Flotation control method, device and equipment for dressing plant, medium and product
By using industrial cameras and image recognition models in the mineral processing plant to automatically adjust the dosage of reagents, the high cost and rough adjustment problems caused by manual operation are solved, and precise control and automation of the flotation process are achieved.
Patent Information
- Application Number
- CN202511180570.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-22
- Publication Date
- 2025-11-21
AI Technical Summary
In the existing mineral processing plant flotation process, the dosing system is operated manually, which results in high labor costs and rough adjustment of reagent dosage, making it difficult to achieve precise control.
Industrial cameras are used to acquire real-time images of the flotation cell, and a pre-trained image recognition model is used to identify the static and dynamic characteristics of the foam, thereby automatically adjusting the dosage of reagents.
It reduces personnel costs for monitoring and inspecting flotation cells, enables precise control of reagent dosage, and improves the automation and accuracy of the flotation process.
Smart Images
Figure CN120984446A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of flotation technology, and in particular to a flotation control method, apparatus, equipment, medium and product for a mineral processing plant. Background Technology
[0002] Flotation units in mineral processing plants are important equipment for separating minerals using physicochemical methods. Utilizing the differences in wettability of mineral surfaces in the slurry, air is aerated to generate bubbles. Target minerals (such as coal and metallic minerals) selectively adhere to these bubbles and float to the surface, forming a foam layer. Impurities, on the other hand, sink to the bottom and are discharged as tailings, thereby improving mineral recovery and grade.
[0003] Currently, most flotation plants still rely on manual operation of the reagent dosing system, requiring dedicated personnel to adjust the reagent dosage on-site. Each shift requires more than two workers to perform high-intensity work (walking an average of 20 kilometers per shift), resulting in high labor costs and relatively crude adjustments to reagent dosage. Summary of the Invention
[0004] In view of this, the purpose of this application is to provide a flotation control method, apparatus, equipment, medium and product for a mineral processing plant, which can at least acquire real-time images of the flotation cell through a camera, and control the dosage of reagents during the flotation process based on the identification and analysis of the real-time images of the flotation cell. This reduces the personnel cost of supervising and inspecting the flotation cell, and also enables more precise control of the dosage of reagents.
[0005] In a first aspect, embodiments of this application provide a flotation control method for a mineral processing plant, the method comprising: An industrial camera is used in flotation control equipment, which is positioned above the flotation cell to capture images of the flotation cell. The method includes: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0006] Optionally, the method includes: The blower frequency is determined based on the current state of the flotation cell.
[0007] Optionally, the static characteristic parameters of the foam include the number of bubbles, bubble size, bubble roundness, foam layer thickness, and foam texture parameters, and the dynamic characteristic parameters of the foam include foam flow rate and foam collapse rate. The determination of the current state of the flotation cell based on the static and dynamic characteristic parameters of the foam in the flotation cell includes: Determine whether the current mineral load is sufficient based on the bubble size; Based on the foam texture parameters, determine whether the current mineral's hydrophobicity is sufficient; Determine whether the foaming agent concentration is appropriate based on the foam flow rate; Determine the viscosity of the liquid based on the roundness of the bubbles; Based on the thickness of the foam layer, determine whether the foaming agent or collector is appropriate, and determine whether there is an excessive amount of fine mud or an excessively high slurry level.
[0008] Optionally, the image recognition model includes a static feature extraction module, a dynamic feature tracking module, and a multimodal fusion model; The static feature extraction module includes a marker watershed algorithm and an HSV color space multi-scale LBP texture analysis algorithm; the dynamic feature tracking module includes a bubble localization algorithm and a multi-target tracking algorithm; and the multimodal fusion model includes a static feature extraction branch, a dynamic temporal feature processing branch, and a feature weighting layer.
[0009] Optionally, the image recognition model is trained through the following steps: Acquire multiple sets of flotation cell training images, the foam dynamic feature parameters corresponding to each set of flotation cell training images, and the foam static feature parameters of each image in each set of images; Each set of flotation cell training images is input into the image recognition model to obtain the training foam dynamic feature parameters of the set of flotation cell training images and the training foam static feature parameters of each image in the set of images; Based on the training foam dynamic feature parameters of each group of flotation cell training images and the training foam static feature parameters of each image in each group, the loss function of the image recognition model is determined, and the image recognition model with the smallest loss function value is determined as the target image recognition model after training.
[0010] Optionally, the loss function value of the image recognition model is determined based on the training foam dynamic feature parameters of each group of flotation cell training images and the training foam static feature parameters of each image in each group of images, including: For each image, a static feature loss function is used to calculate the static loss function value for each image in each group of images; For each set of images, a dynamic feature loss function is used to calculate the dynamic loss function value for each set of images; The loss function value of the image recognition model is determined based on the static loss function value of each image in each image group and the dynamic loss function value of each image group.
[0011] Secondly, this application also provides a flotation control system for a mineral processing plant, which is applied to a flotation control device. The flotation control device includes an industrial camera, which is installed above the flotation cell and is used to collect images of the flotation cell. The device includes: An image acquisition module is used to control the industrial camera to acquire real-time images of the flotation cell at preset time intervals; The parameter determination module is used to determine the static and dynamic characteristic parameters of foam in the flotation cell based on a pre-trained image recognition model and multiple real-time images. The state determination module is used to determine the current state of the flotation cell based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosing control module is used to control the dosage of various reagents based on the current state of the flotation cell.
[0012] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0013] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0014] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0015] The flotation control method, apparatus, equipment, medium, and product for mineral processing plants provided in this application embodiment can at least acquire real-time images of the flotation cell through a camera, and control the dosage of reagents during the flotation process based on the identification and analysis of the real-time images of the flotation cell. This reduces the personnel cost of supervising and inspecting the flotation cell, and also enables more precise control of the dosage of reagents.
[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 A schematic flowchart of a flotation control method for a mineral processing plant provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a flotation control device for a mineral processing plant provided in an embodiment of the present invention; Figure 3 An internal structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0021] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of flotation technology.
[0022] Flotation units in mineral processing plants are important equipment for separating minerals using physicochemical methods. Utilizing the differences in wettability of mineral surfaces in the slurry, air is aerated to generate bubbles. Target minerals (such as coal and metallic minerals) selectively adhere to these bubbles and float to the surface, forming a foam layer. Impurities, on the other hand, sink to the bottom and are discharged as tailings, thereby improving mineral recovery and grade.
[0023] Currently, most flotation plants still rely on manual operation of the reagent dosing system, requiring dedicated personnel to adjust the reagent dosage on-site. Each shift requires more than two workers to perform high-intensity work (walking an average of 20 kilometers per shift), resulting in high labor costs and relatively crude adjustments to reagent dosage.
[0024] Based on this, the embodiments of this application provide a flotation control method, apparatus, equipment, medium and product for a mineral processing plant, which can at least acquire real-time images of the flotation cell through a camera, and control the dosage of reagents during the flotation process based on the identification and analysis of the real-time images of the flotation cell. This reduces the personnel cost of supervising and inspecting the flotation cell, and also enables more precise control of the dosage of reagents.
[0025] Please see Figure 1 , Figure 1 This is a schematic flowchart of a flotation control method for a mineral processing plant provided in an embodiment of this application.
[0026] It should be noted that the flotation control method for mineral processing plants proposed in this application is applied to flotation control equipment, which includes an industrial camera. The industrial camera is installed above the flotation cell and is used to collect images of the flotation cell.
[0027] like Figure 1 As shown in the embodiments of this application, the flotation control method for a mineral processing plant includes: S101. At preset intervals, control the industrial camera to acquire real-time images of the flotation cell.
[0028] Here, the preset time can be between 300 milliseconds and 500 milliseconds.
[0029] S102. Based on a pre-trained image recognition model, determine the static and dynamic characteristic parameters of the foam in the flotation cell according to multiple real-time images.
[0030] Specifically, the image recognition model includes a static feature extraction module, a dynamic feature tracking module, and a multimodal fusion model; The static feature extraction module includes a marker watershed algorithm and an HSV color space multi-scale LBP texture analysis algorithm; the dynamic feature tracking module includes a bubble localization algorithm and a multi-target tracking algorithm; and the multimodal fusion model includes a static feature extraction branch, a dynamic temporal feature processing branch, and a feature weighting layer.
[0031] For example, the watershed marker algorithm code framework (based on OpenCV) includes: using defwatershed_enhanced(img): KFCM clustering to obtain highlighted marker points, eliminating uneven illumination, and morphological reconstruction filtering to correct edges, return segmented_bubbles, etc.
[0032] For example, the bubble localization algorithm can use the YOLOv8 target detection framework (CSPDarknet53 backbone network) to locate the bubble in real time, and the multi-target tracking algorithm can use the DeepSORT-based multi-target tracking algorithm.
[0033] Specifically, the image recognition model can be trained through the following steps: acquiring multiple sets of flotation cell training images, the foam dynamic feature parameters corresponding to each set of flotation cell training images, and the foam static feature parameters of each image in each set of images; inputting each set of flotation cell training images into the image recognition model to obtain the training foam dynamic feature parameters of the set of flotation cell training images and the training foam static feature parameters of each image in the set of images; determining the loss function of the image recognition model based on the training foam dynamic feature parameters of each set of flotation cell training images and the training foam static feature parameters of each image in the set of images, and determining the image recognition model with the smallest loss function value as the trained target image recognition model.
[0034] Specifically, the loss function value of the image recognition model is determined based on the training foam dynamic feature parameters of each group of flotation cell training images and the training foam static feature parameters of each image in each group of images, including: For each image, a static feature loss function is used to calculate the static loss function value for each image in each group of images; For each set of images, a dynamic feature loss function is used to calculate the dynamic loss function value for each set of images; The loss function value of the image recognition model is determined based on the static loss function value of each image in each image group and the dynamic loss function value of each image group.
[0035] As an example, the static loss function value can be calculated using Huber loss to differentiate between the predicted and labeled static features, thereby improving the robustness of outliers.
[0036] As an example, the dynamic loss function value can be used to measure the similarity of temporal features using the DTW (Dynamic Time Warping) distance.
[0037] As an example, a weighted summation method can be used to determine the loss function value of an image recognition model.
[0038] S103. Determine the current state of the flotation cell based on the static and dynamic characteristic parameters of the foam in the flotation cell.
[0039] Specifically, the static characteristic parameters of the foam include the number of bubbles, bubble size, bubble roundness, foam layer thickness, and foam texture parameters, while the dynamic characteristic parameters of the foam include foam flow rate and foam collapse rate.
[0040] The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell, including: determining whether the current mineral loading is sufficient based on the bubble size; determining whether the current mineral hydrophobicity is sufficient based on the foam texture parameters; determining whether the frother concentration is appropriate based on the foam flow rate; determining the liquid viscosity based on the bubble roundness; determining whether the frother or collector is appropriate based on the foam layer thickness; and determining whether there is excessive fine mud or excessively high slurry level.
[0041] S104. Control the dosage of various reagents according to the current state of the flotation cell.
[0042] Optionally, the method includes: determining the blower frequency based on the current state of the flotation cell.
[0043] As an example, when the mineral load is insufficient, the blower frequency needs to be increased to increase the bubble density.
[0044] As an example, when the mineral is not sufficiently hydrophobic, the collector flow rate needs to be increased.
[0045] As an example, when a foaming agent or collector is not suitable, the dosage of the foaming agent or collector can be adjusted.
[0046] As an example, when there is an excessive amount of fine mud or an excessively high slurry level, it is necessary to reduce the amount of reagent and add a dispersant to enhance desliming.
[0047] In this way, this application acquires real-time images of the flotation cell through a camera, and controls the dosage of reagents during the flotation process based on the identification and analysis of the real-time images of the flotation cell. This reduces the personnel cost of supervising and inspecting the flotation cell, and also enables more precise control of the dosage of reagents.
[0048] Based on the same inventive concept, this application also provides a flotation control device for a mineral processing plant, which is part of a flotation control method for a mineral processing plant. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the flotation control device for a mineral processing plant provided below can be found in the limitations of the flotation control method for a mineral processing plant described above, and will not be repeated here.
[0049] Please refer to Figure 2 In one exemplary embodiment, a flotation control device for a mineral processing plant is provided, which is applied to a flotation control equipment. The flotation control equipment includes an industrial camera, which is disposed above the flotation cell and is used to collect images of the flotation cell.
[0050] The device includes: The image acquisition module 20 is used to control the industrial camera to acquire real-time images of the flotation cell at preset intervals; The parameter determination module 30 is used to determine the static characteristic parameters and dynamic characteristic parameters of the foam in the flotation cell based on a pre-trained image recognition model and multiple real-time images. The state determination module 40 is used to determine the current state of the flotation cell based on the static characteristic parameters and dynamic characteristic parameters of the foam in the flotation cell. The dosing control module 50 is used to control the dosage of various reagents according to the current state of the flotation cell.
[0051] The various modules in the flotation control device of the aforementioned mineral processing plant can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0052] In an exemplary embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a flotation control method for a mineral processing plant. The display unit of the computer device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0053] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0054] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0055] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0056] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
[0057] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0058] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0059] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
[0060] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0061] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0063] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A flotation control method for a mineral processing plant, characterized in that, An industrial camera is used in flotation control equipment, which is positioned above the flotation cell to capture images of the flotation cell. The method includes: At preset intervals, the industrial camera is controlled to acquire real-time images of the flotation cell; Based on a pre-trained image recognition model, the static and dynamic characteristic parameters of the foam in the flotation cell are determined according to multiple real-time images. The current state of the flotation cell is determined based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosage of various reagents is controlled according to the current state of the flotation cell.
2. The method according to claim 1, characterized in that, The method includes: The blower frequency is determined based on the current state of the flotation cell.
3. The method according to claim 2, characterized in that, The static characteristic parameters of the foam include the number of bubbles, bubble size, bubble roundness, foam layer thickness, and foam texture parameters; the dynamic characteristic parameters of the foam include foam flow rate and foam collapse rate. The determination of the current state of the flotation cell based on the static and dynamic characteristic parameters of the foam in the flotation cell includes: Determine whether the current mineral load is sufficient based on the bubble size; Based on the foam texture parameters, determine whether the current mineral's hydrophobicity is sufficient; Determine whether the foaming agent concentration is appropriate based on the foam flow rate; Determine the viscosity of the liquid based on the roundness of the bubbles; Based on the thickness of the foam layer, determine whether the foaming agent or collector is appropriate, and determine whether there is an excessive amount of fine mud or an excessively high slurry level.
4. The method according to claim 3, characterized in that, The image recognition model includes a static feature extraction module, a dynamic feature tracking module, and a multimodal fusion model; The static feature extraction module includes a marker watershed algorithm and an HSV color space multi-scale LBP texture analysis algorithm; the dynamic feature tracking module includes a bubble localization algorithm and a multi-target tracking algorithm; and the multimodal fusion model includes a static feature extraction branch, a dynamic temporal feature processing branch, and a feature weighting layer.
5. The method according to claim 4, characterized in that, The image recognition model is trained using the following steps: Acquire multiple sets of flotation cell training images, the foam dynamic feature parameters corresponding to each set of flotation cell training images, and the foam static feature parameters of each image in each set of images; Each set of flotation cell training images is input into the image recognition model to obtain the training foam dynamic feature parameters of the set of flotation cell training images and the training foam static feature parameters of each image in the set of images; Based on the training foam dynamic feature parameters of each group of flotation cell training images and the training foam static feature parameters of each image in each group, the loss function of the image recognition model is determined, and the image recognition model with the smallest loss function value is determined as the target image recognition model after training.
6. The method according to claim 5, characterized in that, Based on the training foam dynamic feature parameters of each set of flotation cell training images and the training foam static feature parameters of each image in each set, the loss function value of the image recognition model is determined, including: For each image, a static feature loss function is used to calculate the static loss function value for each image in each group of images; For each set of images, a dynamic feature loss function is used to calculate the dynamic loss function value for each set of images; The loss function value of the image recognition model is determined based on the static loss function value of each image in each image group and the dynamic loss function value of each image group.
7. A flotation control device for a mineral processing plant, characterized in that, An industrial camera is used in flotation control equipment, which is positioned above the flotation cell to capture images of the flotation cell. The device includes: An image acquisition module is used to control the industrial camera to acquire real-time images of the flotation cell at preset time intervals; The parameter determination module is used to determine the static and dynamic characteristic parameters of foam in the flotation cell based on a pre-trained image recognition model and multiple real-time images. The state determination module is used to determine the current state of the flotation cell based on the static and dynamic characteristic parameters of the foam in the flotation cell. The dosing control module is used to control the dosage of various reagents based on the current state of the flotation cell.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.