty belt travel time of the conveyor belt, the continuous working time of the shallow trough separator and the conveyor belt associated with the target coal bunker is determined. Finally, when the actual working time of the shallow trough separator reaches the continuous working time, the shallow trough separator is controlled to stop working. The shutdown delay of the separator and the material in transit caused by the empty belt travel are explicitly included, allowing for early shutdown and significantly reducing the risk of bunker overflow or low-level material shortage. This reduces the safety risks caused by full bunker or material spillage, meeting the requirements for safe production.

[0153] Example 5

[0154] Figure 9 This is a schematic diagram of the intelligent control device for coal conveying bins provided in this embodiment of the present disclosure, as shown below. Figure 9 As shown, the device includes: a coal bunker determination module 510, a coal bunker evaluation attribute determination module 520, a target coal bunker determination module 530, a predicted material level information determination module 540, and a target control information determination module 550.

[0155] The module for determining the coal bunker to be used is used to determine at least one coal bunker to be used for placing the coal, based on coal information and the material level information of the coal bunkers to be selected for placing the coal. The module for determining the coal bunker evaluation attributes is used to determine the coal bunker evaluation attributes of the at least one coal bunker to be used, based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and equipment health information; wherein the conveyor belt information includes at least conveyor belt load information and operating speed. The module for determining the target coal bunker is used to determine the target coal bunker to be used for placing the coal based on the coal bunker evaluation attributes of the at least one coal bunker to be used. The system includes: at least one target coal bunker corresponding to the target coal material; a predicted material level information determination module, used for periodically inputting the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the speed of the conveyor belt corresponding to the target coal bunker, and the current coal flow speed into a pre-trained material level prediction model during the process of transporting coal to the target coal bunker via a conveyor belt, to obtain the predicted material level information of the target coal bunker within the prediction time period; and a target control information determination module, used for determining the target control information of the shallow trough separator and the conveyor belt associated with the target coal bunker based on the predicted material level information and the preset material level information of the target coal bunker.

[0156] The technical solution of this disclosure embodiment determines at least one unused coal bin for placing coal based on coal material information and the material level information of the unused coal bins. Then, for each unused coal bin, a coal bin evaluation attribute is determined based on the material level information corresponding to the unused coal bin, the conveyor belt information associated with the unused coal bin, and equipment health information. Then, based on the coal bin evaluation attributes of the at least one unused coal bin, at least one target coal bin is determined for placing the target coal material corresponding to the coal material information. Further, for each target coal bin, during the process of transporting coal to the target coal bin via conveyor belt, the material level information of the target coal bin, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bin, and the current coal flow speed are periodically input into a pre-trained material level prediction model to obtain the predicted material level information of the target coal bin within the prediction time period. Finally, based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined. This solves the problems of high labor intensity, increased workload for operators, significant safety hazards due to human negligence, and difficulty in accurately controlling the amount of coal entering the bunker in the existing manual coal distribution method. This embodiment of the present disclosure achieves the following: after determining the target coal bunker for placing the target coal based on the coal bunker's evaluation attributes, the target control information for the shallow trough sorting and conveyor belt is determined based on the predicted material level information. This enables efficient transportation of the target coal to the target coal bunker, reduces errors, improves the safety and accuracy of distribution, and achieves precise bunker entry.

[0157] Based on the above technical solutions, the coal bunker determination module 510 includes: a coal bunker acquisition submodule, used to determine the coal bunker corresponding to the coal quality type according to the coal quality type in the coal information and the coal storage type of at least one coal bunker to be selected.

[0158] Based on the above technical solutions, the device further includes: a material level information determination module, used to scan the coal bunker based on a 3D level scanner deployed at a preset location in the coal bunker to obtain current point cloud data corresponding to the coal bunker; if the material level height change information corresponding to the current point cloud data is greater than a preset change threshold, then the median of the point cloud data is determined based on the current point cloud data in the sliding window and historical point cloud data of a preset number of frames prior to the point cloud data; based on the median of the point cloud data, point cloud data values ​​in the sliding window that exceed the preset range of the median of the point cloud data are replaced to obtain updated point cloud data; a material level estimator based on Kalman filtering processes the updated point cloud data to obtain the material level height value corresponding to the updated point cloud data in the sliding window, and uses the material level height value as the material level information.

[0159] Based on the above technical solutions, the coal bunker assessment attribute determination module 520 includes: a belt congestion attribute determination submodule, an equipment health attribute determination submodule, a coal bunker capacity attribute determination submodule, a path cost attribute determination submodule, and a coal bunker assessment attribute calculation submodule.

[0160] The belt congestion attribute determination submodule is used to determine the belt congestion attribute based on the conveyor belt load information, the maximum belt load capacity and the first function in the conveyor belt information.

[0161] The device health attribute determination submodule is used to determine the device health attribute based on the device failure probability, average repair time and the second function in the device health information.

[0162] The coal bunker capacity attribute determination submodule is used to determine the coal bunker capacity attribute based on the material level height value in the material level information and the height capacity threshold of the coal bunker to be used.

[0163] The path cost attribute determination submodule is used to determine the path cost attribute based on the conveyor belt speed, the distance from the starting point of the conveyor belt to the coal bunker to be used, and the turning angle of the conveyor belt.

[0164] The coal bunker assessment attribute calculation submodule is used to determine the coal bunker assessment attributes based on the belt congestion attribute, the equipment health attribute, the coal bunker capacity attribute, the path cost attribute, and the corresponding weight coefficients; wherein the weight coefficients are determined based on the target algorithm.

[0165] Based on the above technical solutions, the target coal bunker determination module 530 includes: a priority determination submodule and a height capacity threshold judgment submodule.

[0166] The priority determination submodule is used to determine the usage priority of a coal bunker of the same coal quality type based on its coal bunker evaluation attributes.

[0167] The height capacity threshold determination submodule is used to select the coal bunker with the highest priority as the target coal bunker, so that when the material level height value of the target coal bunker reaches the height capacity threshold, the next coal bunker to be used is selected as the target coal bunker according to the priority.

[0168] Based on the above technical solutions, the predicted material level information determination module 540 includes: a material level prediction model prediction submodule, which is used to input the material level information of the target coal bunker at the current time and within a preset time before the current time, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker and the current coal flow speed into the pre-trained material level prediction model to obtain the predicted material level information corresponding to each prediction time within the prediction time.

[0169] Based on the above technical solutions, the target control information determination module 550 includes: a continuous working duration determination submodule and a control module.

[0170] The continuous working duration determination submodule is used to determine the continuous working duration of the shallow trough separator and the conveyor belt associated with the target coal bunker based on the predicted material level information corresponding to the predicted time, the shutdown delay duration of the shallow trough separator, and the empty belt running duration of the conveyor belt.

[0171] The control module is used to control the shallow trough separator to stop working when the actual working time of the shallow trough separator reaches the continuous working time.

[0172] Based on the above technical solutions, the device further includes: a material level prediction model retraining module, used to determine the prediction error based on the predicted material level information corresponding to at least one prediction time within the prediction period and the actual material level information corresponding to at least one prediction time; and to retrain the model parameters of the material level prediction model when the prediction error exceeds a preset error threshold.

[0173] Based on the above technical solutions, the device further includes: a prediction duration adjustment module, used to adjust the prediction duration of the material level prediction model from a first duration to a second duration if the detected coal flow density fluctuation information meets the preset fluctuation conditions; wherein, the first duration is longer than the second duration.

[0174] Based on the above technical solutions, the device further includes: a forced braking trigger module, used when the material level information of the target coal bunker is greater than the height capacity threshold; the material level prediction model fails to converge within multiple prediction periods, and / or the number of prediction errors greater than the preset error threshold is greater than the preset number threshold; the duration for which the belt running speed of the conveyor belt is less than the preset speed threshold meets a preset condition; a fault signal of the shallow trough separator is detected; the forced braking includes: controlling the belt running speed of the transmission belt to adjust from a first speed to a second speed; cutting off the power supply of the shallow trough separator until the coal on the conveyor belt is emptied.

[0175] The intelligent control device for coal transport warehouses provided in this disclosure can execute the intelligent control method for coal transport warehouses provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method execution.

[0176] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0177] Example 6

[0178] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Refer to the following... Figure 10 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 10 The diagram below shows the structure of the terminal device or server 500. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (e.g., vehicle navigation terminals). Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0179] like Figure 10As shown, electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An edit / output (I / O) interface 505 is also connected to bus 504.

[0180] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 10 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0181] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0182] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0183] The electronic device provided in this embodiment and the intelligent control method for coal transport warehouses provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0184] Example 7

[0185] This disclosure provides a computer storage medium storing a computer program that, when executed by a processor, implements the intelligent control method for coal transport bins provided in the above embodiments.

[0186] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0187] In some implementations, the server may communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and may interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0188] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0189] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to:

[0190] Based on the coal information and the material level information of the coal bins to be selected for placing the coal, at least one coal bin to be used for placing the coal is determined.

[0191] For the at least one coal bunker to be used, the coal bunker evaluation attributes are determined based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and the equipment health information; wherein, the conveyor belt information includes at least the conveyor belt load information and the operating speed.

[0192] Based on the coal bunker evaluation attributes of the at least one coal bunker to be used, at least one target coal bunker is determined for placing the target coal material corresponding to the coal material information.

[0193] For the at least one target coal bunker, during the process of transporting coal to the target coal bunker based on the conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time.

[0194] Based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough sorting and conveyor belt associated with the target coal bunker is determined.

[0195] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0196] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0197] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0198] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0199] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0200] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0201] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0202] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. An intelligent control method for coal conveying bins, characterized in that, include: Based on the coal information and the material level information of the coal bins to be selected for placing the coal, at least one coal bin to be used for placing the coal is determined. For the at least one coal bunker to be used, the coal bunker evaluation attributes are determined based on the material level information corresponding to the coal bunker, the conveyor belt information associated with the coal bunker, and the equipment health information; wherein, the conveyor belt information includes at least the conveyor belt load information and the operating speed. Based on the coal bunker evaluation attributes of the at least one coal bunker to be used, at least one target coal bunker is determined for placing the target coal material corresponding to the coal material information. For the at least one target coal bunker, during the process of transporting coal to the target coal bunker based on the conveyor belt, the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker and the current coal flow speed are periodically input into the pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time. Based on the predicted and preset material level information of the target coal bunker, the target control information for the shallow trough separator and conveyor belt associated with the target coal bunker is determined. The step of determining the coal bunker assessment attributes based on the material level information corresponding to the coal bunker to be used, the conveyor belt information associated with the coal bunker to be used, and the equipment health information includes: The belt congestion attribute is determined based on the conveyor belt load information, maximum belt capacity, and the first function in the conveyor belt information. The equipment health attributes are determined based on the equipment failure probability, average repair time, and the second function in the equipment health information. The coal bunker capacity attribute is determined based on the material level height value in the material level information and the height capacity threshold of the coal bunker to be used. The path cost attribute is determined based on the conveyor belt speed, the distance from the starting point of the conveyor belt to the coal bunker to be used, and the turning angle of the conveyor belt. The coal bunker evaluation attributes are determined based on the belt congestion attribute, the equipment health attribute, the coal bunker capacity attribute, the path cost attribute, and the corresponding weighting coefficients. The weighting coefficients are determined based on the target algorithm. The step of determining at least one target coal bunker for placing target coal corresponding to the coal information based on the coal bunker evaluation attributes of the at least one coal bunker to be used includes: For coal bunkers of the same coal quality type, the usage priority of the coal bunkers is determined based on their coal bunker evaluation attributes. The coal bunker with the highest usage priority is designated as the target coal bunker. When the material level in the target coal bunker reaches the height capacity threshold, the next coal bunker to be used is designated as the target coal bunker based on the usage priority. The step of determining the target control information for the shallow trough separator and conveyor belt associated with the target coal bunker based on the predicted and preset material level information includes: Based on the predicted material level information, coal flow velocity, and preset material level information corresponding to the predicted time, the expected full-load duration of the shallow trough separator is determined. Based on the expected full-load duration, the shutdown delay duration of the shallow trough separator, and the empty belt running duration of the conveyor belt, the continuous working duration of the shallow trough separator and the conveyor belt associated with the target coal bunker is determined. When the actual working time of the shallow trough sorter reaches the continuous working time, the shallow trough sorter is controlled to stop working, and when the conveyor belt is empty, a stop command is sent to the conveyor belt to control the conveyor belt to stop.

2. The method according to claim 1, characterized in that, The step of determining at least one coal bin to be used for placing the coal, based on coal information and the material level information of the coal bins to be selected for placing the coal, includes: Based on the coal quality type in the coal information and the coal storage type of at least one coal bunker to be selected, determine the coal bunker to be used corresponding to the coal quality type.

3. The method according to claim 1 or 2, characterized in that, The material level information of the coal bunker to be selected, the coal bunker to be used, and the target coal bunker is determined based on the following method: The coal bunker is scanned using a 3D level scanner deployed at a preset location to obtain current point cloud data corresponding to the coal bunker. If the material level height change information corresponding to the current point cloud data is greater than the preset change threshold, then the median value of the point cloud data is determined based on the current point cloud data in the sliding window and the historical point cloud data of the preset number of frames before the current point cloud data. Based on the median value of the point cloud data, point cloud data values ​​in the sliding window that exceed the preset range of the median value of the point cloud data are replaced to obtain updated point cloud data; The Kalman filter-based level estimator processes the updated point cloud data to obtain the level height value corresponding to the updated point cloud data within the sliding window, and uses the level height value as the level information.

4. The method according to claim 1, characterized in that, The step of inputting the material level information of the target coal bunker, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow velocity into a pre-trained material level prediction model to obtain the predicted material level information of the target coal bunker within the prediction time period includes: The material level information of the target coal bunker at the current time and within a preset time period before the current time, the sorting efficiency of the shallow trough separator, the conveyor belt speed corresponding to the target coal bunker, and the current coal flow speed are input into the pre-trained material level prediction model to obtain the predicted material level information corresponding to each prediction time within the prediction time period.

5. The method according to claim 1, characterized in that, After obtaining the predicted material level information, the method further includes: The prediction error is determined based on the predicted material level information at at least one prediction time and the actual material level information at at least one prediction time within the prediction period. When the prediction error exceeds a preset error threshold, the model parameters of the material level prediction model are retrained.

6. The method according to claim 1, characterized in that, The method further includes: If the detected coal flow density fluctuation information meets the preset fluctuation conditions, the prediction duration of the material level prediction model will be adjusted from the first duration to the second duration. Wherein, the first duration is longer than the second duration.

7. The method according to claim 1, characterized in that, Forced braking is triggered when any of the following conditions are met: The material level information of the target coal bunker is greater than the height capacity threshold; The material level prediction model failed to converge within multiple prediction periods, and / or the number of times the prediction error exceeded a preset error threshold exceeded a preset number threshold. The duration during which the belt speed of the conveyor belt is less than a preset speed threshold meets the preset condition. A fault signal was detected in the shallow trench sorting machine; The forced braking includes: The belt speed of the conveyor belt is adjusted from a first speed to a second speed. Disconnect the power supply to the shallow trough separator until the coal on the conveyor belt is emptied.

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