Deep-sea cuttings particle size information prediction management method and related equipment

Abstract

The embodiment of the present application provides a kind of deep-sea cuttings particle size information prediction management method and related equipment, comprising: real-time acquisition development equipment in current collection environment under the collection data, the collection data includes collection environment data and collection mineral data;Through preset cuttings particle size prediction model, the collection data is predicted and analyzed, determines the prediction distribution data of the cuttings particle size corresponding to collection mineral under current collection environment data;Based on the prediction distribution data, adjust the collection parameter of the development equipment and execute corresponding development strategy.Through the above method steps, combine the environment data and mineral data collected in real time, utilize accurate particle size prediction model, optimize and adjust mining operation, can significantly improve the cuttings recovery rate in deep-sea mining operation, reduce the generation and loss of fine particles, ensure the minimum disturbance to environment in mining process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mineral resource development, and in particular to a deep-sea rock debris particle size information prediction management method and device, an electronic device and a storage medium thereof. BACKGROUND

[0002] With the development of deep-sea mineral resources, how to efficiently carry out mining operations and ensure minimal environmental impact has become a major technical challenge. In the process of deep-sea mining, the development equipment will generate a large amount of rock debris particles, especially fine particles, which can have a serious impact on the ecological environment of the surrounding sea area. In addition, traditional rock debris particle size prediction methods mostly rely on laboratory data or static models, which cannot respond to changes in the deep-sea environment in real time, resulting in low ore recovery rate, poor mining operation efficiency, and increased environmental pollution.

[0003] In the prior art, although there are some rock debris particle size prediction methods, most of them ignore the dynamic changes of the deep-sea environment and the optimization adjustment of the mining equipment. For example, changes in environmental factors such as ore body depth, hydrostatic pressure, and ore characteristics will affect the crushing behavior of the rock and the particle size distribution of the rock debris. These factors are particularly important in the process of deep-sea mining, so traditional methods often fail to effectively cope with the complexity of the deep-sea environment.

[0004] Therefore, the existing deep-sea rock debris particle size information prediction management method has the problem of being unable to predict the particle size distribution of the rock debris in real time and adjust the operating parameters of the mining equipment based on the predicted distribution data. SUMMARY

[0005] The present application provides a deep-sea rock debris particle size information prediction management method to solve the problem of the existing deep-sea rock debris particle size information prediction management method being unable to predict the particle size distribution of the rock debris in real time and adjust the operating parameters of the mining equipment based on the predicted distribution data.

[0006] In a first aspect, the present application provides a deep-sea rock debris particle size information prediction management method, which comprises the following steps:

[0007] Real-time acquisition of collection data of the development equipment under the current collection environment, wherein the collection data includes collection environment data and collection mineral data;

[0008] Predictive analysis of the collection data by a pre-set rock debris particle size prediction model to determine the predicted distribution data of the rock debris particle size corresponding to the collection mineral under the current collection environment data;

[0009] Adjustment of the collection parameters of the development equipment based on the predicted distribution data and execution of the corresponding development strategy.

[0010] Optionally, the real-time acquisition of the collection data of the development equipment under the current collection environment comprises:

[0011] acquire depth data and pressure data of a current acquisition environment where the development device is located through a preset environment sensor;

[0012] In the database, acquisition mineral data that the development device is developing is determined, and the acquisition mineral data includes mineral types and mineral parameter data.

[0013] Optionally, before the acquisition data is predicted and analyzed through the preset rock debris granularity prediction model to determine the predicted distribution data of the rock debris granularity corresponding to the acquisition mineral under the current acquisition environment data, the method further includes:

[0014] tool characteristic parameters of the development device are acquired, and the tool characteristic parameters include a rotational speed and a diameter of the development device;

[0015] a first strain model layer of a rock breaking tool and an ore body is constructed based on the tool characteristic parameters of the development device and the acquisition mineral data, and the first strain model layer is used to describe a strain relationship between the development device and the ore body;

[0016] a second strain model layer of internal collapse of the ore body is constructed based on the acquisition environment data where the development device is located, and the second strain model layer is used to describe an internal self-collapse relationship of the acquisition mineral in the current acquisition environment data;

[0017] the preset rock debris granularity prediction model is determined based on the first strain model layer and the second strain model layer.

[0018] Optionally, the predicted distribution data of the rock debris granularity corresponding to the acquisition mineral under the current acquisition environment data is determined by predicting and analyzing the acquisition data through the preset rock debris granularity prediction model, and includes:

[0019] the acquisition mineral data is processed through the first strain model layer to determine crushing capacity data of the current development device on the current acquisition mineral;

[0020] a first prediction distribution coefficient of the current acquisition mineral is determined based on the crushing capacity data.

[0021] Optionally, the predicted distribution data of the rock debris granularity corresponding to the acquisition mineral under the current acquisition environment data is determined by predicting and analyzing the acquisition data through the preset rock debris granularity prediction model, and includes:

[0022] the acquisition environment data and the acquisition mineral data are processed through the second strain model layer to determine total damage data of the current acquisition mineral under the current acquisition environment data;

[0023] Determine a second prediction distribution coefficient of the current collection mineral based on the total damage data.

[0024] Optionally, the processing of the collection environment data and the collection mineral data by the second strain model layer to determine total damage data of the current collection mineral under the current collection environment data comprises:

[0025] Determine crack density increment data of the current collection mineral according to the depth data and the pressure data under the current collection environment data.

[0026] Determine a crushing time of the current collection mineral by damage accumulation calculation of the crack density increment data according to the development duration.

[0027] Determine the total damage data of the current collection mineral based on the volume of the current collection mineral and the crushing time.

[0028] Optionally, the adjusting of the collection parameters of the development device and the execution of the corresponding development strategy based on the prediction distribution data comprises:

[0029] Adjust the development impact force, the rotation speed, the cutting thickness, the traction speed and the operation mode of the development device based on the prediction distribution data.

[0030] Adjust the depth data of the development device and replace the rock breaking tool with corresponding development capacity according to the prediction distribution data.

[0031] In a second aspect, the present application further provides a deep-sea rock debris particle size information prediction management device, which comprises:

[0032] A first collection module is configured to acquire collection data of a development device under a current collection environment in real time, wherein the collection data comprises collection environment data and collection mineral data.

[0033] A first determination module is configured to perform prediction analysis on the collection data by a preset rock debris particle size prediction model to determine prediction distribution data of rock debris particle size of a collection mineral under current collection environment data.

[0034] A first adjustment module is configured to adjust collection parameters of the development device and execute a corresponding development strategy based on the prediction distribution data.

[0035] In a third aspect, the present application provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the deep-sea rock debris particle size information prediction management method provided by the present application.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the deep-sea rock cuttings grain size information prediction and management method provided by the invention.

[0037] In this invention, real-time data is acquired from the development equipment under the current acquisition environment. This data includes environmental data and mineral data. A pre-set rock cuttings grain size prediction model is used to predict and analyze the acquired data, determining the predicted distribution of rock cuttings grain size corresponding to the acquired minerals under the current environmental data. Based on this predicted distribution data, the acquisition parameters of the development equipment are adjusted, and a corresponding development strategy is executed. Through these steps, combining real-time acquired environmental and mineral data, and utilizing a precise grain size prediction model, mining operations can be optimized and adjusted. This significantly improves rock cuttings recovery rates, reduces fine particle loss, and ensures minimal environmental disturbance during the mining process in deep-sea mining operations. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a deep-sea rock cuttings grain size information prediction and management method provided in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of another deep-sea rock debris grain size information prediction and management device provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, Figure 1 This is a flowchart of a deep-sea rock debris grain size prediction and management method provided by an embodiment of the present invention. The deep-sea rock debris grain size prediction and management method includes the following steps:

[0044] 101、Real-time acquisition of the acquisition data of the development device in the current acquisition environment.

[0045] In the embodiment of the present application, the deep-sea rock debris particle size information prediction management method can be applied to a deep-sea rock debris particle size information prediction management platform. The deep-sea rock debris particle size information prediction management platform has functions such as mineral collection data processing, mineral collection data transmission and reception, and mineral collection data memory storage, and can be constructed based on a server or a server cluster. The server or server cluster can be an electronic device with mineral collection data capability.

[0046] The development device can be machinery and tools used for mineral development, crushing, collection and other operations in the mining process. Specifically, since different development devices correspond to different types of functions, different development devices need to be adapted for the collection of corresponding mineral types. Therefore, in deep-sea resource development operations, different types of development devices with different functions and resistances can be selected according to different deep-sea environments and different mineral types.

[0047] Generally, deep-sea drilling machines can be used for ore drilling and development operations at a depth of 2000 meters under the sea. Since the deep-sea drilling machine is equipped with depth sensors, pressure sensors and ore hardness testers, and has certain resistance to deep-sea environments, it can ensure the efficiency and accuracy of the mining operation.

[0048] The current acquisition environment can refer to the physical and geographical environmental conditions of the development device at a certain time, which can generally include but is not limited to water depth, hydrostatic pressure, temperature, etc. in the mining area.

[0049] The acquisition data can include but is not limited to acquisition environment data and acquisition mineral data, as well as various data obtained in real time during the development process through sensors or other monitoring devices.

[0050] The acquisition environment data can include but is not limited to various physical, chemical and geographical data about the environment of the mining area collected in real time by sensors and monitoring systems during the development process. These data describe the environmental characteristics of the mining area where the development device is located, mainly including water depth, temperature, pressure, flow rate and other parameters. For example, in deep-sea mining operations, development devices such as drilling machines and mining equipment will operate in deep-sea environments, and the collected environmental data can include:

[0051] Water depth data: the depth of the current development device in the mining area is monitored in real time by a depth sensor, for example, 2000 meters;

[0052] Hydrostatic pressure: according to the change of water depth, the hydrostatic pressure is monitored by a pressure sensor, assuming that the hydrostatic pressure is 20 Mpa at a depth of 2000 meters;

[0053] Water temperature data: The water temperature sensor measured the water temperature at 4°C;

[0054] Flow velocity data: The velocity of ocean currents in the deep sea can also affect the diffusion of rock debris, so mining equipment may be equipped with flow velocity sensors to monitor the flow velocity in real time.

[0055] The aforementioned mineral data can refer to real-time characteristic data about the ore or mineral itself acquired through sensors, detection instruments, and other equipment. This includes, but is not limited to, characteristic information such as ore type, hardness, composition, and brittleness / toughness, used to assess the difficulty of ore body development and the grain size distribution of rock cuttings. For example, in deep-sea mining operations, equipment for acquiring mineral data may include:

[0056] Mineral hardness sensor: Used to measure the hardness of ores in real time. For example, if the sensor detects that the hardness of an ore is 8.5 (Mohs hardness), it means that the ore is hard and may require a large impact force to break.

[0057] Mineral composition analyzer: This equipment uses chemical composition analysis technology to detect the content of major components in ores, such as copper, cobalt, and iron. For example, the copper content in the ore is 10%, and the cobalt content is 0.6%.

[0058] Mineral brittleness-toughness tester: measures the degree of brittleness-toughness transition in deep-sea rocks, which is characterized numerically. The higher the degree of transition, the more likely fine grains are to be produced.

[0059] 102. By using a preset rock fragment grain size prediction model to predict and analyze the collected data, the predicted distribution data of the rock fragment grain size corresponding to the collected minerals under the current collection environment data is determined.

[0060] In this embodiment of the invention, the aforementioned preset rock fragment particle size prediction model can refer to a deep learning model capable of predicting the distribution of rock fragment particle size based on the physical properties of the target ore and corresponding environmental data. Generally, the aforementioned preset rock fragment particle size prediction model can analyze mineral and environmental data collected under specific conditions to predict the distribution of rock fragment particle size. Specifically, the aforementioned preset rock fragment particle size prediction model can provide particle size prediction for mining operations under different depths and ore body conditions by integrating rock mechanics principles, ore crushing characteristics, and environmental factors. For example, in deep-sea mining operations, the aforementioned preset rock fragment particle size prediction model can combine analysis of how the high-pressure environment of the deep sea affects the degree of ore crushing, crack propagation, and rock fragment particle size. In shallower waters, rock crushing may form more and larger particles, while in deeper waters, due to increased hydrostatic pressure, the extensive crack propagation of rocks may lead to the generation of fine rock fragment particles.

[0061] In a possible embodiment, the deep-sea rock debris particle size information prediction management platform can predict the particle size distribution of the rock debris based on the current real-time collected environmental data and mineral data by calculating and analyzing based on the preset rock debris particle size prediction model. The analysis process includes inputting the collected data into the model, calculating the distribution proportion of the rock debris in different particle size ranges, and providing data support for subsequent operation adjustment and optimization.

[0062] The predicted distribution data can refer to the rock debris particle size distribution obtained by prediction analysis, which is usually expressed in terms of particle size interval and corresponding distribution proportion. This data reflects the size and distribution of rock debris particles after ore crushing under different environmental conditions, and is the basis for adjusting the operation parameters of mining equipment.

[0063] Specifically, the predicted distribution data can specifically show the size and distribution proportion of the ore rock debris, and can also be prediction information of the distribution direction, for example, the particle size of the ore rock debris in the current area is between 2-5 mm, accounting for 60%, and the distribution is diffused to the lower part of the seabed.

[0064] The rock debris particle size can refer to the size and distribution of rock debris particles generated by ore crushing in the mining operation. Specifically, fine rock debris may be diffused with water flow and have a greater impact on the surrounding environment, while too large rock debris particle size may lead to a decrease in ore recovery rate, for example, after crushing, the rock debris may be mostly concentrated in 2-5 mm, and a small part of the particle size is less than 0.5 mm. According to the hardness, brittleness and mining environmental conditions of the ore, larger particle size rock debris needs to use stronger impact force to crush, and the recovery of fine particle rock debris may face more environmental challenges such as water flow diffusion and pollution. By predicting the rock debris particle size, the parameters of the mining equipment can be effectively adjusted to avoid resource waste and reduce the negative impact on the environment.

[0065] 103、Based on the predicted distribution data, adjust the collection parameters of the development equipment and execute the corresponding development strategy.

[0066] In the embodiment of the present application, the deep-sea rock debris particle size information prediction management platform analyzes the predicted distribution data to obtain the parameters that need to be adjusted for the corresponding development equipment under the current development environment, including but not limited to impact force, mining depth and mining rate, etc., so as to ensure that the distribution of the rock debris particle size under the development intensity meets the expected standard, thereby improving the ore recovery rate, optimizing the mining operation process, and reducing the impact on the environment.

[0067] In the embodiment of the present application, the collection data of the development device in the current collection environment is acquired in real time, and the collection data includes collection environment data and collection mineral data; the collection data is predicted and analyzed by using a preset rock debris particle size prediction model to determine the predicted distribution data of the collection mineral corresponding to the rock debris particle size under the current collection environment data; and the collection parameters of the development device are adjusted based on the predicted distribution data and the corresponding development strategy is executed. Through the above method steps, the mining operation is optimized and adjusted by combining the real-time collected environment data and mineral data, using the accurate particle size prediction model, which can significantly improve the rock debris recovery rate in deep sea mining operation, reduce the loss of fine particles, and ensure the minimum disturbance to the environment in the mining process.

[0068] Optionally, in the step of acquiring the collection data of the development device in the current collection environment in real time, the depth data and pressure data of the current collection environment of the development device can also be collected by using a preset environment sensor; and the collection mineral data of the development device being developed is determined in the database.

[0069] In the embodiment of the present application, the above-mentioned preset environment sensor can be a sensor module integrated on the mining device, which can monitor and collect the environment data of the device in real time. These sensors are pre-calibrated to ensure accurate measurement of environmental conditions such as water depth, pressure and other parameters during actual mining. In deep sea mining, common environment sensors include depth sensors and pressure sensors, which measure the depth of the water area where the device is located and the hydrostatic pressure of the surrounding environment, respectively.

[0070] The above-mentioned depth data can be used to represent the water depth of the water area where the development device is currently located. Generally, the hydrostatic pressure of seawater on the development device is affected by the depth, the greater the water depth, i.e. the greater the depth data, the greater the pressure, and the pressure between the development device and the corresponding mineral will also be greater.

[0071] The above-mentioned pressure data will affect the crack propagation, damage accumulation and rock debris particle size of the mineral, so when analyzing the predicted distribution data, the influence of the pressure data on the distribution of the mineral needs to be considered to ensure the accuracy of the particle size prediction.

[0072] The above-mentioned collection mineral data includes but is not limited to mineral type and mineral parameter data.

[0073] The above-mentioned mineral type can refer to the type of mineral being developed or the classification of mineral resources. Different types of minerals have different physical and chemical properties, which will affect the performance of the mineral during the crushing process. For example, the hardness, brittleness and mineral composition of polymetallic sulfide and cobalt-rich crust may be different, which determines the rock debris particle size generated during crushing.

[0074] The mineral parameter data can be a set of numerical values describing the physical and chemical properties of the minerals, including the density, hardness, compressive strength, fracture toughness coefficient, and crack density of the minerals, which can be measured according to the type and properties of the ore or obtained from a database, and used to characterize the behavior of the minerals during the crushing process. For example, the compressive strength of the ore affects the external force it can withstand during the crushing process, the fracture toughness of the mineral affects the propagation of cracks, and the crack density of the ore is closely related to the formation of the rock particle size.

[0075] In one possible embodiment, the deep-sea rock particle size information prediction management platform collects deep-sea environment data of the development device in real time through a preset environment sensor arranged on the development device, and matches the collected mineral type and mineral parameter data through a background database to determine the matching result.

[0076] Optionally, before the step of predicting and analyzing the collected data through the preset rock particle size prediction model to determine the predicted distribution data of the rock particle size of the collected mineral under the current collected environment data, the method further includes obtaining tool characteristic parameters of the development device; constructing a first strain model layer of the rock breaking tool and the ore body based on the tool characteristic parameters of the development device and the collected mineral data; constructing a second strain model layer of the internal collapse of the ore body based on the collected environment data of the development device; and determining the preset rock particle size prediction model based on the first strain model layer and the second strain model layer.

[0077] In the embodiments of the present application, the tool characteristic parameters can include, but are not limited to, the rotational speed, diameter, and other operation parameters of the development device for describing the development intensity. Specifically, the rotational speed directly affects the contact force and crushing efficiency of the device and the ore body, a higher rotational speed can provide greater impact force to help the device crush the ore more effectively, and the diameter affects the contact area of the device, thereby affecting the range and intensity of ore crushing. Through these parameters, the platform can adjust the operation state of the tool to optimize the prediction of the rock particle size.

[0078] In one possible embodiment, before the development, the deep-sea rock particle size information prediction management platform can build two key strain model layers based on the tool characteristic parameters of the development device and the collected mineral data. These model layers are used to describe the interaction between the development device and the ore body, helping the platform to predict the particle size distribution of the rock.

[0079] The first strain model layer is used to describe the strain relationship between the development device and the ore body. The strain relationship can refer to the change in shape and size of an object under the action of external force, which is usually manifested as the deformation of the object. For deep-sea mining operations, the contact between the development device and the ore body can cause the ore body to deform and produce cracks, and the crack propagation eventually leads to the formation of rock debris. The first strain model layer calculates the strain distribution of the ore body under the action of the development device by considering factors such as the rotational speed of the development device, the diameter of the development device, and the type of mineral (such as the hardness and toughness of the ore). For example, at a higher rotational speed, the strain is larger and the ore is more likely to break; and a smaller device diameter can cause the strain to concentrate in a local area, resulting in poor breaking effect.

[0080] The second strain model layer is used to describe the internal self-disintegration relationship of the collected mineral in the current collection environment data. The hydrostatic pressure and water depth in the deep-sea environment have a significant impact on the microstructure (such as crack density and lattice defects) of the ore. At a higher pressure, the cracks in the ore can accelerate and cause the ore to self-disintegrate. The second strain model layer combines the physical properties of the collected mineral (such as hardness and brittleness) and the environmental data (such as depth and pressure) to simulate the breaking process of the ore body under environmental pressure. Through this layer model, the platform can accurately calculate the self-disintegration rate of the ore and the formation and distribution characteristics of the rock debris under a specific collection environment.

[0081] Optionally, in the step of predicting and analyzing the collection data by the preset rock debris particle size prediction model to determine the predicted distribution data of the rock debris particle size corresponding to the collected mineral under the current collection environment data, the method further includes processing the collected mineral data by the first strain model layer to determine the breaking capacity data of the current development device on the current collected mineral; and determining a first prediction distribution coefficient of the current collected mineral based on the breaking capacity data.

[0082] In the embodiments of the present application, the first strain model layer can be used to analyze and calculate the strain distribution of the development device and the ore body after contact. Specifically, the tool characteristic parameters (such as rotational speed and diameter) of the development device and the physical property data (such as ore hardness and brittleness) of the collected mineral can be input into the strain model to simulate the interaction between the device and the ore body. Specifically, after the ore body is subjected to the action of the device, the ore body deforms and cracks are generated. The model calculates the breaking characteristics of the mineral under the operating conditions based on this strain distribution.

[0083] The crushing capacity data can refer to the crushing effect of the current development device on the current collected mineral under specific mining operation conditions. This data reflects the crushing capacity of the device when processing specific minerals, for example, the amount or degree of crushing of the ore that the device can effectively crush under different rotational speeds and diameters. Specifically, when the rotational speed and diameter of the device are high, the strain force acting on the ore body is large, the crushing capacity is strong, the crack propagation of the ore is more significant, and more small particle debris is generated. Lower rotational speed or smaller device diameter can result in insufficient crushing of the ore, and the proportion of large ore blocks in the ore body is high. The crushing capacity data is a quantitative analysis of the interaction between the device and the ore, reflecting the crushing efficiency of the ore under specific device conditions.

[0084] The first prediction distribution coefficient can refer to the parameterization coefficient of the distribution of the particle size of the debris after the crushing of the mineral, which is determined based on the crushing capacity data. Specifically, the particle size and proportion of the debris after crushing are predicted through the crushing capacity data. This coefficient reflects the influence of the operating parameters of the device (such as rotational speed, diameter, etc.) and the characteristics of the mineral (such as hardness, brittleness) on the particle size of the debris. For example, larger crushing capacity data can correspond to smaller particle size of the debris, while smaller crushing capacity can result in more large particle debris.

[0085] Optionally, in the step of predicting and analyzing the collected data through the preset debris particle size prediction model to determine the predicted distribution data of the particle size of the debris corresponding to the collected mineral under the current collected environmental data, the step further includes processing the collected environmental data and the collected mineral data through a second strain model layer to determine the total damage data of the current collected mineral under the current collected environmental data; and determining a second prediction distribution coefficient of the current collected mineral based on the total damage data.

[0086] In the embodiments of the present application, the total damage data can refer to the overall damage state of the mineral under the current collection environment, which can be obtained by analyzing the damage effect of the mineral characteristics (such as hardness, brittleness, etc.) and environmental factors (such as water depth, pressure, etc.) on the mineral. Specifically, it can be calculated by the second strain model layer, according to the influence of hydrostatic pressure and temperature and other factors on the mineral in the deep sea environment, to determine the expansion of the internal cracks of the ore, for example, when the deep sea pressure is high, the micro-cracks in the ore can accelerate the expansion, leading to the mineral being more easily crushed.

[0087] More specifically, the total damage data described above can be calculated according to different development stages, such as crack propagation, fragmentation, disintegration, etc. In the deep-sea environment, the damage of the ore is not only affected by mechanical fragmentation, but also by environmental pressure. Factors such as hydrostatic pressure and temperature in deep-sea waters, especially high pressure in deep-sea environments, often cause further propagation of micro-cracks within the ore, accelerating the fragmentation of the ore. Therefore, the total damage data not only reflects the fragmentation of the ore under the action of the development equipment, but also takes into account the effect of deep-sea environmental factors on the propagation of cracks in the ore.

[0088] In the deep-sea environment, the action of hydrostatic pressure and pore pressure helps to propagate the micro-cracks within the ore, thereby exacerbating the degree of damage to the ore. Specifically, as the depth increases, the hydrostatic pressure experienced by the ore increases, and the increase in internal cracks is also more significant. By simulating the propagation process of the cracks through the hydrostatic pressure and the hardness of the ore, etc., the crack increment is associated with the degree of damage over time, ultimately leading to the fragmentation of the ore.

[0089] As the development time extends, the ore body is subjected to mechanical impact while the environmental pressure continues to act, causing the cracks in the ore to gradually increase and propagate, thereby exacerbating the damage to the ore body. Therefore, the total damage data is not only related to the physical properties of the ore (such as hardness, brittleness, etc.), but also closely related to the operation time of the development equipment.

[0090] The second prediction distribution coefficient described above can be used to describe the particle size distribution of the ore in the current collection environment. By calculating the total damage data of the ore, the deep-sea particle size information prediction management platform can predict the particle size distribution characteristics of the ore produced during fragmentation. The second prediction distribution coefficient reflects how the crack propagation and mineral damage caused by environmental pressure in the deep-sea environment affect the final particle size of the ore. For example, higher total damage data may correspond to finer particle size, while lower damage may result in more large particle size. Through this coefficient, the deep-sea particle size information prediction management platform can accurately predict the particle size distribution of the ore after fragmentation in the current environment, and adjust the operating parameters of the mining equipment to optimize the mining effect.

[0091] Optionally, in the step of processing the collection environment data and the collection mineral data through the second strain model layer to determine the total damage data of the current collection mineral under the current collection environment data, the crack density increment data of the current collection mineral can also be determined according to the depth data and the pressure data under the current collection environment data; the crack density increment data is calculated for damage accumulation according to the development time to determine the fragmentation time of the current collection mineral; and the total damage data of the current collection mineral is determined based on the volume of the current collection mineral and the fragmentation time.

[0092] In the embodiments of the present application, the crack density increment data can refer to the increase in the number and density of cracks inside the ore under the current collection environment due to the environmental pressure and the operation of the development device. It can be understood that in the deep sea environment, the high hydrostatic pressure is the main factor affecting the crack increment. As the ore is processed by the mining device, cracks will be generated and expanded inside the ore, and the density of these cracks will increase with the increase of the deep sea hydrostatic pressure. The crack expansion speed and the change in the number of cracks can be calculated by the second strain model layer based on the current environmental data (such as depth data and pressure data) and the mineral characteristics, so as to obtain the crack density increment data.

[0093] Specifically, the high pressure in the deep sea environment promotes the rapid expansion of micro-cracks in the ore, so the crack density increases rapidly during this process, and eventually leads to the crushing of the ore. For example, assuming that the current operation depth is 2000 meters, the hydrostatic pressure is 20 Mpa, and the crack density of the ore increases by a certain percentage per hour under this depth condition, the crack density increment data is the quantitative value of the change of the crack number with time.

[0094] In a possible embodiment, the deep sea rock debris particle size information prediction management platform can calculate the damage accumulation of the ore during the development process according to the crack density increment data. Specifically, the ore is continuously subjected to mechanical impact and environmental pressure under the action of the development device, and the number of cracks in the ore gradually increases, and the damage also gradually intensifies. The cumulative process of damage considers the relationship between the crack increment and the time, and the total damage is usually calculated by time integration. For example, during the operation process, the crack density increment data gradually increases with time, and the platform can calculate the damage accumulation of the crack density increment data according to the development time. That is, the damage is added up at each time (for example, every hour), and the cumulative effect of the ore material is considered to obtain the damage accumulation value at the end of the operation.

[0095] The crushing time can refer to the time required for the ore to be completely crushed from the beginning of the external force to the beginning of the operation, which is based on the result of damage accumulation calculation. The platform calculates the time required for the ore to break according to the damage progress of the ore, and specifically, the damage state of the ore is obtained by damage accumulation calculation, and the crushing time required from the beginning of the operation to the complete crushing of the ore is calculated in combination with the physical characteristics (such as brittleness, hardness, etc.) of the ore. For example, if the damage accumulation value of the ore reaches a certain threshold, it will be calculated that the ore may break in the next few seconds or minutes, and it should be noted that the crushing individual is mainly for the subject that has not been developed but has been affected by cracks.

[0096] Optionally, in the step of adjusting the acquisition parameters of the development equipment and performing the corresponding development strategy based on the predicted distribution data, the development impact force, rotation speed, cutting thickness, traction speed, and operation mode of the development equipment can also be adjusted based on the predicted distribution data; the depth data of the development equipment is adjusted, and the rock breaking tool with corresponding development capacity is replaced according to the predicted distribution data.

[0097] In the embodiment of the present application, the deep-sea rock debris particle size information prediction management platform dynamically adjusts multiple operation parameters of the development equipment based on the real-time collected predicted distribution data, thereby optimizing the ore breaking process. First, the deep-sea rock debris particle size information prediction management platform adjusts the development impact force and rotation speed of the development equipment in real time according to the predicted rock debris particle size distribution data. When the predicted data shows that the ore is relatively hard and requires a larger impact force, the deep-sea rock debris particle size information prediction management platform increases the impact force and rotation speed of the equipment to improve the breaking efficiency; when the predicted data shows that the ore is relatively fragile and easy to produce small particles, the deep-sea rock debris particle size information prediction management platform reduces the impact force and rotation speed to avoid excessive breaking and ensure more uniform particle size distribution.

[0098] In addition, the deep-sea rock debris particle size information prediction management platform also adjusts the operation depth of the development equipment according to the predicted distribution data. In the deep-sea environment, the hardness and breaking characteristics of the ore change with the change of water depth, so the deep-sea rock debris particle size information prediction management platform optimizes the working depth of the equipment according to the rock debris particle size prediction results at different depths. When the prediction shows that the ore at a certain layer in the deep sea is hard and requires a higher impact force, the deep-sea rock debris particle size information prediction management platform will adjust the operation depth and select the most suitable operation position for development to ensure that the ore can be broken efficiently.

[0099] The deep-sea rock debris particle size information prediction management platform can also replace the appropriate rock breaking tool according to the change of the predicted distribution of rock debris particle size. When the prediction shows that the ore is broken into more large-particle rock debris, the deep-sea rock debris particle size information prediction management platform will select a more powerful rock breaking tool; conversely, when the particle size is small and the loss of small particles needs to be reduced, the deep-sea rock debris particle size information prediction management platform will use a suitable and relatively mild rock breaking tool to maximize the recovery rate and reduce the environmental impact.

[0100] Through these dynamic adjustments, the deep-sea rock debris particle size information prediction management platform can flexibly adjust the working state of the development equipment according to the real-time prediction data, thereby significantly improving the mining efficiency, optimizing the rock debris particle size distribution, and reducing the negative impact on the environment.

[0101] For example, Figure 2As shown, the embodiment of the present application also provides a deep-sea cutting particle size information prediction management device 200, which comprises:

[0102] The first acquisition module 201 is configured to acquire, in real time, collection data of the development device in a current collection environment, wherein the collection data comprises collection environment data and collection mineral data.

[0103] The first determination module 202 is configured to perform prediction analysis on the collection data by using a preset cutting particle size prediction model, and determine prediction distribution data of the cutting particle size of the collection mineral under the current collection environment data.

[0104] The first adjustment module 203 is configured to adjust the collection parameters of the development device and execute a corresponding development strategy based on the prediction distribution data.

[0105] Optionally, the first acquisition module 201 comprises:

[0106] The first acquisition sub-module is configured to acquire, by using a preset environment sensor, depth data and pressure data of the current collection environment in which the development device is located.

[0107] The first determination sub-module is configured to determine, in a database, collection mineral data of the collection mineral being developed by the development device, wherein the collection mineral data comprises mineral type and mineral parameter data.

[0108] Optionally, the device further comprises:

[0109] The first acquisition module is configured to acquire tool characteristic parameters of the development device, wherein the tool characteristic parameters comprise the rotating speed and the diameter of the development device.

[0110] The first construction module is configured to construct a first strain model layer of the rock breaking tool and the ore body based on the tool characteristic parameters of the development device and the collection mineral data, wherein the first strain model layer is used to describe the strain relationship between the development device and the ore body.

[0111] The second construction module is configured to construct a second strain model layer of internal collapse of the ore body based on the collection environment data in which the development device is located, wherein the second strain model layer is used to describe the internal self-collapse relationship of the collection mineral in the current collection environment data.

[0112] The second determination module is configured to determine the preset cutting particle size prediction model based on the first strain model layer and the second strain model layer.

[0113] Optionally, the first determination module 202 comprises:

[0114] A second determining sub-module is configured to determine the crushing capacity data of the current development device on the current collected mineral by processing the collected mineral data through the first strain model layer.

[0115] A third determining sub-module is configured to determine a first predicted distribution coefficient of the current collected mineral based on the crushing capacity data.

[0116] Optionally, the first determining module 202 comprises:

[0117] A first processing sub-module is configured to determine the total damage data of the current collected mineral under the current collected environment data by processing the collected environment data and the collected mineral data through the second strain model layer.

[0118] A fourth determining sub-module is configured to determine a second predicted distribution coefficient of the current collected mineral based on the total damage data.

[0119] Optionally, the first processing sub-module further comprises:

[0120] A first determining unit is configured to determine the crack density increment data of the current collected mineral according to the depth data and the pressure data under the current collected environment data.

[0121] A second determining unit is configured to determine the crushing time of the current collected mineral by damage accumulation calculation on the crack density increment data.

[0122] A third determining unit is configured to determine the total damage data of the current collected mineral based on the volume of the current collected mineral and the crushing time.

[0123] Optionally, the first adjusting module 203 comprises:

[0124] A first adjusting sub-module is configured to adjust the development impact force, the rotating speed, the cutting thickness, the traction speed and the operation mode of the development device based on the predicted distribution data.

[0125] A second adjusting sub-module is configured to adjust the depth data of the development device and replace the rock breaking tool with corresponding development capacity according to the predicted distribution data.

[0126] As shown in Figure 3 The embodiment of the present application also provides an electronic device 300 comprising a processor, and the processor can execute any one of the deep-sea rock particle size information prediction management methods.

[0127] Specifically, the electronic device 300 comprises a processor 301, a memory 302, and a computer program stored in the memory 302 and capable of running on the processor 301 to execute the deep-sea rock particle size information prediction management method.

[0128] The processor 301 runs the computer program of the deep-sea cutting particle size information prediction management method stored in the memory 302, and performs the following steps:

[0129] Real-time acquisition of the acquisition data of the development device in the current collection environment, wherein the acquisition data includes collection environment data and collection mineral data;

[0130] Predictive analysis of the acquisition data by a preset cutting particle size prediction model to determine the predicted distribution data of the cutting particle size corresponding to the collection mineral under the current collection environment data;

[0131] Based on the predicted distribution data, adjust the collection parameters of the development device and execute the corresponding development strategy.

[0132] Optionally, the processor 301 performs the real-time acquisition of the acquisition data of the development device in the current collection environment, comprising:

[0133] Acquisition of the depth data and pressure data of the current collection environment of the development device by a preset environment sensor;

[0134] In the database, determine the collection mineral data of the development device being developed, wherein the collection mineral data includes mineral type and mineral parameter data.

[0135] Optionally, before the processor 301 performs the predictive analysis of the acquisition data by the preset cutting particle size prediction model to determine the predicted distribution data of the cutting particle size corresponding to the collection mineral under the current collection environment data, the method further comprises:

[0136] Acquisition of the tool characteristic parameters of the development device, wherein the tool characteristic parameters include the rotational speed and diameter of the development device;

[0137] Based on the tool characteristic parameters of the development device and the collection mineral data, a first strain model layer of the rock breaking tool and the ore body is constructed, wherein the first strain model layer is used to describe the strain relationship between the development device and the ore body;

[0138] Based on the collection environment data of the development device, a second strain model layer of the internal collapse of the ore body is constructed, wherein the second strain model layer is used to describe the internal self-collapse relationship of the collection mineral in the current collection environment data;

[0139] Based on the first strain model layer and the second strain model layer, the preset cutting particle size prediction model is determined.

[0140] Optionally, the processor 301 performs the prediction analysis on the collection data by the preset rock debris particle size prediction model to determine the predicted distribution data of the collection mineral corresponding to the rock debris particle size under the current collection environment data, including:

[0141] The first strain model layer is used to process the collection mineral data to determine the crushing capacity data of the current development device on the current collection mineral.

[0142] Based on the crushing capacity data, the first predicted distribution coefficient of the current collection mineral is determined.

[0143] Optionally, the processor 301 also performs the prediction analysis on the collection data by the preset rock debris particle size prediction model to determine the predicted distribution data of the collection mineral corresponding to the rock debris particle size under the current collection environment data, including:

[0144] The second strain model layer is used to process the collection environment data and the collection mineral data to determine the total damage data of the current collection mineral under the current collection environment data.

[0145] Based on the total damage data, the second predicted distribution coefficient of the current collection mineral is determined.

[0146] Optionally, the processor 301 also performs the processing of the collection environment data and the collection mineral data by the second strain model layer to determine the total damage data of the current collection mineral under the current collection environment data, including:

[0147] According to the depth data and the pressure data under the current collection environment data, the crack density increment data of the current collection mineral is determined.

[0148] According to the development time, the crack density increment data is calculated to determine the crushing time of the current collection mineral.

[0149] Based on the volume of the current collection mineral and the crushing time, the total damage data of the current collection mineral is determined.

[0150] Optionally, the processor 301 performs the adjustment of the collection parameters of the development device and the execution of the corresponding development strategy based on the predicted distribution data, including:

[0151] Based on the predicted distribution data, the development impact force, the rotation speed, the cutting thickness, the traction speed, and the operation mode of the development device are adjusted.

[0152] According to the predicted distribution data, the depth data of the development device is adjusted, and the rock breaking tool with corresponding development capacity is replaced.

[0153] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement each process of the deep-sea rock debris particle size information prediction management method provided by the embodiment of the present application or the application end deep-sea rock debris particle size information prediction management method, and the same technical effects can be achieved. To avoid repetition, details are not described herein.

[0154] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and can be stored in a computer readable storage medium. When the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM).

[0155] The above only describes the preferred embodiments of the present application, and of course cannot limit the scope of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope of the present application.

Claims

1. A method for predicting and managing deep-sea rock cuttings grain size information, characterized in that, include: The system acquires data in real time from the development equipment under the current acquisition environment, including acquisition environment data and acquired mineral data. The collected data is analyzed by using a preset rock fragment grain size prediction model to determine the predicted distribution data of rock fragment grain size corresponding to the collected minerals under the current collection environment. Based on the predicted distribution data, the acquisition parameters of the development equipment are adjusted and the corresponding development strategy is executed; Before determining the predicted distribution data of rock fragment grain size corresponding to the collected minerals under the current collection environment by performing predictive analysis on the collected data using a preset rock fragment grain size prediction model, the method further includes: Obtain the tool characteristic parameters of the development equipment, including the rotational speed and diameter of the development equipment; Based on the tool characteristic parameters of the development equipment and the collected mineral data, a first strain model layer between the rock breaking tool and the ore body is constructed. The first strain model layer is used to describe the strain relationship between the development equipment and the ore body. Based on the data collected from the development equipment, a second strain model layer for the internal collapse of the ore body is constructed. The second strain model layer is used to describe the internal self-disintegration relationship of the collected minerals in the current data collected from the environment. Based on the first strain model layer and the second strain model layer, the preset rock cuttings grain size prediction model is determined.

2. The deep-sea rock debris grain size information prediction and management method as described in claim 1, characterized in that, The real-time acquisition of data collected by the development device in the current acquisition environment includes: The development device collects depth and pressure data of the current environment using preset environmental sensors. The database identifies the mineral data being collected by the development equipment, including mineral type and mineral parameter data.

3. The deep-sea rock cuttings grain size information prediction and management method as described in claim 1, characterized in that, The step of predicting and analyzing the collected data using a preset rock fragment grain size prediction model to determine the predicted distribution data of the rock fragment grain size corresponding to the collected minerals under the current collection environment includes: The collected mineral data is processed by the first strain model layer to determine the crushing capacity of the current development equipment for the currently collected minerals. Based on the crushing capacity data, the first predicted distribution coefficient of the currently collected minerals is determined.

4. The deep-sea rock debris grain size information prediction and management method as described in claim 1, characterized in that, The step of predicting and analyzing the collected data using a preset rock fragment grain size prediction model to determine the predicted distribution data of the rock fragment grain size corresponding to the collected minerals under the current collection environment includes: The second strain model layer processes the collected environmental data and collected mineral data to determine the total damage data of the currently collected minerals under the current collected environmental data. Based on the total damage data, a second predicted distribution coefficient for the currently collected minerals is determined.

5. The deep-sea rock debris grain size information prediction and management method as described in claim 4, characterized in that, The process of processing the acquired environmental data and acquired mineral data through the second strain model layer to determine the total damage data of the currently acquired minerals under the current acquired environmental data includes: Based on the depth and pressure data under the current environmental data, determine the crack density increment data of the currently collected mineral; Damage accumulation calculation is performed on the crack density increment data based on the development time to determine the crushing time of the currently collected minerals; Based on the volume of the currently collected mineral and the fragmentation time, the total damage data of the currently collected mineral is determined.

6. The deep-sea rock cuttings grain size information prediction and management method as described in claim 1, characterized in that, The step of adjusting the acquisition parameters of the development device and executing the corresponding development strategy based on the predicted distribution data includes: Based on the predicted distribution data, the development impact force, rotation speed, cutting thickness, traction speed, and operating mode of the development equipment are adjusted. Based on the predicted distribution data, adjust the depth data of the development equipment and replace the rock-breaking tools with those corresponding to the development capabilities.

7. A deep-sea rock debris grain size information prediction and management device, characterized in that, include: The first acquisition module is used to acquire data from the development equipment in the current acquisition environment in real time. The acquisition data includes acquisition environment data and acquired mineral data. The first determining module is used to perform predictive analysis on the collected data through a preset rock fragment grain size prediction model to determine the predicted distribution data of the rock fragment grain size corresponding to the collected minerals under the current collection environment data. The first adjustment module is used to adjust the acquisition parameters of the development device and execute the corresponding development strategy based on the predicted distribution data. The device further includes a first acquisition module, used to acquire tool characteristic parameters of the development equipment, the tool characteristic parameters including the rotational speed and diameter of the development equipment; The first construction module is used to construct a first strain model layer between the rock-breaking tool and the ore body based on the tool characteristic parameters of the development equipment and the collected mineral data. The first strain model layer is used to describe the strain relationship between the development equipment and the ore body. The second construction module is used to construct a second strain model layer for the internal collapse of the ore body based on the acquisition environment data of the development equipment. The second strain model layer is used to describe the internal self-disintegration relationship of the acquired minerals in the current acquisition environment data. The second determining module is used to determine the preset rock cuttings grain size prediction model based on the first strain model layer and the second strain model layer.

8. An electronic device, characterized in that, include: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the deep-sea rock cuttings grain size information prediction and management method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the deep-sea rock cuttings grain size information prediction and management method as described in any one of claims 1 to 6.

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