Ore blending control method and device for ore resources and electronic equipment

Abstract

The invention discloses an ore blending control method and device for ore resources and electronic equipment. The method comprises the following steps: acquiring ore blending parameters and raw material data corresponding to mineral resources; the raw material weight relation between the transport vehicle and the ore blending conveying belt is determined; according to the weight relationship of the raw materials, adjusting the density parameters of the raw materials to obtain updated density parameters; determining a current weight parameter corresponding to the mineral resource according to the updated density parameter, the first raw material volume and the second raw material volume; determining ore blending liquid parameters corresponding to the mineral resources according to the ore blending parameters and the current weight parameters; and according to the current weight parameters and the ore blending liquid parameters, ore blending control is conducted on the mineral resources. The technical problem that ore blending control is inaccurate when ore blending control is carried out on ore resources in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of system automation, and more specifically, to a method, apparatus, and electronic equipment for controlling the blending of ore resources. Background Technology

[0002] In related technologies, ore blending control is a crucial step in ensuring production efficiency, product quality, and resource utilization during the processing and utilization of ore resources. However, in these technologies, there are technical problems with inaccurate ore blending control.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for controlling the blending of ore resources, in order to at least solve the technical problem of inaccurate blending control in related technologies when controlling the blending of ore resources.

[0005] According to one aspect of the present invention, a method for controlling the blending of ore resources is provided, comprising: acquiring blending parameters and raw material data corresponding to the ore resources, wherein the raw material data includes a raw material density parameter, a first raw material volume corresponding to a transport vehicle, and a second raw material volume corresponding to a blending conveyor belt, the transport vehicle being used to transport the raw materials to the blending conveyor belt, and the blending conveyor belt being used to transport the raw materials during the blending process; determining a raw material weight relationship between the transport vehicle and the blending conveyor belt; adjusting the raw material density parameter according to the raw material weight relationship to obtain an updated density parameter; determining a current weight parameter corresponding to the ore resources based on the updated density parameter, the first raw material volume, and the second raw material volume; determining a blending liquid parameter corresponding to the ore resources based on the blending parameters and the current weight parameter; and performing blending control on the ore resources based on the current weight parameter and the blending liquid parameter.

[0006] Optionally, determining the raw material weight relationship between the transport vehicle and the ore blending conveyor belt includes: determining a first raw material weight corresponding to the transport vehicle and a second raw material weight corresponding to the ore blending conveyor belt; determining a deviation characteristic parameter between the first raw material weight and the second raw material weight; and determining the raw material weight relationship between the transport vehicle and the ore blending conveyor belt based on the deviation characteristic parameter.

[0007] Optionally, determining the weight of the second raw material corresponding to the ore blending conveyor belt includes: determining first point cloud data of the ore blending conveyor belt in a first state and second point cloud data of the ore blending conveyor belt in a second state, wherein the first state is a state in which the ore blending conveyor belt is not transporting raw materials, and the second state is a state in which the ore blending conveyor belt is transporting raw materials; determining raw material transport parameters corresponding to the ore blending conveyor belt based on the first point cloud data and the second point cloud data, wherein the raw material transport parameters include raw material flow rate and transport duration; and determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the raw material transport parameters.

[0008] Optionally, determining the weight of the first raw material corresponding to the transport vehicle includes: determining vehicle body point cloud data and raw material point cloud data corresponding to the transport vehicle, wherein the raw material point cloud data is the point cloud data of the raw material loaded on the transport vehicle; determining loading space parameters corresponding to the transport vehicle based on the vehicle body point cloud data; determining raw material space parameters corresponding to the transport vehicle based on the raw material point cloud data; and determining the weight of the first raw material corresponding to the transport vehicle based on the loading space parameters and the raw material space parameters.

[0009] Optionally, determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the raw material transmission parameters includes: determining the unloading position corresponding to the transport vehicle, the unloading start time, and the unloading end time; determining the spatial distance between the unloading position and the ore blending conveyor belt; determining the unloading compensation time based on the spatial distance; and determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the unloading start time, the unloading end time, the unloading compensation time, and the raw material transmission parameters.

[0010] Optionally, determining the current weight parameter corresponding to the ore resource based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material includes: determining a volume deviation parameter between the volume of the first raw material and the volume of the second raw material; determining a target raw material volume based on the volume deviation parameter, the volume of the first raw material, and the volume of the second raw material; and determining the current weight parameter corresponding to the ore resource based on the updated density parameter and the target raw material volume.

[0011] Optionally, determining the blending solution parameters corresponding to the ore resource based on the blending parameters and the current weight parameter includes: determining the weight parameter of the blending solution corresponding to the ore resource based on the blending parameters and the current weight parameter; determining the flow characteristic corresponding to the current weight parameter; determining the flow parameter of the blending solution based on the flow characteristic; and determining the blending solution parameters corresponding to the ore resource based on the weight parameter of the blending solution and the flow parameter of the blending solution.

[0012] According to one aspect of the present invention, a ore blending control device is provided, comprising: an acquisition module for acquiring blending parameters and raw material data corresponding to the ore resources, wherein the raw material data includes a raw material density parameter, a first raw material volume corresponding to a transport vehicle, and a second raw material volume corresponding to a blending conveyor belt, the transport vehicle being used to transport the raw materials to the blending conveyor belt, and the blending conveyor belt being used to transport the raw materials during the blending process; a first determination module for determining the raw material weight relationship between the transport vehicle and the blending conveyor belt; a second determination module for adjusting the raw material density parameter according to the raw material weight relationship to obtain an updated density parameter; a third determination module for determining a current weight parameter corresponding to the ore resources based on the updated density parameter, the first raw material volume, and the second raw material volume; a fourth determination module for determining a blending liquid parameter corresponding to the ore resources based on the blending parameters and the current weight parameter; and a fifth determination module for performing blending control on the ore resources based on the current weight parameter and the blending liquid parameter.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the ore blending control method of any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the ore blending control method described in any of the preceding claims.

[0015] In this embodiment of the invention, ore blending parameters and raw material data corresponding to ore resources are obtained. The raw material data includes raw material density parameters, a first raw material volume corresponding to a transport vehicle, and a second raw material volume corresponding to a blending conveyor belt. The transport vehicle is used to transport the raw materials to the blending conveyor belt, which is used to transport the raw materials during the blending process. The weight relationship between the transport vehicle and the blending conveyor belt is determined. Based on the weight relationship, the raw material density parameters are adjusted to obtain updated density parameters. Based on the updated density parameters, the first raw material volume, and the second raw material volume, the current weight parameters corresponding to the ore resources are determined. Based on the blending parameters and the current weight parameters, the blending liquid parameters corresponding to the ore resources are determined. Based on the current weight parameters and the blending liquid parameters, ore blending control is performed on the ore resources. This method acquires ore blending parameters and raw material data, including raw material density parameters, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the ore blending conveyor belt. It determines the raw material weight relationship between the transport vehicle and the ore blending conveyor belt, establishing a correlation benchmark based on dual volume measurements of the same batch of raw materials. Adjusting the raw material density parameters according to this weight relationship yields updated density parameters, correcting density deviations to ensure density data accurately reflects actual raw material characteristics. Combining the updated density parameters with the first and second raw material volumes determines the current weight parameters, enabling precise measurement of raw material weight. Based on the ore blending parameters and the current weight parameters, the ore blending solution parameters are determined, allowing for precise matching of the ore blending solution addition amount with the raw material weight. Ore blending control is performed based on the current weight parameters and ore blending solution parameters, ultimately avoiding ore blending imbalances and achieving precise ore resource blending control. This solves the technical problem of inaccurate ore blending control in related technologies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a mineral resource blending control method according to an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of the overall architecture of the ore resource blending control system in an optional embodiment of the present invention;

[0019] Figure 3 This is a flowchart of the mutual correction algorithm module in an optional embodiment of the present invention;

[0020] Figure 4 This is a flowchart of a method for controlling the blending of ore resources in an optional embodiment of the present invention;

[0021] Figure 5This is a structural block diagram of an ore blending control device according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0025] Voxel-based integration: Voxel-based integration is a numerical integration method that divides the three-dimensional space into a series of small cubes (voxels), calculates the approximate value of the integrand on each voxel, and then sums the integration results of all voxels to obtain the integral value of the entire region.

[0026] Delaunay triangulation: Delaunay triangulation is a method of dividing a planar point set into a series of non-overlapping triangles. For example, in finite element analysis, the solution domain can be divided into a Delaunay triangular mesh, and then numerical calculations can be performed on each triangle.

[0027] Trapezoid method of integration: The trapezoidal method of integration is a numerical integration method that divides the integration interval into a series of small trapezoids, calculates the area of ​​each trapezoid, and then sums the areas of all trapezoids to obtain the integral value for the entire interval.

[0028] Simpson's method of integration: Simpson's method of integration is a numerical integration method that approximates the definite integral of the original function by dividing the integration interval into a series of small subintervals, approximating the curve of the integrand with a quadratic function in each subinterval, and then calculating the definite integrals of these quadratic functions in the subintervals.

[0029] PID: PID (Proportional-Integral-Derivative) is a feedback control algorithm that adjusts the control quantity by calculating the error between the set value and the actual value.

[0030] Web: The Web, or World Wide Web, is an information system based on the Hypertext Transfer Protocol (HTTP) that allows users to access and share information on the Internet through a browser.

[0031] Example 1

[0032] According to an embodiment of the present invention, an embodiment of a method for controlling the blending of ore resources is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1 This is a flowchart of a mineral resource blending control method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0034] S102, Obtain the ore blending parameters and raw material data corresponding to the ore resources. The raw material data includes the raw material density parameter, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the ore blending conveyor belt. The transport vehicle is used to transport the raw material to the ore blending conveyor belt, and the ore blending conveyor belt is used to transport the raw material during the ore blending process.

[0035] In step S102 of this application, the ore blending parameters and raw material data corresponding to the ore resources are obtained.

[0036] This involves mineral resources, which are the objects of mineral blending control, such as potassium ore or carnallite in potash fertilizer production.

[0037] This involves ore blending parameters, which are various parameters used to control and optimize the ore blending process during ore resource blending. For example, the blending ratio (e.g., the ratio of ore to mother liquor and water). The water can be process water, that is, water related to ore blending and decomposition reaction.

[0038] This includes raw material data, which consists of various data related to ore resources, used to describe the physical and chemical properties of the raw materials, their state during transportation and transmission, including raw material density parameters, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the ore blending conveyor belt.

[0039] This involves raw material density parameters, which are used to reflect the quality characteristics of ore resources, such as the density of the ore. Accurate density parameters are crucial for precise metering and blending control during the ore blending process. Because the physical properties of mineral resources can change over time and with the environment, density parameters need to be measured and calibrated to ensure their accuracy.

[0040] This involves transport vehicles, which are vehicles used to transport mineral resources from mining sites or storage sites to ore blending conveyor belts, with the cargo compartments of the transport vehicles used to load ore.

[0041] This involves the volume of the first raw material, which is the volume of mineral resources loaded on the transport vehicle.

[0042] This involves a ore blending conveyor belt, which is a conveyor belt used to transport mineral resources during the ore blending process. For example, the ore blending conveyor belt can be used to transport ore from the unloading point of the transport vehicle to the subsequent ore blending equipment.

[0043] This involves the second raw material volume, which is the volume of mineral resources on the ore blending conveyor belt. The second raw material volume includes the flow volume and the cumulative volume. The two-dimensional contour data of the ore being transported on the belt can be acquired in real time by a laser contour scanner or other visual sensors installed above the conveyor belt, and the volumetric flow rate and cumulative volume of the ore can be calculated.

[0044] Obtaining ore blending parameters can clarify the ideal ratio between ore and blending solution (such as mother liquor and water), ensuring that the ore blending process meets the process requirements; while obtaining raw material density parameters, first raw material volume, and second raw material volume, etc., can quantitatively describe the state of ore during transportation and transmission, providing a data basis for subsequent ore blending control.

[0045] S104, Determine the weight relationship of raw materials between the transport vehicle and the ore mixing conveyor belt;

[0046] In step S104 provided in this application, the weight relationship of raw materials between the transport vehicle and the ore mixing conveyor belt is determined.

[0047] This involves the relationship between raw material weights, which is the correlation and correspondence between the weight of ore loaded on the transport vehicle and the corresponding weight of ore on the blending conveyor belt. This relationship reflects the weight changes of the ore during transportation and transmission, as well as the deviations and consistency of the transport vehicles and blending conveyor belts in measuring the ore weight.

[0048] Determining the weight relationship between the transport vehicles and the ore blending conveyor belt can provide a basis for subsequent density parameter adjustments and ensure the accuracy of ore weight measurement.

[0049] S106, Based on the weight relationship of the raw materials, the density parameters of the raw materials are adjusted to obtain updated density parameters;

[0050] In step S106 provided in this application, the density parameter of the raw material is adjusted according to the weight relationship of the raw material to obtain the updated density parameter.

[0051] This involves adjusting the raw material density parameter. This adjustment is done by modifying the initially set raw material density parameter based on the weight relationship between the transport vehicle and the ore blending conveyor belt. Because the physical properties of the ore (such as moisture content and particle size) may change during transportation and transport, the actual density may deviate from the initial set value. By analyzing the raw material weight relationship (such as the deviation in the metering results between the transport vehicle and the ore blending conveyor belt), the density parameter can be adjusted to more closely approximate the actual ore density. Furthermore, a deviation threshold tolerance mechanism can be simultaneously set to prevent abnormal data from affecting the adjustment results.

[0052] This involves updating the density parameter, which is the adjusted raw material density parameter.

[0053] By comparing the weight of ore loaded on the transport vehicle with the corresponding weight of ore on the blending conveyor belt, measurement deviations can be identified, and the raw material density parameters can be corrected. This avoids inaccurate blending caused by measurement errors, enabling precise measurement of ore during transportation and transmission, and providing a data foundation for precise blending control.

[0054] S108. Based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material, determine the current weight parameter corresponding to the ore resource.

[0055] In step S108 provided in this application, the current weight parameter corresponding to the ore resource is determined based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material.

[0056] This includes the current weight parameter, which represents the actual weight of the ore resources calculated based on the updated density parameter, the first raw material volume (the volume of ore loaded on the transport vehicle), and the second raw material volume (the volume of ore on the blending conveyor belt) during the ore blending process. This reflects the true weight of the ore in its current state.

[0057] The current weight parameters are determined based on the updated density parameters, the volume of the first raw material, and the volume of the second raw material. By combining the dynamically corrected density data with the results of dual volume measurements, the calculated weight of the ore can be made to match the actual state, avoiding weight distortion caused by density deviation or volume measurement error, thereby achieving accurate quantification of raw material weight during the ore blending process.

[0058] S110, based on the ore blending parameters and the current weight parameters, determine the ore blending solution parameters corresponding to the ore resources;

[0059] In step S110 of this application, the parameters of the ore blending solution corresponding to the ore resources are determined based on the ore blending parameters and the current weight parameters.

[0060] This involves parameters related to the ore blending solution. These parameters are used to describe and control the amount and related characteristics of the blending solution (such as mother liquor, water, and other additives) during the ore blending process. These parameters include: the weight of the blending solution, its volume, flow rate, concentration, and addition time.

[0061] Determining the parameters of the ore blending solution based on the ore blending parameters and the current weight parameters ensures that the mixing of ore and ore blending solution meets the process setting requirements (i.e., ore blending requirements), avoids process instability or resource waste caused by improper proportioning, and achieves precise control of the ore blending process.

[0062] S112, based on the current weight parameters and the parameters of the ore mixing solution, controls the ore blending of resources.

[0063] In step S112 provided in this application, the ore resources are controlled by blending based on the current weight parameters and the blending solution parameters.

[0064] This involves controlling the blending of ore resources. This process involves controlling the proportion of ore resources during processing using an automated or semi-automated system based on current weight parameters and blending liquid parameters, in order to obtain resources that meet the blending requirements.

[0065] By controlling the blending of ore resources based on current weight parameters and blending solution parameters, it is possible to ensure that the ore and blending solution are mixed in a precise ratio, avoid imbalance in the blending ratio, and achieve precision in the blending control of ore resources.

[0066] Through the above steps S102-S112, the ore blending parameters and raw material data corresponding to the ore resources are obtained. The raw material data includes raw material density parameters, a first raw material volume corresponding to the transport vehicle, and a second raw material volume corresponding to the blending conveyor belt. The transport vehicle is used to transport the raw materials to the blending conveyor belt, which is used to transport the raw materials during the blending process. The weight relationship between the transport vehicle and the blending conveyor belt is determined. Based on the weight relationship, the raw material density parameters are adjusted to obtain updated density parameters. Based on the updated density parameters, the first raw material volume, and the second raw material volume, the current weight parameters corresponding to the ore resources are determined. Based on the blending parameters and the current weight parameters, the blending liquid parameters corresponding to the ore resources are determined. Based on the current weight parameters and the blending liquid parameters, the ore resources are blended and controlled. This method acquires ore blending parameters and raw material data, including raw material density parameters, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the ore blending conveyor belt. It determines the raw material weight relationship between the transport vehicle and the ore blending conveyor belt, establishing a correlation benchmark based on dual volume measurements of the same batch of raw materials. Adjusting the raw material density parameters according to this weight relationship yields updated density parameters, correcting density deviations to ensure density data accurately reflects actual raw material characteristics. Combining the updated density parameters with the first and second raw material volumes determines the current weight parameters, enabling precise measurement of raw material weight. Based on the ore blending parameters and the current weight parameters, the ore blending solution parameters are determined, allowing for precise matching of the ore blending solution addition amount with the raw material weight. Ore blending control is performed based on the current weight parameters and ore blending solution parameters, ultimately avoiding ore blending imbalances and achieving precise ore resource blending control. This solves the technical problem of inaccurate ore blending control in related technologies.

[0067] As an optional embodiment, determining the raw material weight relationship between the transport vehicle and the ore blending conveyor belt includes: determining a first raw material weight corresponding to the transport vehicle and a second raw material weight corresponding to the ore blending conveyor belt; determining a deviation characteristic parameter between the first raw material weight and the second raw material weight; and determining the raw material weight relationship between the transport vehicle and the ore blending conveyor belt based on the deviation characteristic parameter.

[0068] In this embodiment, the specific steps for determining the weight relationship of raw materials between the transport vehicle and the ore mixing conveyor belt are described.

[0069] This involves the weight of the first raw material, which is the weight of the ore resources loaded on the transport vehicle, and can be measured by the metering system corresponding to the transport vehicle.

[0070] This involves the weight of a second raw material, which is the weight of the ore resources transported by the ore blending conveyor belt, and can be measured by the metering system corresponding to the ore blending conveyor belt.

[0071] This involves a deviation characteristic parameter, which represents the difference or deviation between the weight of the first raw material and the weight of the second raw material, quantifying the degree of difference in weight measurements of the same batch of raw materials by different metering systems. This deviation characteristic parameter can be represented by a relative deviation coefficient.

[0072] By determining the weight of the first raw material corresponding to the transport vehicle and the weight of the second raw material corresponding to the ore blending conveyor belt, dual weight measurement data of the same batch of ore can be obtained. By calculating the deviation characteristic parameters of the two (such as the relative deviation coefficient), the measurement differences of different metering systems can be accurately identified, thereby accurately quantifying the raw material weight relationship between the transport vehicle and the ore blending conveyor belt.

[0073] As an optional embodiment, determining the weight of the second raw material corresponding to the ore blending conveyor belt includes: determining first point cloud data of the ore blending conveyor belt in a first state and second point cloud data of the ore blending conveyor belt in a second state, wherein the first state is a state in which the ore blending conveyor belt is not conveying raw materials and the second state is a state in which the ore blending conveyor belt is conveying raw materials; determining the raw material conveying parameters corresponding to the ore blending conveyor belt based on the first point cloud data and the second point cloud data, wherein the raw material conveying parameters include raw material flow rate and conveying time; and determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the raw material conveying parameters.

[0074] In this embodiment, the specific steps for determining the weight of the second raw material corresponding to the ore mixing conveyor belt are described.

[0075] The first state involves the conveyor belt not transporting raw materials, meaning it is empty or carrying no ore. In this state, the conveyor belt is not loaded with ore and is mainly used to obtain baseline data for subsequent calculations of ore volume and weight.

[0076] This involves a second state, which is the state where the ore blending conveyor belt is transporting raw materials, i.e., ore is being transported on the conveyor belt. In this state, the volume and flow rate data of the ore can be obtained through sensors to calculate the actual weight of the ore.

[0077] This involves the first point cloud data, which is the point cloud data (such as 3D point cloud data) of the conveyor belt surface acquired when the ore mixing conveyor belt is in its first state (unloaded). This first point cloud data can be obtained by a laser scanner or other 3D sensors and is used to establish a reference model of the conveyor belt.

[0078] This involves second point cloud data, which is point cloud data (such as 3D point cloud data) of the conveyor belt surface acquired when the ore blending conveyor belt is in the second state (load). This second point cloud data can be obtained by a laser scanner or other 3D sensor and includes 3D point cloud data of the conveyor belt surface and the outline of the raw materials carried above it.

[0079] This involves raw material transport parameters, which are relevant parameters used to describe the transport status of ore on the blending conveyor belt, including raw material flow rate and transport time.

[0080] This involves the raw material flow rate, which is the volume of ore passing through the ore blending conveyor belt per unit time.

[0081] This includes the transmission time, which is the total time it takes for the ore to be transported on the blending conveyor belt from the start to the end.

[0082] By determining the first point cloud data under no-load conditions and the second point cloud data under load conditions of the ore blending conveyor belt, a reference plane model of the conveyor belt can be established and three-dimensional data containing the outline of the raw materials can be obtained. By comparing the two sets of point cloud data, the raw material transmission parameters (raw material flow rate and transmission time) can be accurately extracted. Based on these parameters, the cumulative weight of the corresponding batch of raw materials on the ore blending conveyor belt can be accurately calculated, ensuring that the weight of the second raw material and the weight of the first raw material on the transport vehicle form an effective mutual calibration benchmark. This provides accurate weight data support for subsequent density parameter adjustments and ore blending control, avoiding the impact of conveyor belt measurement deviations on the overall ore blending accuracy.

[0083] As an optional embodiment, determining the weight of the first raw material corresponding to the transport vehicle includes: determining the vehicle body point cloud data and the raw material point cloud data corresponding to the transport vehicle, wherein the raw material point cloud data is the point cloud data of the raw material loaded on the transport vehicle; determining the loading space parameters corresponding to the transport vehicle based on the vehicle body point cloud data; determining the raw material space parameters corresponding to the transport vehicle based on the raw material point cloud data; and determining the weight of the first raw material corresponding to the transport vehicle based on the loading space parameters and the raw material space parameters.

[0084] In this embodiment, the specific steps for determining the weight of the first raw material corresponding to the transport vehicle are described.

[0085] This involves vehicle body point cloud data, which is point cloud data (such as 3D point cloud data) reflecting the three-dimensional outline of the transport vehicle, obtained by scanning the vehicle. Specifically, this vehicle body point cloud data can be the point cloud data of the cargo compartment, used to construct a 3D model of the cargo compartment to define the loading range. This vehicle body point cloud data can be obtained through scanning with multi-line LiDAR or binocular stereo vision cameras.

[0086] This involves raw material point cloud data, which is the point cloud data (such as 3D point cloud data) of the ore loaded on the transport vehicle. It reflects the distribution and shape of the ore in the truck bed and is used to calculate the actual volume and space occupation of the ore.

[0087] This involves loading space parameters, which are the available space inside the transport vehicle's cargo compartment for loading ore. These loading space parameters include the length, width, height, and total volume of the cargo compartment.

[0088] This involves raw material space parameters, which are the actual space parameters occupied by the ore in the transport vehicle compartment, including the shape and distribution of the ore.

[0089] Determining the point cloud data of the transport vehicle and the raw materials allows for the construction of a 3D model of the truck bed and precise definition of the loading area, while also reflecting the distribution and shape of the ore within the truck bed. Loading space parameters calculated from the vehicle bed point cloud data clarify the usable volume of the truck bed; raw material space parameters calculated from the raw material point cloud data determine the actual volume and space occupied by the ore. By comparing the loading space parameters and the raw material space parameters, the volume of the ore can be accurately calculated, and combined with density parameters, the weight of the first raw material can be determined. This enables precise measurement of the weight of the ore loaded in the transport vehicle, providing accurate weight data for subsequent ore blending control.

[0090] As an optional embodiment, determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the raw material transmission parameters includes: determining the unloading position corresponding to the transport vehicle, the unloading start time, and the unloading end time; determining the spatial distance between the unloading position and the ore blending conveyor belt; determining the unloading compensation time based on the spatial distance; and determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the unloading start time, the unloading end time, the unloading compensation time, and the raw material transmission parameters.

[0091] In this embodiment, the specific steps for determining the weight of the second raw material corresponding to the ore blending conveyor belt are described.

[0092] This involves the unloading location, which is the specific location where the transport vehicle unloads the raw materials onto the ore mixing conveyor belt. This unloading location can be the location of the unloading point.

[0093] This includes the unloading start time, which is the time when the transport vehicle starts the unloading action and the raw materials begin to fall.

[0094] This includes the unloading end time, which is the time when the transport vehicle completes unloading and the raw materials stop falling.

[0095] This involves spatial distance, which is the distance between the unloading position and the ore mixing conveyor belt, and can be measured by sensors or determined by pre-set system parameters.

[0096] This includes unloading compensation time, which is the additional transmission time caused by the spatial distance between the ore unloading position and the ore blending conveyor belt, used to compensate for the time delay of the ore during the unloading process.

[0097] Determining the unloading location, start and end times of the transport vehicles clarifies the unloading range and time interval for a single batch of raw materials. By determining the spatial distance between the unloading location and the ore blending conveyor belt, the unloading compensation time can be accurately calculated to compensate for the time delay in raw material transmission. Combined with raw material transmission parameters (raw material flow rate, transmission duration), the cumulative weight of the corresponding batch of raw materials on the ore blending conveyor belt can be further accurately determined, ensuring a precise correlation between the weight of the second raw material and the weight of the first raw material on the transport vehicle. This avoids measurement misalignment caused by time and spatial deviations, providing accurate batch weight data support for subsequent determination of raw material weight relationships and adjustment of density parameters.

[0098] As an optional embodiment, determining the current weight parameter corresponding to the ore resource based on the updated density parameter, the first raw material volume, and the second raw material volume includes: determining the volume deviation parameter between the first raw material volume and the second raw material volume; determining the target raw material volume based on the volume deviation parameter, the first raw material volume, and the second raw material volume; and determining the current weight parameter corresponding to the ore resource based on the updated density parameter and the target raw material volume.

[0099] In this embodiment, specific steps are described to determine the current weight parameter corresponding to the ore resource based on the updated density parameter, the first raw material volume, and the second raw material volume.

[0100] This involves a volume deviation parameter, which is the difference or deviation between the volume of the first raw material (the volume of ore loaded on the transport vehicle) and the volume of the second raw material (the volume of ore on the blending conveyor belt).

[0101] This involves the target raw material volume, which is the actual volume of the ore calculated through adjustments and optimizations after considering volume deviation parameters. It is a volume value corrected based on a comprehensive analysis of the first and second raw material volumes, combined with the volume deviation parameters. The target raw material volume is closer to the true volume of the ore in its current state and is used for subsequent weight calculations and ore blending control.

[0102] Determining the volume deviation parameter between the first and second raw material volumes allows for the identification of volume changes in the ore during transportation and transmission, ensuring the accuracy of volume measurement. Adjusting the volumes of the first and second raw materials based on the volume deviation parameter to determine the target raw material volume more accurately reflects the actual volume state of the ore. Combining this with updated density parameters and the target raw material volume to calculate the current weight parameter enables precise weight measurement of ore resources during blending, ensuring the stability and accuracy of blending control.

[0103] As an optional embodiment, the parameters of the blending solution corresponding to the ore resources are determined based on the blending parameters and the current weight parameters, including: determining the weight parameters of the blending solution corresponding to the ore resources based on the blending parameters and the current weight parameters; determining the flow characteristics corresponding to the current weight parameters; determining the flow parameters of the blending solution based on the flow characteristics; and determining the parameters of the blending solution corresponding to the ore resources based on the weight parameters and the flow parameters of the blending solution.

[0104] This embodiment describes the specific steps for determining the parameters of the blending solution corresponding to the ore resources based on the blending parameters and the current weight parameters.

[0105] This involves the weight parameters of the ore blending solution. These parameters are calculated during the ore blending process based on blending parameters (such as the required ratio of ore to blending solution) and current weight parameters (the actual weight of the ore). Examples include the total weight of the blending solution, the weight of the mother liquor in the blending solution, and the weight of water in the blending solution.

[0106] This involves flow characteristics, which are used to represent the dynamic characteristics of ore transport, including instantaneous flow changes and flow stability of ore on the blending conveyor belt.

[0107] This involves the flow rate parameters of the ore preparation solution, which are parameters used to reflect the flow state of the ore preparation solution, including the flow velocity and flow rate of the ore preparation solution.

[0108] Determining the weight parameters of the ore blending solution based on the ore blending parameters and the current weight parameters of the ore can clarify the total weight of the ore blending solution and the weight requirements of components such as mother liquor and water. Furthermore, by using the flow characteristics corresponding to the current weight parameters, the flow parameters of the ore blending solution can be determined, ensuring that the amount of ore blending solution added, the flow rate, and the actual weight and transmission status of the ore are accurately matched, thereby achieving the accuracy and reliability of ore blending.

[0109] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0110] In related technologies, ore blending control is a crucial step in ensuring production efficiency, product quality, and resource utilization during the processing and utilization of ore resources. However, in these technologies, there are technical problems with inaccurate ore blending control.

[0111] There is currently no effective solution to the above problems.

[0112] In view of this, an optional embodiment of the present invention provides a method for ore blending control, which can effectively solve the above-mentioned technical problems. Specifically, the ore blending control method can be implemented through an ore blending control system, which includes: a sensing and execution layer, a data acquisition and edge processing layer, a core mutual calibration layer, a control and optimization layer, and a human-computer interaction and detection layer.

[0113] Figure 2 This is a schematic diagram of the overall architecture of the ore resource blending control system in an optional embodiment of the present invention. Figure 3 This is a flowchart of the mutual correction algorithm module in an optional embodiment of the present invention. Figure 4 This is a flowchart of an optional embodiment of the ore resource blending control method of the present invention, such as... Figure 2 , Figure 3 and Figure 4 As shown, a detailed description follows.

[0114] S1, obtain the ore blending parameters and raw material data corresponding to the ore resources. The raw material data includes the raw material density parameter, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the ore blending conveyor belt. The transport vehicle is used to transport the raw material to the ore blending conveyor belt, and the ore blending conveyor belt is used to transport the raw material during the ore blending process.

[0115] For example, obtaining ore blending parameters and raw material data corresponding to ore resources can be achieved through the belt conveyor vision measurement unit in the perception and execution layer, the transport vehicle vision volume detection unit, and the data acquisition and edge processing layer.

[0116] The belt conveyor vision metering unit is used to determine the volume of the second raw material corresponding to the ore blending conveyor belt, and includes: sensors and auxiliary systems. Among them:

[0117] The preferred sensor is a laser profile scanner with strong resistance to ambient light interference. Its working principle is laser triangulation, which can acquire the two-dimensional profile of the ore on the conveyor belt at high speed and high precision at frequencies of several kilohertz. Furthermore, the sensor is fixedly installed above the feed conveyor (i.e., the ore blending conveyor belt) using a predetermined bracket. The installation height H (the vertical distance from the sensor lens to the belt surface) needs to be precisely calculated and determined based on the belt width and the sensor's field of view, preferably 1.5m-2.5m, to ensure that the scanning line completely covers the effective working width of the belt with appropriate margin. The sensor should be installed perpendicular to the belt plane.

[0118] The auxiliary system includes a strip-shaped LED linear supplemental light, an automatic dust removal device, and an encoder-type speed sensor. The strip-shaped LED linear supplemental light, with a wavelength matched to the laser, is installed near the sensor to provide stable and uniform illumination at night or in low-light conditions, suppressing interference from ambient light variations. The automatic dust removal device, integrated into the sensor's protective cover, periodically sprays clean compressed air to prevent dust from adhering to the laser emitter and receiving lens, ensuring long-term stable measurement performance. The encoder-type speed sensor can be a high-precision encoder-type speed sensor, directly connected to the shaft end of the belt conveyor's tail pulley (driven pulley) via a coupling, to measure the belt speed v(t) in real time, with a speed measurement accuracy better than ±0.1%.

[0119] The visual volume measurement unit for transport vehicles is used to determine the volume of the first raw material corresponding to the transport vehicle, and includes sensors and auxiliary systems. Among them:

[0120] The preferred sensor is a multi-line LiDAR, which offers the advantage of rapidly acquiring a 360° three-dimensional point cloud of the vehicle and its load, unaffected by lighting conditions, and with high ranging accuracy. Specifically, the sensor can be mounted on a gantry at the vehicle unloading point entrance. Furthermore, the LiDAR can be installed in the center of the gantry beam, with the installation height ensuring its field of view completely covers the cargo area of ​​the largest vehicle. Additionally, two high-resolution binocular stereo vision cameras can be symmetrically mounted on both sides of the gantry to calculate depth information using a stereo matching algorithm.

[0121] The auxiliary system includes UHF RFID readers and passive tags, and triggered LED strobe lights. For the UHF RFID readers and passive tags, the tags are installed on each transport vehicle, and the readers are mounted on the gantry. When a vehicle passes by, its vehicle identification number (ID) is automatically read, thus binding the vehicle information. The triggered LED strobe lights can be high-brightness LED strobe lights, triggered synchronously with the camera, providing high-intensity illumination at the moment of image acquisition, completely eliminating the influence of ambient light.

[0122] The data acquisition and edge processing layer determines the first raw material volume corresponding to the transport vehicle and the second raw material volume corresponding to the ore blending conveyor belt based on the measurement and detection results of the belt conveyor visual measurement unit and the transport vehicle visual volume detection unit.

[0123] S2, determine the weight relationship of raw materials between the transport vehicle and the ore mixing conveyor belt;

[0124] Specifically, this can be achieved through a data acquisition and edge processing layer and a core mutual calibration layer. The data acquisition and edge processing layer includes a data acquisition module, a vision algorithm processing module, and an initial quality calculation module. Further, the vision algorithm processing module includes a belt volume calculation submodule and a vehicle volume calculation submodule. The core mutual calibration layer includes a data association and batch management module, and an adaptive mutual calibration algorithm module (i.e., a cross-correlation algorithm module).

[0125] Specifically, when the raw material density parameters include the density parameters of the belt system and the vehicle system, an example is given where the first raw material weight can be the historical raw material weight corresponding to the transport vehicle (denoted as the first historical weight), and the second raw material weight can be the weight corresponding to the ore blending conveyor belt (i.e., the belt) (denoted as the second historical weight). The ore blending control system includes the belt system and the vehicle system. The belt system is a detection and control system corresponding to the ore blending conveyor belt, and the vehicle system is a detection and control system corresponding to the transport vehicle. These will be described in detail below.

[0126] Furthermore, S2 may also include:

[0127] S21, determine the vehicle body point cloud data and raw material point cloud data corresponding to the transport vehicle, wherein the raw material point cloud data is the point cloud data of the raw materials loaded on the transport vehicle; determine the loading space parameters corresponding to the transport vehicle based on the vehicle body point cloud data; determine the raw material space parameters corresponding to the transport vehicle based on the raw material point cloud data; determine the first raw material weight corresponding to the transport vehicle based on the loading space parameters and the raw material space parameters.

[0128] For example, to acquire and segment the overall point cloud data of a transport vehicle, a Random Sample Consensus (RANSAC) algorithm or a deep learning-based segmentation network can be used to accurately separate the cargo compartment point cloud (i.e., vehicle body point cloud data) and the ore pile point cloud (i.e., raw material point cloud data) from the overall point cloud of the vehicle (i.e., the transport vehicle). Based on the cargo compartment point cloud, a 3D model of the cargo compartment is built, and its internal space volume (i.e., loading space parameters) is calculated for volume deduction. Then, for the segmented ore pile point cloud (i.e., raw material point cloud data), an integral method based on voxel meshes or an algorithm that calculates the convex hull volume after generating a 3D mesh using Delaunay triangulation is used to calculate the ore pile volume, thus obtaining the actual volume of the ore. This process can be implemented through the vehicle volume calculation submodule.

[0129] Then, based on the actual volume The formula for determining the visual quality of a vehicle is:

[0130]

[0131] in, For vehicle visual quality (i.e., the weight of the first raw material). This refers to the density parameters of the belt system. This process can be achieved through the initial mass calculation module.

[0132] S22, determine the first point cloud data of the ore blending conveyor belt in the first state and the second point cloud data of the ore blending conveyor belt in the second state, wherein the first state is the state in which the ore blending conveyor belt is not conveying raw materials and the second state is the state in which the ore blending conveyor belt is conveying raw materials; based on the first point cloud data and the second point cloud data, determine the raw material conveying parameters corresponding to the ore blending conveyor belt, wherein the raw material conveying parameters include the raw material flow rate and the conveying time; based on the raw material conveying parameters, determine the second raw material weight corresponding to the ore blending conveyor belt.

[0133] For example: acquire the first and second point cloud data (which can be historical point cloud data of the ore blending conveyor belt), and perform point cloud preprocessing. For instance, filter the raw contour data (i.e., the first and second point cloud data) from the laser scanner (e.g., median filtering or Gaussian filtering) to remove noise. Based on the first point cloud data, perform reference plane calibration, i.e., scan multiple times in an empty conveyor belt state (i.e., the first state) to fit a mathematical model of the conveyor belt plane. Based on the first and second point cloud data, perform effective cross-section calculation, including: for each frame of contour data in the second point cloud data, calculate the effective cross-sectional area enclosed by the ore point cloud above the conveyor belt plane. The volumetric flow rate can be calculated using either the trapezoidal integral method or the Simpson integral method. Then, volumetric flow rate integration is performed; that is, the volumetric flow rate is calculated in real time by incorporating raw material transport parameters (including synchronously acquired belt speed). The formula is:

[0134]

[0135] in, For synchronously collected belt speed; The sampling interval is defined as follows. Further, based on the real-time calculated volumetric flow rate described above, the cumulative volume is obtained by summing the data. This process can be implemented through the belt volume calculation submodule.

[0136] Next, determine the visual quality flow rate of the belt using the following formula:

[0137]

[0138] in, The visual quality flow rate of the belt conveyor (i.e., the weight of raw materials transported by the ore blending conveyor belt per unit time). For the density parameters of the belt system;

[0139] Therefore, the cumulative visual mass of the belt can be determined by the following formula:

[0140]

[0141] in, The visual cumulative mass of the belt (i.e., the weight of the second raw material) is calculated. The above process can be achieved through the initial mass calculation module.

[0142] In addition, the initial mass calculation module is also used to maintain two dynamic density parameters: (For belt systems) and (For vehicle systems). During system initialization, and They are given the same empirical initial values ​​(e.g., obtained through experimental measurement or initial static weighing calibration).

[0143] Furthermore, based on the raw material transport parameters, the weight of the second raw material corresponding to the ore blending conveyor belt is determined, including:

[0144] Determine the unloading location corresponding to the transport vehicle, the unloading start time, and the unloading end time; determine the spatial distance between the unloading location and the ore blending conveyor belt; determine the unloading compensation time based on the spatial distance; and determine the weight of the second raw material corresponding to the ore blending conveyor belt based on the unloading start time, unloading end time, unloading compensation time, and raw material transmission parameters.

[0145] S23, determine the weight of the first raw material corresponding to the transport vehicle and the weight of the second raw material corresponding to the ore blending conveyor belt; determine the deviation characteristic parameter between the weights of the first and second raw materials; and determine the weight relationship between the transport vehicle and the ore blending conveyor belt based on the deviation characteristic parameter. Specifically, this can be achieved through a data acquisition and edge processing layer.

[0146] For example, firstly, the vehicle information of the transport vehicle is bound. When the vehicle passes through the radio frequency identification (RFID) reader, its ID, timestamp T1, and visual measurement results are recorded and stored. Next, unloading event detection is performed. Using hopper level gauges or video detection signals, it is determined when the vehicle begins unloading (timestamp T2, i.e., the unloading start time). Then, belt conveyor batch segmentation is performed. Based on T2 and the time delay (estimated time for ore to travel from the hopper to the belt conveyor, i.e., unloading compensation time), the conveying batch corresponding to the ore from that vehicle is precisely segmented from the cumulative belt conveyor volume data, obtaining the cumulative visual quality of that batch. This process can be achieved through the data association and batch management module, which forms the logical basis for mutual correction and establishes the raw material weight relationship between the transport vehicle and the ore blending conveyor belt through the above steps.

[0147] Therefore, the characteristic parameter for the deviation between the weights of the first and second raw materials can be determined in the following way:

[0148] Determine the related data pairs Based on the correlated data pairs, the deviation characteristic parameter between the weight of the first raw material and the weight of the second raw material is determined using the following formula:

[0149]

[0150] in, This is the relative deviation coefficient (i.e., the deviation characteristic parameter). Ideally, if both systems (i.e., the belt system and the vehicle system) are perfectly accurate, then... =1.

[0151] Furthermore, based on the relative deviation coefficient, the relationship between the raw material weights of the transport vehicles and the ore mixing conveyor belt is determined, that is, the deviation correlation relationship.

[0152] In addition, a fault tolerance mechanism can be set by setting a deviation threshold (preferably 0.7 < 0.7). <1.3). When If the data exceeds this range, it is considered that there may be a large error in the measurement (such as incomplete vehicle scanning, foreign objects on the belt, etc.), the data set is discarded, no parameter update is performed, and an alarm is triggered.

[0153] S3, Based on the weight relationship of the raw materials, the density parameters of the raw materials are adjusted to obtain the updated density parameters;

[0154] Adjusting the raw material density parameters includes updating the density of the vehicle system and the belt system.

[0155] The formula for density updates in the vehicle system is:

[0156]

[0157] in, For the updated density parameters of the vehicle system; These are weighting coefficients (smoothing factors); The density parameters of the vehicle system before the update.

[0158] The formula for updating the density of a belt system is:

[0159]

[0160] in, For the updated density parameters of the belt system; These are weighting coefficients (smoothing factors); The density parameters are those of the belt system before the update.

[0161] and The value range is (0, 1). The closer the value is to 1, the more stable the system and the stronger its noise immunity, but the slower it adapts to changes. Conversely, the smaller the value, the faster the system responds to changes, but it is also more susceptible to gross errors from single measurements. The preferred initial value is 0.8.

[0162] Based on the above steps, updated density parameters are obtained, including updated density parameters of the vehicle system and updated density parameters of the belt system.

[0163] Furthermore, the process of adjusting the raw material density parameter to obtain the updated density parameter can be achieved using an exponentially weighted moving average algorithm. By giving historical data an exponentially decaying weight, it can smooth out random errors and track slow systematic changes.

[0164] Furthermore, after obtaining the updated density parameters, parameter feedback is performed. The updated parameters are then... and The data is fed back to the initial quality calculation module in real time for quality calculation of the next vehicle / batch.

[0165] Based on the above, the measurements of the two systems (i.e., the belt system and the vehicle system) are considered to represent the same physical quantity (the actual mass of a truckload of ore). These are two independent observations. The deviation between them reflects the error of the current density parameter of each system. The density parameter can be progressively corrected using a filtering algorithm to bring the deviation close to zero. This can be achieved through an adaptive mutual correction algorithm module.

[0166] S4. Based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material, determine the current weight parameter corresponding to the ore resource.

[0167] Furthermore, S4 may include:

[0168] Determine the volume deviation parameter between the first raw material volume and the second raw material volume; based on the volume deviation parameter, the first raw material volume, and the second raw material volume, determine the target raw material volume; based on the updated density parameter and the target raw material volume, determine the current weight parameter corresponding to the ore resource.

[0169] S5. Based on the ore blending parameters and the current weight parameters, determine the ore blending solution parameters corresponding to the ore resources;

[0170] Specifically, this can be achieved through a control and optimization layer, which includes an intelligent controller, an optimization setting model, and an actuator drive module. The intelligent controller receives the real-time ore mass flow rate signal from the data processing layer after mutual calibration and optimization. (usually adopted directly) (Because it is a continuous signal). This intelligent controller can be a programmable logic controller (PLC) or a distributed control system (DCS).

[0171] Furthermore, S5 may include:

[0172] Based on the ore blending parameters and the current weight parameters, determine the weight parameters of the blending solution corresponding to the ore resources; determine the flow characteristics corresponding to the current weight parameters; determine the flow parameters of the blending solution based on the flow characteristics; and determine the blending solution parameters corresponding to the ore resources based on the weight parameters and flow parameters of the blending solution.

[0173] For example, the optimal allocation algorithm built into the model can be optimized, with its input being... The output is the theoretical set flow rate of mother liquor and water (i.e., the ore preparation liquid parameters) based on the target solid-liquid ratio set by the process.

[0174] S6 controls the blending of ore resources based on the current weight parameters and the parameters of the blending solution.

[0175] Specifically, this can be achieved through an actuator drive module in the control and optimization layer, which drives and controls the actuators in the perception and execution layer. These actuators include a mother liquor addition unit and a process water addition unit. The mother liquor addition unit can be a corrosion-resistant slurry pump controlled by a frequency converter, and the process water addition unit can be a pipeline system controlled by an intelligent electric regulating valve. The controller of the actuator drive module can employ PID or control algorithms, such as model predictive control (MPC), to control the speed of the mother liquor pump by adjusting the output frequency of the frequency converter and to control the flow rate of the process water by adjusting the opening of the electric valve, thereby achieving instantaneous and precise matching of solvent addition.

[0176] In addition, it can interact with the detection layer through human-computer interaction to display and detect (such as early warning). Based on Web technology or configuration software development, it provides a graphical interface to display the status of each device, flow trend, density parameter change curve, calibration process, production reports, historical data query and system alarms in real time.

[0177] The core step in potash fertilizer production is the decomposition process, which involves reacting raw potash ore (mainly carnallite) with mother liquor and water in precise stoichiometric ratios. The efficiency and thoroughness of this reaction directly determine the potassium recovery rate and the quality of the final product. Therefore, accurate metering of the raw ore entering the decomposition tank is a prerequisite for achieving stable and optimal process conditions. However, potash ore, especially carnallite, typically exhibits a viscous, easily crystallizing, and highly heterogeneous mixture. This property leads to the following problems in the practical application of traditional contact-type continuous metering equipment (such as electronic belt scales):

[0178] (1) Adhesion and accumulation: Damp and sticky ore is very easy to adhere to the weighing frame, idler rollers and sensors of the belt scale, causing zero drift and weighing value distortion. Frequent manual cleaning is required, which is a lot of maintenance work and affects continuous production.

[0179] (2) Wear and corrosion: Hard particles and corrosive components in the ore will accelerate the wear and aging of the weighing sensor and mechanical parts, shorten the equipment life and increase maintenance costs.

[0180] (3) Impact and vibration: The violent impact and vibration caused by the falling of large pieces of ore and the uneven load will introduce significant measurement noise, affecting the accuracy of instantaneous flow rate and cumulative weight.

[0181] Based on the above methods and steps, controlling the blending of raw ore for potash fertilizer production can effectively solve the aforementioned technical problems.

[0182] By employing mineral resource blending control methods, systematic errors caused by changes in material properties, environmental factors, and equipment drift can be automatically compensated, preventing error accumulation and ensuring long-term measurement stability. Furthermore, the blending control system, through data mutual calibration using dual vision systems, forms a self-closing accuracy improvement system independent of external benchmarks. This achieves completely non-contact measurement and closed-loop optimization, avoiding problems such as adhesion, wear, and corrosion associated with contact measurements, and significantly reducing daily maintenance workload. In addition, the blending control system can automatically track and adapt to changes in ore properties caused by different mineral sources, humidity levels, and seasons, demonstrating a high degree of intelligence.

[0183] The following description, with specific examples, will further illustrate this point.

[0184] E1: System Initialization and Calibration

[0185] In the initial stage of system operation, the true mass of ore will be obtained by statically weighing at least 20 truckloads of ore using a truck scale. .Will Volume measured by the visual system and Perform linear fitting separately to obtain and The initial value. Wherein, Let be the ore loading volume of the i-th vehicle. This represents the cumulative transport volume of the batch of ore after the i-th car of ore is unloaded onto the ore mixing conveyor belt.

[0186] E2: Parallel visual measurement and preliminary control:

[0187] The transport vehicle enters the scanning area, and the vision unit acquires its 3D point cloud and performs calculations. In combination with the current situation get .

[0188] The belt conveyor vision unit operates continuously and performs real-time calculations. and The controller then performs preliminary pre-addition control of the solvent based on this information.

[0189] E3: Data Association and Batch Synchronization: The system automatically and accurately associates vehicles with corresponding belt conveyor batches through RFID and hopper signals.

[0190] E4: Dynamic Mutual Correction Execution: After a batch is completed, the system calls the mutual correction algorithm to calculate... and update and .

[0191] E5: Optimized Control: The controller performs more precise feedback control by using updated density parameters for subsequent visual quality calculations.

[0192] E6: Continuous self-learning and optimization: Repeat steps E2-E5. As running data accumulates, the density parameters of the two vision systems are continuously optimized under the drive of the mutual calibration algorithm, and the measurement accuracy of the entire system continues to improve, eventually reaching and stabilizing at a high accuracy level.

[0193] The above optional implementation methods can achieve at least the following beneficial effects:

[0194] (1) Compared with related technologies, the present invention obtains the raw material data, including raw material density parameters, the first raw material volume corresponding to the transport vehicle, and the second raw material volume corresponding to the raw material conveyor belt, by acquiring the ore blending parameters and raw material data, and determines the raw material weight relationship between the transport vehicle and the raw material conveyor belt. It can establish a correlation benchmark based on the dual volume measurement of the same batch of raw materials, and adjust the raw material density parameters according to the weight relationship to obtain the updated density parameters. It can correct the density deviation to ensure that the density data fits the actual raw material characteristics. Combine the updated density parameters with the first and second raw material volumes to determine the current weight parameters, and can achieve accurate measurement of raw material weight. Based on the ore blending parameters and the current weight parameters, the ore blending liquid parameters can be determined, and the amount of ore blending liquid added can be accurately matched with the raw material weight. Based on the current weight parameters and the ore blending liquid parameters, the ore blending control is carried out, and the imbalance of the ore blending ratio is ultimately avoided, and the accuracy of ore resource blending control is achieved. Thus, it solves the technical problem of inaccurate ore blending control in related technologies when ore resources are blended.

[0195] (2) Compared with related technologies, the present invention can obtain dual weight measurement data of the same batch of ore by determining the weight of the first raw material corresponding to the transport vehicle and the weight of the second raw material corresponding to the ore mixing conveyor belt. By calculating the deviation characteristic parameters (such as the relative deviation coefficient) of the two, the measurement differences of different metering systems can be accurately identified, thereby accurately quantifying the raw material weight relationship between the transport vehicle and the ore mixing conveyor belt.

[0196] (3) Compared with related technologies, this invention can establish a reference plane model of the conveyor belt and obtain three-dimensional data containing the outline of the raw materials by determining the first point cloud data under the empty state and the second point cloud data under the loaded state of the ore blending conveyor belt. By comparing the two sets of point cloud data, the raw material transmission parameters (raw material flow rate, transmission time) can be accurately extracted. Based on these parameters, the cumulative weight of the corresponding batch of raw materials on the ore blending conveyor belt can be accurately calculated, ensuring that the weight of the second raw material and the weight of the first raw material of the transport vehicle form an effective mutual calibration benchmark, providing accurate weight data support for subsequent density parameter adjustment and ore blending control, and avoiding the impact of the overall ore blending accuracy on the conveyor belt measurement deviation.

[0197] (4) Compared with related technologies, this invention can determine the unloading location, start and end time of unloading of the transport vehicle, and thus clarify the unloading range and time interval of a single batch of raw materials. By determining the spatial distance between the unloading location and the ore mixing conveyor belt, the unloading compensation time can be accurately calculated to compensate for the time delay of raw material transmission. Combined with the raw material transmission parameters (raw material flow rate, transmission time), the cumulative weight of the corresponding batch of raw materials on the ore mixing conveyor belt can be further accurately determined, ensuring that the weight of the second raw material is accurately correlated with the weight of the first raw material of the transport vehicle, avoiding measurement misalignment caused by time and space deviation, and providing accurate batch weight data support for subsequent determination of raw material weight relationship and adjustment of density parameters.

[0198] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0199] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0200] Example 2

[0201] According to embodiments of the present invention, an apparatus for implementing the above-described ore resource blending control method is also provided. Figure 5 This is a structural block diagram of an ore blending control device according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 502, a first determination module 504, a second determination module 506, a third determination module 508, a fourth determination module 510, and a fifth determination module 512. The device will be described in detail below.

[0202] Acquisition module 502 is used to acquire ore blending parameters and raw material data corresponding to the ore resources. The raw material data includes raw material density parameters, a first raw material volume corresponding to the transport vehicle, and a second raw material volume corresponding to the blending conveyor belt. The transport vehicle is used to transport the raw materials to the blending conveyor belt, which is used to transport the raw materials during the blending process. First determination module 504, connected to the acquisition module 502, is used to determine the raw material weight relationship between the transport vehicle and the blending conveyor belt. Second determination module 506, connected to the first determination module 504, is used to determine the raw material weight relationship based on the weight relationship. The material density parameter is adjusted to obtain an updated density parameter; the third determining module 508, connected to the second determining module 506, is used to determine the current weight parameter corresponding to the ore resource based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material; the fourth determining module 510, connected to the third determining module 508, is used to determine the ore blending liquid parameter corresponding to the ore resource based on the blending parameter and the current weight parameter; the fifth determining module 512, connected to the fourth determining module 510, is used to perform ore blending control on the ore resource based on the current weight parameter and the ore blending liquid parameter.

[0203] It should be noted that the above-mentioned acquisition module 502, first determination module 504, second determination module 506, third determination module 508, fourth determination module 510 and fifth determination module 512 correspond to steps S102 to S112 in the method for implementing ore resource blending control. The multiple modules and the corresponding steps are the same in terms of implementation instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0204] Example 3

[0205] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the ore resource blending control method of any of the above embodiments.

[0206] Example 4

[0207] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the ore blending control method of any of the above embodiments.

[0208] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0209] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0210] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0211] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0212] Furthermore, the functional units in the various embodiments of the present invention 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0213] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0214] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the blending of ore resources, characterized in that, include: Obtain ore blending parameters and raw material data corresponding to ore resources. The raw material data includes raw material density parameters, a first raw material volume corresponding to a transport vehicle, and a second raw material volume corresponding to a ore blending conveyor belt. The transport vehicle is used to transport the raw material to the ore blending conveyor belt, and the ore blending conveyor belt is used to transport the raw material during the ore blending process. Determine the weight relationship of raw materials between the transport vehicle and the ore mixing conveyor belt; Based on the aforementioned raw material weight relationship, the raw material density parameter is adjusted to obtain an updated density parameter; Based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material, the current weight parameter corresponding to the ore resource is determined; Based on the ore blending parameters and the current weight parameters, determine the ore blending solution parameters corresponding to the ore resources; Based on the current weight parameters and the ore mixing liquid parameters, the ore resources are controlled for blending.

2. The method according to claim 1, characterized in that, Determining the raw material weight relationship between the transport vehicle and the ore mixing conveyor belt includes: Determine the weight of the first raw material corresponding to the transport vehicle, and the weight of the second raw material corresponding to the ore blending conveyor belt; Determine the deviation characteristic parameter between the weight of the first raw material and the weight of the second raw material; Based on the deviation characteristic parameters, the raw material weight relationship between the transport vehicle and the ore mixing conveyor belt is determined.

3. The method according to claim 2, characterized in that, Determining the weight of the second raw material corresponding to the ore blending conveyor belt includes: Determine the first point cloud data of the ore blending conveyor belt in a first state and the second point cloud data of the ore blending conveyor belt in a second state, wherein the first state is the state in which the ore blending conveyor belt is not conveying raw materials and the second state is the state in which the ore blending conveyor belt is conveying raw materials. Based on the first point cloud data and the second point cloud data, the raw material transmission parameters corresponding to the ore blending conveyor belt are determined, wherein the raw material transmission parameters include raw material flow rate and transmission duration; Based on the raw material transport parameters, the weight of the second raw material corresponding to the ore blending conveyor belt is determined.

4. The method according to claim 2, characterized in that, Determining the weight of the first raw material corresponding to the transport vehicle includes: Determine the vehicle body point cloud data and raw material point cloud data corresponding to the transport vehicle, wherein the raw material point cloud data is the point cloud data of the raw materials loaded on the transport vehicle; Based on the vehicle body point cloud data, determine the loading space parameters corresponding to the transport vehicle; Based on the raw material point cloud data, determine the raw material spatial parameters corresponding to the transport vehicle; Based on the loading space parameters and the raw material space parameters, the weight of the first raw material corresponding to the transport vehicle is determined.

5. The method according to claim 3, characterized in that, The step of determining the weight of the second raw material corresponding to the ore blending conveyor belt based on the raw material transport parameters includes: Determine the unloading location corresponding to the transport vehicle, and the unloading start time and unloading end time; Determine the spatial distance between the unloading location and the ore mixing conveyor belt; Based on the aforementioned spatial distance, determine the unloading compensation time; Based on the unloading start time, the unloading end time, the unloading compensation time, and the raw material transmission parameters, the weight of the second raw material corresponding to the ore blending conveyor belt is determined.

6. The method according to claim 1, characterized in that, The step of determining the current weight parameter corresponding to the ore resource based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material includes: Determine the volume deviation parameter between the volume of the first raw material and the volume of the second raw material; Based on the volume deviation parameter, the volume of the first raw material, and the volume of the second raw material, the target raw material volume is determined; Based on the updated density parameter and the target raw material volume, the current weight parameter corresponding to the ore resource is determined.

7. The method according to any one of claims 1 to 6, characterized in that, The step of determining the ore blending solution parameters corresponding to the ore resources based on the ore blending parameters and the current weight parameters includes: Based on the ore blending parameters and the current weight parameters, determine the weight parameters of the ore blending solution corresponding to the ore resources; Determine the flow characteristics corresponding to the current weight parameter; Based on the aforementioned flow characteristics, determine the flow parameters of the ore preparation solution; Based on the weight parameters and flow parameters of the ore preparation solution, the parameters of the ore preparation solution corresponding to the ore resources are determined.

8. A mineral resource blending control device, characterized in that, include: The acquisition module is used to acquire ore blending parameters and raw material data corresponding to ore resources. The raw material data includes raw material density parameters, a first raw material volume corresponding to a transport vehicle, and a second raw material volume corresponding to a ore blending conveyor belt. The transport vehicle is used to transport the raw materials to the ore blending conveyor belt, and the ore blending conveyor belt is used to transport the raw materials during the ore blending process. The first determining module is used to determine the raw material weight relationship between the transport vehicle and the ore mixing conveyor belt; The second determining module is used to adjust the density parameter of the raw materials based on the weight relationship of the raw materials to obtain an updated density parameter; The third determining module is used to determine the current weight parameter corresponding to the ore resource based on the updated density parameter, the volume of the first raw material, and the volume of the second raw material. The fourth determining module is used to determine the ore blending liquid parameters corresponding to the ore resources based on the ore blending parameters and the current weight parameters. The fifth determining module is used to control the blending of the ore resources based on the current weight parameters and the blending solution parameters.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the ore blending control method for ore resources as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the ore blending control method for ore resources as described in any one of claims 1 to 7.

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