Production control optimization method and system for manufacturing building sand from coal gangue

Abstract

The invention provides a production control optimization method and system for manufacturing building sand from coal gangue, and the method comprises the steps: obtaining an optimal hardness parameter of sand grains and a corresponding temperature parameter and time parameter, inputting the parameters into a PLC system, and optimizing the temperature parameter and control time through a greedy algorithm, the optimized time parameter and the corresponding temperature parameter are input into a PLC to be controlled, an optimized hardness parameter is obtained, production control is adjusted in real time based on the optimized hardness parameter, a sand grain image after combustion and an image hardness parameter are obtained and compared with a second optimized hardness parameter, and the sand grain image and the image hardness parameter are obtained. Through comparison operation, corresponding time parameters and corresponding temperature parameters can be refined, or coal gangue processing can be continuously controlled in real time, the hardness value of the sand grains is accurate, so that manufacturing sand with proper hardness is obtained, and meanwhile, energy is saved in the production control optimization process.

Description

Technical Field

[0001] The present invention relates to the technical field of production control, and in particular to a production control optimization method and system for producing building sand from coal gangue. Background Art

[0002] Gangue is solid waste discharged during the coal mining and coal washing processes. It contains a large amount of carbon that is not utilized. The obtained gangue is desulfurized and then burned to obtain sand particles. The sand particles are construction sand. Construction sand is the main raw material for concrete and is widely used in civil engineering.

[0003] The traditional production control method of making construction sand from coal gangue mainly relies on fixed parameter control or adjusting time and temperature parameters through manual experience, and consumes a lot of resources. Relying on fixed parameter control or adjusting temperature and time parameters through manual experience is often one-sided, resulting in unstable hardness of sand particles after combustion, and the hardness of the obtained construction sand does not meet the requirements. On the other hand, when burning coal gangue, the traditional production control method is prone to incomplete combustion of coal gangue due to inaccurate time or temperature settings, requiring additional combustion, thereby resulting in unnecessary energy waste. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a production control optimization method for producing construction sand from coal gangue, the method comprising: S1: Obtain the temperature parameter range, time parameter range and sand hardness parameter range of gangue combustion based on historical data, obtain the optimal hardness parameter according to the sand hardness parameter range, and obtain the temperature parameter and time parameter based on the optimal hardness parameter; S2: Inputting the temperature parameter and the time parameter into the PLC system, taking the temperature parameter as the first limiting condition and dividing the time parameter into time periods, optimizing the optimal hardness parameter based on the first limiting condition and the time period control time parameter, and obtaining a first optimized hardness parameter and corresponding time parameter; S3: Using the corresponding time parameter as the second limiting condition and dividing the temperature parameter into temperature cycles, performing secondary optimization on the first optimized hardness parameter based on the second limiting condition and the temperature cycle control temperature parameter to obtain a second optimized hardness parameter and the corresponding temperature parameter; S4: inputting the corresponding time parameters and the corresponding temperature parameters into the PLC system, and controlling the real-time time parameters and the real-time temperature parameters during the gangue processing based on the PLC system; S5: acquiring an image of the burned sand grains based on the image acquisition module, acquiring an image hardness parameter based on the sand grain image, and comparing the image hardness parameter with the second optimized hardness parameter; S6: If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, return to step S2 and refine the corresponding time parameter and the corresponding temperature parameter; S7: If the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, the gangue processing is controlled in real time through step S4.

[0005] As a further solution of the present invention, the temperature parameter range, time parameter range, and sand hardness parameter range for each ton of gangue combustion are obtained based on historical data, the optimal hardness parameter is obtained according to the sand hardness parameter range, and the corresponding temperature parameter and time parameter are obtained based on the optimal hardness parameter, including: Based on historical data, the temperature parameter range during gangue processing, the time parameter range for processing each ton of gangue, and the sand grain hardness parameter range after the gangue processing are completed are obtained, and the optimal hardness parameter is obtained from the sand grain hardness parameter range. The optimal hardness parameter is expressed as the parameter closest to the target sand grain hardness parameter in the sand grain hardness parameter range. At the same time, based on historical data, the temperature parameter corresponding to the optimal hardness parameter and the time parameter used for processing each ton of gangue are obtained, and the temperature parameter and the time parameter are respectively within the temperature parameter range and the time parameter range.

[0006] As a further solution of the present invention, step S2 and step S3 include: Inputting the temperature parameter and the time parameter into the PLC system, controlling the temperature parameter to remain unchanged, and adjusting the time parameter based on a greedy algorithm to obtain a first optimized hardness parameter and a time parameter corresponding to the first optimized hardness parameter; The corresponding time parameter is controlled to remain unchanged, and the temperature parameter is continuously adjusted based on a greedy algorithm to obtain a second optimized hardness parameter and a temperature parameter corresponding to the second optimized hardness parameter.

[0007] As a further solution of the present invention, the adjustment based on the greedy algorithm includes: Using the time parameter as a starting time parameter, dividing every ten minutes into a time period, adjusting the number of time periods of the starting time parameter and obtaining corresponding time nodes, obtaining an optimized hardness parameter for each time node, and obtaining a first optimized hardness parameter based on the optimized hardness parameter for each time node; The temperature parameter is used as the starting temperature parameter, and every ten degrees Celsius is divided into a temperature cycle. The number of temperature cycles of the starting temperature parameter is adjusted and the corresponding temperature nodes are obtained, and the optimized hardness parameter of each temperature node is obtained. The second optimized hardness parameter is obtained based on the optimized hardness parameter of each temperature node.

[0008] As a further solution of the present invention, the method of acquiring the image of the burned sand grains based on the image acquisition module, acquiring the image hardness parameter based on the sand grain image, and comparing the image hardness parameter with the second optimized hardness parameter includes: Based on the sand grain image, the color texture characteristics, morphological texture characteristics, and surface texture characteristics of the coal gangue sand grains after combustion are extracted; based on the color texture characteristics, morphological texture characteristics, and surface texture characteristics, the image hardness parameters of the coal gangue are obtained; based on the image hardness parameters and the second optimized hardness parameter, the deviation calculation is performed to obtain the hardness deviation value of the second optimized hardness parameter; and the hardness deviation value of the second optimized hardness parameter is compared with the preset deviation value.

[0009] As a further solution of the present invention, if the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, returning to step S2 and refining the corresponding time parameter and the corresponding temperature parameter includes: Returning to step S2, obtaining time nodes of two time periods before and after the corresponding time parameter, dividing the time within the two time nodes into a refined time period of two minutes each, increasing or decreasing the number of refined time periods of the corresponding time parameter and obtaining the corresponding time nodes, obtaining the refined hardness parameter for each time node, and extracting the first refined hardness parameter and the corresponding refined time parameter from the refined hardness parameters; Control the refinement time parameter unchanged, obtain the temperature nodes of the two temperature cycles before and after the corresponding temperature parameter, divide the temperature within the two temperature nodes, and each two degrees Celsius is a refinement temperature cycle. Increase or decrease the number of refinement temperature cycles of the corresponding temperature parameter and obtain the corresponding temperature nodes, obtain the refinement hardness parameter of each temperature node, and extract the second refinement hardness parameter and the corresponding refinement temperature parameter from the refinement hardness parameter.

[0010] As a further embodiment of the present invention, the method further comprises: Inputting the corresponding refinement time parameter and the refinement temperature parameter into a PLC system, and controlling the real-time time parameter and the real-time temperature parameter during gangue processing based on the PLC system; If the deviation between the image hardness parameter and the second refined hardness parameter is still not within the preset deviation range, return to step S2 and refine the corresponding refined time parameter and the refined corresponding temperature parameter a second time until the deviation is within the preset deviation range.

[0011] In another aspect, an embodiment of the present invention further provides a production control optimization system for producing construction sand from coal gangue, comprising: A PLC system, wherein the PLC system is used to control real-time time parameters and real-time temperature parameters during gangue processing; An acquisition module, the acquisition module is used to obtain the temperature parameter range, time parameter range and sand hardness parameter range of each ton of coal gangue combustion in historical data, obtain the optimal hardness parameter according to the sand hardness parameter range, and obtain the corresponding temperature parameter and time parameter based on the optimal hardness parameter; an optimization module, wherein the optimization module uses the temperature parameter as a first limiting condition and divides the time parameter into time periods, controls the time parameter based on the first limiting condition and the time period to optimize the optimal hardness parameter, uses the corresponding time parameter as a second limiting condition and divides the temperature parameter into temperature periods, and controls the temperature parameter based on the second limiting condition and the temperature period to perform a secondary optimization on the first optimized hardness parameter; a comparison module, wherein the comparison module compares the image hardness parameter with the second optimized hardness parameter; a division module, wherein when the deviation between the image hardness parameter and the second optimized hardness parameter is not within a preset deviation range, the division module returns to step S2 and refines the corresponding time parameter and the corresponding temperature parameter; A verification module, wherein when the deviation between the image hardness parameter and the second optimized hardness parameter is within a preset deviation range, the verification module performs real-time control on the gangue processing through step S4.

[0012] Based on the above aspects, the embodiment of the present application realizes obtaining the temperature parameter range, time parameter range and sand hardness parameter range of coal gangue combustion through historical data, obtaining the optimal hardness parameter according to the sand hardness parameter range, obtaining the temperature parameter and time parameter through the optimal hardness parameter, inputting the temperature parameter and time parameter into the PLC system, taking the temperature parameter as the first limiting condition and dividing the time parameter into time periods, optimizing the optimal hardness parameter according to the first limiting condition and the time period, obtaining the first optimized hardness parameter and the corresponding time parameter, taking the corresponding time parameter as the second limiting condition and dividing the temperature parameter into temperature periods, controlling the temperature parameter according to the second limiting condition and the temperature period to perform secondary optimization on the first optimized hardness parameter, obtaining the second optimized hardness parameter and Corresponding temperature parameters, corresponding time parameters and corresponding temperature parameters are input into the PLC system, and the real-time time parameters and real-time temperature parameters during gangue processing are controlled by the PLC system. The optimal time parameters and temperature parameters are obtained through the greedy algorithm, and the optimal time parameters and temperature parameters are input into the PLC system to control time and temperature in real time, and obtain the optimal hardness parameters. The temperature parameters and time parameters are obtained through the algorithm to adjust the production control in real time, and the temperature and time values ​​are accurately obtained to obtain sand with suitable hardness for construction. At the same time, the appropriate temperature and time parameters are controlled in the process of production control optimization, which saves the heat energy wasted due to too long time or too high temperature, and saves the energy wasted due to insufficient combustion of carbon in the gangue due to too short time or too low temperature, so as to achieve the purpose of saving energy.

[0013] Based on the image acquisition module, the image of the sand grains after combustion is obtained, the image hardness parameter is obtained based on the sand grain image, and the image hardness parameter is compared with the second optimized hardness parameter. If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, return to step S2 and refine the corresponding time parameter and the corresponding temperature parameter. If the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, the coal gangue processing is controlled in real time through step S4, and the optimal hardness parameter is verified through the image hardness parameter to increase the accuracy of the optimal hardness parameter. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention provides a flowchart of a method for optimizing the production control of building sand from coal gangue.

[0015] Figure 2 The present invention provides a schematic diagram of the execution flow of a greedy algorithm in a production control optimization method for producing construction sand from coal gangue provided by an embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of a production control optimization system for producing construction sand from coal gangue provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a schematic diagram of the execution flow of a production control optimization method for producing construction sand from coal gangue provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the execution flow of a greedy algorithm in a production control optimization method for producing construction sand from coal gangue provided by an embodiment of the present invention. The production control optimization method for producing construction sand from coal gangue is introduced in detail below.

[0018] S1: Based on historical data, the temperature parameter range, time parameter range and sand hardness parameter range of coal gangue combustion are obtained, the optimal hardness parameter is obtained according to the sand hardness parameter range, and the temperature parameter and time parameter are obtained based on the optimal hardness parameter.

[0019] Based on historical data, the temperature parameter range during gangue processing, the time parameter range for processing each ton of gangue, and the sand grain hardness parameter range after the gangue processing are completed are obtained, and the optimal hardness parameter is obtained from the sand grain hardness parameter range. The optimal hardness parameter is expressed as the parameter in the sand grain hardness parameter range that is closest to the target sand grain hardness parameter. At the same time, based on historical data, the temperature parameter corresponding to the optimal hardness parameter and the time parameter used for processing each ton of gangue are obtained.

[0020] In this embodiment, based on the historical data of a coal gangue processing plant, the temperature parameter range of coal gangue processing is 850℃-950℃, the time parameter range of processing one ton of coal gangue is 10h-12h, and the sand grain hardness parameter range is 200HV-400HV. The hardness parameter is expressed in Vickers hardness, and HV is the unit of Vickers hardness.

[0021] Furthermore, the sand hardness parameter 460HV required for construction sand is obtained, and the sand hardness parameter 400HV closest to the sand hardness parameter required for construction sand is selected within the sand hardness parameter range. The temperature parameter 900°C corresponding to the sand hardness parameter 400HV and the time parameter used for processing one ton of coal gangue are obtained as 11 hours.

[0022] S2: Input the temperature parameter and time parameter into the PLC system, use the temperature parameter as the first limiting condition and divide the time parameter into time periods, optimize the optimal hardness parameter based on the first limiting condition and the time period control time parameter, and obtain the first optimized hardness parameter and the corresponding time parameter.

[0023] S3: Taking the corresponding time parameter as the second limiting condition and dividing the temperature parameter into temperature cycles, performing secondary optimization on the first optimized hardness parameter based on the second limiting condition and the temperature cycle control temperature parameter to obtain the second optimized hardness parameter and the corresponding temperature parameter.

[0024] In this embodiment, step S2 and step S3 include: Step S23-1: input the temperature parameter and time parameter into the PLC system, control the temperature parameter to remain unchanged, and adjust the time parameter based on the greedy algorithm to obtain the first optimized hardness parameter and the time parameter corresponding to the first optimized hardness parameter.

[0025] The time parameter is used as the starting time parameter, and every ten minutes is divided into a time period, the number of time periods of the starting time parameter is adjusted and the corresponding time nodes are obtained, and the optimized hardness parameter of each time node is obtained, and the first optimized hardness parameter is obtained based on the optimized hardness parameter of each time node.

[0026] Specifically, the above-mentioned corresponding temperature parameter 900°C and the time parameter 11h used for processing each ton of coal gangue are input into the PLC system. The PLC system controls the temperature parameter 900°C to remain unchanged, and uses the time parameter 11h as the starting time parameter. Every ten minutes is divided into a time period. The time parameter 11h is increased or decreased by n time periods to obtain n time nodes and the optimized hardness parameters of each time node, and the first optimized hardness parameter is obtained from the n optimized hardness parameters. The first optimized hardness parameter is expressed as the maximum value of the n optimized hardness parameters under the condition of 900°C.

[0027] For example, under 900°C conditions, the optimized hardness parameter obtained in 10h40min is 410HV, the optimized hardness parameter obtained in 10h50min is 415HV, the optimized hardness parameter obtained in 11h10min is 415HV, and so on. The first optimized hardness parameter 420HV is obtained from the n optimized hardness parameters, and the time corresponding to the first optimized hardness parameter is 11h30min.

[0028] Step S23-2: Control the corresponding time parameter to remain unchanged, and continuously adjust the temperature parameter based on a greedy algorithm to obtain a second optimized hardness parameter and a temperature parameter corresponding to the second optimized hardness parameter.

[0029] Divide every ten degrees Celsius into a temperature cycle, and use the temperature parameter as the starting temperature parameter, increase or decrease the number of temperature cycles of the starting temperature parameter and obtain the corresponding temperature nodes, and obtain the optimized hardness parameter of each temperature node, and obtain the second optimized hardness parameter based on the optimized hardness parameter of each temperature node.

[0030] Specifically, the time parameter 900℃ and the time 11h30min corresponding to the first optimized hardness parameter are input into the PLC system. The PLC system controls the time 11h30min corresponding to the first optimized hardness parameter to remain unchanged, and uses the temperature parameter 900℃ as the starting temperature parameter. Every ten degrees Celsius is divided into a temperature cycle. The temperature parameter 900℃ is increased or decreased by n temperature cycles to obtain n temperature nodes and the optimized hardness parameter of each temperature node, and the second optimized hardness parameter is obtained from the n optimized hardness parameters. The second optimized hardness parameter is expressed as the maximum value of the n optimized hardness parameters under the condition of 11h30min.

[0031] For example, under the condition of 11h30min, the optimized hardness parameter obtained at 880℃ is 425HV, the optimized hardness parameter obtained at 890℃ is 425HV, the optimized hardness parameter obtained at 910℃ is 435HV, and so on. The second optimized hardness parameter 440HV is obtained from the n optimized hardness parameters, and the temperature corresponding to the second optimized hardness parameter is 920℃.

[0032] It should be noted that the greedy algorithm can effectively deal with some local optimal problems. The hardness of coal gangue is adjusted by temperature and time. The greedy algorithm controls the temperature and time separately to make the time and temperature reach the local optimum, and reflects the overall situation through the local optimum. At the same time, the greedy algorithm decomposes the multidimensional problem, temperature and time, into multiple one-dimensional problems, and can optimize the temperature and time separately, avoiding testing all combinations at the same time, reducing the amount of calculation, making decisions quickly, and quickly finding the local optimal solution.

[0033] Furthermore, the greedy algorithm reduces invalid calculations through directed iteration, and immediately enters a better parameter range after each round of calculation, accelerating the convergence process while avoiding repeated calculations, which can reduce both time and energy consumption.

[0034] S4: Inputting the corresponding time parameters and the corresponding temperature parameters into the PLC system, and controlling the real-time time parameters and the real-time temperature parameters during the gangue processing based on the PLC system.

[0035] For example, the corresponding time parameters and corresponding temperature parameters obtained are 11h30min and 920℃ respectively. 11h30min and 920℃ are input into the PLC system, and the PLC system sets the real-time time parameters and real-time temperature parameters to 11h30min and 920℃ to process the coal gangue.

[0036] S5: Acquire an image of the burned sand grains based on the image acquisition module, acquire an image hardness parameter based on the sand grain image, and compare the image hardness parameter with the second optimized hardness parameter.

[0037] Based on the sand grain image, the color texture characteristics, morphological texture characteristics, and surface texture characteristics of the coal gangue sand grains after combustion are extracted; based on the color texture characteristics, morphological texture characteristics, and surface texture characteristics, the image hardness parameters of the coal gangue are obtained; based on the image hardness parameters and the second optimized hardness parameter, the deviation calculation is performed to obtain the hardness deviation value of the second optimized hardness parameter; and the hardness deviation value of the second optimized hardness parameter is compared with the preset deviation value.

[0038] Specifically, burnt sand grains are randomly selected and images of the burnt sand grains are obtained based on the image acquisition module. The color texture characteristics, morphological texture characteristics, and surface texture characteristics of the burnt sand grains in the sand grain images are analyzed to obtain color texture characteristic parameters, morphological texture characteristic parameters, and surface texture characteristic parameters. The color texture characteristic parameters, morphological texture characteristic parameters, and surface texture characteristic parameters are weightedly summed to obtain image hardness parameters. Multiple burnt sand grains are randomly selected and multiple image hardness parameters are obtained. The image hardness parameters of coal gangue are obtained by taking the average value of the multiple image hardness parameters.

[0039] For example, the image hardness parameter of the obtained coal gangue is 420HV, and the second optimized hardness parameter is 440HV. The hardness deviation value of the second optimized hardness parameter is 20HV, and the preset deviation value between the image hardness parameter and the second optimized hardness parameter is 0-10HV, indicating that the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range; the image hardness parameter of the obtained coal gangue is 435HV, and the second optimized hardness parameter is 440HV. The hardness deviation value of the second optimized hardness parameter is 5HV, indicating that the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range.

[0040] S6: If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, return to step S2 and refine the corresponding time parameter and the corresponding temperature parameter.

[0041] In this embodiment, step S6 includes: Step S61, return to step S2, obtain the time nodes of the two time periods before and after the corresponding time parameter, divide the time within the two time nodes, and each two minutes is a refined time period. Increase or decrease the number of refined time periods of the corresponding time parameter and obtain the corresponding time node, obtain the refined hardness parameter of each time node, and extract the first refined hardness parameter and the corresponding refined time parameter from the refined hardness parameter.

[0042] For example, obtain the corresponding time parameter 11h30min, and obtain the time nodes 11h20min and 11h40min of the two time periods before and after the corresponding time parameter, divide the 20 minutes within 11h20min-11h40min again, and divide every two minutes into a refined time period, and obtain the refined hardness parameters corresponding to the time nodes such as 11h26min, 11h28min, and 11h32min, which are 440HV, 442HV, and 442HV respectively. Obtain the maximum refined hardness parameter 446HV from the refined hardness parameters of each time node. The maximum hardness parameter is the first refined hardness parameter, and at the same time, obtain the refined time parameter 11h36min corresponding to the first refined hardness parameter.

[0043] Step S62: Control the refinement time parameter to remain unchanged, obtain the temperature nodes of the two temperature cycles before and after the corresponding temperature parameter, divide the temperature within the two temperature nodes, and each two degrees Celsius is a refinement temperature cycle. Increase or decrease the number of refinement temperature cycles of the corresponding temperature parameter and obtain the corresponding temperature nodes, obtain the refinement hardness parameter of each temperature node, and extract the second refinement hardness parameter and the corresponding refinement temperature parameter from the refinement hardness parameter.

[0044] For example, under the condition of 11h36min, the corresponding temperature parameter 920℃ is obtained, and the temperature nodes 910℃ and 930℃ of the two temperature cycles before and after the corresponding temperature parameter are obtained. The 20 degrees Celsius within 910℃-930℃ are divided again, and every two degrees Celsius is divided into a refined temperature cycle. The refined hardness parameters corresponding to temperature nodes such as 918℃, 922℃, and 924℃ are obtained, which are 444HV, 446HV, and 448HV respectively. The maximum refined hardness parameter 452HV is obtained from the refined hardness parameters of each temperature node. The maximum hardness parameter is the second refined hardness parameter. At the same time, the refined temperature parameter 925℃ corresponding to the second refined hardness parameter is obtained.

[0045] In step S63, the corresponding refined time parameter and the refined temperature parameter are input into the PLC system. The real-time time parameter and the real-time temperature parameter during gangue processing are controlled based on the PLC system. If the deviation between the image hardness parameter and the second refined hardness parameter is still not within the preset deviation range, the process returns to step S2 and refines the corresponding refined time parameter and the corresponding refined temperature parameter for a second time until the deviation is within the preset deviation range.

[0046] Specifically, the refined corresponding time parameter 11h36min and the corresponding refined temperature parameter 925℃ are input into the PLC system, and 11h36min and 925℃ are input into the PLC system. The PLC system sets the real-time time parameter and the real-time temperature parameter to 11h36min and 925℃ to process the coal gangue.

[0047] It should be noted that if the deviation between the image hardness parameter and the second refined hardness parameter is still not within the preset deviation range, return to step S2 and perform a secondary refinement operation. For example, the time period is secondary refined to 0.5 minutes, and the temperature period is secondary refined to 0.5°C. The secondary refined hardness parameter is obtained until the obtained hardness deviation is within the preset deviation range.

[0048] S7: If the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, the gangue processing is controlled in real time through step S4.

[0049] Furthermore, the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, indicating that the hardness parameter of the sand particles is verified by the image hardness parameter and can be directly used for construction sand. The corresponding temperature parameters and time parameters are used to directly control the gangue processing in real time. The corresponding temperature parameters and time parameters are adjusted in time through the verification method to avoid potential risks and losses.

[0050] It should be noted that by burning coal gangue to obtain construction sand, the hardness of the sand particles after combustion is optimized by controlling the temperature and time during the combustion of coal gangue, and sand for construction with appropriate hardness is obtained. At the same time, appropriate temperature and time parameters are controlled during the production control optimization process, thereby saving heat energy wasted due to too long time or too high temperature, and saving energy wasted due to incomplete combustion of carbon in the coal gangue due to too short time or too low temperature, thereby achieving the purpose of saving energy.

[0051] Figure 3 A schematic diagram of a production control optimization system for producing construction sand from coal gangue provided by some embodiments of the present application that can realize the concept of the present application is shown.

[0052] Specifically, a production control optimization system for producing construction sand from coal gangue includes: The PLC system is used to control real-time time parameters and real-time temperature parameters during gangue processing.

[0053] An acquisition module is used to obtain the temperature parameter range, time parameter range and sand hardness parameter range of each ton of coal gangue combustion in historical data, obtain the optimal hardness parameter according to the sand hardness parameter range, and obtain the corresponding temperature parameter and time parameter based on the optimal hardness parameter.

[0054] An optimization module, wherein the optimization module uses the temperature parameter as a first limiting condition and divides the time parameter into time periods, controls the time parameter based on the first limiting condition and the time period to optimize the optimal hardness parameter, uses the corresponding time parameter as a second limiting condition and divides the temperature parameter into temperature periods, and controls the temperature parameter based on the second limiting condition and the temperature period to perform a second optimization on the first optimized hardness parameter.

[0055] A comparison module is used to compare the image hardness parameter with the second optimized hardness parameter.

[0056] A division module, when the deviation between the image hardness parameter and the second optimized hardness parameter is not within a preset deviation range, returns to step S2 and refines the corresponding time parameter and the corresponding temperature parameter.

[0057] A verification module, wherein when the deviation between the image hardness parameter and the second optimized hardness parameter is within a preset deviation range, the verification module performs real-time control on the gangue processing through step S4.

[0058] The specific usage and function of this embodiment are described below: First, the temperature parameter range, time parameter range and sand hardness parameter range of gangue combustion are obtained through historical data, and the optimal hardness parameter is obtained according to the sand hardness parameter range. The temperature parameter and time parameter are obtained through the optimal hardness parameter, and then the temperature parameter and time parameter are input into the PLC system. The temperature parameter is used as the first limiting condition and the time parameter is divided into time periods. The optimal hardness parameter is optimized according to the first limiting condition and the time period, and the first optimized hardness parameter and the corresponding time parameter are obtained. At the same time, the corresponding time parameter is used as the second limiting condition and the temperature parameter is divided into temperature periods. The temperature parameter is controlled according to the second limiting condition and the temperature period to perform secondary optimization on the first optimized hardness parameter, and the second optimized hardness parameter and the corresponding temperature parameter are obtained. Then the corresponding time parameter and the corresponding temperature parameter are input into the PLC system, and the real-time time parameter and real-time temperature parameter during gangue processing are controlled by the PLC system. The optimal time parameter and temperature parameter are obtained by the greedy algorithm, and the optimal time parameter and temperature parameter are input into In the PLC system, time and temperature are controlled in real time to obtain the optimal hardness parameters. The temperature parameters and time parameters are obtained through the algorithm to adjust the production control in real time, and the hardness value of the sand grains is accurately obtained to obtain sand with appropriate hardness for production. At the same time, the appropriate temperature and time parameters are controlled during the production control optimization process, thereby saving heat energy wasted due to too long time or too high temperature, and saving energy wasted due to insufficient combustion of carbon in the coal gangue due to too short time or too low temperature, thereby achieving the purpose of saving energy. Finally, the image of the burned sand grains is obtained based on the image acquisition module, the image hardness parameter is obtained based on the sand grain image, and the image hardness parameter is compared with the second optimized hardness parameter. If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, return to step S2 and refine the corresponding time parameter and the corresponding temperature parameter. If the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, the coal gangue processing is controlled in real time through step S4, and the optimal hardness parameter is verified through the image hardness parameter to increase the accuracy of the optimal hardness parameter.

[0059] In addition, an embodiment of the present invention further provides an electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method proposed in the first embodiment of the present invention.

[0060] The following is a detailed introduction to the various components of electronic equipment: The term "processor" is the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, the processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the first embodiment of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0061] The processor can execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0062] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0063] The memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor via an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0064] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer instructions can be transferred from one website, computer, server, or data center to another website, server, or data center via a wireless method (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0065] It should be understood that the term "and / or" as used herein simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent the existence of A alone, the existence of both A and B, or the existence of B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the related objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0066] It should be understood that in the embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A production control optimization method for producing construction sand from coal gangue, characterized in that: The method comprises: S1: Obtain the temperature parameter range, time parameter range and sand hardness parameter range of gangue combustion based on historical data, obtain the optimal hardness parameter according to the sand hardness parameter range, and obtain the temperature parameter and time parameter based on the optimal hardness parameter; S2: Inputting the temperature parameter and the time parameter into the PLC system, taking the temperature parameter as the first limiting condition and dividing the time parameter into time periods, optimizing the optimal hardness parameter based on the first limiting condition and the time period control time parameter, and obtaining a first optimized hardness parameter and corresponding time parameter; S3: Using the corresponding time parameter as the second limiting condition and dividing the temperature parameter into temperature cycles, performing secondary optimization on the first optimized hardness parameter based on the second limiting condition and the temperature cycle control temperature parameter to obtain a second optimized hardness parameter and the corresponding temperature parameter; S4: inputting the corresponding time parameters and the corresponding temperature parameters into the PLC system, and controlling the real-time time parameters and the real-time temperature parameters during the gangue processing based on the PLC system; S5: acquiring an image of the burned sand grains based on the image acquisition module, acquiring an image hardness parameter based on the sand grain image, and comparing the image hardness parameter with the second optimized hardness parameter; S6: If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, return to step S2 and refine the corresponding time parameter and the corresponding temperature parameter; S7: If the deviation between the image hardness parameter and the second optimized hardness parameter is within the preset deviation range, the gangue processing is controlled in real time through step S4.

2. The production control optimization method for producing construction sand from coal gangue according to claim 1, characterized in that: The method of obtaining the temperature parameter range, time parameter range, and sand hardness parameter range of gangue combustion based on historical data, obtaining the optimal hardness parameter according to the sand hardness parameter range, and obtaining the temperature parameter and time parameter based on the optimal hardness parameter includes: Based on historical data, the temperature parameter range during gangue processing, the time parameter range for processing each ton of gangue, and the sand grain hardness parameter range after the gangue processing are completed are obtained, and the optimal hardness parameter is obtained from the sand grain hardness parameter range. The optimal hardness parameter is expressed as the parameter closest to the target sand grain hardness parameter in the sand grain hardness parameter range. At the same time, based on historical data, the temperature parameter corresponding to the optimal hardness parameter and the time parameter used for processing each ton of gangue are obtained, and the temperature parameter and the time parameter are respectively within the temperature parameter range and the time parameter range.

3. The production control optimization method for producing construction sand from coal gangue according to claim 1, characterized in that: The steps S2 and S3 include: Inputting the temperature parameter and the time parameter into the PLC system, controlling the temperature parameter to remain unchanged, and adjusting the time parameter based on a greedy algorithm to obtain a first optimized hardness parameter and a time parameter corresponding to the first optimized hardness parameter; The corresponding time parameter is controlled to remain unchanged, and the temperature parameter is continuously adjusted based on a greedy algorithm to obtain a second optimized hardness parameter and a temperature parameter corresponding to the second optimized hardness parameter.

4. The production control optimization method for producing construction sand from coal gangue according to claim 3, characterized in that: Adjustment based on greedy algorithm, including: Using the time parameter as a starting time parameter, dividing every ten minutes into a time period, adjusting the number of time periods of the starting time parameter and obtaining corresponding time nodes, obtaining an optimized hardness parameter for each time node, and obtaining a first optimized hardness parameter based on the optimized hardness parameter for each time node; The temperature parameter is used as the starting temperature parameter, and every ten degrees Celsius is divided into a temperature cycle. The number of temperature cycles of the starting temperature parameter is adjusted and the corresponding temperature nodes are obtained, and the optimized hardness parameter of each temperature node is obtained. The second optimized hardness parameter is obtained based on the optimized hardness parameter of each temperature node.

5. The production control optimization method for producing construction sand from coal gangue according to claim 1, characterized in that: The method of acquiring the burned sand grain image based on the image acquisition module, acquiring the image hardness parameter based on the sand grain image, and comparing the image hardness parameter with the second optimized hardness parameter includes: Based on the sand grain image, the color texture characteristics, morphological texture characteristics, and surface texture characteristics of the coal gangue sand grains after combustion are extracted; based on the color texture characteristics, morphological texture characteristics, and surface texture characteristics, the image hardness parameters of the coal gangue are obtained; based on the image hardness parameters and the second optimized hardness parameter, the deviation calculation is performed to obtain the hardness deviation value of the second optimized hardness parameter; and the hardness deviation value of the second optimized hardness parameter is compared with the preset deviation value.

6. The production control optimization method for producing construction sand from coal gangue according to claim 1, characterized in that: If the deviation between the image hardness parameter and the second optimized hardness parameter is not within the preset deviation range, returning to step S2 and refining the corresponding time parameter and the corresponding temperature parameter includes: Returning to step S2, obtaining time nodes of two time periods before and after the corresponding time parameter, dividing the time within the two time nodes into a refined time period of two minutes each, adjusting the number of refined time periods of the corresponding time parameter and obtaining the corresponding time nodes, obtaining the refined hardness parameter of each time node, and extracting the first refined hardness parameter and the corresponding refined time parameter from the refined hardness parameter; The refinement time parameter is controlled to remain unchanged, the temperature nodes of the two temperature cycles before and after the corresponding temperature parameter are obtained, the temperature within the two temperature nodes is divided, and each two degrees Celsius is a refinement temperature cycle. The number of refinement temperature cycles of the corresponding temperature parameter is adjusted and the corresponding temperature node is obtained, the refinement hardness parameter of each temperature node is obtained, and the second refinement hardness parameter and the corresponding refinement temperature parameter are extracted from the refinement hardness parameter.

7. The production control optimization method for producing construction sand from coal gangue according to claim 6, characterized in that: The method further comprises: Inputting corresponding refinement time parameters and corresponding refinement temperature parameters into a PLC system, and controlling real-time time parameters and real-time temperature parameters during gangue processing based on the PLC system; If the deviation between the image hardness parameter and the second refined hardness parameter is still not within the preset deviation range, return to step S2 and refine the corresponding refined time parameter and the corresponding refined temperature parameter for a second time until the deviation is within the preset deviation range.

8. A production control optimization system for producing construction sand from coal gangue, characterized in that: include: A PLC system, wherein the PLC system is used to control real-time time parameters and real-time temperature parameters during gangue processing; An acquisition module, the acquisition module is used to obtain the temperature parameter range, time parameter range and sand hardness parameter range of each ton of coal gangue combustion in historical data, obtain the optimal hardness parameter according to the sand hardness parameter range, and obtain the corresponding temperature parameter and time parameter based on the optimal hardness parameter; an optimization module, wherein the optimization module uses the temperature parameter as a first limiting condition and divides the time parameter into time periods, controls the time parameter based on the first limiting condition and the time period to optimize the optimal hardness parameter, uses the corresponding time parameter as a second limiting condition and divides the temperature parameter into temperature periods, and controls the temperature parameter based on the second limiting condition and the temperature period to perform a secondary optimization on the first optimized hardness parameter; a comparison module, wherein the comparison module compares the image hardness parameter with the second optimized hardness parameter; a division module, wherein when the deviation between the image hardness parameter and the second optimized hardness parameter is not within a preset deviation range, the division module returns to step S2 and refines the corresponding time parameter and the corresponding temperature parameter; A verification module, wherein when the deviation between the image hardness parameter and the second optimized hardness parameter is within a preset deviation range, the verification module performs real-time control on the gangue processing through step S4.

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