Flotation dosing method, device and equipment for dressing plant, medium and product

Abstract

The invention provides a flotation chemical adding method, device and equipment of a dressing plant, a medium and a product. The flotation chemical adding method comprises the steps that currently input mineral product information is obtained; according to the mineral information, a dosing control data set corresponding to the mineral information is determined from a dosing database, the dosing database comprises multiple dosing control data sets, and the multiple dosing control data sets are determined by a pre-trained machine learning model; and according to the dosing control data set, multiple dosing agents and the dosing quantity of each agent are controlled. According to the method, the dosing database can be determined at least through the pre-trained machine learning model, flotation dosing control is conducted on the mineral products of the mineral product types and the mineral product sources through the dosing database, and the effects of accurately controlling the dosing dosage, improving the flotation dosing precision and improving the target mineral product recovery rate are achieved.

Description

Technical Field

[0001] This application relates to the field of flotation technology, and in particular to a flotation reagent dosing 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. This makes it impossible to achieve timely and accurate reagent dosage adjustments, resulting in poor mineral processing efficiency. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a flotation reagent dosing method, apparatus, equipment, medium and product for a mineral processing plant, which can at least determine the reagent database through a pre-trained machine learning model, and control the flotation reagent dosing of minerals of different mineral types and sources through the reagent database, so as to achieve accurate control of reagent dosage, improve the accuracy of flotation reagent dosing and improve the recovery rate of target minerals.

[0005] In a first aspect, embodiments of this application provide a flotation reagent dosing method for a mineral processing plant, the method comprising: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0006] Optionally, the machine learning model includes multiple sub-machine learning models, each of which is used to determine a set of dosing control data for a mineral source and mineral type.

[0007] Optionally, each sub-machine learning model is trained through the following steps: Acquire multiple sets of training data, each set including the weight of the target mineral obtained from the same mineral source and the same mineral type in multiple different dosing control processes; Each set of training data is input into the sub-machine learning model to obtain the training drug control process corresponding to that set of training data; Based on the training dosing control process corresponding to each set of training data and the weight of the target mineral obtained in various different dosing control processes, the loss function of the sub-machine learning model is determined, and the sub-machine learning model with the smallest loss function value is determined as the trained sub-machine learning model.

[0008] Optionally, each set of dosing control data corresponds to a desired target mineral yield; The method further includes: Calculate the ratio between the actual target mineral production and the expected target mineral production based on the actual target mineral production and the expected target mineral production. Determine whether the ratio is less than a first preset ratio; If the ratio is less than the first preset ratio, the dosing control data set is updated based on the actual dosing control data set and the actual production of the target mineral, as determined by the pre-trained machine learning model.

[0009] Optionally, the method further includes: Determine whether the ratio is greater than a second preset ratio; If the ratio is greater than the second preset ratio, the dosing control data set is updated based on the actual dosing control data set and the actual production of the target mineral, as determined by the pre-trained machine learning model.

[0010] Optionally, the dosing control data set may also include the pulp volume corresponding to multiple flotation times; The method further includes: Obtain the actual pulp volume for each flotation time. Determine whether the absolute value of the difference between the actual slurry volume at each flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than a preset threshold. For each flotation time, if the absolute value of the difference between the actual slurry volume at that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than a preset threshold, then the slurry volume ratio between the actual slurry volume at that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is calculated. The emergency dosing rate is calculated based on the dosing rate corresponding to the flotation time in the dosing control data set and the ratio of the actual slurry volume at the flotation time to the slurry volume corresponding to the flotation time in the dosing control data set. The dosing pump is controlled to administer the drug according to the emergency dosing rate.

[0011] Secondly, this application also provides a flotation control system for a mineral processing plant, the apparatus comprising: The mineral information acquisition module is used to acquire the currently input mineral information; The dosing control data determination module is used to determine, based on the mineral information, a dosing control data group corresponding to the mineral information from a dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. The dosing control module is used to control the dosage of various dosing agents and the dosage of each agent according to the dosing control data set; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[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: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[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: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0014] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0015] The flotation reagent dosing method, apparatus, equipment, media, and products for mineral processing plants provided in this application embodiment can at least determine the reagent dosing database through a pre-trained machine learning model, and control the flotation reagent dosing of minerals of different types and sources through the reagent dosing database, thereby achieving accurate control of reagent dosage, improving the accuracy of flotation reagent dosing, and increasing the recovery rate of target minerals.

[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 reagent addition method in a mineral processing plant provided by 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. This makes it impossible to achieve timely and accurate reagent dosage adjustments, resulting in poor mineral processing efficiency.

[0024] Based on this, the embodiments of this application provide a flotation reagent dosing method, apparatus, equipment, medium and product for a mineral processing plant. It can at least determine the reagent dosing database through a pre-trained machine learning model, and control the flotation reagent dosing of minerals of different types and sources through the reagent dosing database, so as to achieve accurate control of reagent dosage, improve the accuracy of flotation reagent dosing, and improve the recovery rate of target minerals.

[0025] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a flotation reagent dosing method in a mineral processing plant, provided as an embodiment of this application. Figure 1 As shown in the embodiments of this application, the flotation reagent addition method for a mineral processing plant includes: S101. Obtain the currently input mineral information.

[0026] Here, mineral information includes mineral type and mineral source.

[0027] S102. Based on the mineral information, determine the dosing control data group corresponding to the mineral information from the dosing database.

[0028] The dosing database includes multiple sets of dosing control data, which are determined by a pre-trained machine learning model.

[0029] The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent.

[0030] Specifically, the machine learning model includes multiple sub-machine learning models, each of which is used to determine a set of dosing control data for a mineral source and mineral type.

[0031] Each sub-machine learning model is trained through the following steps: Acquire multiple sets of training data, each set including the weight of the target mineral obtained from the same mineral source and the same mineral type in multiple different dosing control processes; Each set of training data is input into the sub-machine learning model to obtain the training drug control process corresponding to that set of training data; Based on the training dosing control process corresponding to each set of training data and the weight of the target mineral obtained in various different dosing control processes, the loss function of the sub-machine learning model is determined, and the sub-machine learning model with the smallest loss function value is determined as the trained sub-machine learning model.

[0032] As an example, the sub-machine learning model can employ a Long Short-Term Memory (LSTM) network or a Gated Recurrent Unit (GRU).

[0033] S103. Based on the dosing control data set, control the dosage of various dosing agents and the dosage of each agent.

[0034] Specifically, each set of dosing control data corresponds to a target mineral yield.

[0035] The method further includes: calculating the ratio between the actual target mineral yield and the expected target mineral yield based on the actual target mineral yield and the expected target mineral yield; determining whether the ratio is less than a first preset ratio; if the ratio is less than the first preset ratio, updating the dosing control data set based on the actual dosing control data set and the actual target mineral yield, determined by a pre-trained machine learning model.

[0036] The method further includes: determining whether the ratio is greater than a second preset ratio; if the ratio is greater than the second preset ratio, then updating the dosing control data set based on the actual dosing control data set and the actual production of the target mineral, as determined by a pre-trained machine learning model.

[0037] Specifically, the dosing control data set also includes the pulp volume corresponding to multiple flotation times.

[0038] The method further includes: obtaining the actual slurry volume for each flotation time; determining whether the absolute value of the difference between the actual slurry volume for each flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than a preset threshold; for each flotation time, if the absolute value of the difference between the actual slurry volume for that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than the preset threshold, then calculating the slurry volume ratio between the actual slurry volume for that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set; calculating an emergency dosing rate based on the dosing rate corresponding to that flotation time in the dosing control data set and the slurry volume ratio between the actual slurry volume for that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set; and controlling the dosing pump to add reagents based on the emergency dosing rate.

[0039] This application can determine the reagent database through a pre-trained machine learning model, and control the flotation reagent dosage of minerals of different mineral types and sources through the reagent database, thereby achieving accurate control of reagent dosage, improving the accuracy of flotation reagent dosing, and increasing the recovery rate of target minerals.

[0040] Based on the same inventive concept, this application also provides a flotation control device for a mineral processing plant's flotation reagent addition method. 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 reagent addition method for a mineral processing plant described above, and will not be repeated here.

[0041] Please refer to Figure 2 In one exemplary embodiment, a flotation control device for a mineral processing plant is provided, the device comprising: The mineral information acquisition module 20 is used to acquire the currently input mineral information; The dosing control data determination module 30 is used to determine the dosing control data group corresponding to the mineral information from the dosing database based on the mineral information. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. The dosing control module 40 is used to control the dosing agents and the dosage of each agent according to the dosing control data set; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0042] 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.

[0043] 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 reagent dosing method in 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.

[0044] 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.

[0045] 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: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0046] 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: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0047] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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 reagent addition method for a mineral processing plant, characterized in that, The method includes: Retrieve the currently input mineral information; Based on the mineral information, a dosing control data group corresponding to the mineral information is determined from the dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. Based on the dosing control data set, control the dosing of various dosing agents and the dosage of each agent; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

2. The method according to claim 1, characterized in that, The machine learning model includes multiple sub-machine learning models, each of which is used to determine a set of dosing control data for a mineral source and mineral type.

3. The method according to claim 2, characterized in that, Each sub-machine learning model is trained using the following steps: Acquire multiple sets of training data, each set including the weight of the target mineral obtained from the same mineral source and the same mineral type in multiple different dosing control processes; Each set of training data is input into the sub-machine learning model to obtain the training drug control process corresponding to that set of training data; Based on the training dosing control process corresponding to each set of training data and the weight of the target mineral obtained in various different dosing control processes, the loss function of the sub-machine learning model is determined, and the sub-machine learning model with the smallest loss function value is determined as the trained sub-machine learning model.

4. The method according to claim 3, characterized in that, Each set of dosing control data corresponds to a target mineral yield; The method further includes: Calculate the ratio between the actual target mineral production and the expected target mineral production based on the actual target mineral production and the expected target mineral production. Determine whether the ratio is less than a first preset ratio; If the ratio is less than the first preset ratio, the dosing control data set is updated based on the actual dosing control data set and the actual production of the target mineral, as determined by the pre-trained machine learning model.

5. The method according to claim 4, characterized in that, The method further includes: Determine whether the ratio is greater than a second preset ratio; If the ratio is greater than the second preset ratio, the dosing control data set is updated based on the actual dosing control data set and the actual production of the target mineral, as determined by the pre-trained machine learning model.

6. The method according to claim 5, characterized in that, The dosing control data set also includes the pulp volume corresponding to multiple flotation times; The method further includes: Obtain the actual pulp volume for each flotation time. Determine whether the absolute value of the difference between the actual slurry volume at each flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than a preset threshold. For each flotation time, if the absolute value of the difference between the actual slurry volume at that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is greater than a preset threshold, then the slurry volume ratio between the actual slurry volume at that flotation time and the slurry volume corresponding to that flotation time in the dosing control data set is calculated. The emergency dosing rate is calculated based on the dosing rate corresponding to the flotation time in the dosing control data set and the ratio of the actual slurry volume at the flotation time to the slurry volume corresponding to the flotation time in the dosing control data set. The dosing pump is controlled to administer the drug according to the emergency dosing rate.

7. A flotation control device for a mineral processing plant, characterized in that, The device includes: The mineral information acquisition module is used to acquire the currently input mineral information; The dosing control data determination module is used to determine, based on the mineral information, a dosing control data group corresponding to the mineral information from a dosing database. The dosing database includes multiple dosing control data groups, which are determined by a pre-trained machine learning model. The dosing control module is used to control the dosage of various dosing agents and the dosage of each agent according to the dosing control data set; The dosing control data set includes multiple dosing reagents corresponding to multiple flotation times and the dosing quantity of each reagent; the mineral information includes mineral type and mineral source.

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.

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