Rare earth ore multi-stage particle size dry screening separation control method and system

Abstract

The application provides a rare earth ore multi-stage particle size dry screening separation control method and system, relates to the screening separation control technical field, acquires ore particle size data of target rare earth ore feed, determines a target feed particle size spectrum based on the ore particle size data; determines a multi-stage screening particle size state vector according to real-time discharge particle size data, performs screening detection according to the target feed particle size spectrum and the multi-stage screening particle size state vector, and obtains a screening deviation rate; determines a screen health index according to a screen characteristic vector, performs information fusion according to the screen health index and the screening deviation rate, and obtains a screening efficiency index of the target rare earth ore feed during dry screening; and determines a screening control strategy according to the screening efficiency index. The application can fuse process deviation and equipment state, realize adaptive optimization and precise control of the rare earth ore dry screening process, and thus improve the efficiency and stability of the rare earth ore dry screening process.

Description

Technical Field

[0001] This application relates to the field of screening and sorting control technology, and more specifically, to a method and system for controlling the multi-stage particle size dry screening and sorting of rare earth ores. Background Technology

[0002] Currently, in the field of dry screening and separation of rare earth ores, existing technologies mainly rely on preset fixed process parameters for operation control. During production, offline sampling and laboratory sieving are typically used to periodically test the particle size distribution of the feed and product, serving as the basis for evaluating screening effectiveness and adjusting parameters. Simultaneously, the operational status of the screening equipment itself generally relies on periodic manual inspections, auditory identification, or simple vibration monitoring to qualitatively determine whether there are abnormalities such as screen damage or blockage. The entire control process exhibits significant lag and experience dependence; process adjustments and equipment maintenance decisions are often based on discrete, non-real-time information, making it difficult to respond promptly and accurately to continuous fluctuations in raw material characteristics and gradual deterioration of equipment performance during production.

[0003] However, current dry screening production of rare earth ores suffers from several drawbacks. Process control is lagging, relying on manual offline sampling and particle size analysis, which fails to detect feed fluctuations in real time and adjust parameters accordingly. This results in unstable screening efficiency and significant fluctuations in product quality. Equipment maintenance is also reactive; problems such as screen damage and blockage are often only discovered when they severely impact production. The lack of predictive maintenance based on real-time health status leads to frequent unplanned downtime, high maintenance costs, and shortened equipment lifespan. Therefore, how to integrate process deviations and equipment status to achieve adaptive optimization and precise control of the dry screening process for rare earth ores, thereby improving its efficiency and stability, is a challenge facing the industry. Summary of the Invention

[0004] This application provides a method and system for controlling the dry screening and separation of rare earth ores with multi-stage particle size. It can integrate process deviations and equipment status to achieve adaptive optimization and precise control of the dry screening process of rare earth ores, thereby improving the efficiency and stability of the dry screening process of rare earth ores.

[0005] In a first aspect, this application provides a method for controlling the multi-stage particle size dry screening and separation of rare earth ores, the method comprising the following steps:

[0006] Obtain the particle size data of the target rare earth ore feed, and determine the target feed particle size spectrum of the target rare earth ore feed based on the particle size data;

[0007] Real-time monitoring of the real-time discharge particle size data of the target rare earth ore feed after classification by multi-stage screening equipment; determination of the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data; screening detection based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process.

[0008] Obtain the screen feature vector of the multi-stage screen, determine the screen health index based on the screen feature vector, and perform information fusion based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore is fed for dry screening.

[0009] Based on the screening efficiency index, a dynamic adjustment screening control strategy for the screening equipment is generated.

[0010] In this embodiment, the particle size data of the target rare earth ore feed is obtained by using a millimeter-wave radar sensor to detect the data of the target rare earth ore feed, thereby obtaining the particle size data of the target rare earth ore feed.

[0011] In this embodiment, determining the target feed particle size profile of the target rare earth ore feed based on the ore particle size data specifically includes:

[0012] Determine the characteristic values ​​of each particle size based on the ore particle size data;

[0013] The target feed particle size spectrum of the target rare earth ore is generated by using various particle size characteristic values.

[0014] In this embodiment, determining the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data specifically includes:

[0015] Based on the real-time discharge particle size data, the efficiency vectors of each screening stage are calculated and generated;

[0016] Based on the real-time discharge particle size data, process deviation is quantified to generate mismatch rate vectors at each level;

[0017] The multi-stage screening particle size state vector of the target rare earth ore feed is determined by the screening efficiency vectors and mismatch rate vectors of each stage.

[0018] In this embodiment, the screening detection based on the target feed particle size spectrum and the multi-stage screening particle size state vector, to obtain the screening deviation rate of the target rare earth ore during the screening process, specifically includes:

[0019] The current feed screening deviation vector is obtained by performing a balance back-calculation based on the multi-stage screening particle size state vector.

[0020] Based on the current feed screening deviation vector and the target feed particle size spectrum, particle size deviation is detected to obtain the particle size deviation index of the target rare earth ore during the screening process.

[0021] By using the screening deviation vector of the current feed and the particle size spectrum of the target feed, the screening efficiency is detected, and the efficiency deviation index of the target rare earth ore during the screening process is obtained.

[0022] The screening deviation rate of the target rare earth ore during the screening process is determined based on the particle size deviation index and the efficiency deviation index.

[0023] In this embodiment, the screening deviation rate is used to characterize the overall deviation of the actual operating state of the rare earth ore dry screening system from the ideal state.

[0024] In this embodiment, the screening efficiency index for dry screening of the target rare earth ore feed is obtained by fusing information based on the screen health index and the screening deviation rate, specifically including:

[0025] The screening deviation rate is reverse normalized to obtain the screening performance index.

[0026] The screening efficiency index is obtained by fusing the screening performance index and the screen health index to obtain the screening efficiency index of the target rare earth ore feed during dry screening.

[0027] In this embodiment, the screening efficiency index is an index used to quantify the overall operating efficiency level of the dry screening system.

[0028] In this embodiment, the screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index specifically includes:

[0029] Initialize each screening threshold;

[0030] The screening control strategy for dynamically adjusting the screening equipment is determined by the screening efficiency index and various screening thresholds.

[0031] Secondly, this application provides a multi-stage dry screening and sorting control system for rare earth ores, used to execute a multi-stage dry screening and sorting control method for rare earth ores, the control system comprising:

[0032] The data acquisition module is used to acquire the particle size data of the target rare earth ore feed and determine the target feed particle size spectrum of the target rare earth ore feed based on the particle size data.

[0033] The screening and detection module is used to monitor the real-time discharge particle size data of the target rare earth ore feed after it has been graded by a multi-stage screening device. Based on the real-time discharge particle size data, the multi-stage screening particle size state vector of the target rare earth ore feed is determined. Screening detection is performed based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process.

[0034] The information fusion module is used to obtain the screen feature vector of the multi-level screen, determine the screen health index based on the screen feature vector, and perform information fusion based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore feed is dry screened.

[0035] The screening control module is used to generate a dynamic adjustment screening control strategy for the screening equipment based on the screening efficiency index.

[0036] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0037] The process involves: acquiring particle size data of the target rare earth ore feed; determining the target feed particle size spectrum based on the particle size data; monitoring the real-time discharge particle size data of the target rare earth ore feed after grading by a multi-stage screening device; determining the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data; performing screening detection based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process; acquiring the screen feature vector of the multi-stage screen; determining the screen health index based on the screen feature vector; fusing information based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore feed is dry-screened; and generating a screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index.

[0038] Therefore, this application firstly establishes a precise process benchmark by systematically collecting ore particle size data and constructing a target feed particle size spectrum. This defines the ideal distribution range of each particle size, enabling real-time judgment of whether the current feed meets the screening process requirements and providing an objective reference for subsequent calculations. Secondly, by analyzing the discharge particle size data of each screen level in real time, a multi-level screening particle size state vector is constructed and compared with the target feed particle size spectrum. This achieves a quantitative diagnosis of the screening process quality, transforming the screening process status into a screening deviation rate index. This allows for real-time evaluation of the degree to which the current screening effect deviates from the ideal state, accurately locating process steps that lead to efficiency decline or quality fluctuations. This provides a precise quantitative basis for subsequent intelligent control and optimization, thereby improving the screening efficiency. The system assesses the efficiency and stability of the process. Furthermore, by constructing a screen feature vector and converting it into a screen health index, it achieves real-time, objective evaluation of the equipment's mechanical condition. It intelligently integrates equipment health status with screening deviation rates, which characterize process quality, to generate a screening efficiency index that reflects both process performance and equipment status. This enables the control system to make more precise decisions, ultimately achieving a balance between production efficiency and equipment stability. Finally, by mapping the screening efficiency index to preset efficiency levels and control thresholds, it achieves automatic decision-making from comprehensive status assessment to tiered and refined control commands, establishing a complete closed-loop control logic. This allows for dynamic adjustment of process parameters, adapting to raw material fluctuations and equipment status, and improving the overall system energy efficiency and operational reliability.

[0039] In summary, the technical solution adopted in this application can integrate process deviations and equipment status to achieve adaptive optimization and precise control of the dry screening process of rare earth ores, thereby improving the efficiency and stability of the dry screening process of rare earth ores. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an exemplary flowchart of a method for controlling the multi-stage particle size dry screening of rare earth ores according to this application;

[0042] Figure 2 This is a flowchart illustrating the process of obtaining the screening deviation rate of the target rare earth ore during the screening process, based on the information provided in this application.

[0043] Figure 3This is a flowchart illustrating the screening efficiency index of the target rare earth ore feed during dry screening, as provided in this application.

[0044] Figure 4 This is a module structure diagram of a multi-stage particle size dry screening and sorting control system for rare earth ores provided in this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0046] This application provides a method and system for controlling the multi-stage dry screening and separation of rare earth ores. The core of this method involves: acquiring the particle size data of the target rare earth ore feed; determining the target feed particle size spectrum based on the particle size data; monitoring the real-time discharge particle size data of the target rare earth ore feed after grading by a multi-stage screening device; determining the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data; performing screening detection based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process; acquiring the screen feature vector of the multi-stage screens; determining the screen health index based on the screen feature vector; fusing the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore feed undergoes dry screening; and generating a screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index.

[0047] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a multi-stage particle size dry screening and separation control method for rare earth ores according to this embodiment of the present application. The control method includes the following steps:

[0048] In step S1, the particle size data of the target rare earth ore feed is obtained, and the target feed particle size spectrum of the target rare earth ore feed is determined based on the particle size data.

[0049] In this embodiment, the particle size data of the target rare earth ore feed is obtained by using a millimeter-wave radar sensor to detect the target rare earth ore feed, thereby obtaining the particle size data of the target rare earth ore feed. Specifically, the millimeter-wave radar sensor installed at the head of the feed conveyor belt can receive the echo spectrum, and then calculate the moments of each echo spectrum. The zero-order moment represents the total power, the first-order moment represents the average conveying speed, and the second-order moment represents the speed variance. Then, the particle size distribution estimation array is calculated by experimental calibration method, usually expressed in the form of a mass percentage array of each particle size, such as: {"+25mm": 15%, "25-10mm": 30%, "10-2mm": 40%, "-2mm": 15%}. The obtained particle size distribution estimation array is then used as the particle size data of the target rare earth ore feed.

[0050] In this embodiment, determining the target feed particle size profile of the target rare earth ore feed based on the ore particle size data can be achieved through the following steps:

[0051] Determine the characteristic values ​​of each particle size based on the ore particle size data;

[0052] The target feed particle size spectrum of the target rare earth ore is generated by using various particle size characteristic values.

[0053] In practical implementation, the particle size characteristic values ​​are determined based on the ore particle size data. That is, for each particle size distribution in the ore particle size data, such as the particle size distribution of the +25mm particle size, the distribution characteristic value is taken as the distribution characteristic value of the +25mm particle size. 10% of the distribution characteristic value is taken as the standard deviation characteristic value. The distribution characteristic value and the standard deviation characteristic value are taken as the particle size characteristic values ​​of each particle size, thus obtaining the particle size characteristic values ​​of each particle size. Then, the target feed particle size spectrum of the target rare earth ore can be generated through each particle size characteristic value. That is, for each particle size, the distribution characteristic value corresponding to the particle size is added to twice the standard deviation characteristic value, and the result is taken as the control upper limit. The distribution characteristic value corresponding to the particle size is subtracted from twice the standard deviation characteristic value, and the result is taken as the control lower limit, thus obtaining the control upper limit and control lower limit of each particle size. The vector composed of the distribution characteristic value, standard deviation characteristic value, control upper limit and control lower limit corresponding to each particle size is taken as the target feed particle size spectrum of the target rare earth ore.

[0054] It should be noted that by systematically collecting ore particle size data and constructing a target feed particle size spectrum, a precise process benchmark was established, the ideal distribution range of each particle size was defined, and it is possible to determine in real time whether the current feed meets the screening process requirements, and provide an objective reference for subsequent calculations.

[0055] In step S2, real-time discharge size data of the target rare earth ore feed is obtained, and the multi-stage screening particle size state vector of the target rare earth ore feed is determined based on the real-time discharge particle size data. Screening detection is performed based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process.

[0056] In practical implementation, real-time discharge rate data of the target rare earth ore feed is obtained. For each level of screen, laser emitters and receiver arrays can be installed at the inlet and outlet of the corresponding screen. Then, the total mass of fine particles on the screen, the total mass of fine particles under the screen, and the particle size distribution under the screen are obtained through inversion algorithm. The total mass of fine particles on the screen, the total mass of fine particles under the screen, and the particle size distribution under the screen are used as the particle size data of the level, thus obtaining the particle size data of each level. The particle size data of each level is then used as the real-time discharge rate data of the target rare earth ore feed.

[0057] In this embodiment, determining the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data can be achieved through the following steps:

[0058] Based on the real-time discharge particle size data, the efficiency vectors of each screening stage are calculated and generated;

[0059] Based on the real-time discharge particle size data, process deviation is quantified to generate mismatch rate vectors at each level;

[0060] The multi-stage screening particle size state vector of the target rare earth ore feed is determined by the screening efficiency vectors and mismatch rate vectors of each stage.

[0061] In specific implementation, firstly, the screening efficiency vector for each stage can be determined based on real-time discharge particle size data. That is, for the i-th stage screen with aperture Di, for each stage of the real-time discharge particle size data, the mass of all fine particles smaller than aperture Di in each stage of the screen is summed to obtain the total mass of all fine particles smaller than aperture Di. The total mass of fine particles smaller than aperture Di in the i-th stage undersize particles is divided by the total mass of all fine particles smaller than aperture Di, and the result is used as the screening efficiency of the i-th stage screen, thus obtaining the screening efficiency of each stage of the screen. All screening efficiencies are used as vector elements to form a vector, and the resulting vector is used as the screening efficiency vector for each stage. Then, the process can be performed based on the real-time discharge particle size data. Deviation quantification generates mismatch rate vectors for each level. Specifically, the percentage of fine particles larger than aperture Di can be extracted from the undersize particle mass distribution data of the i-th level screen in the real-time discharge particle size data. This percentage is then used as the mismatch rate of the i-th level screen, thus obtaining the mismatch rate of each screen. The mismatch rates of each screen are then used as vector elements to form a vector, which is then used as the mismatch rate vector for each level. Finally, the multi-level screening particle size state vector of the target rare earth ore feed can be determined by using the screening efficiency vector and the mismatch rate vector. Specifically, the screening efficiency vector and the mismatch rate vector can be directly concatenated, and the resulting vector is then concatenated with the undersize particle mass distribution data for each level to obtain the multi-level screening particle size state vector of the target rare earth ore feed.

[0062] Preferably, in this embodiment, screening detection is performed based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process, with reference to... Figure 2 As shown in the figure, this is a flowchart illustrating the process of obtaining the screening deviation rate of the target rare earth ore during the screening process in some embodiments of this application. In this embodiment, obtaining the screening deviation rate of the target rare earth ore during the screening process can be achieved by the following steps:

[0063] In step S21, the current feed screening deviation vector is obtained by back-calculating the balance based on the multi-stage screening particle size state vector.

[0064] In step S22, particle size deviation is detected based on the current feed screening deviation vector and the target feed particle size spectrum to obtain the particle size deviation index of the target rare earth ore during the screening process.

[0065] In step S23, the screening efficiency is detected by the screening deviation vector of the current feed and the particle size spectrum of the target feed, and the efficiency deviation index of the target rare earth ore in the screening process is obtained.

[0066] In step S24, the screening deviation rate of the target rare earth ore during the screening process is determined based on the particle size deviation index and the efficiency deviation index.

[0067] In specific implementation, firstly, a balance back-calculation is performed based on the multi-level screening particle size state vector to obtain the screening deviation vector of the current feed. That is, based on the multi-level screening particle size state vector, the mass distribution of the undersize particles at each level is extracted. The total mass of all fine particles smaller than aperture Di is divided by the total mass of all fine particles, and the result is used as the screening particle size distribution of aperture Di, thus obtaining the screening particle size distribution of each aperture. The screening particle size distribution of each aperture is used as vector elements to form a vector. The obtained vector is directly concatenated with the screening efficiency vector of each level, and the resulting vector is used as the screening deviation vector of the current feed. Then, based on the current... The particle size deviation is detected by comparing the current feed sieving deviation vector with the target feed particle size spectrum to obtain the particle size deviation index. Specifically, for each particle size class, the sieving particle size distribution of the corresponding particle size class in the sieving deviation vector is subtracted from the distribution characteristic value of that particle size class in the target feed particle size spectrum. The absolute value of the result is taken as the particle size deviation for that particle size class. This process is repeated for each particle size class. The particle size deviations of all particle size classes are then summed, and the sum is divided by the total number of particle size classes. The resulting value is the particle size deviation index. It should be noted that the particle size deviation index measures the difference between the current actual feed particle size distribution and the target feed particle size spectrum. The degree of deviation between them; furthermore, by using the current feed screening deviation vector and the target feed particle size spectrum to detect screening efficiency, an efficiency deviation index is obtained. That is, for the target feed particle size spectrum, the reference efficiency of each level of screen can be obtained through historical data analysis. For each level of screen, the screening efficiency of that level of screen in the screening efficiency vector is subtracted from the reference efficiency of the corresponding screen. The result is used as the loss margin of that level of screen. When the loss margin is negative, it is taken as 0, thus obtaining the loss margin of each level of screen. All loss margins are summed, and the result is divided by the sum of the reference efficiencies of each level of screen. The result serves as the efficiency deviation index. It should be noted that the efficiency deviation index measures the difference between the actual working efficiency and the theoretical efficiency of the screening system. Finally, the screening deviation rate of the target rare earth ore during the screening process can be determined based on the particle size deviation index and the efficiency deviation index. That is, the particle size deviation index and the efficiency deviation index can be summed, the result divided by 2, and the result is used as the screening deviation rate of the target rare earth ore during the screening process. It should be noted that the screening deviation rate is used to characterize the overall deviation of the actual operating state of the dry screening system for rare earth ore from the ideal state.

[0068] It should be noted that by analyzing the particle size data of the output from each level of screen in real time, a multi-level screening particle size state vector is constructed. This vector is then compared with the target feed particle size spectrum to achieve a quantitative diagnosis of the screening process quality. The screening process status can be transformed into a screening deviation rate index, which can evaluate in real time the degree to which the current screening effect deviates from the ideal state. This allows for precise identification of process links that lead to decreased efficiency or quality fluctuations, providing accurate quantitative basis for subsequent intelligent control and optimization, thereby improving the efficiency and stability of the screening process.

[0069] In step S3, the screen feature vector of the multi-stage screen is obtained, the screen health index is determined according to the screen feature vector, and the information is fused based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore is fed for dry screening.

[0070] In practice, the feature vectors of the multi-level screens are obtained. For each screen, an industrial camera can be installed above the screen to obtain a high-definition image of the screen surface. Image analysis is performed on the high-definition image of the screen surface to identify the proportion of the torn area of ​​the screen to the total area of ​​the screen. The result is used as the screen breakage ratio of the screen, thereby obtaining the breakage ratio of each level of screen. The breakage ratios of each level of screen are then used as vector elements to form a vector, and the resulting vector is used as the feature vector of the multi-level screen.

[0071] In practical implementation, the screen health index can be determined based on the screen feature vector. That is, the screen failure threshold can be obtained through historical data. For each level of screen breakage ratio in the screen feature vector, the screen breakage ratio is divided by the screen failure threshold, and then 1 is subtracted from the result. The result is taken as the screen breakage degree of that level. If the result is negative, it is taken as 0. Thus, the screen breakage degree of each level of screen is obtained. For each level of screen breakage degree, the maximum value is selected as the screen health index.

[0072] Preferably, in this embodiment, information fusion is performed based on the screen health index and the screening deviation rate to obtain the screening efficiency index of the target rare earth ore feed during dry screening, with reference to... Figure 3 As shown in the figure, this is a flowchart illustrating the process of obtaining the screening efficiency index of the target rare earth ore feed during dry screening in some embodiments of this application. Specifically, obtaining the screening efficiency index of the target rare earth ore feed during dry screening in this embodiment can be achieved through the following steps:

[0073] In step S31, the screening deviation rate is reverse normalized to obtain the screening performance index.

[0074] In step S32, the screening performance index and the screen health index are fused together to obtain the screening efficiency index of the target rare earth ore feed during dry screening.

[0075] In practice, firstly, the screening deviation rate can be reversed to obtain the screening performance index. That is, the screening deviation rate can be subtracted from 1, and the result can be used as the screening performance index. Then, the screening performance index and the screen health index can be fused to obtain the screening efficiency index for dry screening of the target rare earth ore feed. That is, the screening performance index can be multiplied by the screen health index, and the result can be used as the screening efficiency index for dry screening of the target rare earth ore feed. It should be noted that the screening efficiency index is used to quantify the overall operating efficiency level of the dry screening system.

[0076] It should be noted that by constructing a screen feature vector and converting it into a screen health index, a real-time and objective assessment of the equipment's mechanical condition is achieved. The health condition of the equipment is intelligently integrated with the screening deviation rate, which characterizes the process quality, to generate a screening efficiency index that reflects the process performance and equipment condition. This enables the control system to make more accurate decisions and ultimately achieve a balance between production efficiency and equipment stability.

[0077] In step S4, a screening control strategy for dynamically adjusting the screening equipment is generated based on the screening efficiency index.

[0078] In this embodiment, the screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index can be implemented using the following steps:

[0079] Initialize each screening threshold;

[0080] The screening control strategy for dynamically adjusting the screening equipment is determined by the screening efficiency index and various screening thresholds.

[0081] In practice, firstly, each screening threshold is initialized, meaning that each screening threshold can be preset based on historical data statistics. Then, the screening control strategy for dynamically adjusting the screening equipment can be determined by the screening efficiency index and each screening threshold. That is, the screening efficiency index can be compared with each screening threshold to obtain the screening control strategy. For example: when the screening efficiency index is greater than 0.6, the screening control strategy is to maintain operation; when the screening efficiency index is less than 0.6 but greater than 0.4, the screening control strategy is to reduce the feed rate by 10% and increase the screen vibration intensity by 5%; when the screening efficiency index is less than 0.4 but greater than 0.1, the screening control strategy is to reduce the feed rate by 50% and adjust the screen vibration intensity to its original position; when the screening efficiency index is less than 0.1, the screening control strategy is to shut down the machine immediately for a safety inspection. Thus, the screening control strategy is obtained.

[0082] It should be noted that by mapping the screening efficiency index to preset efficiency levels and control thresholds, automatic decision-making from comprehensive status assessment to graded and refined control commands is achieved, establishing a complete closed-loop control logic that can dynamically adjust process parameters, adapt to raw material fluctuations and equipment status, and improve the overall energy efficiency and operational reliability of the system.

[0083] Therefore, this application firstly establishes a precise process benchmark by systematically collecting ore particle size data and constructing a target feed particle size spectrum. This defines the ideal distribution range of each particle size, enabling real-time judgment of whether the current feed meets the screening process requirements and providing an objective reference for subsequent calculations. Secondly, by analyzing the discharge particle size data of each screen level in real time, a multi-level screening particle size state vector is constructed and compared with the target feed particle size spectrum. This achieves a quantitative diagnosis of the screening process quality, transforming the screening process status into a screening deviation rate index. This allows for real-time evaluation of the degree to which the current screening effect deviates from the ideal state, accurately locating process steps that lead to efficiency decline or quality fluctuations. This provides a precise quantitative basis for subsequent intelligent control and optimization, thereby improving the screening efficiency. The system assesses the efficiency and stability of the process. Furthermore, by constructing a screen feature vector and converting it into a screen health index, it achieves real-time, objective evaluation of the equipment's mechanical condition. It intelligently integrates equipment health status with screening deviation rates, which characterize process quality, to generate a screening efficiency index that reflects both process performance and equipment status. This enables the control system to make more precise decisions, ultimately achieving a balance between production efficiency and equipment stability. Finally, by mapping the screening efficiency index to preset efficiency levels and control thresholds, it achieves automatic decision-making from comprehensive status assessment to tiered and refined control commands, establishing a complete closed-loop control logic. This allows for dynamic adjustment of process parameters, adapting to raw material fluctuations and equipment status, and improving the overall system energy efficiency and operational reliability.

[0084] In summary, the technical solution adopted in this application can integrate process deviations and equipment status to achieve adaptive optimization and precise control of the dry screening process of rare earth ores, thereby improving the efficiency and stability of the dry screening process of rare earth ores.

[0085] Example 2: This application provides a multi-stage particle size dry screening and sorting control system for rare earth ores, referring to... Figure 4 As shown in the figure, this is a modular structure diagram of a multi-stage dry screening and sorting control system for rare earth ores according to this embodiment of the present application. The control system includes:

[0086] The data acquisition module 100 is used to acquire the particle size data of the target rare earth ore feed and determine the target feed particle size spectrum of the target rare earth ore feed based on the particle size data.

[0087] The screening and detection module 200 is used to monitor the real-time discharge particle size data of the target rare earth ore feed after it has been graded by a multi-stage screening device. Based on the real-time discharge particle size data, the multi-stage screening particle size state vector of the target rare earth ore feed is determined. Screening detection is performed based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process.

[0088] The information fusion module 300 is used to obtain the screen feature vector of the multi-level screen, determine the screen health index based on the screen feature vector, and perform information fusion based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore feed is dry screened.

[0089] The screening control module 400 is used to generate a screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0092] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for controlling the multi-stage particle size dry screening and separation of rare earth ores, characterized in that, The control method includes the following steps: Obtain the particle size data of the target rare earth ore feed, and determine the target feed particle size spectrum of the target rare earth ore feed based on the particle size data; The system monitors the real-time discharge particle size data of the target rare earth ore feed after it has been graded by a multi-stage screening device. Based on the real-time discharge particle size data, it determines the multi-stage screening particle size state vector of the target rare earth ore feed. It then performs a balance back-calculation based on the multi-stage screening particle size state vector to obtain the screening deviation vector of the current feed. Based on the screening deviation vector of the current feed and the target feed particle size spectrum, it performs particle size deviation detection to obtain the particle size deviation index of the target rare earth ore during the screening process. Finally, it performs screening efficiency detection using the screening deviation vector of the current feed and the target feed particle size spectrum to obtain the efficiency deviation index of the target rare earth ore during the screening process. Based on the particle size deviation index and the efficiency deviation index, it determines the screening deviation rate of the target rare earth ore during the screening process. The screening deviation rate is used to characterize the overall deviation of the actual operating state of the rare earth ore dry screening system from the ideal state. Obtain the screen feature vector of the multi-stage screen, determine the screen health index based on the screen feature vector, and perform information fusion based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore is fed for dry screening. Based on the screening efficiency index, a dynamic adjustment screening control strategy for the screening equipment is generated.

2. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, The particle size data of the target rare earth ore feed is obtained by using a millimeter-wave radar sensor to detect the data of the target rare earth ore feed, thereby obtaining the particle size data of the target rare earth ore feed.

3. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, Determining the target feed particle size profile of the target rare earth ore based on the ore particle size data specifically includes: Determine the characteristic values ​​of each particle size based on the ore particle size data; The target feed particle size spectrum of the target rare earth ore is generated by using various particle size characteristic values.

4. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, Determining the multi-stage screening particle size state vector of the target rare earth ore feed based on the real-time discharge particle size data specifically includes: Based on the real-time discharge particle size data, the efficiency vectors of each screening stage are calculated and generated; Based on the real-time discharge particle size data, process deviation is quantified to generate mismatch rate vectors at each level; The multi-stage screening particle size state vector of the target rare earth ore feed is determined by the screening efficiency vectors and mismatch rate vectors of each stage.

5. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, Based on the fusion of the screen health index and the screening deviation rate, the screening efficiency index for dry screening of the target rare earth ore feed is obtained, specifically including: The screening deviation rate is reverse normalized to obtain the screening performance index. The screening efficiency index is obtained by fusing the screening performance index and the screen health index to obtain the screening efficiency index of the target rare earth ore feed during dry screening.

6. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, The screening efficiency index is an index used to quantify the overall operational efficiency level of a dry screening system.

7. The method for controlling the multi-stage particle size dry screening and separation of rare earth ores as described in claim 1, characterized in that, The screening control strategy for dynamically adjusting the screening equipment based on the screening efficiency index specifically includes: Initialize each screening threshold; The screening control strategy for dynamically adjusting the screening equipment is determined by the screening efficiency index and various screening thresholds.

8. A multi-stage dry screening and sorting control system for rare earth ores, used to execute the multi-stage dry screening and sorting control method for rare earth ores as described in any one of claims 1 to 7, characterized in that, The control system includes: The data acquisition module is used to acquire the particle size data of the target rare earth ore feed and determine the target feed particle size spectrum of the target rare earth ore feed based on the particle size data. The screening and detection module is used to monitor the real-time discharge particle size data of the target rare earth ore feed after it has been graded by a multi-stage screening device. Based on the real-time discharge particle size data, the multi-stage screening particle size state vector of the target rare earth ore feed is determined. Screening detection is performed based on the target feed particle size spectrum and the multi-stage screening particle size state vector to obtain the screening deviation rate of the target rare earth ore during the screening process. The information fusion module is used to obtain the screen feature vector of the multi-level screen, determine the screen health index based on the screen feature vector, and perform information fusion based on the screen health index and the screening deviation rate to obtain the screening efficiency index when the target rare earth ore feed is dry screened. The screening control module is used to generate a dynamic adjustment screening control strategy for the screening equipment based on the screening efficiency index.

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