Method and system for improving coal washing efficiency based on optimization of coal washing process

Abstract

The present invention relates to the technical field of coal washing, a method and system for improving washing efficiency based on coal washing process optimization, comprising: washing the raw coal sample according to the initial coal washing time temperature and determining the sodium ion concentration to obtain a sodium ion concentration matrix, extracting the concentration window matrix according to the adaptive sampling window, calculating the window variance of the concentration window matrix, judging whether the window variance is less than the variance threshold, if not less than, constructing an iterative coal washing matrix according to the window control size, updating the concentration window matrix, and returning the calculated window variance, if less than, setting the concentration window matrix as the target window matrix, summarizing the target window matrix set, performing matrix splicing on the target window matrix set, obtaining a target coal washing matrix, identifying the optimal coal washing time temperature, and washing coal according to the optimal coal washing time temperature. The present invention can improve the washing efficiency of the coal washing process.

Description

Technical Field

[0001] The present invention relates to the technical field of coal washing, and in particular to a method and system for improving coal washing efficiency based on coal washing process optimization. Background Art

[0002] Since marine sedimentary coal contains a large amount of Na salt, when burning marine sedimentary coal, the heating surface in the furnace is very likely to have problems such as slagging, corrosion, contamination and ash accumulation, which seriously affects the safe and economical operation of power station boilers.

[0003] In order to reduce the influence of Na salt in marine sedimentary coal, marine sedimentary coal is usually washed to reduce the Na salt content. When washing marine sedimentary coal, the setting of washing temperature and washing time is involved. Appropriate washing temperature and washing time can reduce the Na salt content in marine sedimentary coal to the maximum extent. However, the current determination of washing temperature and washing time is usually to control the washing temperature unchanged, determine the optimal washing time, and then determine the optimal washing temperature under the optimal washing time. However, this method cannot simultaneously control the washing temperature and washing time changes. The orthogonal test method can simultaneously control the washing temperature and washing time changes, but the orthogonal test method has not been used to control the regular changes of specific washing temperature ranges and washing time ranges. Therefore, the current coal washing process has the problem of low washing efficiency. Summary of the invention

[0004] The present invention provides a method and system for improving coal washing efficiency based on coal washing process optimization, the main purpose of which is to improve the coal washing efficiency of the coal washing process.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for improving washing efficiency based on coal washing process optimization, comprising: obtaining a coal washing temperature sequence and a coal washing time sequence, and constructing an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; extracting the initial coal washing time temperature in sequence from the initial coal washing matrix, washing a preset raw coal sample according to the initial coal washing time temperature and determining the sodium ion concentration to obtain a sodium ion concentration matrix; calculating an adaptive sampling window according to the sodium ion concentration matrix; extracting a concentration window matrix in sequence from the sodium ion concentration matrix according to the adaptive sampling window; and calculating the window variance of the concentration window matrix according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, Represents the mean of the matrix elements in the concentration window matrix; determines whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, calculates the window control size of the concentration window matrix according to the preset window size control formula, constructs an iterative coal washing matrix according to the window control size, updates the concentration window matrix using the iterative coal washing matrix, and returns to the above step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula; if the window variance is less than the variance threshold, sets the concentration window matrix as the target window matrix; determines the sodium ion concentration matrix whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window; if the concentration window matrix in the sodium ion concentration matrix has been extracted, summarize the target window matrix to obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing temperature in the target coal washing matrix, perform coal washing according to the optimal coal washing temperature, and complete the improvement of washing efficiency based on the optimization of the coal washing process.

[0006] Optionally, the obtaining of the coal washing temperature sequence and the coal washing time sequence includes: receiving a coal washing temperature interval and a coal washing time interval input by a user, and calculating the coal washing temperature interval and the coal washing time interval according to the coal washing temperature interval and the coal washing time interval using the following formula: ,in, Indicates the coal washing temperature interval, represents the maximum value of the coal washing temperature range, Indicates the minimum value of the coal washing temperature range, represents the rounding function, represents the temperature linear coefficient, Indicates the coal washing time interval, represents the maximum value of the coal washing time interval, Indicates the minimum value of the coal washing time interval, Represents the time linear coefficient; according to the coal washing temperature interval, temperature series sampling is performed in the coal washing temperature range to obtain a coal washing temperature series, and according to the coal washing time interval, time series sampling is performed in the coal washing time range to obtain a coal washing time series.

[0007] Optionally, the preset raw coal sample is washed according to the initial coal washing time and temperature and the sodium ion concentration is determined to obtain a sodium ion concentration matrix, including: identifying the initial coal washing temperature in the initial coal washing time and temperature, and preparing a constant temperature washing liquid according to the initial coal washing temperature, wherein the volume of the constant temperature washing liquid is 1L; identifying the initial coal washing time in the initial coal washing time and temperature, and according to the initial coal washing time, washing and filtering the raw coal sample with the constant temperature washing liquid to obtain a coal sample washing liquid; determining the sodium ion concentration of the coal sample washing liquid, and arranging the sodium ion concentrations at the initial coal washing temperature in a matrix to obtain a sodium ion concentration matrix.

[0008] Optionally, calculating the adaptive sampling window according to the sodium ion concentration matrix includes: identifying the number of matrix transverse elements and the number of matrix longitudinal elements of the sodium ion concentration matrix; and calculating the number of window transverse elements and the number of window longitudinal elements according to the number of matrix transverse elements and the number of matrix longitudinal elements using the following formula: in, Indicates the number of horizontal elements in the window. represents the window adaptation ratio, represents the number of horizontal elements of the matrix, Indicates the number of vertical elements in the window. Represents the number of vertical elements of the matrix; constructs the adaptive sampling window according to the number of horizontal elements and the number of vertical elements of the window.

[0009] Optionally, extracting a concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window includes: dividing the sodium ion concentration matrix according to the number of window transverse elements and the number of window longitudinal elements of the adaptive sampling window to obtain a sodium ion divided matrix; and extracting a concentration window matrix in sequence from the sodium ion divided matrix.

[0010] Optionally, before determining whether the window variance is less than a preset variance threshold, the method further includes: receiving a variance level input by a user, and calculating the variance threshold according to the variance level using the following formula: ,in, represents the variance threshold, represents the limit variance threshold, e represents the natural constant, Indicates the level of variance.

[0011] Optionally, calculating the window control size of the concentration window matrix according to a preset window size control formula includes: calculating the window control size according to the window size control formula using the concentration window matrix and the window variance, wherein the window size control formula is as follows: ,in, represents the number of lateral control elements, Indicates the number of vertical control elements, Represents the window threshold coefficient.

[0012] Optionally, constructing an iterative coal washing matrix according to the window control size includes: calculating the window temperature span according to the coal washing temperature interval and the number of lateral elements of the window using the following formula: ,in, represents the window temperature span; according to the coal washing time interval and the number of vertical elements in the window, the window time span is calculated using the following formula: ,in, Represents the window time span; identifies the window start temperature of the concentration window matrix, and calculates the iterative temperature sequence using the following formula according to the window start temperature, window temperature span and the number of lateral control elements: ,in, represents the pth coal washing temperature in the iterative temperature sequence, Represents the window start temperature; identifies the window start time of the concentration window matrix, and calculates the iterative time series according to the window start time, window time span and the number of vertical control elements using the following formula: ,in, represents the qth coal washing time in the iterative time series, represents the window start time; constructing an iterative coal washing matrix according to the iterative temperature sequence and the iterative time sequence.

[0013] Optionally, performing matrix splicing on the target window matrix set to obtain a target coal washing matrix includes: extracting target window matrices in the target window matrix set in sequence; identifying a matrix corner point time-temperature set of the target window matrix; extracting matrix corner point time-temperatures in sequence in the matrix corner point time-temperature set, and identifying a same-corner point time-temperature matrix set of the matrix corner point time-temperatures in the target window matrix set; and splicing the target window matrix and the same-corner point time-temperature matrix set to obtain a target coal washing matrix.

[0014] To achieve the above-mentioned purpose, the present invention also provides a system for improving washing efficiency based on coal washing process optimization, including: a sodium ion concentration matrix construction module, used to obtain a coal washing temperature sequence and a coal washing time sequence, and construct an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; the initial coal washing time temperature is sequentially extracted from the initial coal washing matrix, and the preset raw coal sample is washed according to the initial coal washing time temperature and the sodium ion concentration is measured to obtain a sodium ion concentration matrix; a window variance calculation module, used to calculate an adaptive sampling window according to the sodium ion concentration matrix; the concentration window matrix is ​​sequentially extracted from the sodium ion concentration matrix according to the adaptive sampling window; the window variance of the concentration window matrix is ​​calculated according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, represents the mean of the matrix elements in the concentration window matrix; a target window matrix acquisition module is used to determine whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, the window control size of the concentration window matrix is ​​calculated according to the preset window size control formula, an iterative coal washing matrix is ​​constructed according to the window control size, the concentration window matrix is ​​updated using the iterative coal washing matrix, and the step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula is returned to the above; if the window variance is less than the variance threshold, the concentration window matrix is ​​set as the target window matrix; determine Determine whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window; the optimal coal washing time-temperature washing module is used to summarize the target window matrix if the concentration window matrix in the sodium ion concentration matrix has been extracted, obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing time-temperature in the target coal washing matrix, and perform coal washing according to the optimal coal washing time-temperature.

[0015] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned method for improving coal washing efficiency based on coal washing process optimization.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for improving coal washing efficiency based on coal washing process optimization.

[0017] The present invention solves the problems described in the background technology. The present invention realizes a comprehensive test of the titanium rod, including surface friction, high temperature test and simulation of airflow environment, by combining the titanium rod testing device, data acquisition, surface friction and high temperature testing unit. The original titanium rod diagram, friction titanium rod diagram, high temperature titanium rod diagram and airflow titanium rod diagram obtained by the monitoring device are used as input data, and the surface damage degree of the titanium rod is evaluated by using the surface detection model of deep learning, so as to screen out the optimal titanium rod. Since the present invention uses a variety of test units and monitoring equipment to conduct a comprehensive test on the titanium rod, including surface friction, high temperature performance and simulation of airflow environment, it can more comprehensively evaluate the performance of the titanium rod. On this basis, the present invention greatly reduces the manpower and time consumption in the test process through the use of automated test processes and deep learning models. Compared with the traditional manual testing method, this method can complete the test more quickly and has higher efficiency in data analysis and titanium rod screening. Therefore, the present invention can improve the intelligence of titanium rod high temperature testing and reduce excessive consumption of time and human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a method for improving coal washing efficiency based on coal washing process optimization provided by an embodiment of the present invention; Figure 2 A functional module diagram of a system for improving coal washing efficiency based on coal washing process optimization provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for improving the washing efficiency based on the optimization of the coal washing process provided by one embodiment of the present invention.

[0019] Explanation of the reference numerals: 1. electronic device; 10. processor; 11. memory; 12. bus.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] The embodiment of the present application provides a method for improving washing efficiency based on the optimization of the coal washing process. The execution subject of the method for improving washing efficiency based on the optimization of the coal washing process includes but is not limited to at least one of the electronic devices such as the server and the terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for improving washing efficiency based on the optimization of the coal washing process can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 As shown, it is a flow chart of a method for improving washing efficiency based on coal washing process optimization provided by an embodiment of the present invention. In this embodiment, the method for improving washing efficiency based on coal washing process optimization includes: S1, obtaining a coal washing temperature sequence and a coal washing time sequence, and constructing an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence.

[0024] It can be explained that the coal washing temperature sequence refers to a preset washing temperature sequence for washing coal, the coal washing time sequence refers to a preset washing time sequence for washing coal, and the coal washing temperature sequence can be 10 , 30 , 50 , 70 , 90 , the coal washing time sequence can be 2h, 4h, 6h, 8h, 10h.

[0025] In detail, in the initial coal washing matrix, the coal washing temperature sequence is arranged horizontally, and the coal washing time sequence is arranged vertically.

[0026] In the embodiment of the present invention, the step of obtaining the coal washing temperature sequence and the coal washing time sequence includes: receiving the coal washing temperature interval and the coal washing time interval input by the user, and calculating the coal washing temperature interval and the coal washing time interval according to the coal washing temperature interval and the coal washing time interval using the following formula: ,in, Indicates the coal washing temperature interval, represents the maximum value of the coal washing temperature range, Indicates the minimum value of the coal washing temperature range, represents the rounding function, represents the temperature linear coefficient, Indicates the coal washing time interval, represents the maximum value of the coal washing time interval, Indicates the minimum value of the coal washing time interval, Represents the time linear coefficient; according to the coal washing temperature interval, temperature series sampling is performed in the coal washing temperature range to obtain a coal washing temperature series, and according to the coal washing time interval, time series sampling is performed in the coal washing time range to obtain a coal washing time series.

[0027] Furthermore, the coal washing temperature interval refers to the washing temperature interval for washing the raw coal sample, and the coal washing time interval refers to the washing time interval for washing the raw coal sample. The coal washing temperature interval can be 10 -90 , the coal washing time interval can be 2h-10h.

[0028] S2. Extracting the initial coal washing time and temperature in the initial coal washing matrix in sequence, washing the preset raw coal sample according to the initial coal washing time and temperature and measuring the sodium ion concentration to obtain a sodium ion concentration matrix.

[0029] Furthermore, the initial coal washing time temperature refers to the washing temperature and washing time of the raw coal sample in the initial coal washing matrix. The sodium ion concentration matrix can be shown in the following table:

[0030] It can be explained that, since the coal washing temperature sequence and the coal washing time sequence can be set by the user, not all of the sodium ion concentration data in the sodium ion concentration matrix are measured data, and some are user-estimated data for ease of understanding.

[0031] Furthermore, the sodium ion concentration matrix shows that the sodium ion concentration does not increase with the increase of washing temperature, nor does it increase with the extension of washing time. The optimal washing temperature is 50 The best washing time is about 6h.

[0032] In an embodiment of the present invention, the preset raw coal sample is washed according to the initial coal washing time and temperature and the sodium ion concentration is determined to obtain a sodium ion concentration matrix, including: identifying the initial coal washing temperature in the initial coal washing time and temperature, and preparing a constant temperature washing liquid according to the initial coal washing temperature, wherein the volume of the constant temperature washing liquid is 1L; identifying the initial coal washing time in the initial coal washing time and temperature, and according to the initial coal washing time, washing and filtering the raw coal sample with the constant temperature washing liquid to obtain a coal sample washing liquid; determining the sodium ion concentration of the coal sample washing liquid, and arranging the sodium ion concentrations at the initial coal washing temperature in a matrix to obtain a sodium ion concentration matrix.

[0033] It can be explained that the mass of the raw coal sample can be 1g.

[0034] S3. Calculate an adaptive sampling window according to the sodium ion concentration matrix.

[0035] It can be understood that the adaptive sampling window refers to a sampling window whose size can be adaptively adjusted according to the size of the sodium ion concentration matrix, as described in detail in the following embodiments.

[0036] In an embodiment of the present invention, the step of calculating the adaptive sampling window according to the sodium ion concentration matrix includes: identifying the number of matrix transverse elements and the number of matrix longitudinal elements of the sodium ion concentration matrix; and calculating the number of window transverse elements and the number of window longitudinal elements according to the number of matrix transverse elements and the number of matrix longitudinal elements using the following formula: ,in, Indicates the number of horizontal elements in the window. represents the window adaptation ratio, represents the number of horizontal elements of the matrix, Indicates the number of vertical elements in the window. Represents the number of vertical elements of the matrix; constructs the adaptive sampling window according to the number of horizontal elements and the number of vertical elements of the window.

[0037] Furthermore, the number of transverse elements of the matrix refers to the number of elements in the transverse direction of the sodium ion concentration matrix, that is, the number of coal washing temperatures in the coal washing temperature sequence, the number of longitudinal elements of the matrix refers to the number of elements in the longitudinal direction of the sodium ion concentration matrix, that is, the number of coal washing times in the coal washing time sequence, the number of transverse elements of the window refers to the number of elements in the transverse direction of the adaptive sampling window, and the number of longitudinal elements of the window refers to the number of elements in the longitudinal direction of the adaptive sampling window.

[0038] It can be understood that the window adaptation ratio should be divisible by both the number of horizontal elements and the number of vertical elements of the matrix, and should not be the reciprocal of the number of horizontal elements or the number of vertical elements of the matrix. For example, when the number of horizontal elements and the number of vertical elements of the matrix are 4, the window adaptation ratio can be 1 or 1 / 2; when the number of horizontal elements and the number of vertical elements of the matrix are 5, the window adaptation ratio can be 5; when the number of horizontal elements and the number of vertical elements of the matrix are 6, the window adaptation ratio can be 1, 1 / 2, or 1 / 3; when the number of horizontal elements and the number of vertical elements of the matrix are 7, the window adaptation ratio can be 1; when the number of horizontal elements and the number of vertical elements of the matrix are 8, the window adaptation ratio can be 1, 1 / 2, or 1 / 4.

[0039] For example, when the number of horizontal elements of the matrix is ​​5, the number of vertical elements of the matrix is ​​5, and the window adaptation ratio is 1 / 5, the number of horizontal elements of the window is 1, and the number of vertical elements of the window is 1.

[0040] S4. Extracting a concentration window matrix in sequence from the sodium ion concentration matrix according to the adaptive sampling window.

[0041] It can be understood that the concentration window matrix refers to a matrix divided from the sodium ion concentration matrix according to the adaptive sampling window. See the following embodiment for details.

[0042] In an embodiment of the present invention, extracting a concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window includes: dividing the sodium ion concentration matrix according to the number of window transverse elements and the number of window longitudinal elements of the adaptive sampling window to obtain a sodium ion divided matrix; and extracting a concentration window matrix in sequence from the sodium ion divided matrix.

[0043] It can be understood that the sodium ion partitioning matrix refers to the window matrix after the sodium ion concentration matrix is ​​partitioned according to the number of horizontal elements of the window and the number of vertical elements of the window. For example: when the window adaptation ratio is 1 / 2, the number of horizontal elements of the matrix and the number of vertical elements of the matrix are 4, the horizontal serial number and the vertical serial number of the concentration window matrix can be 1-2, 1-2; 2-3, 1-2; 3-4, 1-2; 1-2, 2-3; 2-3, 2-3; 3-4, 2-3; 1-2, 3-4; 2-3, 3-4; 3-4, 3-4, where 1-2 and 1-2 represent the concentration window matrices corresponding to the first sodium ion washing temperature and the second sodium ion washing temperature in the coal washing temperature sequence of the sodium ion concentration matrix, and the first sodium ion washing time and the second sodium ion washing time in the coal washing time sequence. When the sodium ion concentration matrix is ​​as shown in Table 1, the sodium ion partitioning matrix corresponding to the horizontal serial number and the vertical serial number are 1-2 and 1-2 respectively is as follows:

[0044] It should be understood that the horizontal serial number 1-2 corresponds to the 10th at the horizontal serial number 1. and 30 at the second horizontal number , vertical serial number 1-2 corresponds to 1.0 mg / L at the first vertical number and 1.2 mg / L at the second vertical number.

[0045] S5. Calculate the window variance of the concentration window matrix according to a pre-constructed window variance formula.

[0046] It can be understood that the window variance refers to the variance of the concentration window matrix.

[0047] Furthermore, the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, Represents the mean of the matrix elements in the concentration window matrix.

[0048] It can be understood that the smaller the window variance is, the smaller the fluctuation of the sodium ion concentration data within the concentration window matrix is.

[0049] S6. Determine whether the window variance is less than a preset variance threshold.

[0050] It can be understood that the smaller the variance threshold is, the higher the requirement for the fluctuation of the sodium ion concentration data within the concentration window matrix is.

[0051] In the embodiment of the present invention, before determining whether the window variance is less than a preset variance threshold, the method further includes: receiving a variance level input by a user, and calculating the variance threshold according to the variance level using the following formula: ,in, represents the variance threshold, represents the limit variance threshold, e represents the natural constant, Indicates the level of variance.

[0052] Furthermore, the larger the variance level, the smaller the variance threshold, and the higher the requirement for the fluctuation of the sodium ion concentration data. The extreme variance threshold means that as the variance level increases, the variance threshold approaches the extreme variance threshold infinitely.

[0053] If the window variance is not less than the variance threshold, execute S7, calculate the window control size of the concentration window matrix according to the preset window size control formula, construct an iterative coal washing matrix according to the window control size, and use the iterative coal washing matrix to update the concentration window matrix.

[0054] It can be explained that the window control size includes the number of horizontal elements and the number of vertical elements after the concentration window matrix is ​​controlled. The iterative coal washing matrix refers to a matrix including iterative temperature series, iterative time series and corresponding sodium ion concentration after the window control size is controlled and the sodium ion concentration is measured.

[0055] In an embodiment of the present invention, the step of calculating the window control size of the concentration window matrix according to a preset window size control formula includes: calculating the window control size according to the window size control formula using the concentration window matrix and the window variance, wherein the window size control formula is as follows: ,in, represents the number of lateral control elements, Indicates the number of vertical control elements, Represents the window threshold coefficient.

[0056] In the embodiment of the present invention, the step of constructing an iterative coal washing matrix according to the window control size includes: calculating the window temperature span according to the coal washing temperature interval and the number of lateral elements of the window using the following formula: ,in, represents the window temperature span; according to the coal washing time interval and the number of vertical elements in the window, the window time span is calculated using the following formula: ,in, Represents the window time span; identifies the window start temperature of the concentration window matrix, and calculates the iterative temperature sequence using the following formula according to the window start temperature, window temperature span and the number of lateral control elements: ,in, represents the pth coal washing temperature in the iterative temperature sequence, Represents the window start temperature; identifies the window start time of the concentration window matrix, and calculates the iterative time series according to the window start time, window time span and the number of vertical control elements using the following formula: ,in, represents the qth coal washing time in the iterative time series, represents the window start time; constructing an iterative coal washing matrix according to the iterative temperature sequence and the iterative time sequence.

[0057] Furthermore, the window temperature span refers to the span of the coal washing temperature in the concentration window matrix, and the window time span refers to the span of the coal washing time in the concentration window matrix. For example, when the coal washing temperature interval is 20 When the number of window lateral elements is 3, the window temperature span is 20 When the coal washing time interval is 2h and the number of vertical elements in the window is 3, the window time span is 2h .

[0058] It can be explained that the window start temperature refers to the minimum coal washing temperature in the concentration window matrix, and the window start time refers to the minimum coal washing time in the concentration window matrix.

[0059] For example, when the window start temperature is 50 , the number of horizontal control elements is 4, and the window temperature span is 20 , the first coal washing temperature in the iterative temperature sequence is 50 , the second coal washing temperature is 55 , the third coal washing temperature is 60 After obtaining the iterative temperature sequence and the iterative time sequence, it is necessary to measure the sodium ion concentration according to the iterative temperature sequence and the iterative time sequence, and fill the measured sodium ion concentration into the corresponding position in the iterative coal washing matrix. For example, when the second coal washing temperature in the iterative temperature sequence is 55 , the second coal washing time is 2.5h, then it can be determined that when the coal washing temperature is 55 , the sodium ion concentration when the coal washing time is 2.5h, and fill the sodium ion concentration into the iterative coal washing matrix 55 And the matrix position corresponding to 2.5h.

[0060] Return to the above step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula.

[0061] If the window variance is less than the variance threshold, S8 is executed to set the concentration window matrix as the target window matrix.

[0062] It can be understood that the target window matrix refers to a concentration window matrix whose window variance is less than a variance threshold.

[0063] S9. Determine whether the concentration window matrix in the sodium ion concentration matrix has been extracted.

[0064] If the concentration window matrix in the sodium ion concentration matrix has not been completely extracted, return to the above step of sequentially extracting the concentration window matrix in the sodium ion concentration matrix according to the adaptive sampling window.

[0065] If the concentration window matrix in the sodium ion concentration matrix is ​​extracted, S10 is executed, the target window matrix is ​​summarized to obtain a target window matrix set, matrix splicing is performed on the target window matrix set to obtain a target coal washing matrix, the optimal coal washing temperature is identified in the target coal washing matrix, coal washing is performed according to the optimal coal washing temperature, and the washing efficiency is improved based on the optimization of the coal washing process.

[0066] It can be understood that the optimal coal washing temperature refers to the coal washing temperature and coal washing time corresponding to the maximum sodium ion concentration in the target coal washing matrix.

[0067] In an embodiment of the present invention, matrix splicing is performed on the target window matrix set to obtain a target coal washing matrix, including: extracting the target window matrix in sequence from the target window matrix set; identifying the matrix corner point time-temperature set of the target window matrix; extracting the matrix corner point time-temperature in sequence from the matrix corner point time-temperature set, and identifying the same corner point time-temperature matrix set of the matrix corner point time-temperature in the target window matrix set; splicing the target window matrix and the same corner point time-temperature matrix set to obtain a target coal washing matrix.

[0068] It can be understood that the matrix corner point time-temperature set refers to the coal washing temperature and coal washing time set at the four corner points of the target window matrix. For example, when the target window matrix is ​​as shown in the following table:

[0069] Furthermore, when Table 2 is the target window matrix, the matrix corner point time temperature set is (2h, 50 )、(2h,70 )、(4h,50 ) and (4h, 70 The same corner point time-temperature matrix set refers to the target window matrix set that has the same matrix corner point time-temperature as the matrix corner point time-temperature set. For example: the matrix corner point time-temperature set of a target window matrix is ​​(2h, 30 )、(2h,50 )、(4h,30 ) and (4h, 50 ), the target window matrix is ​​the same as the target window matrix in Table 2 (2h, 50 )、(4h,50 ) belong to the same matrix corner point time temperature.

[0070] In order to solve the problem described in the background technology, the present invention first obtains a coal washing temperature sequence and a coal washing time sequence, and constructs an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence. In order to obtain the sodium ion concentrations corresponding to different coal washing temperatures and coal washing times in the initial coal washing matrix, the initial coal washing temperatures are directly extracted in sequence from the initial coal washing matrix, and then the preset raw coal sample is washed and the sodium ion concentration is measured according to the initial coal washing temperature to obtain a sodium ion concentration matrix. In order to determine whether the sodium ion concentration matrix can accurately represent the optimal coal washing temperature, the sodium ion concentration matrix can be divided into multiple concentration window matrices, and then the window variance of each concentration window matrix is ​​calculated. Therefore, an adaptive sampling window can be calculated according to the sodium ion concentration matrix, and then the concentration window matrix is ​​extracted in sequence in the sodium ion concentration matrix according to the adaptive sampling window, so as to realize the calculation of the window variance of the concentration window matrix according to the window variance formula. After the window variance is calculated, the window variance can be determined. Is it less than the preset variance threshold? If the window variance is not less than the variance threshold, it indicates that the concentration window variance is too large. Therefore, it is necessary to calculate the window control size of the concentration window matrix according to the window size control formula, and then construct an iterative coal washing matrix according to the window control size. Finally, the concentration window matrix is ​​updated using the iterative coal washing matrix, and the window variance of the concentration window matrix is ​​recalculated. If the window variance is less than the variance threshold, it means that the sodium ion concentration fluctuation of the concentration window matrix is ​​less than the fluctuation standard of the variance threshold. The concentration window matrix can be directly set as the target window matrix. Finally, it is determined whether the concentration window matrix in the sodium ion concentration matrix has been extracted. If the extraction is completed, the target window matrix is ​​summarized to obtain the target window matrix set, and then the target window matrix set is subjected to matrix splicing to obtain the target coal washing matrix. Finally, the optimal coal washing time and temperature are identified in the target coal washing matrix. Coal washing can be performed according to the optimal coal washing time and temperature to complete the improvement of washing efficiency based on the optimization of the coal washing process. Therefore, the present invention can improve the washing efficiency of the coal washing process.

[0071] like Figure 2 , which is a functional module diagram of a system for improving coal washing efficiency based on coal washing process optimization provided by an embodiment of the present invention.

[0072] The system 100 for improving the washing efficiency based on the optimization of the coal washing process of the present invention can be installed in an electronic device. According to the functions implemented, the system 100 for improving the washing efficiency based on the optimization of the coal washing process can include a sodium ion concentration matrix construction module 101, a window variance calculation module 102, a target window matrix acquisition module 103 and an optimal coal washing time and temperature washing module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0073] The sodium ion concentration matrix construction module 101 is used to obtain a coal washing temperature sequence and a coal washing time sequence, and to construct an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; the initial coal washing time temperature is sequentially extracted from the initial coal washing matrix, and the preset raw coal sample is washed and the sodium ion concentration is measured according to the initial coal washing time temperature to obtain a sodium ion concentration matrix; the window variance calculation module 102 is used to calculate an adaptive sampling window according to the sodium ion concentration matrix; the concentration window matrix is ​​sequentially extracted from the sodium ion concentration matrix according to the adaptive sampling window; the window variance of the concentration window matrix is ​​calculated according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, represents the mean of the matrix elements in the concentration window matrix; the target window matrix acquisition module 103 is used to determine whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, the window control size of the concentration window matrix is ​​calculated according to the preset window size control formula, an iterative coal washing matrix is ​​constructed according to the window control size, the concentration window matrix is ​​updated using the iterative coal washing matrix, and the step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula is returned to the above; if the window variance is less than the variance threshold, the concentration window matrix is ​​set as the target window matrix; determine Determine whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window; the optimal coal washing time-temperature washing module 104 is used to summarize the target window matrix if the concentration window matrix in the sodium ion concentration matrix has been extracted, obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing time-temperature in the target coal washing matrix, and perform coal washing according to the optimal coal washing time-temperature.

[0074] In detail, each module in the coal washing efficiency improvement system 100 based on coal washing process optimization in the embodiment of the present invention is used in the same manner as above. Figure 1 The same technical means are used as the method for improving the washing efficiency based on the optimization of the coal washing process described in the text, and can produce the same technical effects, so they will not be repeated here.

[0075] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a method for improving coal washing efficiency based on coal washing process optimization provided by an embodiment of the present invention.

[0076] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for improving coal washing efficiency based on optimization of the coal washing process.

[0077] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the washing efficiency improvement method program based on the optimization of the coal washing process, but also can be used to temporarily store data that has been output or is to be output.

[0078] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, a method for improving washing efficiency based on optimization of the coal washing process, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0079] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0080] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0081] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0082] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0083] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0084] The program of the method for improving the washing efficiency based on the optimization of the coal washing process stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: obtaining a coal washing temperature sequence and a coal washing time sequence, and constructing an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; extracting the initial coal washing time temperature in sequence from the initial coal washing matrix, washing the preset raw coal sample according to the initial coal washing time temperature and determining the sodium ion concentration to obtain a sodium ion concentration matrix; calculating an adaptive sampling window according to the sodium ion concentration matrix; extracting a concentration window matrix in sequence from the sodium ion concentration matrix according to the adaptive sampling window; calculating the window variance of the concentration window matrix according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, represents the mean of the matrix elements in the concentration window matrix; Determine whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, calculate the window control size of the concentration window matrix according to a preset window size control formula, construct an iterative coal washing matrix according to the window control size, use the iterative coal washing matrix to update the concentration window matrix, and return to the above step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula; if the window variance is less than the variance threshold, set the concentration window matrix as the target window matrix; determine whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in sequence in the sodium ion concentration matrix according to the adaptive sampling window; if the concentration window matrix in the sodium ion concentration matrix has been extracted, summarize the target window matrix to obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing time and temperature in the target coal washing matrix, perform coal washing according to the optimal coal washing time and temperature, and complete the improvement of washing efficiency based on coal washing process optimization.

[0085] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0086] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0087] The present invention also provides a computer-readable storage medium, wherein the computer program is stored in the computer program. When the computer program is executed by a processor of an electronic device, the computer program can achieve: obtaining a coal washing temperature sequence and a coal washing time sequence, and constructing an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; extracting initial coal washing time temperatures in the initial coal washing matrix in sequence, washing a preset raw coal sample according to the initial coal washing time temperatures and determining the sodium ion concentration to obtain a sodium ion concentration matrix; calculating an adaptive sampling window according to the sodium ion concentration matrix; extracting a concentration window matrix in sequence in the sodium ion concentration matrix according to the adaptive sampling window; and calculating the window variance of the concentration window matrix according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, Represents the mean of the matrix elements in the concentration window matrix; determines whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, calculates the window control size of the concentration window matrix according to the preset window size control formula, constructs an iterative coal washing matrix according to the window control size, updates the concentration window matrix using the iterative coal washing matrix, and returns to the above step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula; if the window variance is less than the variance threshold, sets the concentration window matrix as the target window matrix; determines the sodium ion concentration matrix whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window; if the concentration window matrix in the sodium ion concentration matrix has been extracted, summarize the target window matrix to obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing temperature in the target coal washing matrix, perform coal washing according to the optimal coal washing temperature, and complete the improvement of washing efficiency based on the optimization of the coal washing process.

[0088] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0089] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0091] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for improving coal washing efficiency based on coal washing process optimization, characterized in that: The method includes: obtaining a coal washing temperature sequence and a coal washing time sequence, constructing an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; extracting initial coal washing time temperatures in the initial coal washing matrix in sequence, washing a preset raw coal sample according to the initial coal washing time temperatures and measuring the sodium ion concentration to obtain a sodium ion concentration matrix; calculating an adaptive sampling window according to the sodium ion concentration matrix; extracting a concentration window matrix in sequence according to the adaptive sampling window; and calculating the window variance of the concentration window matrix according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, Represents the mean of the matrix elements in the concentration window matrix; determines whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, calculates the window control size of the concentration window matrix according to the preset window size control formula, constructs an iterative coal washing matrix according to the window control size, updates the concentration window matrix using the iterative coal washing matrix, and returns to the above step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula; if the window variance is less than the variance threshold, sets the concentration window matrix as the target window matrix; determines the sodium ion concentration matrix whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, return to the above step of extracting the concentration window matrix in the sodium ion concentration matrix in sequence according to the adaptive sampling window; if the concentration window matrix in the sodium ion concentration matrix has been extracted, summarize the target window matrix to obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing temperature in the target coal washing matrix, perform coal washing according to the optimal coal washing temperature, and complete the improvement of washing efficiency based on the optimization of the coal washing process.

2. The method for improving coal washing efficiency based on coal washing process optimization according to claim 1, characterized in that: The obtaining of the coal washing temperature sequence and the coal washing time sequence includes: receiving the coal washing temperature interval and the coal washing time interval input by the user, and calculating the coal washing temperature interval and the coal washing time interval according to the coal washing temperature interval and the coal washing time interval using the following formula: ,in, Indicates the coal washing temperature interval, represents the maximum value of the coal washing temperature range, Indicates the minimum value of the coal washing temperature range, represents the rounding function, represents the temperature linear coefficient, Indicates the coal washing time interval, represents the maximum value of the coal washing time interval, Indicates the minimum value of the coal washing time interval, Represents the time linear coefficient; according to the coal washing temperature interval, temperature series sampling is performed in the coal washing temperature range to obtain a coal washing temperature series, and according to the coal washing time interval, time series sampling is performed in the coal washing time range to obtain a coal washing time series.

3. The method for improving coal washing efficiency based on coal washing process optimization according to claim 2, characterized in that: The method of washing a preset raw coal sample according to the initial coal washing time and temperature and determining the sodium ion concentration to obtain a sodium ion concentration matrix includes: identifying the initial coal washing temperature in the initial coal washing time and temperature, and preparing a constant temperature washing liquid according to the initial coal washing temperature, wherein the volume of the constant temperature washing liquid is 1L; identifying the initial coal washing time in the initial coal washing time and temperature, and according to the initial coal washing time, washing and filtering the raw coal sample with the constant temperature washing liquid to obtain a coal sample washing liquid; determining the sodium ion concentration of the coal sample washing liquid, and arranging the sodium ion concentrations at the initial coal washing temperature in a matrix to obtain a sodium ion concentration matrix.

4. The method for improving coal washing efficiency based on coal washing process optimization according to claim 3, characterized in that: The method of calculating the adaptive sampling window according to the sodium ion concentration matrix includes: identifying the number of matrix transverse elements and the number of matrix longitudinal elements of the sodium ion concentration matrix; and calculating the number of window transverse elements and the number of window longitudinal elements according to the number of matrix transverse elements and the number of matrix longitudinal elements using the following formula: ,in, Indicates the number of horizontal elements in the window. represents the window adaptation ratio, represents the number of horizontal elements of the matrix, Indicates the number of vertical elements in the window. Represents the number of vertical elements of the matrix; constructs the adaptive sampling window according to the number of horizontal elements and the number of vertical elements of the window.

5. The method for improving coal washing efficiency based on coal washing process optimization according to claim 4, characterized in that: The step of sequentially extracting a concentration window matrix from the sodium ion concentration matrix according to the adaptive sampling window comprises: dividing the sodium ion concentration matrix according to the number of window transverse elements and the number of window longitudinal elements of the adaptive sampling window to obtain a sodium ion segmentation matrix; and sequentially extracting a concentration window matrix from the sodium ion segmentation matrix.

6. The method for improving coal washing efficiency based on coal washing process optimization according to claim 5, characterized in that: Before determining whether the window variance is less than a preset variance threshold, the method further includes: receiving a variance level input by a user, and calculating the variance threshold according to the variance level using the following formula: ,in, represents the variance threshold, represents the limit variance threshold, e represents the natural constant, Indicates the level of variance.

7. The method for improving coal washing efficiency based on coal washing process optimization according to claim 6, characterized in that: The step of calculating the window control size of the concentration window matrix according to a preset window size control formula includes: calculating the window control size according to the window size control formula using the concentration window matrix and the window variance, wherein the window size control formula is as follows: ,in, represents the number of lateral control elements, Indicates the number of vertical control elements, Represents the window threshold coefficient.

8. The method for improving coal washing efficiency based on coal washing process optimization according to claim 7, characterized in that: The step of constructing an iterative coal washing matrix according to the window control size includes: calculating the window temperature span according to the coal washing temperature interval and the number of lateral elements of the window using the following formula: ,in, represents the window temperature span; according to the coal washing time interval and the number of vertical elements in the window, the window time span is calculated using the following formula: ,in, Represents the window time span; identifies the window start temperature of the concentration window matrix, and calculates the iterative temperature sequence using the following formula according to the window start temperature, window temperature span and the number of lateral control elements: ,in, represents the pth coal washing temperature in the iterative temperature sequence, Represents the window start temperature; identifies the window start time of the concentration window matrix, and calculates the iterative time series according to the window start time, window time span and the number of vertical control elements using the following formula: ,in, represents the qth coal washing time in the iterative time series, represents the window start time; constructing an iterative coal washing matrix according to the iterative temperature sequence and the iterative time sequence.

9. The method for improving coal washing efficiency based on coal washing process optimization according to claim 8, characterized in that: The target window matrix set is subjected to matrix splicing to obtain a target coal washing matrix, including: extracting the target window matrix in sequence from the target window matrix set; identifying the matrix corner point time-temperature set of the target window matrix; extracting the matrix corner point time-temperature in sequence from the matrix corner point time-temperature set, and identifying the same corner point time-temperature matrix set of the matrix corner point time-temperature in the target window matrix set; and splicing the target window matrix and the same corner point time-temperature matrix set to obtain a target coal washing matrix.

10. A system for improving coal washing efficiency based on coal washing process optimization, characterized in that: The system includes: a sodium ion concentration matrix construction module, which is used to obtain a coal washing temperature sequence and a coal washing time sequence, and to construct an initial coal washing matrix according to the coal washing temperature sequence and the coal washing time sequence, wherein the coal washing temperature sequence in the initial coal washing matrix is ​​arranged horizontally and the coal washing time sequence is arranged vertically; the initial coal washing time temperature is sequentially extracted from the initial coal washing matrix, and a preset raw coal sample is washed and the sodium ion concentration is measured according to the initial coal washing time temperature to obtain a sodium ion concentration matrix; a window variance calculation module, which is used to calculate an adaptive sampling window according to the sodium ion concentration matrix; sequentially extract a concentration window matrix from the sodium ion concentration matrix according to the adaptive sampling window; and calculate the window variance of the concentration window matrix according to a pre-constructed window variance formula, wherein the window variance formula is as follows: ,in, represents the window variance of the i-th concentration window matrix, Indicates the number of horizontal elements in the concentration window matrix, Indicates the number of vertical elements in the concentration window matrix, represents the jth matrix element in the concentration window matrix, represents the mean of the matrix elements in the concentration window matrix; A target window matrix acquisition module is used to determine whether the window variance is less than a preset variance threshold; if the window variance is not less than the variance threshold, the window control size of the concentration window matrix is ​​calculated according to a preset window size control formula, an iterative coal washing matrix is ​​constructed according to the window control size, the concentration window matrix is ​​updated using the iterative coal washing matrix, and the step of calculating the window variance of the concentration window matrix according to the pre-constructed window variance formula is returned to; if the window variance is less than the variance threshold, the concentration window matrix is ​​set as the target window matrix; it is determined whether the concentration window matrix in the sodium ion concentration matrix has been extracted; if the concentration window matrix in the sodium ion concentration matrix has not been extracted, the step of sequentially extracting the concentration window matrix in the sodium ion concentration matrix according to the adaptive sampling window is returned to; an optimal coal washing time and temperature washing module is used to summarize the target window matrix if the concentration window matrix in the sodium ion concentration matrix has been extracted, obtain a target window matrix set, perform matrix splicing on the target window matrix set to obtain a target coal washing matrix, identify the optimal coal washing time and temperature in the target coal washing matrix, and perform coal washing according to the optimal coal washing time and temperature.

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