Mixing ratio control methods and devices

Abstract

This application discloses a method and apparatus for controlling the mixing ratio. The method includes: periodically acquiring raw material component information from multiple raw material silos; for each period, determining the predicted first discharge component of each raw material silo in the current period based on the raw material component information and corresponding discharge volume of each raw material silo; determining the first mixing component information and corresponding mixing ratio cost of the mixture obtained when discharging from multiple raw material silos according to different discharge ratio values, based on the predicted discharge component information of each raw material silo in the current period; and determining the first target discharge ratio value of each raw material silo when the mixing ratio cost is lowest, using the first mixing component information of the mixture satisfying a preset threshold range as a constraint. This application solves the technical problem of related technologies that use manual methods to control the mixing ratio, resulting in lag and poor control effect.

Description

Technical Field

[0001] This application relates to the field of batching control technology, and more specifically, to a method and apparatus for controlling the mixing ratio. Background Technology

[0002] In the fiberglass industry, the content of components in the blend is a key factor affecting the quality of the finished product. However, since blends with component contents that meet the process requirements are difficult to find directly in nature, factories can rationally proportion multiple ores according to the content of components in each ore to obtain blends that meet the process requirements.

[0003] Currently, relevant technicians generally control the batching of crushed stone manually. This involves first manually sampling and analyzing a portion of the crushed stone raw materials in the silo to obtain the content of each component; then selecting several suitable crushed stone raw materials and setting the proportion of each based on experience to prepare the mixture; finally, sampling and analyzing the prepared mixture, comparing the content of key components (i.e., measured values ​​of components within the mixture) with the set values, and manually adjusting the proportion of selected crushed stone raw materials based on the comparison results (i.e., deviation values). For example, if the iron content in the mixture is lower than the set value, the proportion of crushed stone raw materials with higher iron content will be increased during manual operation to improve the overall iron content of the mixture. However, the above-mentioned crushed stone batching control method still has the following problems:

[0004] (1) During the raw material testing stage of crushed stone, manual testing can only accurately detect some of the key components of crushed stone. In addition, since crushed stone is a solid particle, and the stacking method and discharge speed of the crushed stone raw material storage silos are different, the crushed stone raw material in the silos is not mixed evenly, which leads to poor accuracy of manual testing results;

[0005] (2) The use of manual testing in the mixture component content testing stage resulted in excessively long testing time;

[0006] (3) During the manual adjustment of the proportioning, the proportioning control effect is poor due to the large lag in manual adjustment. In particular, in scenarios with a variety of crushed stone raw materials, there are usually multiple adjustment methods to make the component content in the mixture meet the standards. At this time, it is impossible to quickly find the best adjustment method based solely on manual experience, resulting in poor economic efficiency of the adjustment.

[0007] There is currently no effective solution to the above problems. Summary of the Invention

[0008] This application provides a mixing ratio control method and apparatus to at least solve the technical problem that the mixing ratio is controlled manually in related technologies, resulting in lag in ratio control and poor control effect.

[0009] According to one aspect of the embodiments of this application, a mixing ratio control method is provided, comprising: periodically acquiring raw material component information of multiple raw material bins, wherein each raw material bin stores the same type of raw material; within each period, determining first discharge component prediction information of each raw material bin in the current period based on the raw material component information and corresponding discharge amount of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content value of multiple components discharged from the raw material bin in the current period; determining, based on the discharge component prediction information of each raw material bin in the current period, the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing the multiple raw material bins according to different discharge ratio values, and determining the first target discharge ratio value of each raw material bin when the mixing ratio cost is the lowest, with the first mixing component information of the mixture satisfying a preset threshold range as a constraint condition.

[0010] Optionally, the raw material composition information of multiple raw material bins can be acquired periodically, including: for each raw material bin, acquiring raw material composition information obtained by periodically measuring the content of multiple components fed into the current raw material bin using analytical equipment.

[0011] Optionally, the raw material silo is funnel-shaped, consisting of a cylindrical section and a conical section, and includes multiple storage layers. The process of determining the predicted discharge composition of each raw material silo in the current period based on its raw material composition information and corresponding discharge volume in the current cycle includes: for each raw material silo, obtaining the discharge flow rate of the current raw material silo in the current cycle, and determining the discharge volume of the current raw material silo in the current cycle based on the discharge flow rate; calculating the quotient between the total storage volume of the current raw material silo and the number of storage layers, and using the obtained quotient as the storage layer benchmark value of the current raw material silo; determining the number of outflow layers of the current raw material silo in the current cycle based on the storage layer benchmark value and the discharge volume; adjusting the raw material composition information of at least one storage layer corresponding to the conical section based on the number of outflow layers, and determining the predicted discharge composition of the current raw material silo in the target period based on the obtained adjustment result and the weight coefficients of each storage layer corresponding to the conical section.

[0012] Optionally, the process of determining the weight coefficients of each storage layer corresponding to the cone portion includes: determining the target raw material composition information of each storage layer corresponding to the cone portion of the current raw material warehouse based on the raw material composition information of the current raw material warehouse in the current cycle; and determining the weight coefficients of each storage layer corresponding to the cone portion through a regression algorithm based on the target raw material composition information and the adjustment results.

[0013] Optionally, based on the predicted discharge components of each raw material silo in the current cycle, the information of the first mixing component and the corresponding mixing ratio cost of the mixture obtained by mixing multiple raw material silos according to different discharge ratio values ​​are determined. This includes: determining the information of the first mixing component of the mixture obtained by mixing multiple raw material silos according to different discharge ratio values, and calculating the corresponding mixing ratio cost based on the raw material cost of each raw material silo and the discharge ratio value of each raw material silo.

[0014] Optionally, before determining the first target discharge ratio value of each raw material silo when the mixing ratio cost is lowest, based on the constraint that the first mixing component information of the mixture meets a preset threshold range, the method further includes: obtaining second discharge component prediction information of multiple raw material silos in the previous period, wherein the second discharge component prediction information is used to characterize the second predicted content value of multiple components when the raw material silos discharge in the previous period; obtaining second mixing component information of the mixture obtained when multiple raw material silos discharge according to the second target discharge ratio value based on the second discharge component prediction information of each raw material silo, wherein the second mixing component information is used to characterize the measured value of each component of the mixture obtained in the previous period; determining component prediction error information of the mixture based on the second discharge component prediction information and the second mixing component information, wherein the component prediction error information is used to reflect the error value between the second predicted value and the second measured value of each component; and correcting the constraint condition using the component prediction error information of the mixture.

[0015] Optionally, obtaining the second mixture component information of the mixture obtained when multiple raw material bins are discharged according to the second target discharge ratio value based on the second discharge component prediction information of each raw material bin includes: obtaining the second mixture component information obtained by measuring the content of each component of the mixture obtained by mixing multiple raw material bins according to the second target discharge ratio value using an analytical device based on the second discharge component prediction information of each raw material bin.

[0016] According to another aspect of the embodiments of this application, a mixing ratio control device is also provided, comprising: an acquisition module, configured to periodically acquire raw material component information of multiple raw material bins, wherein each raw material bin stores the same type of raw material; a prediction module, configured to, for each period, determine first discharge component prediction information of each raw material bin in the current period based on the raw material component information and corresponding discharge amount of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged by the raw material bin in the current period; and a determination module, configured to, based on the discharge component prediction information of each raw material bin in the current period, determine the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing the multiple raw material bins according to different discharge ratio values, and determine the first target discharge ratio value of each raw material bin when the mixing ratio cost is the lowest, with the first mixing component information of the mixture satisfying a preset threshold range as a constraint condition.

[0017] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-described mixing ratio control method by running the computer program.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, which includes a stored computer program, wherein the computer program, when executed by a processor, implements the above-described mixing ratio control method.

[0019] In this embodiment, by combining algorithms, software, and hardware, the component content of each raw material silo is automatically and accurately predicted. Based on the prediction results, a multivariate linear programming algorithm is used to control the discharge ratio of each raw material silo, ensuring that the final mixing ratio requires the lowest possible cost. Compared to existing manual batching methods, this embodiment is more efficient and accurate, effectively solving the technical problem that manual batching control in related technologies results in lag and poor control performance. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a schematic diagram of an optional mixing and proportioning control system according to an embodiment of this application;

[0022] Figure 2This is a hardware structure block diagram of an optional computer terminal for implementing a mixing ratio control method according to an embodiment of this application;

[0023] Figure 3 This is a schematic flowchart of an optional mixing ratio control method according to an embodiment of this application;

[0024] Figure 4 This is a longitudinal cross-sectional schematic diagram of an optional raw material silo according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of an optional mixing ratio control device according to an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application 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 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.

[0029] Furthermore, all information and data (including but not limited to user device information, user personal information, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with the relevant user or organization. Before obtaining relevant information, it needs to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent from the aforementioned user or organization.

[0030] Example 1

[0031] This application provides a mixing and proportioning control system. Figure 1 This is a schematic diagram of an optional mixing and proportioning control system according to an embodiment of this application, as shown below. Figure 1 As shown. The system 10 includes at least: a raw material conveyor belt 11, a raw material analysis device 12, a raw material silo assembly 13, a mixing conveyor belt 14, a mixing analysis device 15, a mixing silo 16, and a computing device 17. It should be noted that the computing device 17 includes two parts: a host computer and a slave computer. The host computer can be control software, and the slave computer can be a PLC (Programmable Logic Controller) or a DCS (Distributed Control System).

[0032] The above system 10 will be further divided into the following two subsystems:

[0033] (1) The first subsystem consists of a raw material conveyor belt 11, a raw material analysis device 12, a raw material silo assembly 13 (raw material silo 1, raw material silo 1, ... raw material silo m), and a computing device 17, wherein:

[0034] First, the raw material conveyor belt 11 can transport the raw materials to the corresponding raw material bins via the belt;

[0035] Next, during the raw material transport process, the raw materials pass through the raw material analysis equipment 12 (such as a neutron activity analyzer), which periodically samples and tests the component content of the raw materials on the raw material conveyor belt 11. Compared with manual testing of the component content of the raw materials in the raw material bins, the raw material analysis equipment provides more comprehensive, efficient, and accurate detection of the raw material component content. Simultaneously, the raw material analysis equipment 12 can upload the test results (i.e., the raw material component information of each raw material bin) to the lower-level computer of the computing device 17, which can then further upload the acquired test data to the upper-level computer of the computing device 17.

[0036] Then, the raw materials on the raw material conveyor belt 11 are stored in the corresponding raw material bins according to their raw material type (i.e., any raw material bin in the raw material bin set 13, including raw material bin 1, raw material bin 2, ..., raw material bin m). Since the raw materials in the raw material bins will be continuously consumed during the mixing process, the raw material bins can also upload their real-time data (i.e., the discharge flow rate of the raw material bins in each target cycle) to the lower computer of the computing device 17. The lower computer can further upload the acquired real-time data of the bins to the upper computer of the computing device 17.

[0037] Finally, the host computer of the computing device 17 can predict the feeding components of each raw material silo in each target cycle based on the detection results uploaded by the raw material analysis device 12 and the real-time data of the silo, and obtain the corresponding prediction results (i.e., the predicted information of the discharge components of the raw material silo in each target cycle).

[0038] Specifically, since the mixing process involves the preparation of various crushed stone raw materials, different types of crushed stone raw materials will be used during the mixing process. Therefore, multiple raw material silos are needed to store the crushed stone raw materials. In addition, ideally, the number of raw material analysis equipment 12 (such as a neutron activity analyzer) should be the same as the number of raw material silos. However, since the raw material analysis equipment 12 is expensive equipment, the first subsystem proposed in this application adopts a "1-to-m" method (i.e., one neutron analyzer separately detects the component content information of the raw materials stored in m raw material silos) to detect the raw materials.

[0039] (2) The second subsystem consists of a mixing conveyor belt 14, a mixing analysis device 15, a mixing bin assembly 16, and a computing device 17, wherein:

[0040] First, the mixing conveyor belt 14 can transport mixtures with different component contents to the corresponding mixing bins;

[0041] Next, during the mixing and conveying process, the mixture passes through the mixing analysis device 15 (such as a neutron activity analyzer), and the mixing analysis device 15 periodically samples and detects the component content of the mixture on the mixing conveyor belt 14. The detection results (i.e., the mixing component information) are then uploaded to the lower computer of the computing device 17. The lower computer can further upload the obtained detection results (i.e., the mixing component information) to the upper computer of the computing device 17.

[0042] Then, the mixture on the mixing conveyor belt 14 is stored in the corresponding mixing bin 16 according to its component content range.

[0043] Finally, the computing device 17 can adjust the proportion of each raw material hopper by optimizing the control algorithm based on the detection results uploaded by the mixing analysis device 15 and the preset component content range of the mixture, so that the component content of the adjusted mixture meets the preset component content range.

[0044] In the mixing and proportioning control system 10, which consists of a first subsystem and a second subsystem, the system combines algorithms, software, and hardware to automatically and accurately predict the component content at the discharge of each raw material silo. Based on the prediction results, a multivariate linear programming algorithm is used to control the discharge proportion of each raw material silo, ensuring that the final mixing ratio requires the lowest possible cost. Compared to existing manual batching methods, this embodiment is more efficient and accurate, effectively solving the technical problem that manual batching control in related technologies results in lag and poor control performance.

[0045] Example 2

[0046] According to an embodiment of this application, a method embodiment for controlling the mixing ratio 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.

[0047] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 2 A hardware block diagram of a computer terminal (or mobile device) for implementing a mixing ratio control method is shown. Figure 2 As shown, the computer terminal 20 (or mobile device 20) may include one or more processors 202 (shown as 202a, 202b, ..., 202n in the figure) 202 (processor 202 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 204 for storing data, and a transmission device 206 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 2 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 20 may also include... Figure 2 The more or fewer components shown, or having the same Figure 2 The different configurations shown.

[0048] It should be noted that the aforementioned one or more processors 202 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 20 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0049] The memory 204 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the mixing ratio control method in the embodiments of this application. The processor 202 executes various functional applications and data processing by running the software programs and modules stored in the memory 204, thereby realizing the mixing ratio control method of the above-mentioned application. The memory 204 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 204 may further include memory remotely located relative to the processor 202, and these remote memories can be connected to the computer terminal 20 via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0050] The transmission device 206 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 20. In one example, the transmission device 206 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 206 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0051] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 20 (or mobile device).

[0052] Under the above operating environment, Figure 3 This is a schematic flowchart of an optional mixing ratio control method according to an embodiment of this application, as shown below. Figure 3 As shown, the method includes at least steps S302-S306, wherein:

[0053] Step S302: Periodically acquire raw material composition information from multiple raw material bins.

[0054] In the technical solution provided in step S302, before and during the mixing process, the computing device can periodically obtain the raw material composition information of each raw material bin in each cycle. This raw material composition information is used to characterize the content of each component fed into the raw material bin. For example, in the fiberglass industry, if it is necessary to mix various crushed stone raw materials to obtain a high-quality mixture of specific key components, the key components of various crushed stone raw materials, such as the content values ​​of iron oxide (Fe2O3), aluminum oxide (AL2O3), and potassium oxide (K2O), can be obtained in advance.

[0055] Step S304: Within each cycle, determine the predicted information of the first discharge component of each raw material silo in the current cycle based on the raw material composition information and the corresponding discharge volume of each raw material silo in the current cycle.

[0056] In the technical solution provided in step S304, during the mixing and batching process, as the raw materials in the raw material silo are continuously consumed, the raw material silo can report its material level information to the computing device in real time. The computing device can then determine the discharge volume of the raw material silo within the target cycle based on the material level information. Simultaneously, it determines whether the material level information is below a preset lower limit to decide whether to control the raw material conveyor belt to replenish the corresponding raw materials. If the raw material silo needs replenishment, the raw material composition information within the silo will change accordingly. Therefore, in each batching stage (i.e., batching cycle), the computing device can predict the discharge composition information of the raw material silo by analyzing the discharge volume and raw material composition information within the current cycle, obtaining predicted discharge composition information. This predicted discharge composition information characterizes the predicted content values ​​of multiple components when the raw material silo discharges within the target cycle.

[0057] Step S306: Based on the predicted discharge components of each raw material silo in the current cycle, determine the first mixing component information and the corresponding mixing ratio cost of the mixture obtained when multiple raw material silos discharge according to different discharge ratio values. Then, with the first mixing component information of the mixture meeting a preset threshold range as a constraint, determine the first target discharge ratio value of each raw material silo when the mixing ratio cost is the lowest.

[0058] In the technical solution provided in step S306, during the mixing and batching process, in order to ensure that the component content of the mixture meets the predetermined range, multiple sets of proportioning results that can meet the range may occur. Therefore, the computing device can first determine the mixing component information and corresponding mixing ratio cost of the mixture obtained when multiple raw material bins are discharged according to different discharge ratio values ​​based on the predicted discharge component information of each raw material bin. Then, with the component content of the mixture meeting the predetermined range as a constraint, the proportioning result with the lowest mixing ratio cost is taken as the final output result, thereby obtaining the mixture that meets the process requirements and has the lowest cost, and realizing precise control of the mixing configuration.

[0059] The steps of the methods described in the embodiments of this application will be further described below.

[0060] As an optional implementation, in the technical solution provided in step S302 above, for each raw material bin, the raw material component information is obtained by periodically measuring the content of multiple components fed into the current raw material bin using an analytical device.

[0061] Specifically, before and during the mixing process, raw material analysis equipment (such as a neutron activity analyzer) can periodically sample the raw materials at certain sampling intervals (such as once per minute) as they are transferred to their respective raw material bins (i.e., each bin stores the same type of raw material). This sampling measures the raw material composition information fed into each bin, which characterizes the content of each component of the raw material at the time of feeding. The raw material analysis equipment then uploads the raw material composition information from each bin to a computing device. Therefore, the computing device can obtain the raw material composition information of each bin within the target period.

[0062] Optionally, the raw material silo in this embodiment can be funnel-shaped, consisting of a cylindrical section and a conical section. Due to the transportation characteristics of the raw material conveyor belt, the raw materials transported into the raw material silo are stacked layer by layer, that is, the bottom layer is filled first, and then the layers are stacked one after another. Therefore, the raw material silo includes multiple storage layers, such as... Figure 4 As shown.

[0063] Assuming the raw material silo has n layers, the component content information of each layer can be represented by the following matrix using a mathematical model:

[0064]

[0065] Where n represents the number of storage layers in the raw material warehouse, {A i B i , ...} represent the measured values ​​of components A, B, ... of the raw materials stored in the i-th storage layer, where λ i The component is represented as {A}i B i The proportion of raw materials in the i-th storage layer, ..., is given by the formula λ, where 0 ≤ λ. i ≤1, where, when λ i When = 0, it indicates that the component is {A} i B i The raw materials of , ...} have flowed out of the i-th storage layer; when λ i When = 1, it means that the i-th storage layer only stores the component {A}. i B i The raw materials of , ...}.

[0066] During the feeding and discharging processes, the computing device can easily describe the composition information of the raw materials in each storage layer of the raw material silo by maintaining the above matrix.

[0067] The feeding and discharging processes of the raw material silos will be simulated and analyzed based on the matrix form of the raw material silos described above.

[0068] The raw material feeding process refers to the process where the raw materials enter the raw material silo after their component content is detected by raw material analysis equipment. Assume the sampling interval of the raw material analysis equipment is T. s_in (i.e., the duration of each cycle), the flow rate of the raw material transported by the belt (i.e., the feed flow rate) is V. in The matrix of raw material warehouses for the previous period is shown below:

[0069]

[0070] Where x represents the index of the topmost storage layer in the raw material warehouse that is not a full layer, and r i The component is represented as {A} i B i Let , ...,} represent the proportion of raw materials within the i-th storage layer. In this case, after feeding, the number of storage layers in the raw material silo will increase accordingly. The storage layer is M, which represents the baseline value of the storage layer in the raw material warehouse, and is equal to the quotient of the total storage volume of the raw material warehouse and the number of storage layers.

[0071] If the number of new layers r new and r i The sum of these values ​​does not exceed 1. At this point, the newly added raw materials cannot fill the i-th storage layer. Therefore, the r value in the i-th storage layer of the raw material warehouse... i The content of this component will increase, and the content of other components will also change accordingly. Therefore, the updated results are as follows:

[0072]

[0073] Among them, the above A i (r x Bi (r x ...respectively represent components of {A} i B i The content values ​​of each component in the raw material of A, ..., ... in (r in B in (r in ...representing the addition of raw material component {A} in B in The content values ​​of each component in the raw materials of , ...}.

[0074] If the number of new layers r in and r i If the sum of the raw materials exceeds 1, the newly added raw materials will first fill the i-th storage layer, and the excess raw materials will continue to be added to the (i+1)-th storage layer. The update result is as follows:

[0075]

[0076] During the raw material discharge process, i.e., the raw material falls from the raw material silo and forms a mixture, the sampling time for the raw material discharge process is assumed to be T, as the raw material in the silo is continuously consumed. s_out The output flow rate of the raw material from the raw material silo (i.e., the discharge flow rate) is V. out The matrix of raw material warehouses for the previous period is shown below:

[0077]

[0078] Where x represents the index of the topmost storage layer in the raw material warehouse that is not a full layer, and r i The component is represented as {A} i B i Let $\frac{1}{2}$ represent the percentage of raw materials stored in the $i$-th storage layer. In this case, after discharge, the number of storage layers in the raw material silo will decrease accordingly. Layer. Due to r out The value of can be less than 1 or greater than 1, where, in r out When the value of r is greater than 1, the outflow layer number r can be increased. out Divided into integer and decimal parts, that is, during the discharge process, the bottom floor (r) of the raw material bin is first cleared. out The percentage and content data of ) were extracted, and r was removed. out -floor(r out Find the corresponding data, and then shift the remaining data in the storage layer downwards.

[0079] For example, when r out When ≤1, first clear the bottom floor(r) outThe content and percentage data of layer ) are then removed. out -floor(r out The proportions are used to obtain the following matrix expression:

[0080]

[0081] Then, the remaining storage layers are shifted downwards to obtain the following matrix expression:

[0082]

[0083] Based on the above theoretical knowledge, in the technical solution provided in step S304 of this application embodiment, the following steps S3041-S3044 can be executed for each raw material bin to obtain the predicted information of the output composition of the raw material bin in each cycle, including:

[0084] Step S3041: Obtain the discharge flow rate of the current raw material silo in the current cycle, and determine the discharge volume of the current raw material silo in the current cycle based on the discharge flow rate;

[0085] Step S3042: Calculate the quotient of the current total storage volume of the raw material warehouse and the number of storage layers, and use the quotient as the base value of the current storage layers of the raw material warehouse;

[0086] Step S3043: Determine the number of outflow layers of the current raw material silo in the current cycle based on the storage layer reference value and the output quantity;

[0087] Step S3044: Adjust the raw material composition information of at least one storage layer corresponding to the cone section according to the number of outflow layers, and determine the predicted outflow composition information of the current raw material silo within the target cycle based on the obtained adjustment results and the weight coefficients of each storage layer corresponding to the cone section.

[0088] In this embodiment, since the raw material falls from the cone section during discharge from the raw material silo, the computing device can, based on the characteristics of the cone discharge, determine the predicted information of the discharged components and the corresponding storage layer (which is generally preferably set to three layers, such as...) of the cone section. Figure 4 The information relates to the raw material composition of the bottom three layers of the silos shown. Therefore, when discharging raw materials from each silo, the predicted composition of the discharged raw materials can be calculated using the following formula:

[0089]

[0090] …

[0091] Where l represents the number of storage layers corresponding to the cone portion, A j B j , ... represent the content values ​​of components A, B, ... of the raw material in the j-th storage layer, respectively.pre B pre ... represent the predicted content values ​​of components A, B, ... of the raw material when it is discharged from the raw material silo; w j This represents the preset weight coefficient corresponding to the j-th (counting sequentially upwards from the bottom of the cone) storage layer corresponding to the cone portion.

[0092] In this embodiment, the accuracy of the prediction results can be improved by adjusting the preset weighting coefficients. Regarding the aforementioned w... j In this embodiment of the application, the preset weight coefficients of each storage layer corresponding to the conical portion of each raw material silo can be determined by the following method:

[0093] First, based on the raw material composition information of the current raw material warehouse in the current cycle, determine the target raw material composition information of each storage layer corresponding to the cone part of the current raw material warehouse;

[0094] Then, based on the target raw material composition information and adjustment results, the weight coefficients of each storage layer corresponding to the cone part are determined by regression algorithm.

[0095] Specifically, the computing device can directly obtain the target raw material composition (measurement) information of the raw material in the storage layer corresponding to the cone section of the current raw material silo based on the raw material composition (measurement) information of the current raw material silo within the current cycle. Therefore, by combining the target raw material composition information and the adjustment result (i.e., the result after adjusting the raw material composition information of multiple storage layers corresponding to the cone section based on the discharge flow rate), the objective function is to minimize the error between the predicted discharge composition information and the target raw material composition information. The weight coefficients corresponding to each storage layer corresponding to the cone section of the current raw material silo are calculated using the following regression algorithm:

[0096]

[0097] Among them, F T represents a column vector of all 1s; w represents a weight coefficient vector of order l; These represent the deviation penalty coefficients corresponding to components A, B, ... respectively; Both are p×l matrices, where p represents the number of outgoing batches calculated using the ceiling function based on the number of outgoing layers in the current cycle (i.e., rounding up the number of outgoing layers), and l represents the number of storage layers corresponding to the cone-shaped portion of the current raw material silo. This represents the matrix obtained by adjusting the raw material composition information of the l-layer storage layer corresponding to the cone portion based on the discharge flow rate during p discharge processes; A tr B tr , ... represent the measured values ​​obtained by using analytical equipment to measure the components A, B, ... of the raw materials in the storage layer.

[0098] As an optional implementation, in the technical solution provided in step S306 above, the computing device can calculate the first target output ratio that meets the process requirements and has the lowest cost within the current cycle in the following manner:

[0099] Step S3061: Determine the first mixing component information of the mixture obtained by mixing the first discharge component prediction information of multiple raw material bins according to different discharge ratio values, and calculate the corresponding mixing ratio cost based on the raw material cost of each raw material bin and the discharge ratio value of each raw material bin.

[0100] Step S3062: Using the information of the mixed components of the mixture meeting the preset threshold range as a constraint, determine the target output ratio of each raw material silo when the ratio cost is the lowest.

[0101] In steps S3061-S3062 above, the computing device transforms the batching problem of mixing materials into adjusting the output ratios of multiple raw material bins so that the component content of the mixed material meets a preset threshold range. Therefore, this problem can be transformed into a mathematical programming problem, where the decision is the output ratio of multiple raw material bins. Since the content of each component in the mixed material usually needs to meet a certain range during the batching process, multiple ratios may be able to meet this range. In this case, the cost of each raw material can be considered, and the result with the minimum cost ratio is taken as the final output. Therefore, the objective function of the above mathematical programming problem can be expressed as:

[0102]

[0103] Among them, the above c j This represents the raw material cost of the j-th raw material warehouse.

[0104] Furthermore, the set range of component content in the mixture constitutes the constraint condition of the aforementioned mathematical programming problem, which can be expressed by the following formula:

[0105]

[0106] Among them, the above A LB B LB , … represent the lower threshold values ​​of components A, B, … respectively; A UB B UB , … represent the upper threshold values ​​of components A, B, … respectively; A j B j The predicted content values ​​of components A, B, ... of the raw material when the j-th raw material silo is fed are given, which can be determined by step S304 above; r j This represents the feed ratio value of the j-th raw material silo; Let A, B, ... represent the content values ​​of components A, B, ... in the mixture obtained when the predicted output components of m raw material silos are discharged according to the output ratio values ​​of each raw material silo. Therefore, through the above constraints, it is ensured that, under ideal conditions, the predicted content values ​​of each component A, B, ... are all within the set threshold range.

[0107] Therefore, the above mathematical programming problem can be expressed by the following formula:

[0108]

[0109]

[0110] Furthermore, in practical applications, interference from environmental factors such as equipment and unmeasurable disturbances may cause errors between the predicted and actual measured values ​​of the mixture components. This error can be referred to as prediction error in the control field. Therefore, in this embodiment, the impact of this prediction error on subsequent prediction results is considered, and the prediction error obtained in the previous cycle is used to correct the prediction result of the current cycle, thereby increasing the accuracy of the prediction.

[0111] Alternatively, the computing device can obtain a mathematical programming problem with feedback correction in the following way:

[0112] First, the prediction information of the second discharge component of multiple raw material silos in the previous cycle is obtained. The prediction information of the second discharge component is used to characterize the second predicted content value of multiple components when the raw material silos discharge in the previous cycle.

[0113] Next, the second mixture component information of the mixture obtained when multiple raw material bins are discharged according to the second target discharge ratio value based on the second discharge component prediction information of each raw material bin is obtained. The second mixture component information is used to characterize the measured values ​​of each component of the mixture obtained in the previous cycle, which can be measured using a mixture analysis device (i.e. a mixture neutron activity analysis device).

[0114] Then, based on the second discharge component prediction information and the second mixture component information, the component prediction error information of the mixture is determined, wherein the component prediction error information is used to reflect the error value between the second predicted value and the second measured value of each component.

[0115] Finally, the constraint conditions are corrected using the component prediction error information of the mixture.

[0116] Therefore, the expression for the mathematical programming problem with feedback correction described above can be written as:

[0117]

[0118] Among them, A PV B PV, ... represent the measured values ​​of components A, B, ... of the mixture obtained in the previous cycle, respectively; r j,PV This represents the feed ratio of the j-th raw material silo in the previous cycle; These represent the content values ​​of components A, B, ... of the mixture obtained when the predicted discharge components of m raw material silos are discharged according to the first discharge ratio value (i.e., the output result obtained in the previous cycle); These represent the prediction error values ​​of components A, B, ... of the mixture obtained in the previous cycle.

[0119] The computing device can periodically solve the above mathematical programming problem through the above mixing ratio control algorithm, quickly and accurately obtain the target output ratio value of each raw material bin, realize high-precision control of the batching process, and thus improve the stability of the component content of the mixture.

[0120] Example 3

[0121] Based on Embodiment 1 of this application, an embodiment of a mixing ratio control device is also provided, which executes the mixing ratio control method of the above embodiment during operation. Wherein, Figure 5 This is a schematic diagram of an optional mixing ratio control device according to an embodiment of this application, as shown below. Figure 5 As shown, the mixing ratio control device includes at least an acquisition module 51, a prediction module 52, and a determination module 53, wherein:

[0122] The acquisition module 51 is used to periodically acquire raw material composition information from multiple raw material bins, wherein each raw material bin stores the same type of raw material;

[0123] Prediction module 52 is used to determine the first discharge component prediction information of each raw material silo in the current cycle based on the raw material component information and the corresponding discharge amount of each raw material silo in the current cycle. The first discharge component prediction information is used to characterize the predicted content value of multiple components discharged from the raw material silo in the current cycle.

[0124] The determination module 53 is used to determine the first mixing component information and the corresponding mixing ratio cost of the mixture obtained when multiple raw material silos are discharged according to different discharge ratio values ​​based on the predicted discharge component information of each raw material silo in the current cycle, and to determine the first target discharge ratio value of each raw material silo when the mixing ratio cost is the lowest, with the first mixing component information of the mixture meeting the preset threshold range as a constraint condition.

[0125] It should be noted that each module in the above-mentioned mixing and proportioning control device can be a program module (for example, a set of program instructions to implement a certain function) or a hardware module. For the latter, it can be manifested in the following forms, but is not limited to them: each of the above modules is manifested as a processor, or the functions of each of the above modules are implemented by a processor.

[0126] Example 4

[0127] According to an embodiment of this application, a non-volatile storage medium is also provided, which stores a program, wherein when the program runs, it controls the device where the non-volatile storage medium is located to execute the mixing ratio control method in Embodiment 2.

[0128] Optionally, the device containing the non-volatile storage medium executes the following steps by running the program: periodically acquiring raw material component information of multiple raw material bins, wherein each raw material bin stores the same type of raw material; within each period, determining the first discharge component prediction information of each raw material bin in the current period based on the raw material component information and corresponding discharge amount of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged from the raw material bin in the current period; determining the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing the multiple raw material bins according to different discharge ratio values ​​based on the discharge component prediction information of each raw material bin in the current period, and determining the first target discharge ratio value of each raw material bin when the mixing ratio cost is lowest by using the first mixing component information of the mixture meeting a preset threshold range as a constraint condition.

[0129] According to an embodiment of this application, a computer program product is also provided, which includes a stored computer program, wherein when the computer program is executed by a processor, it implements the mixing ratio control method in Embodiment 2.

[0130] Optionally, the computer program performs the following steps: periodically acquiring raw material component information from multiple raw material bins, wherein each raw material bin stores the same type of raw material; within each period, determining the first discharge component prediction information of each raw material bin in the current period based on the raw material component information and corresponding discharge volume of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged from the raw material bin in the current period; determining the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing multiple raw material bins according to different discharge ratio values ​​based on the discharge component prediction information of each raw material bin in the current period, and determining the first target discharge ratio value of each raw material bin when the mixing ratio cost is lowest by using the first mixing component information of the mixture satisfying a preset threshold range as a constraint condition.

[0131] According to an embodiment of this application, a processor is also provided for running a program, wherein the program executes the mixing ratio control method in embodiment 2 during runtime.

[0132] Optionally, the program executes the following steps during runtime: periodically acquiring raw material component information from multiple raw material bins, wherein each raw material bin stores the same type of raw material; within each period, determining the first discharge component prediction information for each raw material bin in the current period based on the raw material component information and corresponding discharge volume of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged from the raw material bin in the current period; determining the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing multiple raw material bins according to different discharge ratio values ​​based on the discharge component prediction information of each raw material bin in the current period, and determining the first target discharge ratio value of each raw material bin when the mixing ratio cost is lowest by using the first mixing component information of the mixture satisfying a preset threshold range as a constraint condition.

[0133] According to an embodiment of this application, an electronic device is also provided, wherein, Figure 6 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application, such as... Figure 6 As shown, the electronic device includes one or more processors; a memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to run the programs, wherein the programs are configured to execute the mixing ratio control method in Embodiment 2 above during runtime.

[0134] Optionally, the processor is configured to execute the following steps via a computer program: periodically acquiring raw material component information from multiple raw material bins, wherein each raw material bin stores the same type of raw material; within each period, determining the first discharge component prediction information of each raw material bin in the current period based on the raw material component information and corresponding discharge volume of each raw material bin in the current period, wherein the first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged from the raw material bin in the current period; determining the first mixing component information and corresponding mixing ratio cost of the mixture obtained by mixing the multiple raw material bins according to different discharge ratio values ​​based on the discharge component prediction information of each raw material bin in the current period, and determining the first target discharge ratio value of each raw material bin when the mixing ratio cost is lowest by using the first mixing component information of the mixture satisfying a preset threshold range as a constraint condition.

[0135] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0136] In the above embodiments of this application, 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.

[0137] 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 example, 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 couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0138] 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.

[0139] Furthermore, the functional units in the various embodiments of this application 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.

[0140] 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 this application, in essence, or the part that contributes to related technologies, 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0141] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the original content of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling the mixing ratio, characterized in that, include: The raw material composition information of multiple raw material bins is periodically acquired. Each raw material bin stores the same type of raw material. The raw material bin is funnel-shaped, consisting of a cylindrical part and a conical part, and includes multiple storage layers. For each cycle, the first discharge component prediction information of each raw material silo in the current cycle is determined based on the raw material component information and corresponding discharge volume of each raw material silo in the current cycle, including: for each raw material silo, obtaining the discharge flow rate of the current raw material silo in the current cycle, and determining the discharge volume of the current raw material silo in the current cycle based on the discharge flow rate; calculating the quotient of the total storage volume of the current raw material silo and the number of storage layers, and using the obtained quotient as the storage layer reference value of the current raw material silo; determining the outflow layer number of the current raw material silo in the current cycle based on the storage layer reference value and the discharge volume; adjusting the raw material component information of at least one storage layer corresponding to the cone portion based on the outflow layer number, and determining the first discharge component prediction information of the current raw material silo in the target cycle based on the obtained adjustment result and the weight coefficient of each storage layer corresponding to the cone portion, wherein the first discharge component prediction information is used to characterize the predicted content value of multiple components discharged from the raw material silo in the current cycle; Based on the predicted information of the first discharge component of each raw material silo in the current cycle, the information of the first mixing component and the corresponding mixing ratio cost of the mixture obtained by discharging multiple raw material silos according to different discharge ratio values ​​are determined. The first target discharge ratio value of each raw material silo is determined when the mixing ratio cost is the lowest, with the first mixing component information of the mixture meeting a preset threshold range as a constraint. The process of determining the weight coefficients of each storage layer corresponding to the cone portion includes: determining the target raw material composition information of each storage layer corresponding to the cone portion of the current raw material warehouse based on the raw material composition information of the current raw material warehouse in the current period; and determining the weight coefficients of each storage layer corresponding to the cone portion through a regression algorithm based on the target raw material composition information and the adjustment result.

2. The method according to claim 1, characterized in that, Periodically acquire raw material composition information from multiple raw material warehouses, including: For each of the raw material bins, the raw material composition information is obtained by periodically measuring the content of multiple components in the current raw material bin feed using analytical equipment.

3. The method according to claim 1, characterized in that, Based on the predicted discharge components of each of the raw material silos in the current cycle, the information of the first mixture component and the corresponding mixture ratio cost of the mixture obtained when discharging from multiple raw material silos according to different discharge ratio values ​​are determined, including: When the predicted first discharge component information of multiple raw material silos is determined to be discharged according to different discharge ratio values, the first mixture component information of the mixture obtained by mixing is determined, and the corresponding mixture ratio cost is calculated based on the raw material cost of each raw material silo and the discharge ratio value of each raw material silo.

4. The method according to claim 1, characterized in that, Before determining the first target discharge ratio value of each raw material silo when the mixing ratio cost is lowest, based on the constraint that the information of the first mixing component of the mixture meets a preset threshold range, the method further includes: Obtain second discharge component prediction information of multiple raw material bins in the previous cycle, wherein the second discharge component prediction information is used to characterize the second predicted content value of multiple components when the raw material bins discharge in the previous cycle; The second mixture component information is obtained when multiple raw material bins are discharged according to a second target discharge ratio value based on the second discharge component prediction information of each raw material bin. The second mixture component information is used to characterize the measured values ​​of each component of the mixture obtained in the previous cycle. Based on the second discharge component prediction information and the second mixture component information, the component prediction error information of the mixture is determined, wherein the component prediction error information is used to reflect the error value between the second predicted value and the second measured value of each component; The constraint conditions are corrected using the component prediction error information of the mixture.

5. The method according to claim 4, characterized in that, Obtaining the second mixture component information of the mixture obtained when discharging from multiple raw material bins according to a second target discharge ratio value based on the second discharge component prediction information of each raw material bin includes: The second mixture component information is obtained by measuring the content of each component in the mixture obtained by discharging multiple raw material bins according to the second target discharge ratio value using analysis equipment to predict the second discharge component of each of the raw material bins.

6. A mixing and proportioning control device, characterized in that, include: The acquisition module is used to periodically acquire raw material composition information of multiple raw material bins, wherein each raw material bin stores the same type of raw material, the raw material bin is funnel-shaped consisting of a cylindrical part and a conical part, and the raw material bin includes multiple storage layers; The prediction module is used to determine the first discharge component prediction information of each raw material silo in the current cycle for each cycle, based on the raw material composition information and corresponding discharge volume of each raw material silo in the current cycle. This includes: for each raw material silo, obtaining the discharge flow rate of the current raw material silo in the current cycle, and determining the discharge volume of the current raw material silo in the current cycle based on the discharge flow rate; calculating the quotient between the total storage volume of the current raw material silo and the number of storage layers, and using the obtained quotient as the storage layer reference value of the current raw material silo; determining the outflow layer number of the current raw material silo in the current cycle based on the storage layer reference value and the discharge volume; adjusting the raw material composition information of at least one storage layer corresponding to the cone portion based on the outflow layer number, and determining the first discharge component prediction information of the current raw material silo in the target cycle based on the obtained adjustment result and the weight coefficient of each storage layer corresponding to the cone portion. The first discharge component prediction information is used to characterize the predicted content values ​​of multiple components discharged from the raw material silo in the current cycle. The determination module is used to determine the first mixing component information and the corresponding mixing ratio cost of the mixture obtained by mixing multiple raw material bins when discharging materials from multiple raw material bins according to different discharge ratio values, based on the first discharge component prediction information of each raw material bin in the current cycle, and to determine the first target discharge ratio value of each raw material bin when the mixing ratio cost is the lowest, with the first mixing component information of the mixture meeting a preset threshold range as a constraint condition. The process of determining the weight coefficients of each storage layer corresponding to the cone portion includes: determining the target raw material composition information of each storage layer corresponding to the cone portion of the current raw material warehouse based on the raw material composition information of the current raw material warehouse in the current period; and determining the weight coefficients of each storage layer corresponding to the cone portion through a regression algorithm based on the target raw material composition information and the adjustment result.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a computer program, wherein the device containing the non-volatile storage medium executes the mixing ratio control method according to any one of claims 1 to 5 by running the computer program.

8. A computer program product, characterized in that, include: A computer program, wherein when executed by a processor, the computer program implements the mixing ratio control method according to any one of claims 1 to 5.

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