Intelligent grinding control method, device, medium and equipment

Abstract

This disclosure provides an intelligent grinding control method, apparatus, medium, and equipment. The method includes: collecting equipment operating status and process flow information of the grinding process system, processing the information to obtain historical data samples; then using the historical data samples to determine various operating conditions of the grinding process system and their corresponding sample ranges, and determining the parameter adjustment range accordingly; next, acquiring real-time equipment status and process information to obtain real-time data samples, determining the real-time operating conditions; finally, based on the real-time operating conditions and the sample parameter adjustment ranges, determining whether the real-time data samples conform to the parameter adjustment range corresponding to the real-time operating conditions, and adjusting the operating parameters of the grinding process system accordingly. This disclosure, through real-time intelligent feedback regulation, makes the operation and control of the grinding process system reliable, thereby improving the yield and quality of grinding products, reducing unit ore energy consumption and process fluctuations, achieving process optimization of the production process, and increasing the economic benefits of enterprises.

Description

Technical Field

[0001] This disclosure relates to the field of ore processing, and more specifically, to an intelligent grinding control method, apparatus, medium, and equipment. Background Technology

[0002] Grinding is the process of gradually reducing ore particle size through the impact and abrasion of media (such as steel balls, steel bars, and gravel) and the ore itself, in order to meet the requirements of subsequent operations. In mineral processing, the main purpose of grinding is to liberate valuable minerals from gangue minerals as much as possible to meet the needs of subsequent beneficiation processes.

[0003] Currently, domestic and international mining grinding processes are mainly divided into two types: one is the conventional three-stage closed-circuit crushing + ball milling process, and the other is the semi-autogenous grinding + ball milling process. The semi-autogenous grinding + ball milling process can accept larger feed particle sizes (the maximum particle size is generally 200mm to 350mm), replacing the fine crushing and screening operations in conventional grinding processes, thus simplifying the process flow, reducing dust pollution, and reducing the land area required. Therefore, it has been widely used globally. However, the semi-autogenous grinding + ball milling process is quite sensitive to changes in feed properties (including feed size, hardness, grindability, etc.). With changes in the size and hardness of the raw ore feed, problems may arise such as easy fluctuations in the production process flow, large fluctuations in equipment operating parameters, relatively complex on-site production operation and control, untimely real-time manual adjustment and control of the grinding system, and unstable concentration of the grinding product. These issues affect the stability of the grinding process flow, and consequently, the overall process indicators (such as grade and recovery rate) and economic benefits of the beneficiation plant. Summary of the Invention

[0004] This disclosure provides at least one intelligent grinding control method, apparatus, medium, and equipment. Through real-time intelligent feedback adjustment, the grinding process system is reliably controlled, thereby increasing the yield and quality of grinding products, reducing energy consumption per unit ore and process fluctuations, optimizing the production process, and increasing the economic benefits of enterprises.

[0005] This disclosure provides an intelligent grinding control method, including:

[0006] Multiple sets of historical information from the grinding process system are acquired; and each set of historical information is processed to obtain multiple sets of historical information samples; the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples.

[0007] Based on the multiple sets of historical information samples, the different operating conditions of the grinding process system and the sample range of the grinding process system corresponding to the different operating conditions are determined respectively. Based on each operating condition and its corresponding sample range of the grinding process system, the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition is determined according to a preset method.

[0008] Real-time information of the grinding process system is acquired, and the real-time information is processed to obtain real-time information samples; the real-time operating status of the grinding process system is determined based on the real-time equipment information samples; the real-time information includes real-time equipment operating status information and real-time process flow information; the real-time information samples include real-time equipment operating status information samples and real-time process flow information samples.

[0009] Based on the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition, it is determined whether the real-time equipment operating status information sample meets the adjustment range of the sample parameters corresponding to the real-time operating condition; and the operating parameters of the grinding process system are adjusted according to the determination result.

[0010] In some possible embodiments, the information processing for each group of historical information includes:

[0011] For each set of historical device operating status information, data fuzzing processing is performed to obtain historical device operating status information with error information removed, and the historical device operating status information with error information removed is defuzzed to obtain a sample of the historical device operating status information.

[0012] For each set of historical process flow information, data fuzzing is performed to obtain historical process flow information with error information removed, and the historical process flow information with error information removed is then defuzzified to obtain a sample of the historical process flow information.

[0013] In some possible embodiments, the operating conditions include low, medium, high, and extremely high states; the sample range of the grinding process system includes a low sample range, a medium sample range, a high sample range, and an extremely high sample range; determining the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition based on a preset method includes:

[0014] When the operating condition is a low state, the adjustment range of the low sample parameters is determined based on a preset method and the low sample range.

[0015] When the operating condition is in the medium state, the adjustment range of the medium sample parameters is determined based on the preset method and the medium sample range.

[0016] When the operating condition is in the state, the adjustment range of the high sample parameter is determined based on the preset method and the high sample range;

[0017] When the operating condition is extremely high, the adjustment range of the extremely high sample parameters is determined based on a preset method and the extremely high sample range.

[0018] In some possible embodiments, after determining whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time operating condition based on the sample parameter adjustment range of the grinding process system corresponding to each operating condition, the process includes:

[0019] Based on the real-time operating condition, a target sample parameter adjustment range corresponding to the real-time operating condition is matched; the target sample parameter adjustment range includes one of the following: low sample parameter adjustment range, medium sample parameter adjustment range, high sample parameter adjustment range, and extremely high sample parameter adjustment range;

[0020] If the real-time equipment operating status information sample meets the target sample parameter adjustment range, the operating parameters of the grinding process system are adjusted based on the real-time equipment operating status information sample and the real-time process flow information sample.

[0021] In some possible embodiments, the operating parameters of the grinding process system include belt feeder frequency, feed lump size, water volume, and rotational speed.

[0022] In some possible embodiments, adjusting the operating parameters of the grinding process system based on the judgment result includes:

[0023] After the grinding process system has been in operation for a preset time, the historical sample range and real-time sample range of the grinding process system are obtained, and the sample parameter adjustment range of each grinding process system is adjusted based on the historical sample range and the real-time sample range.

[0024] In some possible embodiments, the grinding process system includes a single-stage ball mill system, a single-stage semi-autogenous grinding system, a semi-autogenous grinding-ball mill system, and a ball mill-ball mill system.

[0025] This disclosure provides an intelligent grinding control device, including:

[0026] The historical information acquisition module is used to acquire multiple sets of historical information from the grinding process system; and to process each set of historical information to obtain multiple sets of historical information samples; the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples.

[0027] The adjustment range determination module is used to determine different operating conditions of the grinding process system and the sample range of the grinding process system corresponding to the different operating conditions based on the multiple sets of historical information samples, and to determine the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition based on a preset method according to each operating condition and its corresponding sample range of the grinding process system.

[0028] A real-time information acquisition module is used to acquire real-time information of the grinding process system, process the real-time information to obtain real-time information samples, and determine the real-time operating status of the grinding process system based on the real-time equipment information samples. The real-time information includes real-time equipment operating status information and real-time process flow information. The real-time information samples include real-time equipment operating status information samples and real-time process flow information samples.

[0029] The parameter information judgment module is used to determine whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time operating status based on the sample parameter adjustment range of the grinding process system corresponding to each operating condition; and to adjust the operating parameters of the grinding process system according to the judgment result.

[0030] In some possible embodiments, the historical information acquisition module is specifically used for:

[0031] For each set of historical device operating status information, data fuzzing processing is performed to obtain historical device operating status information with error information removed, and the historical device operating status information with error information removed is defuzzed to obtain a sample of the historical device operating status information.

[0032] For each set of historical process flow information, data fuzzing is performed to obtain historical process flow information with error information removed, and the historical process flow information with error information removed is then defuzzified to obtain a sample of the historical process flow information.

[0033] In some possible embodiments, the operating conditions include low, medium, high, and extremely high states; the sample range of the grinding process system includes a low sample range, a medium sample range, a high sample range, and an extremely high sample range; the adjustment range determination module is specifically used for:

[0034] When the operating condition is a low state, the adjustment range of the low sample parameters is determined based on a preset method and the low sample range.

[0035] When the operating condition is in the medium state, the adjustment range of the medium sample parameters is determined based on the preset method and the medium sample range.

[0036] When the operating condition is high, the adjustment range of the high sample parameter is determined based on a preset method and the high sample range.

[0037] When the operating condition is extremely high, the adjustment range of the extremely high sample parameters is determined based on a preset method and the extremely high sample range.

[0038] In some possible embodiments, the parameter information determination module is further used for:

[0039] Based on the real-time operating condition, a target sample parameter adjustment range corresponding to the real-time operating condition is matched; the target sample parameter adjustment range includes one of the following: low sample parameter adjustment range, medium sample parameter adjustment range, high sample parameter adjustment range, and extremely high sample parameter adjustment range;

[0040] If the real-time equipment operating status information sample meets the target sample parameter adjustment range, the operating parameters of the grinding process system are adjusted based on the real-time equipment operating status information sample and the real-time process flow information sample.

[0041] In some possible embodiments, the operating parameters of the grinding process system include belt feeder frequency, feed lump size, water volume, and rotational speed.

[0042] In some possible embodiments, the parameter information determination module is further used for:

[0043] After the grinding process system has been in operation for a preset time, the historical sample range and real-time sample range of the grinding process system are obtained, and the sample parameter adjustment range of each grinding process system is adjusted based on the historical sample range and the real-time sample range.

[0044] In some possible embodiments, the grinding process system includes a single-stage ball mill system, a single-stage semi-autogenous grinding system, a semi-autogenous grinding-ball mill system, and a ball mill-ball mill system.

[0045] This disclosure provides an electronic device, including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the intelligent grinding control method described in any of the above possible embodiments is executed.

[0046] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the intelligent grinding control method as described in any of the possible embodiments above.

[0047] The intelligent grinding control method, device, electronic equipment, and storage medium provided in this disclosure collect equipment operating status and process flow information of the grinding process system, process the information to obtain historical data samples, then use the historical data samples to determine various operating conditions of the grinding process system and their corresponding sample ranges, and determine the parameter adjustment range accordingly; next, acquire real-time equipment status and process information to obtain real-time data samples, determine the real-time operating conditions, and finally, based on the real-time operating conditions and sample parameter adjustment ranges, determine whether the real-time data samples conform to the parameter adjustment range corresponding to the real-time operating conditions, and adjust the working parameters of the grinding process system accordingly. Thus, this disclosure makes the grinding process system operation control reliable through real-time intelligent feedback adjustment, thereby improving the yield and quality of grinding products, reducing unit ore energy consumption and process fluctuations, and achieving process optimization of the production process. This enables unmanned operation and automated, intelligent, and information-based production management of the entire grinding process system in the mine beneficiation plant, solving the "bottleneck" problem of intelligent grinding in mines and improving the level of digital operation in mines.

[0048] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0050] Figure 1 A flowchart of an intelligent grinding control method provided by an embodiment of this disclosure is shown;

[0051] Figure 2 The flowchart illustrates a specific method for determining the parameter adjustment range in the intelligent grinding control method provided in this embodiment of the present disclosure.

[0052] Figure 3 A schematic diagram of the structure of an intelligent grinding control device provided in an embodiment of this disclosure is shown;

[0053] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0056] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0057] Grinding is an operation that uses the impact and abrasive action of media (steel balls, steel bars, gravel) and the ore itself to reduce the particle size of the ore until it is ground to a size that meets the requirements of subsequent processing. In mineral processing, the purpose of grinding is to maximize the liberation of valuable minerals from gangue minerals in the ore, so as to provide a qualified product particle size that meets the requirements of subsequent beneficiation processes.

[0058] Research has revealed two main types of grinding processes in domestic and international mines: one is the conventional three-stage closed-circuit crushing + ball milling process, and the other is the semi-autogenous grinding + ball milling process. The semi-autogenous grinding + ball milling system can accept larger feed particle sizes (generally 200mm-350mm), replacing fine crushing and screening operations in conventional grinding processes, simplifying the process flow, reducing dust pollution, and minimizing land occupation, thus gaining widespread application globally. However, the semi-autogenous grinding + ball milling system is highly sensitive to changes in feed properties (including feed size, hardness, grindability, etc.). As the size and hardness of the raw ore change, the production process is prone to fluctuations, equipment operating parameters fluctuate significantly, on-site production operation and control are relatively complex, manual real-time adjustment and control of the grinding system are untimely, and the fineness of the ground product is unstable. These problems have remained largely unresolved, and the volatility of the grinding process severely hinders the overall process indicators (grade and recovery rate) and economic benefits of the entire concentrator.

[0059] Based on the above research, this disclosure provides an intelligent grinding control method, device, medium, and equipment. First, by collecting equipment operating status and process flow information of the grinding process system, historical data samples are obtained through information processing. Then, the historical data samples are used to determine various operating conditions of the grinding process system and their corresponding sample ranges, and the parameter adjustment range is determined accordingly. Second, real-time equipment status and process information are acquired to obtain real-time data samples, and the real-time operating conditions are determined. Finally, based on the real-time operating conditions and the sample parameter adjustment range, it is determined whether the real-time data samples conform to the parameter adjustment range corresponding to the real-time operating conditions, and the operating parameters of the grinding process system are adjusted accordingly.

[0060] In this embodiment, real-time intelligent feedback adjustment makes the grinding process system reliable in operation and control, thereby improving the output and quality of grinding products, reducing energy consumption per unit ore and process fluctuations, and realizing process optimization of the production process. This enables the unmanned operation and automation, intelligence and informatization of the entire grinding process system of the mine beneficiation plant, solves the "bottleneck" problem of intelligent grinding in the mine, and improves the level of digital operation of the mine.

[0061] To facilitate understanding of this embodiment, the executing entity of the intelligent grinding control method provided in this disclosure will first be described in detail. The executing entity of the intelligent grinding control method provided in this disclosure is an electronic device. This electronic device can be a terminal device or a server. The terminal device can also be a mobile device, a user terminal, a terminal, a handheld device, a computing device, an in-vehicle device, a wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of electronic devices and servers.

[0062] The intelligent grinding control method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings. See also Figure 1 The diagram shown is a flowchart of an intelligent grinding control method provided in an embodiment of this disclosure. The intelligent grinding control method includes the following steps S101 to S104:

[0063] S101, acquire multiple sets of historical information of the grinding process system; and process each set of historical information to obtain multiple sets of historical information samples.

[0064] It is understood that the grinding process system can be one of a single-stage ball mill system, a single-stage semi-autogenous grinding system, a semi-autogenous grinding-ball mill system, or a ball mill-ball mill system.

[0065] Here, the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples. Specifically, the equipment operating status information covers various key parameters during the operation of the grinding process system. These parameters include, but are not limited to: belt feeder operating frequency, belt conveyor operating frequency, semi-autogenous mill power, semi-autogenous mill operating frequency, semi-autogenous mill shaft pressure, ball mill power, ball mill shaft pressure, number of hydrocyclone groups, and hydrocyclone pressure. This information reflects the status and performance of each part of the grinding process system during operation, which is of great significance for understanding equipment operation and optimizing the production process. Secondly, the process flow information may include, but is not limited to: feed particle size, feed rate, grinding concentration, pump tank level, and grinding product particle size. Here, feed particle size refers to the particle size of the raw material, while feed rate indicates the mass of ore processed by the grinding system per unit time. Grinding concentration reflects the content of solid particles in the liquid during grinding and is one of the important parameters affecting grinding efficiency. Pump tank level refers to the liquid level of the slurry in the pump tank, which directly relates to the stability of the entire system. Grinding product particle size refers to the particle size of the final product obtained after grinding and is one of the important indicators for measuring the grinding process efficiency. This process information affects the efficiency and product quality of the grinding process and is of great significance for monitoring and regulating the operation of the grinding system.

[0066] In some other embodiments, the grinding process system also includes additional operating parameters. Specifically, these may include level gauge data to monitor the liquid level in various parts of the grinding process system, thereby adjusting the liquid content during the grinding process; slurry pump frequency, which refers to the operating frequency of the slurry pump, used to control the slurry delivery speed and flow rate; valve opening degree, which refers to the degree of opening of valves controlling the fluid flow in the grinding process system, affecting the fluid flow rate and speed; and flow meters, used to measure the fluid flow rate, which is crucial for understanding the fluid delivery situation and process control. Furthermore, particle size analyzer data may also be included to analyze and monitor the particle size and distribution of the output material of the grinding process system, in order to evaluate the grinding effect and product quality.

[0067] For example, when processing the historical information for each group, the following (1) to (2) may be included:

[0068] (1) For each group of historical equipment operation status information, perform data fuzzing processing to obtain historical equipment operation status information with error information removed, and perform defuzzing processing on the historical equipment operation status information with error information removed to obtain the historical equipment operation status information sample.

[0069] (2) For each group of historical process information, perform data fuzzing processing to obtain historical process information with error information removed, and perform defuzzing processing on the historical process information with error information removed to obtain the historical process information sample.

[0070] It is understood that the obfuscation process is a privacy protection technique designed to reduce the sensitivity of data by transforming or replacing it, making the information in the original data less easily identifiable. Next, the obfuscated information undergoes error detection and correction to remove potential data errors or outliers, ensuring data accuracy and reliability. Finally, the processed data is deblurred, restoring the obfuscated information to a understandable and analyzable form, yielding an information sample for subsequent analysis and modeling. In this way, the information, after both obfuscation and deblurring processes, maintains data quality and usability, providing a reliable foundation for subsequent data analysis and modeling.

[0071] S102, based on the multiple sets of historical information samples, determine the different operating conditions of the grinding process system and the sample range of the grinding process system corresponding to the different operating conditions, and based on each operating condition and its corresponding sample range of the grinding process system, determine the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition using a preset method.

[0072] For example, different operating conditions of the grinding process system can be identified based on historical equipment operating status information samples and corresponding historical process flow information samples. These operating conditions refer to the working status of the grinding process system equipment at various stages of different processes, determined by professional experience and analysis of historical equipment operating status and process flow information. In the grinding process system, the operating condition reflects changes in the system's operation, load, and performance characteristics. In this disclosure, the operating condition may include low, medium, high, and extremely high states. Then, based on human experience and historical data, the corresponding operating condition of the grinding process system equipment can be determined according to historical equipment operating status information and historical process flow information. Furthermore, statistical analysis of the historical equipment operating status information under different operating conditions is performed to determine the sample range of the grinding process system corresponding to different operating conditions. Specifically, the sample range of the grinding process system can be determined based on the upper and lower limits of the historical equipment operating information corresponding to each operating condition. For example, when determining the range of the grinding feed rate for a certain project, the upper limit is set to 410t / h and the lower limit to 300t / h. Under extremely high conditions, the feed rate needs to be reduced to 300t / h to reduce the power of the mill and make the power reach the safe operating state of the equipment. Under low conditions, the feed rate is increased to 410t / h to maximize the energy efficiency of the system. Reaching more than 410t / h will cause high mill shaft pressure, affecting the inherent safety of equipment operation. The feed rate is adjusted by changing the operating frequency of the feeder.

[0073] It is understood that the preset method is based on professional analysis of the collected information by experts or on-site personnel. This may include analysis of historical data, application of experience and knowledge, and consideration of process specifications. Then, a dynamic adjustable rectangular control method is used to determine the sample parameter adjustment range and process control logic of the grinding system corresponding to each operating condition, so as to more accurately adjust the system parameters during actual operation. For example, in the low state, the low sample parameter adjustment range is determined according to the preset method and the low sample range; in the medium state, the medium sample parameter adjustment range is determined according to the preset method and the medium sample range; and so on, corresponding parameter adjustment ranges are also determined in the high and extremely high states, namely the high sample parameter adjustment range and the extremely high sample parameter adjustment range.

[0074] S103, acquire the real-time information of the grinding process system, and process the real-time information to obtain real-time information samples; determine the real-time operating status of the grinding process system based on the real-time equipment information samples.

[0075] For example, the real-time information includes real-time equipment operating status information and real-time process flow information; after acquiring the real-time equipment operating status information and real-time process flow information of the grinding process system, information processing is performed on the real-time equipment operating status information and the real-time process flow information respectively to obtain the real-time information sample, which includes a real-time equipment operating status information sample and a real-time process flow information sample; since the principle of information processing in this step is similar to that in step S101 above, only the processing object is different, please refer to the processing method in step S101, which will not be repeated here.

[0076] After obtaining samples of real-time equipment operating status information and real-time process flow information, the real-time operating status of the current grinding process system is analyzed based on this information to determine its current operating status, such as low, medium, high, or extremely high.

[0077] S104, based on the sample parameter adjustment range of the grinding process system corresponding to each working condition, determine whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time working condition; and adjust the working parameters of the grinding process system according to the determination result.

[0078] Here, the operating parameters of the grinding process system may include the belt feeder frequency, feed lump size, water volume, and rotational speed. For details, please refer to [link to relevant documentation]. Figure 2 The diagram shown is a flowchart of the specific method for determining the parameter adjustment range disclosed in this invention, which may include the following steps S1041 to S1042:

[0079] S1041, Based on the real-time operating condition, match the target sample parameter adjustment range corresponding to the real-time operating condition.

[0080] Here, the adjustment range of sample parameters corresponding to the real-time operating status of the grinding process system is matched to the adjustment range of sample parameters under the stated operating status. This adjustment range is then used as the target adjustment range, which is one of the following: low, medium, high, or extremely high sample parameter adjustment ranges. By matching the target adjustment range to the real-time operating status, the accuracy of parameter adjustment is ensured. This means that the system does not simply adjust based on static setpoints but dynamically matches the current actual situation, improving the effectiveness and precision of parameter adjustment.

[0081] S1042, if the real-time equipment operating status information sample meets the target sample parameter adjustment range, adjust the working parameters of the grinding process system based on the real-time equipment operating status information sample and the real-time process flow information sample.

[0082] It is understandable that, when the real-time equipment operating status information sample meets the target sample parameter adjustment range, the operating parameters of the grinding process system are adjusted based on the real-time equipment operating status information sample and the real-time process flow information sample. For example, if the current real-time operating condition corresponds to the medium speed state, but the real-time equipment operating status information sample indicates that the mill speed is high, and the medium speed state corresponds to the medium speed sample parameter adjustment range, which requires the mill speed to be at a low level to maintain low energy consumption operation of the system, then the speed needs to be adjusted to adapt to the optimal operating requirements of the medium speed state. Similarly, if the real-time equipment operating status information sample shows that the feed particle size is low, but the sample parameter adjustment range of the medium speed state can accept a higher feed particle size, then the feed particle size also needs to be adjusted to ensure that the mill is in the optimal operating state, achieving efficient production and good processing results.

[0083] In this way, the parameter adjustments disclosed herein are real-time and are adjusted according to the current operating conditions and working status of the grinding process system, which can ensure that the grinding process system can operate continuously and stably and achieve optimal production efficiency and product quality.

[0084] In some other embodiments, besides adjusting the operating parameters of the grinding process system based on the judgment results, the adjustment range of sample parameters for each grinding process system can be further adjusted based on the historical and real-time sample ranges after a period of operation. By analyzing the historical and real-time sample ranges, professionals can understand the performance of different grinding process systems under different process flows. This allows professionals to personalize the adjustment range of sample parameters according to the working environment and requirements of the grinding process system, thereby better adapting to its operational needs. Furthermore, the analysis of historical and real-time sample ranges can reveal the past and current operating conditions of the grinding process system, as well as its potential performance bottlenecks and optimization potential. Based on this information, the adjustment range of sample parameters can be optimized to better reflect the actual situation, thereby improving the stability and efficiency of the system.

[0085] The intelligent grinding control method, device, electronic equipment, and storage medium provided in this disclosure collect equipment operating status and process flow information of the grinding process system, process the information to obtain historical data samples, then use the historical data samples to determine various operating conditions of the grinding process system and their corresponding sample ranges, and determine the parameter adjustment range accordingly; next, obtain real-time equipment status and process information to obtain real-time data samples, determine the real-time operating conditions, and finally, based on the real-time operating conditions and sample parameter adjustment ranges, determine whether the real-time data samples conform to the parameter adjustment range corresponding to the real-time operating conditions, and adjust the working parameters of the grinding process system accordingly.

[0086] Thus, the embodiments of this disclosure make the grinding process system reliable in operation and control through real-time intelligent feedback adjustment, thereby improving the output and quality of grinding products, reducing energy consumption per unit ore and process fluctuations, and realizing process optimization of the production process. This enables the entire grinding process system of the mine beneficiation plant to be unattended and achieve automated, intelligent and information-based production management, solving the "bottleneck" problem of intelligent grinding in mines and improving the level of digital operation of mines.

[0087] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0088] Based on the same inventive concept, this disclosure also provides an intelligent grinding control device corresponding to the intelligent grinding control method. Since the principle of the device in this disclosure for solving the problem is similar to that of the intelligent grinding control method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0089] Reference Figure 3 The diagram shown is a schematic of an intelligent grinding control device 300 provided in an embodiment of this disclosure. The device includes:

[0090] The historical information acquisition module 301 is used to acquire multiple sets of historical information of the grinding process system; and to process each set of historical information to obtain multiple sets of historical information samples; the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples.

[0091] The adjustment range determination module 302 is used to determine different operating conditions of the grinding process system and the sample range of the grinding process system corresponding to the different operating conditions based on the multiple sets of historical information samples, and to determine the sample parameter adjustment range of the grinding process system corresponding to each operating condition based on a preset method according to each operating condition and its corresponding sample range of the grinding process system.

[0092] The real-time information acquisition module 303 is used to acquire real-time information of the grinding process system, process the real-time information to obtain real-time information samples, and determine the real-time operating status of the grinding process system based on the real-time equipment information samples. The real-time information includes real-time equipment operating status information and real-time process flow information. The real-time information samples include real-time equipment operating status information samples and real-time process flow information samples.

[0093] The parameter information judgment module 304 is used to determine whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time operating status based on the sample parameter adjustment range of the grinding process system corresponding to each operating condition; and to adjust the operating parameters of the grinding process system according to the judgment result.

[0094] In some possible embodiments, the historical information acquisition module 301 is specifically used for:

[0095] For each set of historical device operating status information, data fuzzing processing is performed to obtain historical device operating status information with error information removed, and the historical device operating status information with error information removed is defuzzed to obtain a sample of the historical device operating status information.

[0096] For each set of historical process flow information, data fuzzing is performed to obtain historical process flow information with error information removed, and the historical process flow information with error information removed is then defuzzified to obtain a sample of the historical process flow information.

[0097] In some possible embodiments, the operating conditions include low, medium, high, and extremely high states; the sample range of the grinding process system includes a low sample range, a medium sample range, a high sample range, and an extremely high sample range; the adjustment range determination module 302 is specifically used for:

[0098] When the operating condition is a low state, the adjustment range of the low sample parameters is determined based on a preset method and the low sample range.

[0099] When the operating condition is in the medium state, the adjustment range of the medium sample parameters is determined based on the preset method and the medium sample range.

[0100] When the operating condition is high, the adjustment range of the high sample parameter is determined based on a preset method and the high sample range.

[0101] When the operating condition is extremely high, the adjustment range of the extremely high sample parameters is determined based on a preset method and the extremely high sample range.

[0102] In some possible embodiments, the parameter information determination module 304 is further configured to:

[0103] Based on the real-time operating condition, a target sample parameter adjustment range corresponding to the real-time operating condition is matched; the target sample parameter adjustment range includes one of the following: low sample parameter adjustment range, medium sample parameter adjustment range, high sample parameter adjustment range, and extremely high sample parameter adjustment range;

[0104] If the real-time equipment operating status information sample meets the target sample parameter adjustment range, the operating parameters of the grinding process system are adjusted based on the real-time equipment operating status information sample and the real-time process flow information sample.

[0105] In some possible embodiments, the operating parameters of the grinding process system include belt feeder frequency, feed lump size, water volume, and rotational speed.

[0106] In some possible embodiments, the parameter information determination module 304 is further configured to:

[0107] After the grinding process system has been in operation for a preset time, the historical sample range and real-time sample range of the grinding process system are obtained, and the sample parameter adjustment range of each grinding process system is adjusted based on the historical sample range and the real-time sample range.

[0108] In some possible embodiments, the grinding process system includes a single-stage ball mill system, a single-stage semi-autogenous grinding system, a semi-autogenous grinding-ball mill system, and a ball mill-ball mill system.

[0109] Based on the same technical concept, embodiments of this disclosure also provide an electronic device. (Refer to...) Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including a processor 401, a memory 402, and a bus 403. The memory 402 is used to store execution instructions and includes a main memory 4021 and an external memory 4022. The main memory 4021, also called internal memory, is used to temporarily store computational data in the processor 401, as well as data exchanged with external memory 4022 such as a hard disk. The processor 401 exchanges data with the external memory 4022 through the main memory 4021.

[0110] In this embodiment, the memory 402 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 401. That is, when the electronic device 400 is running, the processor 401 communicates with the memory 402 through the bus 403, so that the processor 401 executes the application code stored in the memory 402, and then executes the method described in any of the foregoing embodiments.

[0111] The memory 402 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0112] Processor 401 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0113] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 400. In other embodiments of this application, the electronic device 400 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0114] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the intelligent grinding control method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0115] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the intelligent grinding control method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0116] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0120] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, 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 this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A smart grinding control method, characterized in that, include: Multiple sets of historical information from the grinding process system are acquired; and each set of historical information is processed to obtain multiple sets of historical information samples; the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples. Based on the multiple sets of historical information samples, different operating conditions of the grinding process system and the corresponding sample ranges of the grinding process system are determined. Then, based on each operating condition and its corresponding sample range, a preset method is used to determine the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition. The operating conditions include low, medium, high, and extremely high states; the sample ranges include low, medium, high, and extremely high sample ranges. Real-time information of the grinding process system is acquired, and the real-time information is processed to obtain real-time information samples; the real-time operating status of the grinding process system is determined based on the real-time information samples; the real-time information includes real-time equipment operating status information and real-time process flow information; the real-time information samples include real-time equipment operating status information samples and real-time process flow information samples. Based on the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition, determine whether the real-time equipment operating status information sample meets the adjustment range of the sample parameters corresponding to the real-time operating condition; and adjust the operating parameters of the grinding process system according to the determination result. After the grinding process system has been in operation for a preset time, the historical sample range and real-time sample range of the grinding process system are obtained, and the sample parameter adjustment range of each grinding process system is adjusted based on the historical sample range and the real-time sample range. The step of determining the sample parameter adjustment range of the grinding process system corresponding to each operating condition based on a preset method includes: When the operating condition is a low state, the adjustment range of the low sample parameters is determined based on a preset method and the low sample range. When the operating condition is in the medium state, the adjustment range of the medium sample parameters is determined based on the preset method and the medium sample range. When the operating condition is high, the adjustment range of the high sample parameter is determined based on a preset method and the high sample range. When the operating condition is extremely high, the adjustment range of the extremely high sample parameters is determined based on a preset method and the extremely high sample range.

2. The method according to claim 1, characterized in that, The information processing for each group of historical information includes: For each set of historical device operating status information, data fuzzing processing is performed to obtain historical device operating status information with error information removed, and the historical device operating status information with error information removed is defuzzed to obtain a sample of the historical device operating status information. For each set of historical process flow information, data fuzzing is performed to obtain historical process flow information with error information removed, and the historical process flow information with error information removed is then defuzzified to obtain a sample of the historical process flow information.

3. The method according to claim 1, characterized in that, After determining whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time operating condition based on the sample parameter adjustment range of the grinding process system for each operating condition, the process includes: Based on the real-time operating condition, a target sample parameter adjustment range corresponding to the real-time operating condition is matched; the target sample parameter adjustment range includes one of the following: low sample parameter adjustment range, medium sample parameter adjustment range, high sample parameter adjustment range, and extremely high sample parameter adjustment range; If the real-time equipment operating status information sample meets the target sample parameter adjustment range, the operating parameters of the grinding process system are adjusted based on the real-time information sample.

4. The method according to claim 3, characterized in that, The operating parameters of the grinding process system include the frequency of the belt feeder, the size of the ore pieces, the water volume, and the rotation speed.

5. The method according to claim 1, characterized in that, The grinding process system includes a single-stage ball mill system, a single-stage semi-autogenous grinding system, a semi-autogenous grinding-ball mill system, and a ball mill-ball mill system.

6. An apparatus for executing the intelligent grinding control method according to any one of claims 1-5, characterized in that, include: The historical information acquisition module is used to acquire multiple sets of historical information from the grinding process system; and to process each set of historical information to obtain multiple sets of historical information samples; the historical information includes historical equipment operating status information and historical process flow information; the historical information samples include historical equipment operating status information samples and historical process flow information samples. The adjustment range determination module is used to determine different operating conditions of the grinding process system and the sample range of the grinding process system corresponding to the different operating conditions based on the multiple sets of historical information samples. Based on each operating condition and its corresponding sample range, the module determines the adjustment range of the sample parameters of the grinding process system corresponding to each operating condition using a preset method. The operating conditions include low, medium, high, and extremely high states; the sample ranges include low, medium, high, and extremely high sample ranges. A real-time information acquisition module is used to acquire real-time information of the grinding process system, process the real-time information to obtain real-time information samples, and determine the real-time operating status of the grinding process system based on the real-time information samples. The real-time information includes real-time equipment operating status information and real-time process flow information. The real-time information samples include real-time equipment operating status information samples and real-time process flow information samples. The parameter information judgment module is used to determine whether the real-time equipment operating status information sample meets the sample parameter adjustment range corresponding to the real-time operating status based on the sample parameter adjustment range of the grinding process system for each operating condition; and to adjust the operating parameters of the grinding process system according to the judgment result. The parameter information judgment module is also used to obtain the historical sample range and real-time sample range of the grinding process system after the grinding process system has been working for a preset time, and to adjust the sample parameter adjustment range of each grinding process system based on the historical sample range and the real-time sample range. Specifically, the adjustment range determination module is used for: When the operating condition is a low state, the adjustment range of the low sample parameters is determined based on a preset method and the low sample range. When the operating condition is in the medium state, the adjustment range of the medium sample parameters is determined based on the preset method and the medium sample range. When the operating condition is high, the adjustment range of the high sample parameter is determined based on a preset method and the high sample range. When the operating condition is extremely high, the adjustment range of the extremely high sample parameters is determined based on a preset method and the extremely high sample range.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

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