Method, device and medium for determining flotation control parameters by analytical equipment

Abstract

The present application relates to the field of flotation automation control technology. In particular, it relates to a method, device and medium for determining flotation control parameters through an analytical device. The analytical device is applied to a flotation column, and the analytical device includes a first acquisition unit and a processor. The current image information of the foam layer is acquired through the first acquisition unit. The method includes: inputting the current image information into an image recognition model, and outputting the current ash content corresponding to the current image information through the image recognition model; determining the current ash content difference between the current ash content and the target ash content threshold according to the current ash content and the target ash content threshold; if the current ash content difference satisfies the parameter adjustment condition, one or more control parameters corresponding to the current ash content difference are acquired from a first database according to the current ash content difference, wherein the parameter adjustment condition includes that the current ash content difference is not within the target difference range. This scheme completes real-time monitoring and control of the flotation process through an analytical device.

Description

Technical Field

[0001] The present application relates to the technical field of flotation automation control, and in particular to a method, device and medium for determining flotation control parameters by analyzing equipment. Background Art

[0002] Froth flotation is a method of separating minerals from impurities or one mineral from another. Unpurified ore powder is stirred with water and reagents. The reagents preferentially adhere to the target ore and change its surface properties. Air is then introduced into the mixture, and the target ore floats to the surface of the mixture with bubbles to form foam. The target ore can be obtained after the foam is removed. During the flotation process, the foam layer can well reflect the quality of the flotation effect.

[0003] At present, the monitoring of the foam layer is mainly through manual observation. However, manual observation has a large subjective influence and low accuracy. In addition, it is impossible to ensure real-time monitoring of the foam layer, and it is also impossible to complete the real-time automatic determination of the flotation control parameters. Summary of the invention

[0004] In order to solve the above problems, the present application provides a method, a device and a medium for determining flotation control parameters by analyzing equipment.

[0005] According to one aspect of the present application, a method for determining flotation control parameters by an analysis device is provided. The analysis device is applied to a flotation column. The analysis device includes a first acquisition unit and a processor. The first acquisition unit is arranged at the top of a foam layer in the flotation column. The first acquisition unit is electrically connected to the processor. Current image information of the foam layer is acquired by the first acquisition unit. The method includes:

[0006] Input the current image information into the image recognition model, and output the current gray value corresponding to the current image information through the image recognition model;

[0007] Determine a current ash content difference between the current ash content and the target ash content threshold value according to the current ash content and the target ash content threshold value;

[0008] If the current gray content difference satisfies the parameter adjustment conditions, one or more control parameters corresponding to the current gray content difference are obtained from the first database according to the current gray content difference, and the one or more control parameters are used as the target control parameters corresponding to the current image information, wherein the parameter adjustment conditions include that the current gray content difference is not within the target difference range.

[0009] According to one aspect of the present application, an analysis device is provided, which is applied to a flotation column. The analysis device includes a first acquisition unit and a processor. The first acquisition unit is arranged on the top of a foam layer in the flotation column. The first acquisition unit is electrically connected to the processor. Current image information of the foam layer is acquired through the first acquisition unit. The device also includes:

[0010] Processor; and

[0011] A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the operations of any of the methods described above.

[0012] According to one aspect of the present application, a computer-readable medium storing instructions is provided, wherein when the instructions are executed, the system performs the operation of any of the methods described above.

[0013] Compared with the prior art, the present application automatically determines the flotation control parameters through an analysis device. The analysis device includes a first acquisition unit and a processor, and the current image information of the foam layer is acquired through the first acquisition unit. The current image information of the foam layer is input into the image recognition model, and the corresponding current ash content is output through the image recognition model. The current ash content difference between the current ash content and the target ash content threshold quality inspection is determined. By detecting whether the current ash content difference meets the parameter adjustment condition, it is monitored in real time whether the flotation control parameters need to be determined to control the flotation process. If the current ash content difference meets the parameter adjustment condition, one or more control parameters corresponding to the current ash content difference are obtained from the first database according to the current ash content difference, and the one or more control parameters are used as the target control parameters. This solution completes the real-time monitoring and control of the flotation process through the analysis device. Specifically, based on the image recognition model, the corresponding current ash content is obtained through the current image information; based on the first database, the corresponding target control parameter is determined by the current ash content difference between the current ash content and the target ash content threshold, so as to complete the real-time monitoring and control of the flotation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0015] Figure 1 A flow chart showing a method for determining flotation control parameters by analyzing equipment according to one embodiment of the present application;

[0016] Figure 2 A schematic diagram showing the structure of an analysis device according to an embodiment of the present application is shown;

[0017] Figure 3 A schematic diagram showing the structure of a processor according to an embodiment of the present application is shown;

[0018] Figure 4 An exemplary system is shown that can be used to implement the various embodiments described in this application.

[0019] Explanation of the reference numerals: 1. first acquisition unit; 21. grating transmitter; 22. grating receiver; 23. liquid level meter; 3. processor; 4. flotation column; 41. foam layer. DETAILED DESCRIPTION

[0020] The present application is described in further detail below in conjunction with the accompanying drawings.

[0021] In a typical configuration of the present application, the terminal, the device of the service network and the trusted party all include one or more processors (eg, a central processing unit (CPU)), an input / output interface, a network interface and a memory.

[0022] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory. Memory is an example of a computer-readable medium.

[0023] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, Phase-Change Memory (PCM), Programmable Random Access Memory (PRAM), Static Random-Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of random access memory (RAM), Read-Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0024] The devices referred to in this application include but are not limited to terminals, network devices, or devices formed by integrating terminals and network devices through a network. The terminal includes but is not limited to any mobile electronic product that can interact with a user (for example, interact with a user through a touchpad), such as a smart phone, a tablet computer, etc. The mobile electronic product can use any operating system, such as an Android operating system, an iOS operating system, etc. Among them, the network device includes an electronic device that can automatically perform numerical calculations and information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc. The network device includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud composed of multiple servers; here, the cloud is composed of a large number of computers or network servers based on cloud computing (Cloud Computing), wherein cloud computing is a type of distributed computing, a virtual supercomputer composed of a group of loosely coupled computer sets. The network includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, VPN network, wireless self-organizing network (Ad Hoc network), etc. Preferably, the device may also be a program running on the terminal, network device, or a device formed by integrating the terminal and network device, network device, touch terminal, or network device and touch terminal through a network.

[0025] Of course, those skilled in the art should understand that the above-mentioned devices are only examples, and other existing or future devices that are applicable to the present application should also be included in the scope of protection of the present application and are included here by reference.

[0026] In the description of the present application, “plurality” means two or more, unless otherwise clearly and specifically defined.

[0027] Figure 1A method for determining flotation control parameters by using an analytical device according to an embodiment of the present application is shown. The method includes step S11, step S12, and step S13. The analysis device is applied to the flotation column 4, and the analysis device includes a first acquisition unit with reference numerals: 1 and a processor 3. The first acquisition unit with reference numerals: 1 is arranged on the top of the foam layer in the flotation column 4, and the first acquisition unit with reference numerals: 1 is electrically connected to the processor 3. The current image information of the foam layer is acquired through the first acquisition unit with reference numerals: 1. The method includes: in step S11, the current image information is input into the image recognition model, and the current ash content corresponding to the current image information is output through the image recognition model; in step S12, the current ash content difference between the current ash content and the target ash content threshold is determined according to the current ash content and the target ash content threshold; in step S13, if the current ash content difference satisfies the parameter adjustment condition, one or more control parameters corresponding to the current ash content difference are acquired from the first database according to the current ash content difference, and the one or more control parameters are used as the target control parameters corresponding to the current image information, wherein the parameter adjustment condition includes that the current ash content difference is not within the target difference interval. Figure 2 FIG. 2 shows a schematic diagram of the structure of an analysis device according to an embodiment of the present application. Figure 2As shown, the analysis device includes a first acquisition unit reference numeral description: 1, a processor 3, and the first acquisition unit reference numeral description: 1 is electrically connected to the processor 3. In some embodiments, the first acquisition unit reference numeral description: 1 includes but is not limited to a visual sensor and a camera. In some embodiments, the first acquisition unit reference numeral description: 1 is equipped with a high-resolution zoom lens to flexibly adjust the shooting distance and clarity according to actual process requirements. The first acquisition unit reference numeral description: 1 is set at the top of the foam layer to capture and obtain the current image information about the foam layer (for example, the current image information about the surface of the foam layer is captured by the first acquisition unit reference numeral description: 1). In some embodiments, the foam layer at the center of the foam layer is uniform and the foam is fine. In order to make the captured current image information more conducive to improving the accuracy of the analysis result, in some embodiments, the first acquisition unit reference numeral description: 1 is located in the middle of the top of the foam layer to make the image of the foam layer at the center position clearer. In some embodiments, after acquiring the current image information, the processor 3 can also only intercept the image information within a certain range around the center position, and use the intercepted image information as the current image information (for example, around the center point of the image information sent by the first acquisition unit reference numeral description: 1, the image information of a preset size is intercepted as the current image information). In some embodiments, the execution body of the present solution includes a processor 3. For example, the first acquisition unit is illustrated with the following reference numerals: 1 The current image information about the foam layer obtained by shooting is sent to the processor 3, and the processor 3 inputs the current image information into the image recognition model to obtain the current ash content; the current ash content difference is determined, and it is judged whether the current ash content difference meets the parameter adjustment condition. If so, the target control parameter corresponding to the current image information is obtained from the first database according to the current ash content difference. Here, those skilled in the art can understand that the flotation column 4 includes a flotation column, and the flotation column includes a pulp layer and a foam layer located on the upper side of the pulp layer. 41 This embodiment monitors the flotation foam layer in real time through an analysis device, and determines the target control parameter in real time according to the monitoring result, so as to achieve the purpose of real-time monitoring and control of the flotation process.

[0028] Specifically, in step S11, the current image information is input into the image recognition model, and the current ash content corresponding to the current image information is output through the image recognition model. In some embodiments, the ash content includes the amount of non-combustible matter remaining after the material is burned at high temperature. In some embodiments, the image recognition model is obtained by training based on a deep learning algorithm. For example, a U-Net network is trained with a large number of training images with target ash values ​​to obtain the image recognition model. The current ash content corresponding to the current image information is obtained by inputting the current image information into the image recognition model.

[0029] In step S12, the current ash difference between the current ash and the target ash threshold is determined according to the current ash and the target ash threshold. In some embodiments, the target ash threshold is preset. In some embodiments, by judging whether the current ash matches the target ash threshold, it is judged whether it is necessary to control the flotation process by determining the control parameters. For example, if the current ash matches the target ash threshold, there is no need to determine the control parameters. If the current ash does not match the target ash threshold, the target control parameters are triggered to adjust the current ash to the target ash threshold to improve the flotation effect. In some embodiments, the current ash matches the target ash threshold, including that the difference between the current ash and the target ash threshold (e.g., the current ash difference) is within the target difference interval. In some embodiments, after the current ash is subtracted from the target ash threshold, the absolute value of the subtraction result is taken as the current ash difference.

[0030] In step S13, if the current ash difference satisfies the parameter adjustment condition, one or more control parameters corresponding to the current ash difference are obtained from the first database according to the current ash difference, and the one or more control parameters are used as the target control parameters corresponding to the current image information, wherein the parameter adjustment condition includes that the current ash difference is not within the target difference interval. In some embodiments, if the current ash content does not match the target ash content threshold, the target control parameter is determined, in other words, if the current ash difference satisfies the parameter adjustment condition, the target control parameter is determined. For example, if the current ash difference is not within the target difference interval, it is determined that the current ash content satisfies the parameter adjustment condition, and the trigger processor 3 queries and obtains one or more control parameters corresponding to the current ash difference from the first database according to the current ash difference, so as to control the flotation column 4 based on the one or more control parameters. In some embodiments, after determining the target control parameter, the analysis device controls the flotation column 4 of the flotation machine based on the target control parameter (for example, sending the target control parameter including the target air intake volume to the flotation column 4 of the flotation machine).

[0031] In some embodiments, the first database includes multiple gray difference intervals and one or more control parameters corresponding to each gray difference interval. The one or more control parameters corresponding to the current gray difference value are obtained from the first database according to the current gray difference value, including: querying the first gray difference interval where the current gray difference value is located from multiple gray difference intervals in the first database according to the current gray difference value, and using one or more control parameters with a mapping relationship with the first gray difference interval as one or more control parameters corresponding to the current gray difference value. In some embodiments, as shown in Table 1, multiple mapping relationships are established in the first database, each mapping relationship includes a gray difference interval and one or more control parameters corresponding to the gray difference interval. The processor queries the first gray difference interval where the current gray difference value is located from the first database according to the current gray difference value. For example, if the current gray difference value is 2 and the first database includes the gray difference interval [1,3], it is determined that the current gray difference value 2 is in the gray difference interval [1,3], and the gray difference interval is used as the first gray difference interval. Furthermore, one or more control parameters that have a mapping relationship with the first gray content difference interval are used as one or more control parameters corresponding to the current gray content difference.

[0032] Table 1 The first database

[0033]

[0034] Of course, those skilled in the art will understand that the first database shown in Table 1 above is only an example, and is intended to illustrate that the first database includes multiple ash difference intervals and one or more control parameters corresponding to each ash difference interval.

[0035] In some embodiments, the image recognition model is trained by the following method: according to a plurality of training image information and a target gray value corresponding to each training image information, a model is trained based on a deep learning algorithm to obtain an image recognition model. In some embodiments, the image recognition model uses a U-Net network model, where those skilled in the art can understand that the U-Net network model is a convolutional neural network. In this embodiment, the U-Net network model is trained with a large number of training images with target gray values ​​to obtain the image recognition model. Specifically, the U-Net network model can perform image segmentation and extract texture features of the image, thereby training the U-Net network model based on a large number of training image information with target gray values ​​to obtain the image recognition model. A large amount of training image information includes a large number of foam layer images containing foams of different sizes, textures, and shapes.

[0036] In some embodiments, the analysis device further includes a second acquisition unit, which includes a grating system and a liquid level meter 23. The grating system and the liquid level meter 23 are electrically connected to the processor 3 respectively, and the current top height information of the foam layer is obtained through the grating system, and the current bottom height information of the foam layer is obtained through the liquid level meter 23. The method further includes step S14 (not shown). In step S14: the current thickness information of the foam layer is determined according to the current top height information and the current bottom height information; if the target control parameter includes the target thickness information of the foam layer, and the thickness difference between the target thickness information and the current thickness information is greater than the target thickness threshold, control instruction information is sent to the flotation column 4 to match the current thickness information with the target thickness information. In some embodiments, the matching of the current thickness information with the target thickness information includes but is not limited to the thickness difference between the current thickness information and the target thickness information being equal to or less than the target thickness threshold. In some embodiments, as Figure 2As shown, the analysis device also includes a grating system and a level gauge 23. The level gauge 23 includes but is not limited to an electronic float, an ultrasonic level gauge, a radar level gauge, a static pressure level gauge 23, etc. In some embodiments, the grating system includes but is not limited to an STC series measurement and detection grating. In some embodiments, the level gauge 23 floats on the surface of the slurry layer, and the current surface height of the slurry layer is used as the current bottom height information of the foam layer. Here, those skilled in the art can understand that the current surface height information of the slurry layer can be directly obtained through the level gauge 23, so as to use the current surface height information as the current bottom height information of the foam layer, and send the current bottom height information to the processor 3. In this embodiment, the current top height information of the foam layer is obtained through the grating system. For the specific introduction of this part, please refer to the corresponding embodiment below, which will not be repeated here. For example, after the current top height information of the foam layer is obtained through the grating system, the current bottom height information of the foam layer is obtained through the level gauge 23, and the difference between the current top height information and the current bottom height information is used as the current thickness information of the foam layer. In some embodiments, the target control parameter obtained through the above-mentioned first database may include the target thickness information of the foam layer. If the target control parameter includes target thickness information, detect whether the thickness difference between the target thickness information and the current thickness information is greater than the target thickness threshold. If the thickness difference between the target thickness information and the current thickness information is greater than the target thickness threshold, it is determined that the target thickness information does not match the current thickness information, and the corresponding control instruction information is sent to the flotation column 4 to match the current thickness information with the target thickness information. In some embodiments, if the target thickness information does not match the current thickness information, and the target thickness information is greater than the current thickness information, a first control instruction information is sent to the flotation column 4, wherein the first control instruction information includes control instruction information for increasing the thickness of the foam layer until the current thickness information matches the target thickness information. In other embodiments, if the target thickness information does not match the current thickness information, and the target thickness information is less than the current thickness information, a second control instruction information is sent to the flotation column 4, wherein the second control instruction information includes control instruction information for reducing the thickness of the foam layer until the current thickness information matches the target thickness information. Here, those skilled in the art can understand that the purpose of adjusting the thickness of the foam layer in the flotation column can be achieved by adjusting the slurry level, and the technology for adjusting the thickness of the foam layer in the flotation column by adjusting the slurry level in the prior art is relatively mature. For example, in the process of adjusting the thickness of the foam layer, the processor 3 still monitors the current thickness information of the foam layer in real time through the second acquisition unit, and when the current thickness information matches the target thickness information, stops sending the control instruction information for adjusting the thickness to the flotation column 4, so as to complete the adjustment of the thickness of the foam layer.

[0037] In some embodiments, the grating system includes one or more grating components, each of which includes a grating transmitter 21 vertically arranged on one side of the foam layer, and a grating receiver 22 vertically arranged on the other side of the foam layer, the grating transmitter 21 includes a plurality of light emitting modules arranged in an array for emitting light signals, and the grating receiver 22 includes a plurality of light receiving modules arranged in an array for receiving light signals, and obtaining the current top height information of the foam layer through the grating system includes: obtaining at least one light signal information through the light receivers of one or more grating components, wherein each light signal information includes first identification information of the grating receiver 22 that receives the light signal information, and second identification information of the light receiving module that receives the light signal information; for each light signal information, obtaining a light signal from the first identification information contained in the light signal information; The second database is queried to obtain multiple second identification information that has a mapping relationship with the first identification information, wherein the second database includes one or more first identification information, and multiple second identification information that has a mapping relationship with each first identification information; for each light signal information, at least one candidate height information that has a mapping relationship with the at least one second identification information is queried from the candidate data information according to the at least one second identification information contained in the light signal information, and the at least one candidate height information is used as the target height information corresponding to the light signal information, wherein the second database also includes the candidate height information that has a mapping relationship with each second identification information; the current top height information of the foam layer is determined according to the one or more target height information corresponding to the one or more light signal information, wherein each target height information includes at least one candidate height information. Here, those skilled in the art can understand that light cannot penetrate the foam layer. In this embodiment, the top height information of the foam layer is determined according to one or more light rays that pass through the top surface of the foam layer. The grating transmitter 21 and the grating receiver 22 in a grating component are respectively arranged vertically on both sides of the foam layer. The grating transmitter 21 includes a plurality of light emitting modules arranged in an array, and the plurality of light emitting modules arranged in an array are also arranged vertically on one side of the foam layer. Similarly, the plurality of light receiving modules arranged in an array of the grating receiver 22 are also arranged vertically on the other side of the foam layer, and the light emitting modules and light receiving modules of the relatively arranged grating components correspond one to one in height position. In some embodiments, as shown in Table 2, the height information of each light receiving module (for example, the candidate thickness information) is pre-recorded in the second database, and the height information corresponding to the second identification information of the light receiving module receiving the light signal information can be obtained from the second database, thereby obtaining at least one height information passing through the surface of the foam layer. After the received light signal information is converted into the target height information through the second database, the top height information of the foam layer is determined according to one or more target height information.In some embodiments, since the surface of the foam layer is usually uneven, multiple target height information can be obtained by setting multiple grating components to determine the top height information of the foam layer based on the multiple target height information.

[0038] Table 2 The second database

[0039]

[0040] Of course, those skilled in the art can understand that the second database shown in Table 2 above is only an example, and is intended to illustrate that the second database includes a plurality of first identification information, and one or more second identification information corresponding to each first identification information, and candidate height information corresponding to each second identification information, so as to obtain the corresponding candidate height information based on the first identification information and the second identification information of the received optical signal information, thereby converting the optical signal into height information to obtain the top height information of the foam layer.

[0041] In some embodiments, the current top height information of the foam layer is determined based on one or more target height information corresponding to one or more light signal information, including: using the minimum candidate height information among the one or more target height information as the current top height information of the foam layer. For example, one or more light signal information is received and acquired through one or more grating components, and based on each light signal information, one or more candidate height information corresponding to the light signal information is acquired through a second database to acquire one or more candidate height information corresponding to each light signal information. The minimum candidate height information is determined from the one or more candidate height information, and the candidate height information is used as the current top height information of the foam layer.

[0042] In some embodiments, the current top height information of the foam layer is determined based on one or more target height information corresponding to one or more light signal information, including: for each target height information, determining the minimum candidate height information contained in the target height information to obtain one or more minimum candidate height information; calculating the average height of the one or more minimum candidate height information, and using the average height as the current top height information of the foam layer. For example, in some embodiments, in order to improve accuracy, the average height of the minimum candidate height information contained in each target height information is calculated, and the average height is used as the current top height information of the foam layer.

[0043] In some embodiments, the target control parameter corresponds to a target reaction time, and the method further includes step S15 (not shown). In step S15, within the target reaction time, the reaction image information is input into the image recognition model, and the reaction ash corresponding to the reaction image information is output through the image recognition model, wherein the reaction image information is obtained by the first acquisition unit within the target reaction time; the reaction ash difference between the reaction ash and the target ash threshold is determined according to the reaction ash and the target ash threshold; if the reaction ash difference is greater than the current ash difference, one or more control parameters corresponding to the reaction ash difference are obtained from the first database according to the reaction ash difference, and the one or more control parameters are used as new target control parameters to control the flotation column based on the new target control parameters. For example, in order to adjust the control parameters more finely, in the first database, each group of control parameters corresponds to a reaction time, and while obtaining one or more control parameters corresponding to the current ash difference from the first database according to the current ash difference, the reaction time corresponding to the one or more control parameters is also obtained, and the one or more control parameters are used as target control parameters, and the reaction time is used as the target reaction time. In some embodiments, the reaction image information includes image information about the foam layer acquired by the first acquisition unit during the reaction time. The reaction ash includes the ash value output by inputting the reaction image information into the image recognition model during the target reaction time. The reaction ash difference includes the absolute value of the difference between the reaction ash and the target ash threshold during the target reaction time. In some embodiments, the analysis device usually detects 2-3 times during the target reaction time. For example, by inputting the reaction image information into the image recognition model, it is detected whether the target control parameter determined based on the current image information has an ideal effect on the flotation process, thereby preventing time from being wasted due to erroneous debugging. In some embodiments, by detecting whether the reaction ash difference is greater than the current ash difference, it is detected whether the control parameter needs to be re-determined. For example, if the reaction ash difference is greater than the current ash difference, it means that the target control parameter determined based on the current image information has an effect on the flotation process that is opposite to the ideal effect, and the control parameter needs to be re-determined. If the ash difference is less than or equal to the current ash difference, it means that the target control parameter determined based on the current image information has a positive effect on the flotation process, and there is no need to re-determine the control parameter.

[0044] In some embodiments, the target control parameter includes at least one of the following:

[0045] Air intake;

[0046] Dosage; for example, when there is only one drug, the target control parameter includes the dosage of the drug.

[0047] The type of medicine and the dosage corresponding to the medicine type; for example, when there are multiple medicines, the target control parameter includes the dosage corresponding to a certain medicine type. It can be one or more medicine types and the dosage corresponding to each medicine type.

[0048] The target thickness information of the foam layer.

[0049] Of course, those skilled in the art will understand that the control parameters described above are only examples, and other control parameters that may appear before or after or in the future, if applicable to the present application, are also within the scope of protection of the present application and are included herein by reference.

[0050] Figure 3 A structural schematic diagram of a processor according to an embodiment of the present application is shown, the analysis device is applied to a flotation column, the analysis device includes a first acquisition unit and a processor, the first acquisition unit is arranged on the top of the foam layer in the flotation column, the first acquisition unit is electrically connected to the processor, and the current image information of the foam layer is acquired by acquiring the first acquisition unit, the device includes a one-one module, a one-two module, and a one-three module, the one-one module is used to input the current image information into an image recognition model, and output the current ash content corresponding to the current image information through the image recognition model; the one-two module is used to determine the current ash content difference between the current ash content and the target ash content threshold according to the current ash content and the target ash content threshold; the one-three module is used to obtain one or more control parameters corresponding to the current ash content difference from the first database according to the current ash content difference if the current ash content difference meets the parameter adjustment condition, and use the one or more control parameters as the target control parameters corresponding to the current image information, wherein the parameter adjustment condition includes that the current ash content difference is not within the target difference interval.

[0051] Here, the specific implementations corresponding to module 11, module 12, and module 13 are the same as or similar to the specific implementations of step S11, step S12, and step S13, and are therefore not repeated here and are included herein by reference.

[0052] In some embodiments, the first database includes multiple gray content difference intervals and one or more control parameters corresponding to each gray content difference interval. The one or more control parameters corresponding to the current gray content difference are obtained from the first database according to the current gray content difference, including: querying the first gray content difference interval where the current gray content difference is located from multiple gray content difference intervals in the first database according to the current gray content difference, and using one or more control parameters that have a mapping relationship with the first gray content difference interval as the one or more control parameters corresponding to the current gray content difference.

[0053] Here, the specific implementation corresponding to the one-three modules is the same as or similar to the specific implementation of step S13, and thus will not be described in detail and is included herein by reference.

[0054] In some embodiments, the image recognition model is trained by the following method: according to multiple training image information and the target gray value corresponding to each training image information, a model is trained based on a deep learning algorithm to obtain an image recognition model.

[0055] In some embodiments, the analysis device also includes a second acquisition unit, which includes a grating system and a liquid level meter. The grating system and the liquid level meter are electrically connected to the processor respectively. The current top height information of the foam layer is obtained through the grating system, and the current bottom height information of the foam layer is obtained through the liquid level meter. The device also includes a four-module (not shown), and the four-module is used to determine the current thickness information of the foam layer based on the current top height information and the current bottom height information; if the target control parameter includes the target thickness information of the foam layer, and the thickness difference between the target thickness information and the current thickness information is greater than the target thickness threshold, control instruction information is sent to the flotation column to match the current thickness information with the target thickness information.

[0056] Here, the specific implementation corresponding to the one-fourth module is the same as or similar to the specific implementation of step S14, and thus will not be described in detail and is included herein by reference.

[0057] In some embodiments, the grating system includes one or more grating components, each of which includes a grating transmitter vertically arranged on one side of the foam layer, and a grating receiver vertically arranged on the other side of the foam layer, the grating transmitter includes a plurality of light emitting modules arranged in an array for emitting light signals, and the grating receiver includes a plurality of light receiving modules arranged in an array for receiving light signals, and obtaining the current top height information of the foam layer through the grating system includes: obtaining at least one light signal information through the light receivers of one or more grating components, wherein each light signal information includes first identification information of the grating receiver that receives the light signal information, and second identification information of the light receiving module that receives the light signal information; for each light signal information, selecting a second identification information from a second database according to the first identification information contained in the light signal information Query and obtain multiple second identification information that has a mapping relationship with the first identification information, wherein the second database includes one or more first identification information, and multiple second identification information that has a mapping relationship with each first identification information; for each light signal information, query and obtain at least one candidate height information that has a mapping relationship with the at least one second identification information from the candidate data information according to at least one second identification information included in the light signal information, and use the at least one candidate height information as the target height information corresponding to the light signal information, wherein the second database also includes candidate height information that has a mapping relationship with each second identification information; determine the current top height information of the foam layer according to the one or more target height information corresponding to the one or more light signal information, wherein each target height information includes at least one candidate height information.

[0058] Here, the specific implementation corresponding to the one-fourth module is the same as or similar to the specific implementation of step S14, and thus will not be described in detail and is included herein by reference.

[0059] In some embodiments, determining the current top height information of the foam layer according to one or more target height information corresponding to one or more light signal information includes: taking the smallest candidate height information among the one or more target height information as the current top height information of the foam layer.

[0060] Here, the specific implementation corresponding to the one-fourth module is the same as or similar to the specific implementation of step S14, and thus will not be described in detail and is included herein by reference.

[0061] In some embodiments, the current top height information of the foam layer is determined based on one or more target height information corresponding to one or more light signal information, including: for each target height information, determining the minimum candidate height information contained in the target height information to obtain one or more minimum candidate height information; calculating the average height of the one or more minimum candidate height information, and using the average height as the current top height information of the foam layer.

[0062] Here, the specific implementation corresponding to the one-fourth module is the same as or similar to the specific implementation of step S14, and thus will not be described in detail and is included herein by reference.

[0063] In some embodiments, the device also includes a five-module (not shown), and the target control parameter corresponds to a target reaction time. The five-module is used to input the reaction image information into the image recognition model within the target reaction time, and output the reaction ash corresponding to the reaction image information through the image recognition model, wherein the reaction image information is acquired by the first acquisition unit within the target reaction time; determine the reaction ash difference between the reaction ash and the target ash threshold according to the reaction ash and the target ash threshold; if the reaction ash difference is greater than the current ash difference, obtain one or more control parameters corresponding to the reaction ash difference from the first database according to the reaction ash difference, and use the one or more control parameters as new target control parameters, so as to control the flotation column based on the new target control parameters.

[0064] Here, the specific implementation corresponding to module 15 is the same as or similar to the specific implementation of step S15, and thus will not be described in detail and is included herein by reference.

[0065] In some embodiments, the target control parameter includes at least one of the following:

[0066] Air intake;

[0067] Dosage of medicine;

[0068] The type of medicine and the dosage corresponding to the type of medicine;

[0069] Target thickness information for the foam layer.

[0070] In addition to the methods and devices described in the above embodiments, the present application also provides a computer-readable storage medium, which stores computer code. When the computer code is executed, the method described in any of the preceding items is executed.

[0071] The present application also provides a computer program product. When the computer program product is executed by a computer device, the method described in any of the preceding items is executed.

[0072] The present application also provides a computer device, the computer device comprising:

[0073] one or more processors;

[0074] a memory for storing one or more computer programs;

[0075] When the one or more computer programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any of the preceding items.

[0076] Figure 4 An exemplary system that can be used to implement various embodiments described in this application is shown;

[0077] like Figure 4 In some embodiments shown, the system 300 can be used as any of the devices in the various described embodiments. In some embodiments, the system 300 may include one or more computer-readable media (e.g., system memory or NVM / storage device 320) with instructions and one or more processors (e.g., (one or more) processors 305) coupled to the one or more computer-readable media and configured to execute instructions to implement modules to perform the actions described in this application.

[0078] For one embodiment, system control module 310 may include any suitable interface controller to provide any suitable interface to at least one of processor(s) 305 and / or any suitable device or component in communication with system control module 310 .

[0079] The system control module 310 may include a memory controller module 330 to provide an interface to the system memory 315. The memory controller module 330 may be a hardware module, a software module, and / or a firmware module.

[0080] The system memory 315 may be used, for example, to load and store data and / or instructions for the system 300. For one embodiment, the system memory 315 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the system memory 315 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0081] For one embodiment, system control module 310 may include one or more input / output (I / O) controllers to provide interfaces to NVM / storage device 320 and communication interface(s) 325 .

[0082] For example, NVM / storage device 320 may be used to store data and / or instructions. NVM / storage device 320 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drives (HDDs), one or more compact disk (CD) drives, and / or one or more digital versatile disk (DVD) drives).

[0083] NVM / storage device 320 may include storage resources that are physically part of the device on which system 300 is installed, or it may be accessible to the device without being part of the device. For example, NVM / storage device 320 may be accessed over a network via communication interface(s) 325.

[0084] Communication interface(s) 325 may provide an interface for system 300 to communicate over one or more networks and / or with any other suitable devices. System 300 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0085] For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 (e.g., the memory controller module 330). For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 to form a system-in-package (SiP). For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310. For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310 to form a system on chip (SoC).

[0086] In various embodiments, the system 300 may be, but is not limited to: a server, a workstation, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the system 300 may have more or fewer components and / or a different architecture. For example, in some embodiments, the system 300 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0087] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In one embodiment, the software program of the present application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.

[0088] In addition, a part of the present application may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes but is not limited to source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0089] Communication media include media by which communication signals containing, for example, computer readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media may include guided transmission media such as cables and wires (e.g., fiber optic, coaxial, etc.) and wireless (unguided transmission) media that can propagate energy waves, such as acoustic, electromagnetic, RF, microwave, and infrared. Computer readable instructions, data structures, program modules, or other data may be embodied as a modulated data signal in, for example, a wireless medium such as a carrier wave or similar mechanism such as embodied as part of spread spectrum technology. The term "modulated data signal" refers to a signal whose one or more characteristics are changed or set in such a manner as to encode information in the signal. Modulation may be analog, digital, or a hybrid modulation technique.

[0090] By way of example and not limitation, computer-readable storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memory, such as random access memory (RAM, DRAM, SRAM); and non-volatile memory, such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or later developed that can store computer-readable information / data for use with a computer system.

[0091] Here, according to one embodiment of the present application, a device is included, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the methods and / or technical solutions based on the aforementioned multiple embodiments of the present application.

[0092] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application.

Claims

1. A method for determining flotation control parameters by means of an analytical device, characterized in that: The analysis device is applied to a flotation column, and includes a first acquisition unit and a processor. The first acquisition unit is disposed on the top of a foam layer in the flotation column, and the first acquisition unit is electrically connected to the processor. Current image information of the foam layer is acquired by acquiring the first acquisition unit. The method includes: Inputting the current image information into an image recognition model, and outputting a current gray value corresponding to the current image information through the image recognition model; Determine a current ash content difference between the current ash content and the target ash content threshold value according to the current ash content and the target ash content threshold value; If the current gray content difference satisfies the parameter adjustment condition, one or more control parameters corresponding to the current gray content difference are obtained from the first database according to the current gray content difference, and the one or more control parameters are used as target control parameters corresponding to the current image information, wherein the parameter adjustment condition includes that the current gray content difference is not within the target difference interval; The analysis device further includes a second acquisition unit, the second acquisition unit includes a grating system and a liquid level meter, the grating system and the liquid level meter are electrically connected to the processor respectively, the current top height information of the foam layer is obtained through the grating system, and the current bottom height information of the foam layer is obtained through the liquid level meter, and the method further includes: Determine current thickness information of the foam layer according to the current top height information and the current bottom height information; If the target control parameter includes target thickness information of the foam layer, and the thickness difference between the target thickness information and the current thickness information is greater than a target thickness threshold, sending control instruction information to the flotation column so that the current thickness information matches the target thickness information; The grating system includes one or more grating components, each of which includes a grating transmitter vertically arranged on one side of the foam layer, and a grating receiver vertically arranged on the other side of the foam layer, the grating transmitter includes a plurality of light emitting modules arranged in an arrangement for emitting light signals, and the grating receiver includes a plurality of light receiving modules arranged in an arrangement for receiving light signals, and the current top height information of the foam layer is obtained through the grating system, including: Acquire at least one optical signal information through the optical receivers of the one or more grating components, wherein each optical signal information includes first identification information of the grating receiver that receives the optical signal information and second identification information of the optical receiving module that receives the optical signal information; For each optical signal information, query and obtain multiple second identification information having a mapping relationship with the first identification information from a second database according to the first identification information included in the optical signal information, wherein the second database includes one or more first identification information and multiple second identification information having a mapping relationship with each first identification information; For each optical signal information, query and obtain at least one candidate height information having a mapping relationship with the at least one second identification information from the candidate data information according to at least one second identification information contained in the optical signal information, and use the at least one candidate height information as the target height information corresponding to the optical signal information, wherein the second database also includes the candidate height information having a mapping relationship with each second identification information; The current top height information of the foam layer is determined according to one or more target height information corresponding to the one or more light signal information, wherein each target height information includes at least one candidate height information.

2. The method according to claim 1, characterized in that The first database includes a plurality of ash difference intervals and one or more control parameters corresponding to each ash difference interval, and the step of obtaining one or more control parameters corresponding to the current ash difference from the first database according to the current ash difference includes: According to the current ash content difference, a first ash content difference interval where the current ash content difference is located is queried from multiple ash content difference intervals in the first database, and one or more control parameters that have a mapping relationship with the first ash content difference interval are used as one or more control parameters corresponding to the current ash content difference.

3. The method according to claim 1, characterized in that The image recognition model is trained by the following method: According to multiple training image information and the target gray value corresponding to each training image information, a model is trained based on a deep learning algorithm to obtain the image recognition model.

4. The method according to claim 1, characterized in that: The determining the current top height information of the foam layer according to the one or more target height information corresponding to the one or more light signal information comprises: The smallest candidate height information among the one or more target height information is used as the current top height information of the foam layer.

5. The method according to claim 1, characterized in that The determining the current top height information of the foam layer according to the one or more target height information corresponding to the one or more light signal information comprises: For each target height information, determine the minimum candidate height information included in the target height information to obtain one or more minimum candidate height information; An average height of the one or more minimum candidate height information is calculated, and the average height is used as the current top height information of the foam layer.

6. The method according to claim 1, characterized in that The target control parameter corresponds to a target reaction time, and the method further includes: Inputting reaction image information into an image recognition model within the target reaction time, and outputting reaction ash corresponding to the reaction image information through the image recognition model, wherein the reaction image information is acquired by the first acquisition unit within the target reaction time; Determine a reaction ash difference between the reaction ash and the target ash threshold according to the reaction ash and the target ash threshold; If the reaction ash difference is greater than the current ash difference, one or more control parameters corresponding to the reaction ash difference are obtained from the first database according to the reaction ash difference, and the one or more control parameters are used as new target control parameters to control the flotation column based on the new target control parameters.

7. A computer device for determining flotation control parameters by means of an analytical device, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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