Ore separation system control method and device and ore separation system

Abstract

The invention provides an ore separation system control method and device and an ore separation system, and relates to the technical field of ore separation. The ore separation system comprises a porous intelligent ore separator, an ore separation box, a plurality of elutriation magnetic separators and a plurality of tailing AI state detection systems; an ore feeding control valve is arranged between each elutriation magnetic separator and the intelligent ore separator; the method comprises the steps that overflow tailing state variables, collected by a tailing AI state detection system, of the elutriation magnetic separator are obtained; the underflow concentration and the bottom valve opening degree corresponding to each elutriation magnetic separator are obtained; and the ore feeding amount of each elutriation magnetic separator is calculated and determined through a mathematical model according to the overflow tailing state variable, the underflow concentration and the bottom valve opening corresponding to each elutriation magnetic separator, and the opening of the ore feeding control valve and / or the online number of the elutriation magnetic separators are / is adjusted according to the ore feeding amount of each elutriation magnetic separator. Intelligent ore separation and accurate control over the ore feeding amount can be achieved, and the concentrate grade and the recovery rate of the elutriation magnetic separator are improved.

Description

Technical Field

[0001] This invention relates to the field of mineral sorting technology, and in particular to a mineral sorting system control method, device and mineral sorting system. Background Technology

[0002] To improve concentrate grade, iron ore beneficiation plants extensively utilize washing magnetic separators as quality control equipment in their production processes. As beneficiation plants expand, multiple washing magnetic separators often need to operate simultaneously on a single production line. Problems such as coupling interference from multiple washing magnetic separators sharing a water source and uneven ore separation are unavoidable. Therefore, ensuring stable operation of washing magnetic separators while achieving better separation performance is receiving increasing attention.

[0003] Traditional mineral separators, due to their rudimentary structure, difficult operation, and lack of specific design, cannot meet the fine feeding requirements of washing magnetic separators. Generally, feeding and stopping the washing magnetic separator are controlled manually on-site or remotely. During mineral grinding and beneficiation, the high density of heavy metal ores, the rapid settling of coarse-grained minerals, and the large fluctuations in ore quantity, coupled with varying slurry transport distances, result in significant differences in the particle size, quantity, and grade of the feed allocated to each washing magnetic separator. These irregular fluctuations severely deteriorate the feeding conditions of the washing magnetic separator, necessitating frequent adjustments to operating parameters. Operators cannot operate each machine 24 hours a day. Improper operation not only affects beneficiation indicators but can also lead to production accidents such as material blockage and ore runoff. Therefore, effectively controlling the mineral separator and the separation process is a key issue in the application of washing magnetic separators. Summary of the Invention

[0004] This invention provides a method, apparatus, and system for controlling a mineral separation system, in order to address how to improve the efficiency of mineral separation process control and solve the problem of uniform feeding of the washing magnetic separator, thereby improving the grade of concentrate.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling a mineral sorting system, the mineral sorting system comprising a multi-hole intelligent mineral sorter, multiple washing magnetic separators, and multiple tailings AI status detection systems; each washing magnetic separator is connected to the intelligent mineral sorter via a feed pipeline and a feed control valve; the method includes: Acquire the overflow tailings status variables of the washing magnetic separator collected by the tailings AI status detection system; Obtain the underflow concentration and bottom valve opening for each washing magnetic separator; The feed rate of each washing magnetic separator is determined by mathematical modeling based on the overflow tailings state variables, underflow concentration, and bottom valve opening of each washing magnetic separator. The opening of the feed control valve and / or the number of online washing magnetic separators are then adjusted according to the feed rate of each washing magnetic separator.

[0006] In one possible implementation, the overflow tailings state variables include one or more of the following: overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology.

[0007] In one possible implementation, adjusting the opening of the feed control valve and / or the number of online washing magnetic separators based on the feed rate of each washing magnetic separator includes: When the feed rate of a washing magnetic separator is greater than or less than the set feed rate, the target adjustment amount of the corresponding feed control valve is determined according to the feed rate of the washing magnetic separator, and the opening of the feed control valve is adjusted according to the target adjustment amount. In one possible implementation, the step of determining the target washing magnetic separator and the opening degree of the corresponding target feed control valve based on the feed rates of multiple washing magnetic separators when the feed rates of multiple washing magnetic separators are greater than or less than the set feed rate includes: When the feed rate of multiple washing magnetic separators is less than the set feed rate, the washing magnetic separator that is online and has the minimum feed rate is identified as the target washing magnetic separator. The target feed control valve corresponding to the target washing magnetic separator is closed, and the target washing magnetic separator is automatically taken offline. When the feed rate of multiple washing magnetic separators exceeds the set feed rate, the washing magnetic separator that is currently in the off state is identified as the target washing magnetic separator. The target washing magnetic separator is then put into operation, and the corresponding target feed control valve of the target washing magnetic separator is opened and its opening degree is adjusted.

[0008] In one possible implementation, before the control valve for the target feed of the target washing magnetic separator is opened and its opening degree is adjusted, the method further includes: The difference between the feed rate of multiple washing magnetic separators and the set feed rate is determined, the sum of the feed rate differences is calculated, and the target feed control valve opening is determined based on the preset opening calculation algorithm and the sum of the feed rate differences.

[0009] In one possible implementation, after adjusting the opening of the feed control valve and / or the number of online washing magnetic separators according to the feed rate of each washing magnetic separator, the method further includes: Monitor the liquid level of the material in the ore distribution box; Based on the set upper and lower limits of the liquid level, the opening degree of the feed control valve of the washing magnetic separator and the number of online washing magnetic separators are controlled to keep the material liquid level within the set liquid level range.

[0010] In one possible implementation, the material level in the ore distribution box is determined based on a level gauge; The method further includes: The feed pump frequency is determined based on the material level in the ore distribution box and the set level range.

[0011] In one possible implementation, each feed control valve of the multi-hole intelligent ore separator is equipped with an anti-clogging flushing device; the method further includes: Before the feed control valve is opened, an anti-clogging flushing start command is generated to control the anti-clogging flushing device to flush the corresponding feed control valve.

[0012] In one possible implementation, the method further includes: Monitor the feed rate and feed control valve opening of each washing magnetic separator; wherein, the feed rate is the amount of liquid material input into the washing magnetic separator by the multi-hole intelligent ore separator through the feed control valve; When the feed rate and the opening of the feed control valve of a certain washing magnetic separator do not match, an anti-blocking flushing start command is generated to control the anti-blocking flushing device to flush the corresponding feed control valve.

[0013] Secondly, embodiments of the present invention provide a mining system control device, comprising: The overflow status acquisition module is used to acquire the overflow tailings status variables of the washing magnetic separator collected by the tailings AI status detection system. The washing magnetic separator parameter acquisition module is used to acquire the underflow concentration and bottom valve opening of each washing magnetic separator. The control module is used to calculate and determine the feed rate of each washing magnetic separator based on the overflow tailings state variables, underflow concentration and bottom valve opening of each washing magnetic separator through a mathematical model, and to adjust the opening of the feed control valve and / or the number of online washing magnetic separators according to the feed rate of each washing magnetic separator.

[0014] Thirdly, embodiments of the present invention provide a mineral sorting system, including a multi-hole intelligent mineral sorter, multiple washing magnetic separators, multiple tailings AI status detection systems, and the mineral sorting system control device described in the second aspect; The tailings AI status detection system includes one or more of the following: tailings concentration transmitter, particle size transmitter, tailings grade transmitter, pressure transmitter, and tailings AI image detection and recognition system. The tailings AI image detection and recognition system includes: a sampling and detection device, a supercomputer, an intelligent management server, and an algorithm model; the tailings AI image detection and recognition system updates the sampling time according to a set time; and updates the algorithm model according to the operating parameters of the ore sorting system and the washing magnetic separator.

[0015] In this embodiment of the invention, the ore sorting system utilizes multiple washing magnetic separators and tailings AI status detection systems within a multi-hole intelligent ore sorter. The number of washing magnetic separators does not exceed the number of ore sorting outlets of the intelligent ore sorter. Each washing magnetic separator is connected to the intelligent ore sorter's feed control valve via a feed pipeline, thus constructing a precise and controllable ore sorting infrastructure. This method first uses the tailings AI status detection system to collect overflow tailings status variables, then combines this with the operating parameters of the washing magnetic separators to calculate and determine the feed rate using a mathematical model. Finally, it adjusts the opening of the feed control valve or the number of online washing magnetic separators accordingly.

[0016] The beneficial effects of this invention are as follows: By acquiring the overflow tailings state variables collected by the tailings AI state detection system from the washing magnetic separator, including one or more material parameters such as overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology, these parameters can reflect the separation effect of the washing magnetic separator. Based on the overflow tailings state variables, underflow concentration, and bottom valve opening of each washing magnetic separator, the feed rate of each washing magnetic separator is calculated using big data technology through a mathematical model, solving the problem of inaccurate feed rate measurement in existing technologies. Furthermore, the opening of the feed control valve of the separator and / or the number of online washing magnetic separators are adjusted according to the feed rate of each washing magnetic separator, achieving the goal of closed-loop control of the feed rate according to the optimal processing capacity of the washing magnetic separator. This achieves precise control of the separation process, thereby comprehensively improving mineral separation efficiency, concentrate grade, and product recovery rate. The washing magnetic separator and separation system operate automatically without manual intervention. Regardless of whether the actual ore feed increases or decreases, the system automatically adjusts the number of online washing magnetic separators, which can save energy and water consumption. Attached Figure Description

[0017] Figure 1 This is an application scenario diagram of the mining system control method provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation of a mining system control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a mining system control device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that the washing magnetic separator in this invention is one type of mineral processing equipment. The solution provided in this embodiment is also applicable to other mineral processing equipment with overflow systems, such as dewatering tanks, desliming tanks, magnetic separation columns, flotation machines, flotation columns, and magnetic flotation columns. In practical applications, the overflow product can be the target product or tailings. All the above-mentioned equipment and applications fall within the scope of protection of this patent.

[0019] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] Figure 1 This is an application scenario diagram of the mining system control method provided in an embodiment of the present invention. For example... Figure 1 As shown, the ore sorting system includes a multi-hole intelligent ore sorter, multiple washing magnetic separators, multiple tailings AI status detection systems, multiple PLC control cabinets, and an intelligent ore sorter control system. Figure 1 The solid black lines represent physical structural connections, while the dashed red lines represent communication connections.

[0021] In one possible implementation, the tailings AI status detection system includes one or more of a tailings concentration transmitter, a particle size transmitter, a tailings grade transmitter, and a tailings AI image detection and recognition system. The tailings AI image detection and recognition system includes a sampling and detection device, a supercomputer, an intelligent management server, and an algorithm model. The tailings AI image detection and recognition system updates the algorithm model according to a set update time based on the operating parameters of the tailings system. The algorithm model is used to output data such as overflow concentration, particle size, grade, and flow rate.

[0022] Each washing magnetic separator is connected to the intelligent ore separator via a feed control valve. A tailings AI status detection system and a PLC control cabinet are installed for each washing magnetic separator. Therefore, the number of tailings AI status detection systems and PLC control cabinets are the same as the number of washing magnetic separators. The tailings AI status detection systems and PLC control cabinets respectively acquire tailings images and control the operating status of their respective washing magnetic separators. The PLC control cabinets are responsible for controlling the operating parameters of the washing magnetic separators based on data from the tailings AI status detection systems. They also forward data from the tailings AI image detection and recognition system to the intelligent ore separator control system, which then controls the multi-hole intelligent ore separator, specifically the feed control valves and liquid level detection.

[0023] Figure 1 The following example uses four washing magnetic separators. In other possible implementations, more than four washing magnetic separators can be installed to meet the needs of the mining site.

[0024] Multiple washing and magnetic separators are connected to the same multi-hole intelligent ore separator, such as... Figure 1 As shown, the multi-hole intelligent ore separator is a 4-hole intelligent ore separator. In actual implementation, the number of ore separation outlets of the washing magnetic separator is less than or equal to the number of outlets of the intelligent ore separator. This ensures that each washing magnetic separator can be connected to one ore separation outlet, while also reserving interfaces for future additions of washing magnetic separators.

[0025] Optionally, the number of outlets for the multi-hole intelligent ore separator can be 5-hole or 6-hole, etc. By increasing the number of outlets of a single ore separator, more washing magnetic separators can be accommodated, reducing the overall number of ore separators, making the system structure more compact, reducing the space occupied by equipment installation and the complexity of pipeline connections between multiple ore separators, and allowing maintenance of a single device to cover all ore separator outlets, simplifying the maintenance process.

[0026] Optionally, the multi-hole intelligent ore separator is a 4-hole intelligent ore separator. When the number of washing magnetic separators is greater than 4, multiple multi-hole intelligent ore separators are set up. Adopting a "multiple standard 4-hole ore separators in parallel" mode offers greater flexibility, allowing the number of separators to be increased or decreased according to the actual number of washing magnetic separators, avoiding the problem of idle outlets on a single multi-outlet separator. A single 4-hole separator handles a more balanced load, ensuring greater uniformity in ore separation. Furthermore, a failure in a single separator only affects a small number of associated washing magnetic separators, resulting in higher overall system reliability.

[0027] In the specific implementation process, with Figure 1 The following scenario is used as an example: To achieve uniform feeding of ore to 4 washing magnetic separators; to judge the ore quantity fluctuation by monitoring the overflow tailings status of the washing magnetic separators; to automatically adjust the number of washing magnetic separators working online; to automatically start and stop the washing magnetic separators, including automatically switching on and off the intelligent plunger valve (i.e., ore feeding control valve) of the ore separator separator and the water supply valve of the washing magnetic separator.

[0028] When the AI ​​image detection and recognition system includes the AI ​​image detection and recognition system, the intelligent ore separator control system is used to collect tailings images and equipment operating parameters collected by the tailings AI image detection and recognition system in each AI image detection and recognition system, and coordinate and determine the opening degree of the feed control valve and the adjustment scheme of the washing magnetic separator based on the tailings images and equipment operating parameters.

[0029] In actual implementation, the mining system, in addition to Figure 1 In addition to the components shown, the system also includes equipment such as a ore distribution box, a feed pump for the washing magnetic separator, and a ore separator for the filter. The solution provided in this application aims to achieve remote, interlocked control of equipment such as the feed pump for the washing magnetic separator, the liquid level in the ore distribution box, the ore separator for the washing magnetic separator, and the ore separator for the filter through an intelligent ore distribution control system. The ore distribution system, combined with the washing magnetic separator system and the tailings AI status detection system, ensures long-term, balanced, efficient, and low-consumption operation of the system, guaranteeing concentrate quality and aligning with environmentally friendly, energy-saving, and high-efficiency principles.

[0030] The above is an introduction to the application scenarios of the mining system control method provided in the embodiments of this application. The mining system control method will be described below with reference to the accompanying drawings.

[0031] Figure 2 This is a flowchart illustrating the implementation of a mining system control method according to an embodiment of the present invention, as shown below. Figure 2 As shown, it includes the following steps: S201, Obtain the overflow tailings state variables of the washing magnetic separator collected by the tailings AI state detection system.

[0032] The execution entity in the various embodiments of this application can be a server, processor, microprocessor, or other device with data processing capabilities. In actual implementation, the specific implementation method of the execution entity can be selected according to actual needs. This embodiment does not impose any particular limitation on this; any device with data processing capabilities is acceptable. For ease of understanding, the embodiments of this application use... Figure 1 The intelligent ore sorting control system will be used as an example for explanation.

[0033] In the specific implementation process, the state variables of overflow tailings include one or more of the material parameters such as overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology. These parameters can reflect the separation effect of the washing magnetic separator.

[0034] In the specific implementation process, the intelligent ore separator control system sends start commands to the control systems (i.e., PLC control cabinets) of each washing magnetic separator. The tailings AI status detection system monitors the tailings of the washing magnetic separator in real time and outputs data such as overflow concentration, particle size, grade, and flow rate to the washing magnetic separator control system. The washing magnetic separator control system then uploads this data to the intelligent ore separator control system in real time via the industrial communication network, or the tailings AI status detection system uploads it directly to the intelligent ore separator control system. After receiving the images collected by the tailings AI image detection and recognition system, the intelligent ore separator control system calls the built-in image analysis program to process the images. By identifying the distribution density and other characteristics of the tailings particles in the image, it determines the overflow tailings status variables of the corresponding washing magnetic separator and stores this status data in the local database after associating it with the corresponding washing magnetic separator number.

[0035] S202, obtain the underflow concentration and bottom valve opening corresponding to each washing magnetic separator.

[0036] The intelligent ore separator control system communicates with the corresponding PLC control cabinets of each washing magnetic separator to obtain the data (i.e., underflow concentration) detected by the underflow outlet concentration sensor collected by the PLC control cabinet, as well as the bottom valve opening signal fed back by the bottom valve actuator.

[0037] S203, determine the feed rate of each washing magnetic separator based on the overflow tailings state variables, underflow concentration and bottom valve opening of each washing magnetic separator, and adjust the opening of the feed control valve and / or the number of online washing magnetic separators based on the feed rate of each washing magnetic separator.

[0038] The controller's intelligent ore separator control system has a pre-set feed rate calculation model. The model takes three sets of parameters as input: the overflow tailings state variable corresponding to each washing magnetic separator, the underflow concentration uploaded by the PLC control cabinet, and the bottom valve opening. The model calculates the optimal feed rate required for each washing magnetic separator under the current operating conditions by analyzing the relationship between the three parameters. This optimal feed rate must match the rated processing capacity of the washing magnetic separator.

[0039] In practical implementation, corresponding to the overflow tailings state variables (including overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology), feed rate calculation models are established based on the underflow concentration and bottom valve opening. This facilitates the determination of the feed rate for the washing magnetic separator when the overflow tailings state variables are obtained, by combining the corresponding feed rate calculation models. When multiple overflow tailings state variables are collected, the average of the calculated feed rates is used as the optimal feed rate for the corresponding washing magnetic separator.

[0040] The intelligent ore separator control system generates adjustment commands based on the calculated optimal feed rate of each washing magnetic separator and the actual opening degree and corresponding feed rate data of the current feed control valve uploaded by each PLC control cabinet.

[0041] In the specific implementation process, if the deviation between the current feed rate and the optimal feed rate of all washing magnetic separators is within the allowable range, and the number of online washing magnetic separators can meet the processing needs, the intelligent ore separator control system sends an opening fine-tuning command to the corresponding PLC control cabinet, and the PLC control cabinet controls the feed control valve to complete the fine-tuning.

[0042] If the overall feed rate of the existing washing magnetic separators has a large deviation, and there are washing magnetic separators that are not in operation, the intelligent ore separator control system will simultaneously generate feed control valve opening adjustment commands and washing magnetic separator start / stop commands. Through the corresponding PLC control cabinet, the system will control the opening of the feed control valve and the online status of the idle washing magnetic separators to achieve precise matching of the feed rate.

[0043] In this embodiment, the ore separation system utilizes multiple washing magnetic separators and multiple tailings AI status detection systems within a multi-hole intelligent ore separator. The number of washing magnetic separators is kept within the number of ore separation outlets of the intelligent ore separator. Feed control valves are configured between each washing magnetic separator and the intelligent ore separator, thus constructing a precise and controllable ore separation infrastructure. This method first uses the tailings AI status detection system to collect overflow tailings status information, then combines this with the operating parameters of the washing magnetic separators to determine the feed rate, and finally adjusts the opening of the feed control valves or the number of online washing magnetic separators accordingly. By acquiring overflow tailings status variables from the washing magnetic separators collected by the tailings AI status detection system, including one or more material parameters such as overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology, these parameters can reflect the separation effect of the washing magnetic separators. Based on the overflow tailings state variables, underflow concentration, and bottom valve opening of each washing magnetic separator, the feed rate of each washing magnetic separator is calculated using big data technology through a mathematical model. This solves the problem of inaccurate feed rate measurement in existing technologies. Furthermore, the opening of the feed control valve of the separator and / or the number of online washing magnetic separators are adjusted according to the feed rate of each washing magnetic separator, achieving closed-loop control of the feed rate according to the optimal processing capacity of the washing magnetic separator. This achieves precise control of the mineral separation process, thereby comprehensively improving mineral separation efficiency, concentrate grade, and product recovery rate. The washing magnetic separator and mineral separation system operate automatically without manual intervention. Regardless of whether the actual feed rate increases or decreases, the system automatically adjusts the number of online washing magnetic separators, saving energy and water consumption.

[0044] Based on the aforementioned embodiments, the process of adjusting the ore feed rate is further refined.

[0045] In one possible implementation, adjusting the opening of the feed control valve and / or the number of online washing magnetic separators based on the feed rate of each washing magnetic separator includes: When the feed rate of a washing magnetic separator is greater than or less than the set feed rate, the target adjustment amount of the corresponding feed control valve is determined according to the feed rate, and the opening of the feed control valve is adjusted according to the target adjustment amount. When the feed rate of multiple washing magnetic separators is greater than or less than the set feed rate, the target washing magnetic separator and the opening degree of the corresponding target feed control valve are determined based on the feed rate of the multiple washing magnetic separators.

[0046] The intelligent ore separator control system has a preset allowable deviation range for the feed rate. When each PLC control cabinet collects the feed rate data of the corresponding washing magnetic separator in real time and uploads it to the intelligent ore separator control system, when the intelligent ore separator control system determines that the feed rate of a certain washing magnetic separator exceeds the deviation range, it determines that an abnormal feed rate signal is triggered and starts the corresponding adjustment process.

[0047] When the intelligent ore separator control system detects, through data monitoring uploaded from each PLC control cabinet, that only one washing magnetic separator has a feed rate greater than or less than the set feed rate, and that the feed rate of that washing magnetic separator has an upward adjustment margin, the intelligent ore separator control system first retrieves the historical feed rate data of that washing magnetic separator and the current overflow concentration and underflow concentration data uploaded by the PLC control cabinet. Using its built-in feed rate adjustment algorithm, it calculates the target adjustment amount of the feed control valve required to return the feed rate of that washing magnetic separator to the set value. For example, when it detects that the feed rate of the washing magnetic separator is greater than the set value, it calculates the specific amount by which the opening of the feed control valve needs to be reduced. The intelligent ore separator control system then sends this adjustment command to the corresponding PLC control cabinet, which sends a control signal to the actuator of the feed control valve. The control valve gradually reduces its opening at a set rate. During this process, the PLC control cabinet collects feed rate change data in real time and feeds it back to the intelligent ore separator control system until the intelligent ore separator control system confirms that the feed rate is stable within the set value range.

[0048] When the intelligent ore separator control system detects, via data uploaded from the PLC control cabinet, that the feed rates of multiple washing magnetic separators are simultaneously greater than or less than the set feed rate, it no longer adjusts independently through a single PLC control cabinet, but instead performs overall coordinated control through the intelligent ore separator control system. The intelligent ore separator control system first assigns numbers to all washing magnetic separators with abnormal feed rates and their corresponding PLC control cabinets, calculates the feed rate deviation values ​​uploaded by each device via the PLC control cabinet, and combines this with the total processing capacity of all currently operating washing magnetic separators and the total ore supply data to determine the number of target washing magnetic separators, their specific numbers, and the corresponding PLC control cabinets that need adjustment. Simultaneously, it calculates the required opening value of the target feed control valve. For example, when the feed rates of multiple washing magnetic separators are all less than the set value, the intelligent ore separator control system determines whether it is necessary to shut down some equipment to concentrate the ore supply; when the feed rates of multiple machines are all greater than the set value, it determines whether it is necessary to turn on idle equipment to share the load. Subsequently, the intelligent ore separator control system sends the working status adjustment command of the target washing magnetic separator and the opening adjustment command of the feed control valve to the corresponding PLC control cabinet, which then executes the specific control operations. Specifically, when the feed rate of a single washing magnetic separator deviates from the set value, the target adjustment amount of the corresponding feed control valve is directly determined and the opening is adjusted. This one-to-one precise adjustment method can quickly correct the feed deviation of a single device and prevent the deviation from spreading and affecting the overall system. When the feed rates of multiple washing magnetic separators are abnormal, the target washing magnetic separator to be adjusted and the opening of the corresponding target feed control valve are determined to achieve coordinated control of multiple devices.

[0049] In this embodiment, a differentiated adjustment strategy is adopted for different situations of abnormal feed rate of the washing magnetic separator. This case-by-case approach abandons the extensive mode of uniform adjustment, making the adjustment more targeted and efficient. The rapid response when a single unit is abnormal can reduce the impact of local deviations on the overall separation effect, and the coordinated control when multiple units are abnormal can ensure the overall balance of feed rate of the system, further improving the accuracy and stability of the ore separation system control.

[0050] The above embodiments introduce differentiated adjustment strategies. The following describes the specific control process for adjusting the number of online washing magnetic separators, which is applicable to working conditions where the supply of ore fluctuates greatly.

[0051] In one possible implementation, when the feed rates of multiple washing magnetic separators are greater than or less than the set feed rate, the target washing magnetic separator whose operating state needs to be adjusted and the opening degree of the corresponding target feed control valve are determined based on the feed rates of the multiple washing magnetic separators, including: When the feed rate of multiple washing magnetic separators is less than the set feed rate, the washing magnetic separator that is online and has the minimum feed rate is identified as the target washing magnetic separator. The target feed control valve of the target washing magnetic separator is closed, and the target washing magnetic separator is taken offline. When the feed rate of multiple washing magnetic separators exceeds the set feed rate, the washing magnetic separator that is currently off is identified as the target washing magnetic separator. The target washing magnetic separator is then brought online, and the corresponding target feed control valve is opened and its opening degree is adjusted. Specifically, the target feed control valve is adjusted to the set opening degree.

[0052] When the intelligent ore sorting control system detects, through data uploaded from each PLC control cabinet, that multiple washing magnetic separators are experiencing feed rates lower than their set feed rates, and this abnormal state persists for the duration preset by the intelligent ore sorting control system, it indicates that the total supply of ore is insufficient. Continuing to operate all washing magnetic separators would result in each device operating at a low load, increasing energy consumption and reducing sorting efficiency. At this point, the intelligent ore sorting control system automatically retrieves the real-time feed rate data uploaded from the corresponding PLC control cabinets of all abnormal washing magnetic separators, sorts the data, selects the washing magnetic separator with the lowest feed rate as the target washing magnetic separator, and locates its corresponding PLC control cabinet. Subsequently, the intelligent ore separator control system sends a shutdown command to the PLC control cabinet corresponding to the target washing magnetic separator. Upon receiving the command, the PLC control cabinet controls the washing magnetic separator to stop operating according to a preset program. After detecting that the washing magnetic separator has completely stopped, the PLC control cabinet then controls its corresponding feed control valve to close, and simultaneously feeds back the equipment status to the intelligent ore separator control system to ensure that there is no ore leakage or equipment impact during the shutdown process. After shutting down the target equipment, the intelligent ore separator control system redistributes the remaining ore to other operating washing magnetic separators, sending opening adjustment commands to the PLC control cabinets corresponding to these devices. The PLC control cabinets then control the feed control valves to adjust the opening, so that the feed rate of each operating device is increased to near the set feed rate.

[0053] When the intelligent ore separator control system detects, through data uploaded from the PLC control cabinet, that the feed rate of multiple washing magnetic separators exceeds the set feed rate, and that the feed rate still fails to return to the set value after the intelligent ore separator control system sends an opening adjustment command, it indicates that the current total ore supply is too large, and the existing operating equipment is under overload. Continued operation may lead to a decrease in separation quality and equipment damage. At this time, the intelligent ore separator control system queries the working status data uploaded by all washing magnetic separators through their corresponding PLC control cabinets, selects a washing magnetic separator that is in a closed state and in normal operating condition as the target washing magnetic separator, and determines its corresponding PLC control cabinet. The intelligent ore separator control system first sends an ore control valve opening adjustment command to the PLC control cabinet corresponding to the target washing magnetic separator. The PLC control cabinet then controls the valve to adjust to the set opening. Subsequently, the intelligent ore separator control system sends a start command to the PLC control cabinet, which controls the target washing magnetic separator to start running according to the preset start-up procedure and feeds back the operating status to the intelligent ore separator control system in real time. After the target washing magnetic separator is started and stabilized, the intelligent ore separator control system sends an opening adjustment command to the PLC control cabinet corresponding to all operating washing magnetic separators. The PLC control cabinet then coordinates the adjustment of the opening of the feed control valve to ensure that the feed rate of each device is stable within the set feed rate range, thereby achieving a balanced distribution of ore.

[0054] In particular, the control logic is further refined to address the situation where the feed rates of multiple washing magnetic separators are abnormal. When the feed rates of multiple separators are less than the set value, the washing magnetic separator with the smallest feed rate and its corresponding feed control valve are shut down. This allows the limited ore to be concentrated and distributed to the remaining equipment with relatively sufficient feed rates, avoiding low sorting efficiency and energy waste caused by all equipment operating under low load. When the feed rates of multiple separators are greater than the set value, one washing magnetic separator that is currently shut down and its corresponding feed control valve are turned on and adjusted to the set opening degree. This allows the load of the ore to be shared by adding equipment, preventing the existing equipment from deteriorating in sorting quality and aggravating equipment wear due to overload operation.

[0055] In this embodiment, precise control of the feed rate extreme value and equipment operating status ensures a high degree of matching between the number of online devices and the amount of ore, guaranteeing the sorting effect while reducing equipment operating losses.

[0056] In practice, if this calculation is not performed and a fixed opening degree is set directly, there may be situations where the new equipment feeds too much ore, resulting in insufficient ore supply to other equipment, or the ore supply is too low and still cannot alleviate the overload problem of the original equipment.

[0057] In one possible implementation, before the target feed control valve of the control target washing magnetic separator is opened and its opening degree is adjusted, the following is also included: Determine the difference between the feed rate of multiple washing magnetic separators and the set feed rate, calculate the sum of the differences of each feed rate, and determine the target opening of the target feed control valve based on the preset opening calculation algorithm and the sum of the feed rate differences.

[0058] In the specific implementation process, when the intelligent ore separator control system selects the target washing magnetic separator that is in a closed state and in normal condition, it does not directly send the opening adjustment command to its corresponding PLC control cabinet, but first executes the opening pre-calculation process.

[0059] First, the intelligent ore separator control system retrieves real-time data from all washing magnetic separators whose feed rates exceed the set values. This data is continuously collected and uploaded by the corresponding PLC control cabinets, including the current actual feed rate and the system's preset standard feed rate for each malfunctioning washing magnetic separator. Then, the intelligent ore separator control system uses its built-in difference calculation module to calculate the difference between the actual feed rate and the set feed rate for each malfunctioning washing magnetic separator. The difference result is positive (because the actual feed rate exceeds the set value). After the calculation is complete, the system automatically sums all positive differences to obtain the total feed rate difference for multiple malfunctioning devices. This sum directly reflects the total surplus of current ore supply relative to the processing capacity of existing operating equipment, and is also the benchmark for the ore load that the target washing magnetic separator needs to share.

[0060] In practical implementation, the intelligent ore separator control system has a pre-set opening calculation algorithm. This algorithm is calibrated based on fundamental parameters such as the rated processing capacity of the washing magnetic separator and the flow characteristic curve of the feed control valve. Optionally, an adjustment coefficient is determined based on the rated processing capacity of the washing magnetic separator and the flow characteristic curve of the feed control valve. The target opening is determined by multiplying the adjustment coefficient by the difference in feed rate. The system inputs the sum of the calculated differences into the algorithm, which calculates the set opening to which the target feed control valve needs to be adjusted by relating the surplus ore quantity to the opening of the feed control valve. This set opening ensures that after the target washing magnetic separator starts, it can precisely handle all the surplus ore quantity, avoiding situations where insufficient handling leads to overloading of the original equipment, or excessive handling leads to the feed rate of the original equipment falling below the set value.

[0061] After the opening calculation is completed, the intelligent ore separator control system sends an adjustment command containing the set opening parameters to the PLC control cabinet corresponding to the target washing magnetic separator. Upon receiving the command, the PLC control cabinet controls the feed control valve to adjust to the set opening at a preset rate and feeds back the actual valve opening to the intelligent ore separator control system. After confirming that the opening meets the standard, the intelligent ore separator control system sends a start command to the washing magnetic separator to the PLC control cabinet, ensuring that the feed rate of all operating equipment is ultimately balanced and stable.

[0062] In this embodiment, when a new target washing magnetic separator is started, the differences between the feed rates of multiple abnormal washing magnetic separators and their set values ​​are first determined and summed. Then, based on a preset algorithm and the sum of these differences, the set opening degree of the target feed control valve is determined. This method of calculating the total deviation before determining the opening degree ensures that the feed rate of the new equipment can accurately match the total material surplus of the system, avoiding under- or over-adjustment caused by setting the opening degree based on experience. By accurately calculating the opening degree through the sum of the differences, the uniform distribution of material among all operating equipment can be achieved, further improving the accuracy of feed adjustment and ensuring the stability of the overall separation process.

[0063] In one possible implementation, after adjusting the opening of the feed control valve and / or the number of online washing magnetic separators, the following is also included: Monitor the liquid level of materials in the ore distribution box; Based on the set upper and lower limits of the liquid level, the opening degree of the feed control valve of the washing magnetic separator and the number of online washing magnetic separators are controlled to keep the material liquid level within the set liquid level range.

[0064] In one possible implementation, the material level in the ore bin is determined based on a level gauge; The method also includes: The feed pump frequency is determined based on the material level in the ore distribution box and the set level range.

[0065] The ore sorting box serves as the front-end feeding and buffering device of the ore sorting system. It is equipped with a level gauge, which is connected to the PLC control cabinet of the corresponding area to collect material level data in the pump pool in real time and upload it to the intelligent ore sorting control system.

[0066] Upon receiving signals from each PLC control cabinet indicating that the feed adjustment is complete (e.g., valve opening is stable, and the washing magnetic separator is operating normally), the intelligent ore separator control system immediately retrieves the real-time material level data uploaded by the ore separator level gauge via the PLC control cabinet and compares it with the system's preset safe level range. The preset safe level range includes an upper and lower threshold. The upper threshold is lower than the maximum volume of the pump tank to prevent material overflow, while the lower threshold is higher than the level at which the feed pump risks idling to ensure pump safety.

[0067] When the intelligent sorting system determines that the actual material level is higher than the preset upper threshold, it indicates that the current feed rate of the feed pump is greater than the consumption rate of the sorting system, and the feed pump frequency needs to be reduced to decrease the feed amount. When the actual level is lower than the preset lower threshold, it indicates that the feed rate is less than the consumption rate, and the feed pump frequency needs to be increased to increase the feed amount. When the level is within the safe range, the current feed pump frequency is maintained. The intelligent sorting system determines the target pump frequency by using a built-in pump frequency calculation model combined with the level deviation value (the difference between the actual level and the median value of the safe range). This model has been calibrated to ensure that the level can quickly return to the safe range after adjustment.

[0068] The intelligent ore separator control system sends the target pump frequency command to the PLC control cabinet corresponding to the feed pump. Upon receiving the command, the PLC control cabinet controls the feed pump's frequency converter to adjust the frequency according to a preset rate, and collects real-time data on the feed pump's operating frequency and pump pool level changes, feeding this data back to the intelligent ore separator control system. The intelligent ore separator control system continuously monitors the level data until the level stabilizes within a safe range, completing one adjustment cycle.

[0069] In this embodiment, by adjusting the frequency of the feed pump through liquid level feedback, the liquid level of the material in the pump pool can be maintained within a set range, providing a continuous and stable supply of ore for the subsequent ore separation process. This avoids abnormal liquid level in the pump pool caused by mismatch in the feeding rate after the feed adjustment, and improves the operational continuity and reliability of the entire process system.

[0070] In one possible implementation, each feed control valve of the multi-hole intelligent ore separator is equipped with an anti-clogging flushing device; the method also includes: Before the feed control valve is opened, an anti-clogging flushing start command is generated to control the anti-clogging flushing device to flush the corresponding feed control valve.

[0071] In this embodiment, the anti-clogging flushing device includes a solenoid valve and a flushing nozzle. Before each opening of the feed control valve, the solenoid valve of the anti-clogging flushing device is opened, and the feed control valve to be opened is flushed through the flushing nozzle to prevent material accumulation from causing the feed control valve to malfunction.

[0072] In one possible implementation, the method also includes: Monitor the feed rate and feed control valve opening of each washing magnetic separator; the feed rate is the amount of liquid material input into the washing magnetic separator by the multi-hole intelligent separator through the feed control valve. When the feed rate and feed control valve opening of a certain washing magnetic separator are mismatched, an anti-blocking flushing start command is generated to control the anti-blocking flushing device to flush the corresponding feed control valve. The mismatch between the feed rate and feed control valve opening of the washing magnetic separator includes: determining the predicted feed rate based on the feed control valve opening; and determining a mismatch between the feed rate and feed control valve opening when the feed rate of the washing magnetic separator is less than the predicted feed rate, and the difference between the predicted and predicted feed rates is greater than a set value.

[0073] In this embodiment, when the opening of the feed control valve is large, but the system detects that the feed rate of the washing magnetic separator corresponding to the feed control valve is small, it is determined that there may be a blockage in the corresponding feed control valve and pipeline, and the anti-blockage flushing device is controlled to automatically start the flushing function to flush and unclog the pipeline.

[0074] The above mainly describes the methods for adjusting the feed rate; the following section explains the method for determining the overflow concentration.

[0075] In one possible implementation, determining the overflow concentration of the corresponding washing magnetic separator based on the overflow tailings image includes: The overflow tailings image is input into a pre-trained concentration recognition model, which outputs the overflow concentration. The concentration recognition model is trained based on the laboratory concentration measurements of the overflow product and the corresponding overflow tailings image feature extraction results.

[0076] In actual implementation, after the intelligent tailings separator control system sends a start command to the tailings AI status detection system, the overflow tailings images collected by the device are first transmitted to the corresponding PLC control cabinet. The PLC control cabinet performs image format standardization processing (such as size unification and noise reduction), and then uploads them to the image processing module of the intelligent tailings separator control system through the industrial communication network. Alternatively, the overflow tailings images collected by the device are transmitted to the image processing module of the intelligent tailings separator control system, and the intelligent tailings separator control system performs image format standardization processing.

[0077] The intelligent ore separator control system has a pre-stored concentration recognition model ready for use. This model is not trained in real time, but rather pre-trained and calibrated based on numerous laboratory concentration measurements of overflow products and corresponding overflow tailings image feature extraction results. When the intelligent ore separator control system receives an image, it automatically inputs the standardized image into the concentration recognition model. The model analyzes key information such as tailings particle distribution and grayscale characteristics in the input image by calling upon the learned "image feature-concentration" correlation rules. After the model analysis is complete, it directly outputs the overflow concentration data of the corresponding washing magnetic separator. The intelligent ore separator control system then correlates this concentration data with the corresponding washing magnetic separator number and detection time to calculate the feed rate, ensuring the accuracy of the feed rate calculation. Compared to traditional manual detection, this method eliminates the need for manual sampling and testing, significantly improving the real-time performance and accuracy of overflow concentration detection.

[0078] In this embodiment, the overflow tailings image is input into a pre-trained concentration recognition model to determine the overflow concentration. This model is trained based on laboratory concentration measurements of the overflow product and corresponding image feature extraction results. The laboratory concentration measurements provide accurate label data for the model, enabling it to fully learn the correlation between overflow concentration and image features. Compared to traditional manual detection or simple sensor detection methods, this model-based recognition method not only outputs detection results quickly but also effectively avoids the subjectivity and delays of manual detection, as well as the susceptibility of simple sensors to environmental interference. Accurate and real-time overflow concentration data provides a reliable basis for subsequent feed rate calculations, reducing feed adjustment deviations caused by concentration detection errors, thereby improving the accuracy of the entire ore distribution control process.

[0079] Based on the aforementioned embodiments, before acquiring the overflow tailings image of the washing magnetic separator collected by the tailings AI status detection system, it is necessary to perform offline training of the concentration recognition model.

[0080] In one possible implementation, before acquiring the overflow tailings image of the washing magnetic separator collected by the tailings AI state detection system, the following is also included: Acquire historical images of overflow tailings and corresponding laboratory concentration measurements of overflow products; Based on the color value of each pixel, the historical overflow tailings image is divided to determine one or more target regions, and the proportion of one or more target regions is determined; where the proportion is the area proportion or the volume proportion. The data items are the proportion of the target area and the corresponding laboratory concentration measurement value, and training and testing sets are constructed accordingly. The initial model is trained using the training and test sets to obtain the concentration recognition model.

[0081] During the offline training of the model, staff members collect historical overflow tailings images under different working conditions through the tailings AI status detection system. At the same time, they use laboratory testing equipment to measure the concentration of the overflow products under the corresponding working conditions, obtain accurate laboratory concentration values, and form a one-to-one correspondence between "image-concentration" raw data pairs. After being sorted by staff, these raw data are uploaded to the data storage module of the intelligent ore sorting control system.

[0082] The intelligent tailings separator control system calls the image segmentation module to process historical overflow tailings images. Based on the color value difference of each pixel, the image is divided into one or more target regions with similar color characteristics (such as tailings particle aggregation areas and clear water areas). The built-in algorithm calculates the area or volume ratio of each target region in the entire image. This ratio can intuitively reflect the distribution density of tailings particles in the image and is a key feature of the correlation concentration.

[0083] Subsequently, the intelligent mineral sorting control system uses "target area proportion + corresponding laboratory concentration measurement value" as a complete data item, organizes all raw data pairs, and randomly divides them into training set and test set according to preset proportions (e.g., 80% for training set and 20% for test set). During the division process, it ensures that data of different concentration ranges are evenly distributed in the two sets to avoid data offset affecting model performance.

[0084] The intelligent mineral sorting control system loads a pre-set initial model and first inputs training data into the model for training. The model parameters are continuously adjusted using a backpropagation algorithm to gradually reduce the error between the model's output concentration prediction and the actual laboratory concentration measurement. During training, the model performance is periodically verified using test set data. If the test error exceeds a preset threshold, the image segmentation parameters or data partitioning ratio are adjusted until the model's performance on both the training and test sets meets the preset requirements, completing the training of the concentration recognition model. The model is then solidified and deployed to the real-time detection module of the intelligent mineral sorting control system.

[0085] Optionally, a convolutional neural network (CNN) model can be used as the initial model. Through the local receptive field design of the convolutional layers, the CNN model can focus on local distribution features in the overflow tailings image. Simultaneously, its parameter sharing mechanism can significantly reduce the number of model parameters, reducing computational load and avoiding overfitting when processing high-resolution tailings images. The pooling layers of the CNN model can reduce the dimensionality and abstract the extracted local features, strengthening key features and weakening secondary interferences, making the model more robust to common noise and slight displacement interferences in industrial field images. This better adapts to the overflow image recognition needs under different operating conditions in the mining system, ultimately improving the accuracy and stability of concentration recognition.

[0086] In this embodiment, the model training process first acquires historical images of overflow tailings and corresponding laboratory concentration measurements. Then, it divides the target region based on pixel color values ​​and determines its proportion. Next, it constructs training and testing sets using the target region proportion and laboratory concentration measurements as data items to train the initial model. By using historical data to cover image features under different concentration conditions, the model's generalization ability is ensured. Dividing the target region based on pixel color values ​​and calculating its proportion accurately extracts key image features related to concentration, avoiding interference from irrelevant features on the model's recognition accuracy. Through the standardized training process using training and testing sets, model parameters can be continuously optimized, improving the model's recognition accuracy and stability. The model trained using this process can more accurately identify overflow concentrations, improving the control precision of the mining system.

[0087] In one possible implementation, historical overflow tailings images are segmented based on the color value of each pixel to determine one or more target regions, including: Extract the target pixel values ​​associated with iron and / or sulfur elements; Historical overflow tailings images are divided based on target pixel values ​​to determine one or more target regions.

[0088] Because iron and sulfur exist in specific mineral forms in overflow tailings, these minerals exhibit unique color characteristics in images, corresponding to a fixed range of pixel values. Staff can pre-enter these pixel value ranges into the intelligent tailings sorting system as a basis for extraction. After loading historical overflow tailings images, the image processing module of the intelligent tailings sorting system traverses all pixels in the image according to the preset pixel value range, extracting target pixel values ​​associated with iron and / or sulfur, forming a target pixel set. Simultaneously, pixels unrelated to these two elements (such as background noise pixels and pixels in clear water areas) are filtered out to reduce interference from irrelevant information.

[0089] Based on the extracted target pixel values, the intelligent ore separator control system uses a region growth algorithm or a threshold segmentation algorithm to aggregate adjacent target pixels with similar pixel values ​​to form one or more continuous target regions. These regions are the areas where iron and sulfur-bearing minerals accumulate in the overflow tailings.

[0090] In this embodiment, compared to dividing regions based on overall pixel color values, this targeted pixel value extraction makes the divided target regions more closely match the actual needs of concentration detection, making the features learned by the model more targeted and effective, and further improving the model's accuracy in identifying overflow concentration.

[0091] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0092] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0093] Figure 3 A schematic diagram of the structure of the mining system control device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 3 As shown, the mining system control device 3 includes: The overflow status acquisition module 301 is used to acquire the overflow tailings status variables of the washing magnetic separator collected by the tailings AI status detection system; wherein, the overflow tailings status variables include one or more of overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology.

[0094] The washing magnetic separator parameter acquisition module 302 is used to acquire the underflow concentration and bottom valve opening of each washing magnetic separator.

[0095] The control module 303 is used to calculate and determine the feed rate of each washing magnetic separator through a mathematical model based on the overflow tailings state variables, underflow concentration and bottom valve opening of each washing magnetic separator, and to adjust the opening of the feed control valve and / or the number of online washing magnetic separators according to the feed rate of each washing magnetic separator.

[0096] In one possible implementation, the control module 303 is specifically used for: When the feed rate of a washing magnetic separator is greater than or less than the set feed rate, the target adjustment amount of the corresponding feed control valve is determined according to the feed rate of the washing magnetic separator, and the opening of the feed control valve is adjusted according to the target adjustment amount. When the feed rate of multiple washing magnetic separators is greater than or less than the set feed rate, the target washing magnetic separator and the opening degree of the corresponding target feed control valve are determined based on the feed rate of the multiple washing magnetic separators.

[0097] In one possible implementation, the control module 303 is specifically used for: When the feed rate of multiple washing magnetic separators is less than the set feed rate, the washing magnetic separator that is online and has the minimum feed rate is identified as the target washing magnetic separator. The target feed control valve of the target washing magnetic separator is closed, and the target washing magnetic separator is taken offline. When the feed rate of multiple washing magnetic separators exceeds the set feed rate, the washing magnetic separator that is currently in the off state is identified as the target washing magnetic separator. The target washing magnetic separator is then put into operation, and the corresponding target feed control valve of the target washing magnetic separator is opened and its opening degree is adjusted.

[0098] In one possible implementation, the control module 303 is also used to determine the difference between the feed rate of multiple washing magnetic separators and the set feed rate, calculate the sum of the differences of each feed rate, and determine the target opening of the target feed control valve based on the preset opening calculation algorithm and the sum of the feed rate differences.

[0099] In one possible implementation, the control module 303 is also used to monitor the material level in the ore distribution box; based on the set upper and lower limits of the liquid level, it controls the opening of the feed control valve of the washing magnetic separator and the number of online washing magnetic separators, so as to control the material level within the set liquid level range.

[0100] In one possible implementation, the control module 303 is also used to determine the feed pump frequency based on the material level in the ore distribution box and a set level range.

[0101] In one possible implementation, the overflow status acquisition module 301 is specifically used to input the overflow tailings image into a pre-trained concentration recognition model and output the overflow concentration. The concentration recognition model is trained based on the laboratory concentration measurements of the overflow product and the corresponding overflow tailings image feature extraction results.

[0102] In one possible implementation, a training module is also included, which is used to acquire historical overflow tailings images and corresponding laboratory concentration measurements of overflow products before acquiring overflow tailings images of the washing magnetic separator collected by the tailings AI status detection system. Based on the color value of each pixel, the historical overflow tailings image is divided to determine one or more target regions, and the proportion of one or more target regions is determined; where the proportion is the area proportion or the volume proportion. The data items are the proportion of the target area and the corresponding laboratory concentration measurement value, and training and testing sets are constructed accordingly. The initial model is trained using the training and test sets to obtain the concentration recognition model.

[0103] In one possible implementation, a training module is specifically used to extract target pixel values ​​associated with iron and / or sulfur; based on the target pixel values, historical overflow tailings images are segmented to determine one or more target regions.

[0104] In this embodiment, the ore separation system utilizes multiple washing magnetic separators and multiple tailings AI status detection systems within a multi-hole intelligent ore separator. The number of washing magnetic separators is kept within the number of ore separation outlets of the intelligent ore separator. Feed control valves are configured between each washing magnetic separator and the intelligent ore separator, thus constructing a precise and controllable ore separation infrastructure. This method first uses the tailings AI status detection system to collect overflow tailings status information, then combines this with the operating parameters of the washing magnetic separators to determine the feed rate, and finally adjusts the opening of the feed control valves or the number of online washing magnetic separators accordingly. By acquiring overflow tailings status variables from the washing magnetic separators collected by the tailings AI status detection system, including one or more material parameters such as overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology, these parameters can reflect the separation effect of the washing magnetic separators. Based on the overflow tailings state variables, underflow concentration, and bottom valve opening of each washing magnetic separator, the feed rate of each washing magnetic separator is calculated using big data technology through a mathematical model. This solves the problem of inaccurate feed rate measurement in existing technologies. Furthermore, the opening of the feed control valve of the separator and / or the number of online washing magnetic separators are adjusted according to the feed rate of each washing magnetic separator, achieving closed-loop control of the feed rate according to the optimal processing capacity of the washing magnetic separator. This achieves precise control of the mineral separation process, thereby comprehensively improving mineral separation efficiency, concentrate grade, and product recovery rate. The washing magnetic separator and mineral separation system operate automatically without manual intervention. Regardless of whether the actual feed rate increases or decreases, the system automatically adjusts the number of online washing magnetic separators, saving energy and water consumption.

[0105] This application also provides a mineral sorting system, including a multi-hole intelligent mineral sorter, multiple washing magnetic separators, multiple tailings AI status detection systems, and the mineral sorting system control device provided in the aforementioned embodiments; The tailings AI status detection system includes one or more of a tailings concentration transmitter, a particle size transmitter, a tailings grade transmitter, a pressure transmitter, and a tailings AI image detection and recognition system. The tailings AI image detection and recognition system includes a sampling and detection device, a supercomputer, an intelligent management server, and an algorithm model. The algorithm model includes at least the concentration recognition model provided in the aforementioned embodiments, used to identify the overflow concentration. The tailings AI image detection and recognition system updates the sampling time according to the set parameters; and updates the algorithm model based on the operating parameters of the ore sorting system and the washing magnetic separator.

[0106] Optionally, the algorithm model may also include models such as quality recognition model, granularity recognition model, and flow recognition model.

[0107] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 4As shown, the electronic device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0108] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0109] Electronic device 4 may include, but is not limited to, processor 40 and memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0110] The processor 40 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0111] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0112] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0113] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0114] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0115] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0116] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0117] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A control method of a mineral separation system, characterized by, The ore separation system comprises a multi-hole intelligent ore separator, an ore separation box, multiple elution magnetic separators, and multiple tailing AI state detection systems; each elution magnetic separator is connected with the intelligent ore separator through a feeding pipe and a feeding control valve; the method comprises: acquiring overflow tailing state variables of the elution magnetic separators collected by the tailing AI state detection systems; the overflow tailing state variables comprise one or more of overflow concentration, particle size, grade, color, liquid level, and overflow surface morphology; acquiring the underflow concentration and the underflow valve opening degree corresponding to each elution magnetic separator; determining the feeding amount of each elution magnetic separator according to the overflow tailing state variables, the underflow concentration, and the underflow valve opening degree corresponding to each elution magnetic separator through a mathematical model, and adjusting the opening degree of the feeding control valve and / or the online number of the elution magnetic separators according to the feeding amount of each elution magnetic separator.

2. The control method of a sorting system according to claim 1, characterized by, The adjustment of the opening degree of the feeding control valve and / or the online number of the elution magnetic separators according to the feeding amount of each elution magnetic separator comprises: when the feeding amount of one elution magnetic separator is greater than or less than the set feeding amount, determining the target adjustment amount of the corresponding feeding control valve according to the feeding amount of the elution magnetic separator, and adjusting the opening degree of the feeding control valve according to the target adjustment amount; when the feeding amount of multiple elution magnetic separators is greater than or less than the set feeding amount, determining the target elution magnetic separator to be adjusted and the opening degree of the corresponding target feeding control valve according to the feeding amount of the multiple elution magnetic separators.

3. The control method of a sorting system according to claim 2, characterized in that, The determination of the target elution magnetic separator to be adjusted and the opening degree of the corresponding target feeding control valve according to the feeding amount of multiple elution magnetic separators when the feeding amount of the multiple elution magnetic separators is greater than or less than the set feeding amount comprises: when the feeding amount of multiple elution magnetic separators is less than the set feeding amount, determining the elution magnetic separator corresponding to the minimum feeding amount in the online state as the target elution magnetic separator, controlling the corresponding target feeding control valve of the target elution magnetic separator to be closed, and controlling the target elution magnetic separator to be offline; when the feeding amount of multiple elution magnetic separators is greater than the set feeding amount, determining one elution magnetic separator in the closed state as the target elution magnetic separator, controlling the target elution magnetic separator to be online, and controlling the corresponding target feeding control valve of the target elution magnetic separator to be opened and adjusted.

4. The control method of a sorting system according to claim 3, wherein Before the control of the opening of the corresponding target feeding control valve of the target elution magnetic separator, the method further comprises: determining the difference between the feeding amount of multiple elution magnetic separators and the set feeding amount, calculating the sum of the feeding amount differences, and determining the target opening degree of the target feeding control valve based on a preset opening degree calculation algorithm and the sum of the feeding amount differences.

5. The control method of a sorting system according to claim 1, wherein, After the adjustment of the opening degree of the feeding control valve and / or the online number of the elution magnetic separators according to the feeding amount of each elution magnetic separator, the method further comprises: monitoring the material liquid level in the ore separation box; controlling the opening degree of the feeding control valve of the elution magnetic separator and the online number of the elution magnetic separator based on the set upper liquid level limit and the set lower liquid level limit, so as to control the material liquid level within the set liquid level range.

6. The control method of a sorting system according to claim 5, wherein The material liquid level in the ore separation box is measured based on a liquid level meter; The method further comprises: determining the feeding pump frequency based on the material liquid level in the ore separation box and the set liquid level range.

7. The control method of a sorting system according to claim 5, wherein Each ore feeding control valve of the multi-hole intelligent ore distributor is provided with a anti-blocking flushing device; the method further comprises: generating an anti-blocking flushing start instruction before the ore feeding control valve is opened to control the anti-blocking flushing device to flush the corresponding ore feeding control valve.

8. The control method of a sorting system according to claim 7, wherein, The method further comprises: monitoring the ore feeding amount and the opening degree of the ore feeding control valve corresponding to each washing magnetic separator; wherein the ore feeding amount is the amount of material liquid input into the washing magnetic separator by the multi-hole intelligent ore distributor through the ore feeding control valve; when the ore feeding amount and the opening degree of the ore feeding control valve corresponding to a certain washing magnetic separator do not match, generating an anti-blocking flushing start instruction to control the anti-blocking flushing device to flush the corresponding ore feeding control valve.

9. A control device for a division system, characterized by comprising: The ore distribution system comprises a multi-hole intelligent ore distributor, an ore distribution box, multiple washing magnetic separators, and multiple tailings AI state detection systems; each washing magnetic separator is provided with an ore feeding control valve between the intelligent ore distributor; the device comprises: an overflow state acquisition module for acquiring the overflow tailings state variable of the washing magnetic separator collected by the tailings AI state detection system; a washing magnetic separator parameter acquisition module for acquiring the underflow concentration and the underflow valve opening degree corresponding to each washing magnetic separator; a control module for determining the ore feeding amount of each washing magnetic separator according to the overflow tailings state variable, the underflow concentration, and the underflow valve opening degree corresponding to each washing magnetic separator, and adjusting the opening degree of the ore feeding control valve and / or the online number of the washing magnetic separator according to the ore feeding amount of each washing magnetic separator.

10. A mineral separation system characterized by, It comprises: a multi-hole intelligent ore distributor, an ore distribution box, multiple washing magnetic separators, multiple tailings AI state detection systems, and the ore distribution system control device of claim 9; wherein the tailings AI state detection system comprises one or more of a tailings concentration transmitter, a particle size transmitter, a tailings grade transmitter, a pressure transmitter, and a tailings AI image detection and recognition system; The tailings AI image detection and recognition system comprises a sampling detection device, a super-brain computer, an intelligent management server, and an algorithm model; the tailings AI image detection and recognition system updates the sampling time according to the running parameters of the ore distribution system and the washing magnetic separator; and the algorithm model is updated according to the running parameters of the ore distribution system and the washing magnetic separator.

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