Barite beneficiation method and system based on optimization of beneficiation test process

Abstract

The present invention relates to the technical field of barite selection, a barite selection method and system based on the optimization of the selection test process, including: receiving a barite set to be processed based on a barite selection environment, obtaining a sample to be identified according to the barite set to be processed, identifying the sample to be identified using an identification unit, obtaining a barite type, obtaining a selection process database, importing the barite type into the selection process database, obtaining a plurality of selection process parameters corresponding to the barite type, and importing the plurality of selection process parameters after removing the cleaning parameters into the initial selection equipment, obtaining the startup selection equipment, obtaining the selected barite based on the startup selection equipment, and completing the barite selection based on the optimization of the selection test process based on the selected barite. The present invention can confirm the optimal selection parameters corresponding to the multiple selection processes and each selection process of the target barite from the selection process database according to the selection test conducted in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of barite concentration, and in particular to a barite concentration method and system based on optimization of a concentration test process. Background Art

[0002] Barite is a non-metallic mineral with barium sulfate as its main component. It is widely used in various industries. However, barite is not a renewable energy source, so improving the efficiency of barite selection is of utmost importance.

[0003] At present, barite is mostly selected by washing, crushing, hand selection and flotation. Different barite has different selection strategies according to its associated minerals.

[0004] Although the above method can achieve the selection of barite, if a barite sample is collected from a mine and a selection test is conducted on the barite sample to confirm the selection process of the barite, many professionals will be required to stay on site, which will waste a lot of manpower and material resources. If only the same method is used for selection operation, the recovery efficiency of barite will be reduced. Therefore, a method that can provide selection strategies for different barites is needed. Summary of the invention

[0005] The present invention provides a barite beneficiation method based on beneficiation test process optimization, the main purpose of which is to confirm multiple beneficiation processes corresponding to target barite and the optimal beneficiation parameters corresponding to each beneficiation process from a beneficiation process database according to pre-conducted beneficiation tests.

[0006] To achieve the above object, the present invention provides a barite beneficiation method based on optimization of a beneficiation test process, comprising:

[0007] Confirming the barite beneficiation environment, wherein the barite beneficiation environment includes: an identification unit, a cleaning unit and an initial beneficiation device;

[0008] Based on the barite concentration environment, a barite set to be processed is received, a sample to be identified is obtained according to the barite set to be processed, and a recognition unit is used to identify the sample to be identified to obtain the type of barite;

[0009] Obtain a selection process database, import the barite type into the selection process database, and obtain a plurality of selection process parameters corresponding to the barite type, wherein before obtaining the selection process database, it also includes:

[0010] Obtain a target water volume sequence, extract target water volumes from the target water volume sequence in sequence, and perform the following operations on the extracted target water volumes:

[0011] An initial sample is obtained based on the extracted target water volume and the pre-constructed cleaning sample, a cleaning agent of a preset percentage is added to the initial sample to obtain a sample to be cleaned, and the sample to be cleaned is equally divided to obtain a plurality of unit cleaning samples;

[0012] The following operations are performed on the unit cleaning samples in the multiple unit cleaning samples:

[0013] Extracting a target speed from a preset speed gradient sequence, importing a unit cleaning sample into a cleaning unit, starting the cleaning unit using the target speed and a preset cleaning time, performing a cleaning operation on the unit cleaning sample in the cleaning unit using the started cleaning unit, and allowing the cleaned unit cleaning sample to stand to obtain a standing stratified sample, and extracting a barite sample from the standing stratified sample;

[0014] The barite density of the barite sample is detected by using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target rotation speed, cleaning time, and target water volume;

[0015] Summarize the unit experiment parameters to obtain the unit experiment data set;

[0016] Based on the unit experimental data set, the cleaning parameters corresponding to the initial sample are confirmed;

[0017] Wherein, the method of detecting the barite density of the barite sample by using a pre-constructed density detection method comprises:

[0018] Obtain an initial Leigh density bottle, and calibrate the initial Leigh density bottle using a pre-built calibration method to obtain a calibrated density bottle;

[0019] The pre-constructed weighing solution is introduced into the calibration density bottle, and the calibration density bottle after the weighing solution is introduced is subjected to a constant temperature heating operation to obtain a heating bottle, and the heating bottle is photographed by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle;

[0020] After performing a preprocessing operation on the initial weighing image, a binary weighing image is obtained, a concave liquid surface of the weighing solution is identified from the binary weighing image, a concave liquid surface scale line is obtained, and a first scale line and a second scale line are extracted from the calibration scale line based on the concave liquid surface scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization;

[0021] Estimate the starting value using the pre-built estimation method, the first scale line, and the second scale line;

[0022] Weigh the barite sample to obtain the mass of the barite, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset;

[0023] The bottle to be tested is photographed by a visual measurement system to obtain an image to be tested, and the measurement value of the bottle to be tested is calculated by using an estimation method and the image to be tested;

[0024] Calculate the density of barite based on the starting value, the measured value and the mass of barite;

[0025] Extracting cleaning parameters from a plurality of selected process parameters, and importing the cleaning parameters into a cleaning unit, and performing a cleaning operation on a barite set to be processed based on the cleaning unit after the cleaning parameters are imported, to obtain cleaned barite;

[0026] Import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform beneficiation operations on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0027] Optionally, estimating the starting value by using a pre-built estimation method, a first scale line, and a second scale line comprises:

[0028] Acquire an estimated region according to the first scale line and the second scale line, divide the estimated region using a preset precision, and obtain a unit estimation sequence, wherein the unit estimation sequence includes a plurality of unit estimation scales;

[0029] The unit estimation scales are extracted from the unit estimation sequence in sequence, and the Euclidean distances between the extracted unit estimation scales and the concave meniscus scale lines are calculated, the Euclidean distances are summarized to obtain a Euclidean distance set, the minimum Euclidean distance is extracted from the Euclidean distance set, and the unit estimation scale corresponding to the minimum Euclidean distance is confirmed to obtain a starting value.

[0030] Optionally, the step of confirming the cleaning parameters corresponding to the initial sample based on the unit experimental data set includes:

[0031] Unit experiment parameters are extracted from the unit experiment data set in sequence, and a parameter matrix is ​​constructed based on the extracted unit experiment parameters, wherein the parameter matrix is:

[0032]

[0033] in, represents the parameter matrix, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates Indicator type;

[0034] Summarize and concatenate the parameter matrix to obtain the evaluation matrix corresponding to the unit experimental data set, extract column vectors from the evaluation matrix in sequence to obtain the indicator column vector, perform standardization on the extracted indicator column vector to obtain the standard column vector, summarize and concatenate the standard column vector to obtain the standard matrix, where the indicator column vector is represented as follows:

[0035]

[0036] in, Indicates The indicator column vector corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type;

[0037] Calculate the weight corresponding to each indicator type in the multiple indicator types based on the standard matrix to obtain multiple indicator type weights;

[0038] An evaluation formula is constructed based on multiple indicator type weights, and the evaluation formula is used to calculate the evaluation scores of all unit experimental parameters in the unit experimental data set. The evaluation scores are summarized to obtain an evaluation score set, wherein the evaluation score set includes multiple evaluation scores, and the evaluation scores correspond to the unit experimental parameters one by one. The evaluation formula is as follows:

[0039]

[0040] in, Indicates The evaluation score corresponding to the unit experimental parameter, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type;

[0041] The highest evaluation score is extracted from the evaluation score set, and the unit experimental parameter corresponding to the highest evaluation score is confirmed to obtain the cleaning parameter.

[0042] Optionally, the calculation formula of the indicator type weight is as follows:

[0043] in, Indicates The indicator type weight corresponding to the indicator type, Indicates that there are a total of Unit experimental parameters, Indicates the standard matrix Line Elements of a column.

[0044] Optionally, performing a standardization operation on the extracted indicator column vector to obtain a standard column vector, and aggregating and concatenating the standard column vectors to obtain a standard matrix includes:

[0045] Identify the indicator properties corresponding to the extracted indicator column vector;

[0046] If the indicator property is a positive indicator, the positive indicator elements are extracted from the extracted indicator column vector in sequence, and the positive standard elements are calculated using the pre-built forward standardization formula and the extracted positive indicator elements, and the positive standard elements are summarized to obtain a forward standard vector, where the forward standardization formula is as follows:

[0047] in, represents a positive standard element, Represents the extracted positive indicator element, represents the smallest positive index element in the index column vector, Represents the largest positive indicator element in the indicator column vector;

[0048] If the indicator is a negative indicator, the pre-built negative normalization formula and the extracted indicator column vector are used to obtain the negative normalization vector, where the negative normalization formula is as follows:

[0049] in, represents a negative standard element, Represents a negative indicator element, Represents the smallest negative index element in the index column vector, Represents the largest negative index element in the index column vector;

[0050] The positive standard vectors and negative standard vectors are aggregated and concatenated to obtain a standardization matrix.

[0051] Optionally, obtaining a selected process database includes:

[0052] Obtain multiple barite names and multiple selected categories, wherein the multiple selected categories include: washing, centrifugation, magnetic attraction, flotation, drying, grinding, screening, crushing, enrichment and jigging;

[0053] Performing digitization operations on the plurality of selected categories by using a pre-built digitization method to obtain a plurality of digitized categories, extracting a first value set, a second value set, and other value sets from the plurality of digitized categories,

[0054] Recursively extract one or more first representative values ​​from the first value set, and identify the one or more first representative values ​​in a pre-constructed first sequence to obtain an identified first sequence;

[0055] Recursively extract one or more second representative values ​​from the second value set, and identify the one or more second representative values ​​in the pre-constructed second sequence to obtain an identified second sequence;

[0056] Extracting other values ​​from the other value set in sequence, and constructing other value pairs based on the extracted other values, wherein the other value pairs include other values ​​and storage data, the storage data is preset to 0, and summarizing the other value pairs to obtain other value pair sets;

[0057] A selection process database is constructed using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names.

[0058] Optionally, the step of constructing a selection process database using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names includes:

[0059] Extract barite names from the multiple barite names in sequence to obtain target barite, and perform the following operations on the target barite:

[0060] Acquire multiple selection processes corresponding to the target barite, wherein the multiple selection processes are included in the multiple selection categories;

[0061] Selected processes are extracted from multiple selected processes in sequence, and the following operations are performed on the extracted selected processes:

[0062] Identify the first value, the second value, and the selected other values ​​corresponding to the extracted selected process, identify and identify the first value in the first identification sequence to obtain the first point corresponding to the first value, identify and identify the second value in the second identification sequence to obtain the second point corresponding to the second value, identify and identify other value pairs corresponding to the selected other values ​​in other value set pairs to obtain the third point, and sequentially connect the first point, the second point, and the third point to obtain the initial path corresponding to the extracted selected process;

[0063] Obtaining the selection parameters corresponding to the target barite in the extracted selection process, and using the selection parameters to update the storage data corresponding to the third site, and updating the initial path based on the updated storage data to obtain a unit path;

[0064] Summarize the unit paths corresponding to the target barite to obtain the unit selection process database corresponding to the target barite;

[0065] The unit selection process databases are aggregated to obtain the selection process databases corresponding to multiple target barite.

[0066] To achieve the above object, the present invention also provides a barite beneficiation system based on optimization of a beneficiation test process, comprising:

[0067] A barite type acquisition module is used to confirm a barite selection environment, wherein the barite selection environment includes: an identification unit, a cleaning unit, and an initial selection device. A barite set to be processed is received based on the barite selection environment, and a sample to be identified is obtained according to the barite set to be processed. The sample to be identified is identified by using an identification unit to obtain a barite type;

[0068] A selection process parameter module is used to obtain a selection process database, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained, wherein before obtaining the selection process database, it also includes: obtaining a target water volume sequence, extracting the target water volume from the target water volume sequence in sequence, and performing the following operations on the extracted target water volume: obtaining an initial sample based on the extracted target water volume and a pre-constructed cleaning sample, adding a preset percentage of cleaning agent to the initial sample, obtaining a sample to be washed, and equally dividing the sample to be washed to obtain a plurality of unit cleaning samples, and performing the following operations on the unit cleaning samples in the plurality of unit cleaning samples: extracting the target water volume from a preset speed gradient sequence; speed, after the unit cleaning sample is introduced into the cleaning unit, the cleaning unit is started using the target speed and the preset cleaning time, the started cleaning unit is used to perform a cleaning operation on the unit cleaning sample in the cleaning unit, and the cleaned unit cleaning sample is allowed to stand to obtain a static stratified sample, and a barite sample is extracted from the static stratified sample, and the barite density of the barite sample is detected using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target speed, cleaning time and target water volume, the unit experimental parameters are summarized to obtain a unit experimental data set, and the cleaning parameters corresponding to the initial sample are confirmed based on the unit experimental data set, wherein the use of the pre The constructed density detection method detects the barite density of a barite sample, comprising: obtaining an initial Leigh density bottle, calibrating the initial Leigh density bottle using a pre-constructed calibration method to obtain a calibration density bottle, introducing a pre-constructed weighing solution into the calibration density bottle, and performing a constant temperature heating operation on the calibration density bottle after the weighing solution is introduced to obtain a heating bottle, photographing the heating bottle using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line arranged on the heating bottle, performing a pre-processing operation on the initial weighing image to obtain a binary weighing image, identifying a concave liquid surface of the weighing solution from the binary weighing image to obtain a concave liquid surface scale line, extracting a concave liquid surface scale line from the calibration scale line based on the concave liquid surface scale line, and obtaining a binary weighing image. Take the first scale line and the second scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization, use the pre-built estimation method, the first scale line and the second scale line to estimate the starting value, weigh the barite sample, obtain the barite mass, introduce the weighed barite sample into the heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain the bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset, and the bottle to be tested is photographed by a visual measurement system to obtain a test image, and the measurement value of the bottle to be tested is calculated by using the estimation method and the test image, and the barite density is calculated based on the starting value, the measurement value and the barite mass;

[0069] A cleaning barite acquisition module is used to extract cleaning parameters from a plurality of selected process parameters, and import the cleaning parameters into a cleaning unit, and perform a cleaning operation on the barite set to be processed based on the cleaning unit after the cleaning parameters are imported to obtain clean barite;

[0070] The beneficiation equipment acquisition module is used to import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform a beneficiation operation on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0071] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0072] a memory storing at least one instruction; and

[0073] The processor executes the instructions stored in the memory to implement the above-mentioned barite beneficiation method based on optimization of the beneficiation test process.

[0074] 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, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned barite beneficiation method based on optimization of the beneficiation test process.

[0075] The present invention solves the problem described in the background technology, first confirms the barite selection environment, wherein the barite selection environment includes: an identification unit, a cleaning unit and an initial selection device, based on the barite selection environment, a barite set to be processed is received, and a sample to be identified is obtained according to the barite set to be processed, and the sample to be identified is identified by the identification unit to obtain the barite type, and different barites have different corresponding selection processes in different mines, so it is necessary to confirm the type of barite. Obtain a selection process database, import the barite type into the selection process database, obtain a plurality of selection process parameters corresponding to the barite type, extract cleaning parameters from a plurality of selection process parameters, and import the cleaning parameters into the cleaning unit, and perform a cleaning operation on the barite set to be processed based on the cleaning unit after importing the cleaning parameters, and obtain clean barite, no matter what kind of barite type is, it needs to be cleaned after being extracted from the mine, but because the barite types are different, the corresponding cleaning parameters are different, and the same type of barite types are cleaned according to the pre-set cleaning parameters, which can achieve the purification of barite, thereby improving the density of barite. The multiple selected process parameters after removing the cleaning parameters are imported into the initial selected equipment, the selected equipment is started, the cleaned barite is imported into the start-up selected equipment and the start-up selected equipment is used to perform a selected operation on the cleaned barite after importing, and the selected barite is obtained. The selected barite after the selection is completed based on the selected barite after the selection. The selected parameters corresponding to the selected process and the selected process of the target barite are confirmed by the test, and stored in the selected process database, so as to realize that when receiving a large number of the same barite, the corresponding selected process can be called out from the selected process database, and then the selected operation of different target barites can be adapted. Therefore, the present invention can confirm the optimal selected parameters corresponding to the multiple selected processes and each selected process of the target barite from the selected process database according to the selected test performed in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic flow chart of a barite concentration method based on optimization of a concentration test process provided by one embodiment of the present invention;

[0077] Figure 2 A functional module diagram of a barite beneficiation system based on optimization of a beneficiation test process provided by one embodiment of the present invention;

[0078] Figure 3 A schematic diagram of the structure of an electronic device for implementing the barite beneficiation method based on optimization of the beneficiation test process provided in one embodiment of the present invention.

[0079] 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

[0080] 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.

[0081] The embodiment of the present application provides a barite selection method based on the optimization of the selection test process. The execution subject of the barite selection method based on the optimization of the selection test process includes but is not limited to at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server, a terminal, etc. In other words, the barite selection method based on the optimization of the selection test process can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0082] Reference Figure 1 As shown, a schematic diagram of a process flow of a barite beneficiation method based on optimization of a beneficiation test process is provided in one embodiment of the present invention. In this embodiment, the barite beneficiation method based on optimization of a beneficiation test process includes:

[0083] S1. Confirming the barite beneficiation environment, wherein the barite beneficiation environment includes: an identification unit, a cleaning unit and an initial beneficiation device.

[0084] It should be noted that the barite beneficiation environment is an environment composed of multiple units for beneficiating barite. The recognition unit is a unit for retrieving the type of barite from the database in combination with image features. The cleaning unit is a unit for cleaning the barite collected from the mine.

[0085] S2. Based on the barite concentration environment, a barite set to be processed is received, samples to be identified are obtained according to the barite set to be processed, and the samples to be identified are identified by using an identification unit to obtain barite types.

[0086] It should be noted that after collecting ore containing barite in the mine, the ore containing barite is introduced into the barite selection environment, and the ore containing barite is selected by the barite selection environment. Therefore, the barite set to be processed is the ore containing barite collected from the mine, which also includes other non-barite impurities, such as quartz, etc., and the impurities contained in the barite to be processed that can be collected by different mines are different. The sample to be identified is a sample extracted from the barite set to be processed for identification. Optionally, the recognition unit has a built-in target detection algorithm, and after taking the image of the sample to be identified, the feature of the sample to be identified is extracted, and the barite with the same feature as the sample to be identified is retrieved from the pre-built database, thereby identifying the barite type of the sample to be identified, and at the same time, the infrared spectrum is used to detect the sample to be identified, so as to comprehensively confirm the type of barite.

[0087] It is understandable that the type of barite is the type of barite. The reason why the type of barite needs to be confirmed is that different barites have different corresponding beneficiation processes in different mines, so the type of barite needs to be confirmed.

[0088] S3. Obtain a selected process database.

[0089] Furthermore, before obtaining the selected process database, the process further includes:

[0090] Obtain a target water volume sequence, extract target water volumes from the target water volume sequence in sequence, and perform the following operations on the extracted target water volumes:

[0091] An initial sample is obtained based on the extracted target water volume and the pre-constructed cleaning sample, a cleaning agent of a preset percentage is added to the initial sample to obtain a sample to be cleaned, and the sample to be cleaned is equally divided to obtain a plurality of unit cleaning samples;

[0092] The following operations are performed on the unit cleaning samples in the multiple unit cleaning samples:

[0093] Extracting a target speed from a preset speed gradient sequence, importing a unit cleaning sample into a cleaning unit, starting the cleaning unit using the target speed and a preset cleaning time, performing a cleaning operation on the unit cleaning sample in the cleaning unit using the started cleaning unit, and allowing the cleaned unit cleaning sample to stand to obtain a standing stratified sample, and extracting a barite sample from the standing stratified sample;

[0094] The barite density of the barite sample is detected by using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target rotation speed, cleaning time, and target water volume;

[0095] Summarize the unit experiment parameters to obtain the unit experiment data set;

[0096] The cleaning parameters corresponding to the initial samples are confirmed based on the unit experimental data set.

[0097] It is understandable that no matter what type of barite is, it needs to be cleaned after being extracted from the mine. However, due to the different types of barite, the corresponding cleaning parameters are different. The same type of barite can be cleaned according to the pre-set cleaning parameters to purify the barite, thereby increasing the density of the barite.

[0098] It is understandable that the cleaning sample is a sample constructed when determining the cleaning parameters to represent the barite set to be processed obtained from a mine at a certain place, and the cleaning agent is a cleaning agent used to clean the initial sample, such as a cleaning type debonding agent. The target water volume sequence is a sequence composed of different solid-liquid ratios between the cleaning sample and the clean water. For example, 100 mg of water is added to 100 mg of barite, and the solid-liquid ratio is 1:1. After mixing the target water volume and the cleaning sample, the initial sample is obtained. The purpose of equally dividing the sample to be washed is to extract different target speeds from the speed gradient sequence by the experimental method of controlling variables to wash the sample to be washed, so as to explore the influence of different target speeds on the density of barite when recovering barite. The speed gradient sequence includes multiple target speeds, and the multiple target speeds are different. In this experiment, the cleaning time of each cleaning sample in multiple unit cleaning samples is the same. The purpose of the unit cleaning sample after static cleaning is to separate barite from other low-density impurities by static method, so as to make the sediment stratification more obvious.

[0099] It should be noted that the unit experimental parameter is an experimental parameter constructed by the barite density obtained under the conditions of target rotation speed, cleaning time and target water volume. The cleaning parameter is the optimal unit experimental parameter in the unit experimental data set. Please refer to the subsequent embodiments for the evaluation criteria of the optimal unit experimental parameter.

[0100] Further, the method of detecting the barite density of the barite sample using a pre-constructed density detection method includes:

[0101] Obtain an initial Leigh density bottle, and calibrate the initial Leigh density bottle using a pre-built calibration method to obtain a calibrated density bottle;

[0102] The pre-constructed weighing solution is introduced into the calibration density bottle, and the calibration density bottle after the weighing solution is introduced is subjected to a constant temperature heating operation to obtain a heating bottle, and the heating bottle is photographed by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle;

[0103] After performing a preprocessing operation on the initial weighing image, a binary weighing image is obtained, a concave liquid surface of the weighing solution is identified from the binary weighing image, a concave liquid surface scale line is obtained, and a first scale line and a second scale line are extracted from the calibration scale line based on the concave liquid surface scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization;

[0104] Estimate the starting value using the pre-built estimation method, the first scale line, and the second scale line;

[0105] Weigh the barite sample to obtain the mass of the barite, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset;

[0106] The bottle to be tested is photographed by a visual measurement system to obtain an image to be tested, and the measurement value of the bottle to be tested is calculated by using an estimation method and the image to be tested;

[0107] The barite density is calculated based on the starting value, the measured value and the barite mass.

[0108] It should be noted that the initial Leigh density bottle is an uncalibrated Leigh density bottle, which is a device for measuring the density of barite. The calibration method calibrates the initial Leigh density bottle by correcting the Leigh density bottle by an average distribution capacity error correction method, which is a prior art and will not be described in detail here. The weighing solution is a solution insoluble in barite, for example, anhydrous kerosene.

[0109] It should be noted that performing a constant temperature heating operation on the calibration density bottle after the weighing liquid is introduced, and performing a constant temperature heating operation at the same temperature on the heating bottle after the barite sample is introduced, can eliminate the influence of temperature on the volume of anhydrous kerosene, thereby achieving a more accurate measurement of the barite density. Optionally, the constant temperature heating time of the constant temperature heating operation in the embodiment of the present invention is thirty minutes, and the constant temperature heating temperature is 20°C.

[0110] Further, the visual measurement system is a system for measuring the volume of a barite sample through an image. The initial weighing image is a picture obtained by photographing the heating bottle at the angle of the concave liquid surface of the weighing solution from the calibration scale line of the heating bottle. The calibration scale line is a series of scale lines marked on the heating bottle. The concave liquid surface scale line is a straight line fitted into the concave liquid surface of the weighing solution in the heating bottle in the initial weighing picture. It should be noted that after the initial weighing image is pre-processed, the concave liquid surface of the weighing solution can be more clearly represented in the binary weighing image. The first scale line and the second scale line are two scale lines adjacent to the concave liquid surface scale line. For example, the human eye observes that the concave liquid surface is between 10mm and 10.5mm, and there is no other calibration scale line between 10ml and 10.5ml, then the first scale line and the second scale line are 10ml and 10.5ml respectively.

[0111] It is understandable that the starting value is the value corresponding to the concave liquid surface of the weighing solution estimated by the visual measurement system before the barite sample is not introduced into the heating bottle. Optionally, the barite sample can be weighed using a precision balance, and the mass of barite is the mass of barite. It should be noted that the purpose of the ultrasonic vibration operation is to eliminate the bubbles in the weighing liquid in the action of introducing the barite sample into the heating bottle. The image to be measured is the image of the bottle to be measured. Similarly, the heating bottle is photographed at the angle of the concave liquid surface of the weighing solution at the level of the calibration scale line of the bottle to be measured. The measured value is the value corresponding to the concave liquid surface of the weighing solution in the bottle to be measured estimated by the visual measurement system after the barite sample is introduced.

[0112] It should be noted that the calculation formula for the density of barite is:

[0113] Further, the estimating the starting value by using the pre-built estimation method, the first scale line and the second scale line includes:

[0114] Acquire an estimated region according to the first scale line and the second scale line, divide the estimated region using a preset precision, and obtain a unit estimation sequence, wherein the unit estimation sequence includes a plurality of unit estimation scales;

[0115] The unit estimation scales are extracted from the unit estimation sequence in sequence, and the Euclidean distances between the extracted unit estimation scales and the concave meniscus scale lines are calculated, the Euclidean distances are summarized to obtain a Euclidean distance set, the minimum Euclidean distance is extracted from the Euclidean distance set, and the unit estimation scale corresponding to the minimum Euclidean distance is confirmed to obtain a starting value.

[0116] It should be noted that the estimated area is the area corresponding to the Euclidean distance between the first scale line and the second scale line, and the accuracy is the minimum unit during estimation. For example, the estimated area is the area corresponding to the distance between 10ml and 10.5ml, that is, the concave liquid surface of the weighing liquid is between 10ml and 10.5ml, and 10ml and 10.5ml are both calibration scales, and there are no other calibration scales between 10ml and 10.5ml. The preset accuracy is 0.1, then the estimated area between 10ml and 10.5ml is divided into units of 0.1ml, and the unit estimation sequence after division is: 10ml, 10.1ml, 10.2ml, 10.3ml, 10.4ml, 10.5ml, and 10ml, 10.1ml, 10.2ml, 10.3ml, 10.4ml, 10.5ml are all unit estimation scales. If the concave liquid surface of the weighing liquid is between 10ml and 10.1ml, the Euclidean distances between the concave liquid surface scale line and the unit estimated scales of 10ml, 10.1ml, 10.2ml, 10.3ml, 10.4ml, and 10.5ml are calculated to obtain multiple Euclidean distances, and the smallest Euclidean distance is taken as the starting value. At this time, the concave liquid surface of the weighing liquid is between 10ml and 10.1ml. Therefore, if the Euclidean distance between the concave liquid surface scale line and 10ml is less than the distance between the concave liquid surface scale line and 10.1ml, the starting value is output as 10ml.

[0117] Furthermore, the step of confirming the cleaning parameters corresponding to the initial sample based on the unit experimental data set includes:

[0118] Unit experiment parameters are extracted from the unit experiment data set in sequence, and a parameter matrix is ​​constructed based on the extracted unit experiment parameters, wherein the parameter matrix is:

[0119] in, represents the parameter matrix, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates Indicator type;

[0120] Summarize and concatenate the parameter matrix to obtain the evaluation matrix corresponding to the unit experimental data set, extract column vectors from the evaluation matrix in sequence to obtain the indicator column vector, perform standardization on the extracted indicator column vector to obtain the standard column vector, summarize and concatenate the standard column vector to obtain the standard matrix, where the indicator column vector is represented as follows:

[0121] in, Indicates The indicator column vector corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type;

[0122] Calculate the weight corresponding to each indicator type in the multiple indicator types based on the standard matrix to obtain multiple indicator type weights;

[0123] An evaluation formula is constructed based on multiple indicator type weights, and the evaluation formula is used to calculate the evaluation scores of all unit experimental parameters in the unit experimental data set. The evaluation scores are summarized to obtain an evaluation score set, wherein the evaluation score set includes multiple evaluation scores, and the evaluation scores correspond to the unit experimental parameters one by one. The evaluation formula is as follows:

[0124] in, Indicates The evaluation score corresponding to the unit experimental parameter, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type;

[0125] The highest evaluation score is extracted from the evaluation score set, and the unit experimental parameter corresponding to the highest evaluation score is confirmed to obtain the cleaning parameter.

[0126] It can be understood that the indicator type represents the type corresponding to the elements in the parameter matrix. For example, if the parameters corresponding to the parameter matrix are cleaning parameters, and the cleaning parameters include barite density, target speed, cleaning time and target water volume, then the indicator types are: barite density, target speed, cleaning time and target water volume.

[0127] It should be noted that the summary and concatenation of parameter matrices is to arrange the parameter matrices corresponding to the unit experimental parameters in the unit experimental data set in columns. At this time, the matrix formed after arrangement is the evaluation matrix corresponding to the unit experimental data set. The indicator column vector is the column vector of the evaluation matrix. The standard matrix is ​​the matrix obtained after the indicator column vectors in the evaluation matrix are all standardized.

[0128] For example, the existing parameter matrix And the parameter matrix , then the evaluation matrix obtained by splicing is:

[0129] in, represents the evaluation matrix.

[0130] Extract the first The indicator column vector corresponding to the indicator type is as follows:

[0131] in, Indicates Column vector of indicators corresponding to the indicator type.

[0132] If the standardized The standard column vector corresponding to the indicator column vector corresponding to the indicator type is:

[0133] in, Represents the standardized The standard column vector corresponding to the indicator column vector corresponding to the indicator type, Represents the parameter matrix in the standard column vector Middle The element corresponding to the indicator type, Represents the parameter matrix in the standard column vector Middle The element corresponding to the indicator type.

[0134] The standard column vectors are aggregated and concatenated to obtain a standard matrix, which is:

[0135] It should be noted that the indicator type weight is the weight of the indicator type among all indicator types. The highest evaluation score is the largest evaluation score in the evaluation score set.

[0136] Furthermore, the calculation formula of the indicator type weight is as follows:

[0137] in, Indicates The indicator type weight corresponding to the indicator type, Indicates that there are a total of Unit experimental parameters, Indicates the standard matrix Line Elements of a column.

[0138] Furthermore, the extracted indicator column vectors are subjected to a standardization operation to obtain a standard column vector, and the standard column vectors are summarized and concatenated to obtain a standard matrix, including:

[0139] Identify the indicator properties corresponding to the extracted indicator column vector;

[0140] If the indicator property is a positive indicator, the positive indicator elements are extracted from the extracted indicator column vector in sequence, and the positive standard elements are calculated using the pre-built forward standardization formula and the extracted positive indicator elements, and the positive standard elements are summarized to obtain a forward standard vector, where the forward standardization formula is as follows:

[0141] in, represents a positive standard element, Represents the extracted positive indicator element, represents the smallest positive index element in the index column vector, Represents the largest positive indicator element in the indicator column vector;

[0142] If the indicator is a negative indicator, the pre-built negative normalization formula and the extracted indicator column vector are used to obtain the negative normalization vector, where the negative normalization formula is as follows:

[0143]

[0144] in, represents a negative standard element, Represents a negative indicator element, Represents the smallest negative index element in the index column vector, Represents the largest negative index element in the index column vector;

[0145] The positive standard vectors and negative standard vectors are aggregated and concatenated to obtain a standardization matrix.

[0146] It should be noted that the indicator properties include positive indicators or negative indicators. When the indicator properties are positive indicators, it means that the larger the value corresponding to the indicator type, the more beneficial it is to the selection results, such as barite density. When the indicator properties are negative indicators, it means that the smaller the value corresponding to the indicator type, the more beneficial it is to the selection results, such as target water volume. This is because the larger the target water volume, the more clean water is required and the higher the energy consumption.

[0147] It can be understood that the positive indicator element is the element in the indicator column vector when the extracted indicator column vector is a positive indicator. After replacing all positive indicator elements in the extracted indicator column vector with corresponding positive standard elements, a positive standard vector is obtained, so the positive standard elements correspond to the positive indicator elements one by one.

[0148] It should be noted that the minimum positive indicator element is the positive indicator element with the smallest value among all the positive indicator elements, and the maximum positive indicator element is the positive indicator element with the largest value among all the positive indicator elements.

[0149] It is understandable that the negative standard vector, the negative standard element, the negative index element, the smallest negative index element and the largest negative index element have the same meanings as those in the positive standardization formula and will not be elaborated here.

[0150] It should be noted that after mapping all standard column vectors to the positions of the corresponding extracted indicator column vectors in the evaluation matrix, the resulting matrix is ​​a standardized matrix.

[0151] Furthermore, the obtaining of the selected process database includes:

[0152] Obtain multiple barite names and multiple selected categories, wherein the multiple selected categories include: washing, centrifugation, magnetic attraction, flotation, drying, grinding, screening, crushing, enrichment and jigging;

[0153] Performing digitization operations on the plurality of selected categories by using a pre-constructed digitization method to obtain a plurality of digitized categories, extracting a first value set, a second value set, and other value sets from the plurality of digitized categories,

[0154] Recursively extract one or more first representative values ​​from the first value set, and identify the one or more first representative values ​​in a pre-constructed first sequence to obtain an identified first sequence;

[0155] Recursively extract one or more second representative values ​​from the second value set, and identify the one or more second representative values ​​in the pre-constructed second sequence to obtain an identified second sequence;

[0156] Extracting other values ​​from the other value set in sequence, and constructing other value pairs based on the extracted other values, wherein the other value pairs include other values ​​and storage data, the storage data is preset to 0, and summarizing the other value pairs to obtain other value pair sets;

[0157] A selection process database is constructed using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names.

[0158] It should be noted that the name of barite is the name corresponding to different barites. For example, the barite with associated minerals of dolomite and pyrite in a mine in Province G, and the barite with associated minerals of quartz and calcite in a mine in Province X. When the above two types of barite are concentrated, the corresponding concentration processes are different, but the mineral content of barite samples from the same mine is relatively consistent, so the name of barite is used to distinguish different barite samples.

[0159] It is understandable that the selection category is the name for the selection operation of barite, and each of the multiple selection categories is a prior art, which will not be explained one by one here. The digitization method is a method of converting text into numbers. For example, "cleaning" is converted using Unicode encoding, and then all the converted letters are removed so that the converted "cleaning" has a unique identifier in the selection process database.

[0160] It should be noted that the first value set is a set obtained by extracting the first value of all numerical categories. For example, multiple numerical categories are: 1111, 11245, 1143333. Then the first value in the multiple numerical categories is 1, 1, 1. At this time, the first value set is: 1, 1, 1. Similarly, the second value set is: 1, 1, 1. After removing the first value and the second value in each numerical category, the remaining values ​​are other values. Therefore, the other value sets are: 11, 245, 43333.

[0161] It is understood that recursion is an operation of finding different values ​​among multiple values ​​and outputting them. For example, if the first value set is: 1, 1, 1, then there is only one first representative value, and the first representative value is 1. If the second value set is: 1, 1, 3, then there are two second representative values, and the two second representative values ​​are: 1 and 3 respectively.

[0162] It should be noted that the first sequence is a sequence consisting of positive integers from 0 to 9, and the same is true for the second sequence.

[0163] Exemplarily, the one or more first representative values ​​are identified in the first sequence as follows: if the first representative value is 1, then 1 is identified in the first sequence. Optionally, a pointer is used to identify 1 in the first sequence, and the identified first sequence is the identified first sequence. The second identification sequence is similar and will not be described here. If the other value extracted is 1103, then the other value pair is: (1103, 0), 1103 in the other value pair represents other values, and 0 represents preset storage data.

[0164] Further, the method of constructing a selection process database using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names includes:

[0165] Extract barite names from the multiple barite names in sequence to obtain target barite, and perform the following operations on the target barite:

[0166] Acquire multiple selection processes corresponding to the target barite, wherein the multiple selection processes are included in the multiple selection categories;

[0167] Selected processes are extracted from multiple selected processes in sequence, and the following operations are performed on the extracted selected processes:

[0168] Identify the first value, the second value, and the selected other values ​​corresponding to the extracted selected process, identify and identify the first value in the first identification sequence to obtain the first point corresponding to the first value, identify and identify the second value in the second identification sequence to obtain the second point corresponding to the second value, identify and identify other value pairs corresponding to the selected other values ​​in other value set pairs to obtain the third point, and sequentially connect the first point, the second point, and the third point to obtain the initial path corresponding to the extracted selected process;

[0169] Obtaining the selection parameters corresponding to the target barite in the extracted selection process, and using the selection parameters to update the storage data corresponding to the third site, and updating the initial path based on the updated storage data to obtain a unit path;

[0170] Summarize the unit paths corresponding to the target barite to obtain the unit selection process database corresponding to the target barite;

[0171] The unit selection process databases are aggregated to obtain the selection process databases corresponding to multiple target barite.

[0172] It is understandable that how to obtain the cleaning parameters of the cleaning sample is described in detail in the present embodiment, so that the cleaning parameters of the barite set to be processed corresponding to the cleaning sample can be consulted from the selected process database, and at the same time, after confirming the multiple selected processes of the barite set to be processed, the parameters corresponding to the different selected processes can also be obtained by the same evaluation score, for example, the multiple selected processes of the barite set to be processed include centrifugal operation, then the centrifugal sample can be obtained between obtaining the barite sample to be processed, and the evaluation matrix corresponding to the centrifugal operation is constructed according to the centrifugal speed, centrifugal temperature, and the type and quantity of consumables to be added during the centrifugal operation, so as to obtain the centrifugal parameters according to the evaluation matrix corresponding to the centrifugal operation, and the centrifugal parameters are stored in the selected process database. The acquisition methods of the selected parameters of the remaining selected processes are all consistent, and will not be repeated here.

[0173] It is understandable that multiple selection processes are selection processing processes for target barite. Before constructing a selection process database, the embodiment of the present invention confirms the selection process of the target barite and the selection parameters corresponding to the selection process through experiments, and stores them in the selection process database, so that when receiving a large batch of the same barite, the corresponding selection process can be called out from the selection process database, and then it can adapt to the selection operations of different target barite.

[0174] For example, the multiple concentration processes corresponding to barite with associated minerals of dolomite and pyrite in a mine in Province G are: washing, centrifugation and magnetic attraction, and the multiple concentration processes corresponding to barite with associated minerals of quartz and calcite in a mine in Province X are: washing, drying, grinding, jigging and flotation. Therefore, different target barites correspond to different multiple concentration processes.

[0175] It should be noted that the first value is the first value of the digitized selection process, and the second value and other selected values ​​are obtained in the same way as in the previous example, which will not be repeated here.

[0176] It is understandable that the first position is the position of the extracted first value in the first sequence of the identifier, for example, using a pointer for identification. The second position is similar, and will not be repeated here. The third position is the position of the selected other value in the other value pair corresponding to the selected other value.

[0177] Exemplarily, the numerically selected process is: 1487, then find 1 in the first sequence of identifiers, and identify 1 in the first sequence as the first site, then find 4 in the second sequence of identifiers, and identify 4 in the second sequence as the second site, and then find other value pairs in other value pairs: (87,0), and 87 in other value pairs is the third site. The first site, the second site, and the third site are sequentially connected as follows: take 1 in the first sequence of identifiers as the parent node, take 4 in the second sequence of identifiers as the child node, connect 1 in the first sequence of identifiers and 4 in the second sequence of identifiers, then take 4 in the second sequence of identifiers as the parent node, take other value pairs in other value pairs: 87 in (87,0) as the child node, connect 4 in the second sequence of identifiers and other value pairs: 87 in (87,0), and the resulting path is the initial path corresponding to the extracted selected process, and at this time it is: 1 in the first sequence of identifiers - 4 in the second sequence of identifiers - (87,0).

[0178] It is understandable that the storage data corresponding to the third position is used to store the selected parameters of the extracted selected process. For example, the selected process is cleaning, and the digitized selected process is: 1487, and the cleaning parameters are: target speed a, cleaning time b and target water volume c. Then update the other value pairs: (87,0) to: (87, target speed a, cleaning time b, target water volume c). At this time, the unit path is: 1 in the first sequence of identifiers-4 in the second sequence of identifiers-(87, target speed a, cleaning time b, target water volume c).

[0179] It should be noted that the above operations are performed on all the selected processes in the multiple selected processes corresponding to the target barite, and the multiple unit paths obtained are summarized to form a unit selected process database corresponding to the target barite. In actual operation, it is possible to query the multiple selected processes of the target barite in the unit selected process database, and it is also possible to query the selected parameters of each of the multiple selected processes.

[0180] S4. After the barite types are imported into the selection process database, a plurality of selection process parameters corresponding to the barite types are obtained.

[0181] It can be understood that the multiple beneficiation process parameters are the multiple beneficiation processes that are retrieved from the beneficiation process database based on the barite sample, as well as the parameters of each of the multiple beneficiation processes.

[0182] S5, extracting cleaning parameters from the multiple selected process parameters, and importing the cleaning parameters into the cleaning unit, and performing a cleaning operation on the barite set to be processed based on the cleaning unit after the cleaning parameters are imported to obtain cleaned barite.

[0183] It should be noted that the washed barite is a collection of barite to be processed after washing.

[0184] S6. Import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain a start-up beneficiation equipment, import the cleaned barite into the start-up beneficiation equipment and use the start-up beneficiation equipment to perform a beneficiation operation on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0185] It should be noted that the multiple beneficiation process parameters include cleaning parameters. The barite set to be processed can only be beneficiated after being cleaned. Therefore, the multiple beneficiation process parameters after removing the cleaning parameters need to be imported into the initial beneficiation equipment.

[0186] It is understandable that the initial selection equipment includes multiple units, wherein the multiple units include: centrifugal unit, magnetic unit, flotation unit, drying unit, grinding unit, screening unit, crushing unit, enrichment unit and jigging unit, and the units correspond one-to-one to the selection categories.

[0187] It should be noted that the centrifugal unit is used to centrifuge barite, and can separate the low-density solid phase (such as clay and drill cuttings) in the drilling fluid from the barite. The flotation unit is a unit used to perform flotation operations on barite, and flotation is an operation for selectively capturing barite. The magnetic unit is a unit used to separate barite and other minerals by utilizing the principle that barite and associated minerals have different magnetic properties. Barite is a non-magnetic mineral, so when there are magnetic minerals in the associated minerals, the magnetic unit can be used to screen the barite. The remaining units are similar in structure to the centrifugal unit, flotation unit and magnetic unit, and are all units used for the concentration process of barite, which will not be repeated here.

[0188] It should be noted that after the multiple concentrating process parameters after removing the cleaning parameters are imported into the initial concentrating equipment, the multiple units corresponding to the multiple concentrating processes for cleaning barite will be started in the initial concentrating equipment, and the storage data corresponding to the multiple unit paths for cleaning barite will be imported into the multiple units to start the concentrating equipment.

[0189] Exemplarily, the multiple concentration processes corresponding to the barite in a mine in Province X are: washing, drying, jigging and flotation. Therefore, the multiple concentration process parameters after removing the washing parameters are: drying parameters, jigging parameters and flotation parameters. After the drying parameters, jigging parameters and flotation parameters are imported into the initial concentration equipment, the drying unit, jigging unit and flotation unit in the initial concentration equipment will be started, and the drying parameters will be imported into the drying unit, the jigging parameters will be imported into the jigging unit, and the flotation parameters will be imported into the flotation unit to start the concentration equipment.

[0190] The present invention solves the problem described in the background technology, first confirms the barite selection environment, wherein the barite selection environment includes: an identification unit, a cleaning unit and an initial selection device, based on the barite selection environment, a barite set to be processed is received, and a sample to be identified is obtained according to the barite set to be processed, and the sample to be identified is identified by the identification unit to obtain the barite type, and different barites have different corresponding selection processes in different mines, so it is necessary to confirm the type of barite. Obtain a selection process database, import the barite type into the selection process database, obtain a plurality of selection process parameters corresponding to the barite type, extract cleaning parameters from a plurality of selection process parameters, and import the cleaning parameters into the cleaning unit, and perform a cleaning operation on the barite set to be processed based on the cleaning unit after importing the cleaning parameters, and obtain clean barite, no matter what kind of barite type is, it needs to be cleaned after being extracted from the mine, but because the barite types are different, the corresponding cleaning parameters are different, and the same type of barite types are cleaned according to the pre-set cleaning parameters, which can achieve the purification of barite, thereby improving the density of barite. The multiple selected process parameters after removing the cleaning parameters are imported into the initial selected equipment, the selected equipment is started, the cleaned barite is imported into the start-up selected equipment and the start-up selected equipment is used to perform a selected operation on the cleaned barite after importing, and the selected barite is obtained. The selected barite after the selection is completed based on the selected barite after the selection. The selected parameters corresponding to the selected process and the selected process of the target barite are confirmed by the test, and stored in the selected process database, so as to realize that when receiving a large number of the same barite, the corresponding selected process can be called out from the selected process database, and then the selected operation of different target barites can be adapted. Therefore, the present invention can confirm the optimal selected parameters corresponding to the multiple selected processes and each selected process of the target barite from the selected process database according to the selected test performed in advance.

[0191] like Figure 2 1 is a functional module diagram of a barite concentration system based on optimization of a concentration test process provided by an embodiment of the present invention.

[0192] The barite beneficiation system 100 based on the optimization of the beneficiation test process of the present invention can be installed in an electronic device. According to the functions implemented, the barite beneficiation system 100 based on the optimization of the beneficiation test process can include a barite type acquisition module 101, a beneficiation process parameter module 102, a barite cleaning acquisition module 103 and a beneficiation equipment acquisition 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.

[0193] The barite type acquisition module 101 is used to confirm the barite selection environment, wherein the barite selection environment includes: an identification unit, a cleaning unit and an initial selection device, receiving a barite set to be processed based on the barite selection environment, obtaining a sample to be identified according to the barite set to be processed, and using the identification unit to identify the sample to be identified to obtain the barite type;

[0194] The selection process parameter module 102 is used to obtain a selection process database, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained, wherein before obtaining the selection process database, it also includes: obtaining a target water volume sequence, extracting the target water volume from the target water volume sequence in sequence, and performing the following operations on the extracted target water volume: obtaining an initial sample based on the extracted target water volume and a pre-constructed cleaning sample, adding a preset percentage of cleaning agent to the initial sample, obtaining a sample to be washed, dividing the sample to be washed equally, obtaining a plurality of unit cleaning samples, and performing the following operations on the unit cleaning samples in the plurality of unit cleaning samples: extracting the target water volume from the preset speed gradient sequence; Extract the target speed, import the unit cleaning sample into the cleaning unit, start the cleaning unit using the target speed and the preset cleaning time, use the started cleaning unit to perform a cleaning operation on the unit cleaning sample in the cleaning unit, and let the cleaned unit cleaning sample stand to obtain a static stratified sample, and extract the barite sample from the static stratified sample, and use a pre-constructed density detection method to detect the barite density of the barite sample, and construct unit experimental parameters based on the barite density, wherein the unit experimental parameters include: barite density, target speed, cleaning time and target water volume, summarize the unit experimental parameters, obtain a unit experimental data set, and confirm the cleaning parameters corresponding to the initial sample based on the unit experimental data set, wherein the The barite density of a barite sample is detected by using a pre-constructed density detection method, including: obtaining an initial Leigh density bottle, calibrating the initial Leigh density bottle by using a pre-constructed calibration method to obtain a calibration density bottle, introducing a pre-constructed weighing solution into the calibration density bottle, and performing a constant temperature heating operation on the calibration density bottle after the weighing solution is introduced to obtain a heating bottle, photographing the heating bottle by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle, performing a pre-processing operation on the initial weighing image to obtain a binary weighing image, identifying a concave liquid surface of the weighing solution from the binary weighing image to obtain a concave liquid surface scale line, and obtaining a calibration scale line based on the concave liquid surface scale line from the calibration scale line. Extract the first scale line and the second scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization, estimate the starting value using a pre-built estimation method, the first scale line and the second scale line, weigh the barite sample to obtain the barite mass, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset, and the bottle to be tested is photographed using a visual measurement system to obtain a test image, and the measurement value of the bottle to be tested is calculated using the estimation method and the test image, and the barite density is calculated based on the starting value, the measurement value and the barite mass;

[0195] The cleaned barite acquisition module 103 is used to extract cleaning parameters from a plurality of selected process parameters, and import the cleaning parameters into a cleaning unit, and perform a cleaning operation on the barite set to be processed based on the cleaning unit after the cleaning parameters are imported to obtain cleaned barite;

[0196] The beneficiation equipment acquisition module 104 is used to import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform a beneficiation operation on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0197] In detail, each module in the barite beneficiation system 100 based on the optimization of the beneficiation test process described in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the barite selection method based on the optimization of the selection test process described in the above are used and can produce the same technical effects, so they will not be repeated here.

[0198] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing a barite concentration method based on optimization of a concentration test process provided by an embodiment of the present invention.

[0199] 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 barite concentration method program based on optimization of a concentration test process.

[0200] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 can be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 can also be an external storage device of the electronic device 1 in other embodiments, 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 barite selection method program based on the optimization of the selection test process, but also can be used to temporarily store data that has been output or is to be output.

[0201] 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 (such as a barite selection method program based on the optimization of the selection test process, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0202] 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.

[0203] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The barite beneficiation method program based on beneficiation test process optimization stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0208] Confirming the barite beneficiation environment, wherein the barite beneficiation environment includes: an identification unit, a cleaning unit and an initial beneficiation device;

[0209] Based on the barite concentration environment, a barite set to be processed is received, a sample to be identified is obtained according to the barite set to be processed, and a recognition unit is used to identify the sample to be identified to obtain the type of barite;

[0210] A selection process database is obtained, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained;

[0211] Before obtaining the selected process database, the method further includes:

[0212] Obtain a target water volume sequence, extract target water volumes from the target water volume sequence in sequence, and perform the following operations on the extracted target water volumes:

[0213] An initial sample is obtained based on the extracted target water volume and the pre-constructed cleaning sample, a cleaning agent of a preset percentage is added to the initial sample to obtain a sample to be cleaned, and the sample to be cleaned is equally divided to obtain a plurality of unit cleaning samples;

[0214] The following operations are performed on the unit cleaning samples in the multiple unit cleaning samples:

[0215] Extracting a target speed from a preset speed gradient sequence, importing a unit cleaning sample into a cleaning unit, starting the cleaning unit using the target speed and a preset cleaning time, performing a cleaning operation on the unit cleaning sample in the cleaning unit using the started cleaning unit, and allowing the cleaned unit cleaning sample to stand to obtain a standing stratified sample, and extracting a barite sample from the standing stratified sample;

[0216] The barite density of the barite sample is detected by using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target rotation speed, cleaning time, and target water volume;

[0217] Summarize the unit experiment parameters to obtain the unit experiment data set;

[0218] Based on the unit experimental data set, the cleaning parameters corresponding to the initial sample are confirmed;

[0219] Wherein, the method of detecting the barite density of the barite sample by using a pre-constructed density detection method comprises:

[0220] Obtain an initial Leigh density bottle, and calibrate the initial Leigh density bottle using a pre-built calibration method to obtain a calibrated density bottle;

[0221] The pre-constructed weighing solution is introduced into the calibration density bottle, and the calibration density bottle after the weighing solution is introduced is subjected to a constant temperature heating operation to obtain a heating bottle, and the heating bottle is photographed by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle;

[0222] After performing a preprocessing operation on the initial weighing image, a binary weighing image is obtained, a concave liquid surface of the weighing solution is identified from the binary weighing image, a concave liquid surface scale line is obtained, and a first scale line and a second scale line are extracted from the calibration scale line based on the concave liquid surface scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization;

[0223] Estimate the starting value using the pre-built estimation method, the first scale line, and the second scale line;

[0224] Weigh the barite sample to obtain the mass of the barite, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset;

[0225] The bottle to be tested is photographed by a visual measurement system to obtain an image to be tested, and the measurement value of the bottle to be tested is calculated by using an estimation method and the image to be tested;

[0226] Calculate the density of barite based on the starting value, the measured value and the mass of barite;

[0227] Extracting cleaning parameters from a plurality of selected process parameters, and importing the cleaning parameters into a cleaning unit, and performing a cleaning operation on a barite set to be processed based on the cleaning unit after the cleaning parameters are imported, to obtain cleaned barite;

[0228] Import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform beneficiation operations on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0229] 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.

[0230] 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).

[0231] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0232] Confirming the barite beneficiation environment, wherein the barite beneficiation environment includes: an identification unit, a cleaning unit and an initial beneficiation device;

[0233] Based on the barite concentration environment, a barite set to be processed is received, a sample to be identified is obtained according to the barite set to be processed, and a recognition unit is used to identify the sample to be identified to obtain the type of barite;

[0234] A selection process database is obtained, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained;

[0235] Before obtaining the selected process database, the method further includes:

[0236] Obtain a target water volume sequence, extract target water volumes from the target water volume sequence in sequence, and perform the following operations on the extracted target water volumes:

[0237] An initial sample is obtained based on the extracted target water volume and the pre-constructed cleaning sample, a cleaning agent of a preset percentage is added to the initial sample to obtain a sample to be cleaned, and the sample to be cleaned is equally divided to obtain a plurality of unit cleaning samples;

[0238] The following operations are performed on the unit cleaning samples in the multiple unit cleaning samples:

[0239] Extracting a target speed from a preset speed gradient sequence, importing a unit cleaning sample into a cleaning unit, starting the cleaning unit using the target speed and a preset cleaning time, performing a cleaning operation on the unit cleaning sample in the cleaning unit using the started cleaning unit, and allowing the cleaned unit cleaning sample to stand to obtain a standing stratified sample, and extracting a barite sample from the standing stratified sample;

[0240] The barite density of the barite sample is detected by using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target rotation speed, cleaning time, and target water volume;

[0241] Summarize the unit experiment parameters to obtain the unit experiment data set;

[0242] Based on the unit experimental data set, the cleaning parameters corresponding to the initial sample are confirmed;

[0243] Wherein, the method of detecting the barite density of the barite sample by using a pre-constructed density detection method comprises:

[0244] Obtain an initial Leigh density bottle, and calibrate the initial Leigh density bottle using a pre-built calibration method to obtain a calibrated density bottle;

[0245] The pre-constructed weighing solution is introduced into the calibration density bottle, and the calibration density bottle after the weighing solution is introduced is subjected to a constant temperature heating operation to obtain a heating bottle, and the heating bottle is photographed by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle;

[0246] After performing a preprocessing operation on the initial weighing image, a binary weighing image is obtained, a concave liquid surface of the weighing solution is identified from the binary weighing image, a concave liquid surface scale line is obtained, and a first scale line and a second scale line are extracted from the calibration scale line based on the concave liquid surface scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization;

[0247] Estimate the starting value using the pre-built estimation method, the first scale line, and the second scale line;

[0248] Weigh the barite sample to obtain the mass of the barite, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset;

[0249] The bottle to be tested is photographed by a visual measurement system to obtain an image to be tested, and the measurement value of the bottle to be tested is calculated by using an estimation method and the image to be tested;

[0250] Calculate the density of barite based on the starting value, the measured value and the mass of barite;

[0251] Extracting cleaning parameters from a plurality of selected process parameters, and importing the cleaning parameters into a cleaning unit, and performing a cleaning operation on a barite set to be processed based on the cleaning unit after the cleaning parameters are imported, to obtain cleaned barite;

[0252] Import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform beneficiation operations on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

[0253] 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.

[0254] 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.

[0255] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or 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.

[0256] 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.

[0257] 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 barite beneficiation method based on optimization of beneficiation test process, characterized in that: The method comprises: Confirming the barite beneficiation environment, wherein the barite beneficiation environment includes: an identification unit, a cleaning unit and an initial beneficiation device; Based on the barite concentration environment, a barite set to be processed is received, a sample to be identified is obtained according to the barite set to be processed, and a recognition unit is used to identify the sample to be identified to obtain the type of barite; A selection process database is obtained, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained; Before obtaining the selected process database, the method further includes: Obtain a target water volume sequence, extract target water volumes from the target water volume sequence in sequence, and perform the following operations on the extracted target water volumes: An initial sample is obtained based on the extracted target water volume and the pre-constructed cleaning sample, a cleaning agent of a preset percentage is added to the initial sample to obtain a sample to be cleaned, and the sample to be cleaned is equally divided to obtain a plurality of unit cleaning samples; The following operations are performed on each of the unit cleaning samples in the multiple unit cleaning samples: Extracting a target speed from a preset speed gradient sequence, importing a unit cleaning sample into a cleaning unit, starting the cleaning unit using the target speed and a preset cleaning time, performing a cleaning operation on the unit cleaning sample in the cleaning unit using the started cleaning unit, and allowing the cleaned unit cleaning sample to stand to obtain a standing stratified sample, and extracting a barite sample from the standing stratified sample; The barite density of the barite sample is detected by using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target rotation speed, cleaning time, and target water volume; Summarize the unit experiment parameters to obtain the unit experiment data set; Based on the unit experimental data set, the cleaning parameters corresponding to the initial sample are confirmed; Wherein, the method of detecting the barite density of the barite sample by using a pre-constructed density detection method comprises: Obtain an initial Leigh density bottle, and calibrate the initial Leigh density bottle using a pre-built calibration method to obtain a calibrated density bottle; The pre-constructed weighing solution is introduced into the calibration density bottle, and the calibration density bottle after the weighing solution is introduced is subjected to a constant temperature heating operation to obtain a heating bottle, and the heating bottle is photographed by using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line provided on the heating bottle; After performing a preprocessing operation on the initial weighing image, a binary weighing image is obtained, a concave liquid surface of the weighing solution is identified from the binary weighing image, a concave liquid surface scale line is obtained, and a first scale line and a second scale line are extracted from the calibration scale line based on the concave liquid surface scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization; Estimate the starting value using the pre-built estimation method, the first scale line, and the second scale line; Weigh the barite sample to obtain the mass of the barite, introduce the weighed barite sample into a heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain a bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset; The bottle to be tested is photographed by a visual measurement system to obtain an image to be tested, and the measurement value of the bottle to be tested is calculated by using an estimation method and the image to be tested; Calculate the density of barite based on the starting value, the measured value and the mass of barite; Extracting cleaning parameters from a plurality of selected process parameters, and importing the cleaning parameters into a cleaning unit, and performing a cleaning operation on a barite set to be processed based on the cleaning unit after the cleaning parameters are imported, to obtain cleaned barite; Import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform beneficiation operations on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

2. The barite beneficiation method based on beneficiation test process optimization according to claim 1, characterized in that, The estimating the starting value by using the pre-built estimation method, the first scale line and the second scale line comprises: Acquire an estimated region according to the first scale line and the second scale line, divide the estimated region using a preset precision, and obtain a unit estimation sequence, wherein the unit estimation sequence includes a plurality of unit estimation scales; The unit estimation scales are extracted from the unit estimation sequence in sequence, and the Euclidean distances between the extracted unit estimation scales and the concave meniscus scale lines are calculated, the Euclidean distances are summarized to obtain a Euclidean distance set, the minimum Euclidean distance is extracted from the Euclidean distance set, and the unit estimation scale corresponding to the minimum Euclidean distance is confirmed to obtain a starting value.

3. The barite beneficiation method based on beneficiation test process optimization as claimed in claim 2, characterized in that, The step of confirming the cleaning parameters corresponding to the initial sample based on the unit experimental data set includes: Unit experiment parameters are extracted from the unit experiment data set in sequence, and a parameter matrix is ​​constructed based on the extracted unit experiment parameters, wherein the parameter matrix is: in, represents the parameter matrix, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates the first unit experimental parameter Parameters corresponding to the indicator type, Indicates Indicator type; Summarize and concatenate the parameter matrix to obtain the evaluation matrix corresponding to the unit experimental data set, extract column vectors from the evaluation matrix in sequence to obtain the indicator column vector, perform standardization on the extracted indicator column vector to obtain the standard column vector, summarize and concatenate the standard column vector to obtain the standard matrix, where the indicator column vector is represented as follows: in, Indicates The indicator column vector corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type, Represents the parameter matrix Middle Parameters corresponding to the indicator type; Calculate the weight corresponding to each indicator type in the multiple indicator types based on the standard matrix to obtain multiple indicator type weights; An evaluation formula is constructed based on multiple indicator type weights, and the evaluation formula is used to calculate the evaluation scores of all unit experimental parameters in the unit experimental data set. The evaluation scores are summarized to obtain an evaluation score set, wherein the evaluation score set includes multiple evaluation scores, and the evaluation scores correspond to the unit experimental parameters one by one. The evaluation formula is as follows: in, Indicates The evaluation score corresponding to the unit experimental parameter, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type, Indicates The indicator type weight corresponding to the indicator type, Indicates The first unit experimental parameter Parameters corresponding to the indicator type; The highest evaluation score is extracted from the evaluation score set, and the unit experimental parameter corresponding to the highest evaluation score is confirmed to obtain the cleaning parameter.

4. The barite beneficiation method based on beneficiation test process optimization as claimed in claim 3, characterized in that, The calculation formula of the indicator type weight is as follows: in, Indicates The indicator type weight corresponding to the indicator type, Indicates that there are a total of Unit experimental parameters, Indicates the standard matrix Line Elements of a column.

5. The barite beneficiation method based on beneficiation test process optimization as claimed in claim 4, characterized in that, The step of performing a standardization operation on the extracted indicator column vector to obtain a standard column vector, and summarizing and concatenating the standard column vectors to obtain a standard matrix includes: Identify the indicator properties corresponding to the extracted indicator column vector; If the indicator property is a positive indicator, the positive indicator elements are extracted from the extracted indicator column vector in sequence, and the positive standard elements are calculated using the pre-built forward standardization formula and the extracted positive indicator elements, and the positive standard elements are summarized to obtain a forward standard vector, where the forward standardization formula is as follows: in, represents a positive standard element, Represents the extracted positive indicator element, represents the smallest positive index element in the index column vector, Represents the largest positive indicator element in the indicator column vector; If the indicator is a negative indicator, the pre-built negative normalization formula and the extracted indicator column vector are used to obtain the negative normalization vector, where the negative normalization formula is as follows: in, represents a negative standard element, Represents a negative indicator element, Represents the smallest negative index element in the index column vector, Represents the largest negative index element in the index column vector; The positive standard vectors and negative standard vectors are aggregated and concatenated to obtain a standardization matrix.

6. The barite beneficiation method based on beneficiation test process optimization according to claim 5, characterized in that: The obtaining of the selected process database comprises: Obtain multiple barite names and multiple selected categories, wherein the multiple selected categories include: washing, centrifugation, magnetic attraction, flotation, drying, grinding, screening, crushing, enrichment and jigging; Performing digitization operations on the plurality of selected categories by using a pre-constructed digitization method to obtain a plurality of digitized categories, extracting a first value set, a second value set, and other value sets from the plurality of digitized categories, Recursively extract one or more first representative values ​​from the first value set, and identify the one or more first representative values ​​in a pre-constructed first sequence to obtain an identified first sequence; Recursively extract one or more second representative values ​​from the second value set, and identify the one or more second representative values ​​in the pre-constructed second sequence to obtain an identified second sequence; Extracting other values ​​from the other value set in sequence, and constructing other value pairs based on the extracted other values, wherein the other value pairs include other values ​​and storage data, the storage data is preset to 0, and summarizing the other value pairs to obtain other value pair sets; A selection process database is constructed using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names.

7. The barite beneficiation method based on beneficiation test process optimization according to claim 6, characterized in that: The method of constructing a selection process database by using the first sequence of identifiers, the second sequence of identifiers, other value pair sets, and the plurality of barite names includes: The barite names are sequentially extracted from the multiple barite names to obtain target barite, and the following operations are performed on the target barite: Acquire multiple selection processes corresponding to the target barite, wherein the multiple selection processes are included in the multiple selection categories; Selected processes are extracted from multiple selected processes in sequence, and the following operations are performed on the extracted selected processes: Identify the first value, the second value, and the selected other values ​​corresponding to the extracted selected process, identify and identify the first value in the first identification sequence to obtain the first point corresponding to the first value, identify and identify the second value in the second identification sequence to obtain the second point corresponding to the second value, identify and identify other value pairs corresponding to the selected other values ​​in other value set pairs to obtain the third point, and sequentially connect the first point, the second point, and the third point to obtain the initial path corresponding to the extracted selected process; Obtaining the selection parameters corresponding to the target barite in the extracted selection process, and using the selection parameters to update the storage data corresponding to the third site, and updating the initial path based on the updated storage data to obtain a unit path; Summarize the unit paths corresponding to the target barite to obtain the unit selection process database corresponding to the target barite; The unit selection process databases are aggregated to obtain the selection process databases corresponding to multiple target barite.

8. A barite beneficiation system based on beneficiation test process optimization, characterized in that: The system comprises: A barite type acquisition module is used to confirm a barite selection environment, wherein the barite selection environment includes: an identification unit, a cleaning unit, and an initial selection device. A barite set to be processed is received based on the barite selection environment, and a sample to be identified is obtained according to the barite set to be processed. The sample to be identified is identified by using an identification unit to obtain a barite type; A selection process parameter module is used to obtain a selection process database, and after the barite type is imported into the selection process database, a plurality of selection process parameters corresponding to the barite type are obtained, wherein before obtaining the selection process database, it also includes: obtaining a target water volume sequence, extracting the target water volume from the target water volume sequence in sequence, and performing the following operations on the extracted target water volume: obtaining an initial sample based on the extracted target water volume and a pre-constructed cleaning sample, adding a preset percentage of cleaning agent to the initial sample, obtaining a sample to be washed, and equally dividing the sample to be washed to obtain a plurality of unit cleaning samples, and performing the following operations on the unit cleaning samples in the plurality of unit cleaning samples: extracting the target water volume from a preset speed gradient sequence; speed, after the unit cleaning sample is introduced into the cleaning unit, the cleaning unit is started using the target speed and the preset cleaning time, the started cleaning unit is used to perform a cleaning operation on the unit cleaning sample in the cleaning unit, and the cleaned unit cleaning sample is allowed to stand to obtain a static stratified sample, and a barite sample is extracted from the static stratified sample, and the barite density of the barite sample is detected using a pre-constructed density detection method, and unit experimental parameters are constructed based on the barite density, wherein the unit experimental parameters include: barite density, target speed, cleaning time and target water volume, the unit experimental parameters are summarized to obtain a unit experimental data set, and the cleaning parameters corresponding to the initial sample are confirmed based on the unit experimental data set, wherein the use of the pre The constructed density detection method detects the barite density of a barite sample, comprising: obtaining an initial Leigh density bottle, calibrating the initial Leigh density bottle using a pre-constructed calibration method to obtain a calibration density bottle, introducing a pre-constructed weighing solution into the calibration density bottle, and performing a constant temperature heating operation on the calibration density bottle after the weighing solution is introduced to obtain a heating bottle, photographing the heating bottle using a pre-constructed visual measurement system to obtain an initial weighing image, wherein the initial weighing image includes a calibration scale line arranged on the heating bottle, performing a pre-processing operation on the initial weighing image to obtain a binary weighing image, identifying a concave liquid surface of the weighing solution from the binary weighing image to obtain a concave liquid surface scale line, extracting a concave liquid surface scale line from the calibration scale line based on the concave liquid surface scale line, and obtaining a binary weighing image. Take the first scale line and the second scale line, wherein the preprocessing operation includes: noise reduction, grayscale and binarization, use the pre-built estimation method, the first scale line and the second scale line to estimate the starting value, weigh the barite sample, obtain the barite mass, introduce the weighed barite sample into the heating bottle, perform ultrasonic vibration and constant temperature heating operations on the heating bottle after the barite sample is introduced, and obtain the bottle to be tested, wherein the vibration power, vibration duration, constant temperature heating duration and constant temperature heating temperature are preset, and the bottle to be tested is photographed by a visual measurement system to obtain a test image, and the measurement value of the bottle to be tested is calculated by using the estimation method and the test image, and the barite density is calculated based on the starting value, the measurement value and the barite mass; A cleaning barite acquisition module is used to extract cleaning parameters from a plurality of selected process parameters, and import the cleaning parameters into a cleaning unit, and perform a cleaning operation on the barite set to be processed based on the cleaning unit after the cleaning parameters are imported to obtain clean barite; The beneficiation equipment acquisition module is used to import multiple beneficiation process parameters after removing the cleaning parameters into the initial beneficiation equipment to obtain the starting beneficiation equipment, import the cleaned barite into the starting beneficiation equipment and use the starting beneficiation equipment to perform a beneficiation operation on the imported cleaned barite to obtain selected barite, and complete the barite beneficiation based on the optimization of the selection test process based on the selected barite.

Images