Intelligent ore blending data management method and device

Abstract

The invention provides an intelligent ore blending data management method and device. The method comprises the steps that ore raw material information of the intelligent ore blending warehouse is monitored, wherein the ore raw material information comprises the types of ore raw materials, the price cost of each ore raw material, the matching upper limit of each ore raw material, the matching lower limit of each ore raw material, the components of each ore raw material and the allowed configuration amount of each ore raw material at the current moment; the received new order is responded, product information needed by the new order is determined, and the product information comprises the demand quantity of the product, the components of the product and the component proportion of the product; the ore raw material information and the product information serve as input and are input into a pre-trained ore blending model, an ore blending result output by the ore blending model is obtained, and the ore blending result comprises selected target ore raw materials, the proportion of all the target ore raw materials and the configuration amount of all the target ore raw materials; and according to the ore blending result, a production workshop of the intelligent ore blending warehouse is controlled to execute ore blending.

Description

Technical Field

[0001] The present application relates to the field of data processing, and more specifically, to an intelligent ore blending data management method and device. Background Art

[0002] In order to improve product quality and reduce production costs, various industrial fields have strictly controlled the selection and proportion of mineral raw materials. In industries such as metallurgy, chemical industry, cement, and electricity, the quality and proportion of mineral raw materials directly affect the performance and cost of products. Therefore, a digital mineral matching method is needed, which takes precise mineral matching as the main constraint, accurately analyzes the cost-effectiveness of mineral raw materials, provides a basis for mineral raw material procurement, and achieves the optimal production cost result.

[0003] At present, in traditional ore blending methods, manual experience and simple mathematical models are usually used to determine the proportion of ore raw materials. However, these methods have many defects and shortcomings. First, manual experience is easily affected by subjective factors, resulting in inaccurate and unstable proportioning results. Secondly, simple mathematical models are difficult to take into account the diversity and complexity of ore raw materials, and cannot accurately predict the performance and cost of products. In addition, traditional ore blending methods still have problems such as low efficiency, large amount of calculation, and difficulty in optimization, which cannot meet the requirements of modern industrial production for high efficiency and high precision.

[0004] Therefore, the present application provides an intelligent ore blending data management method to solve one of the above-mentioned technical problems. Summary of the invention

[0005] The purpose of this application is to provide an intelligent ore blending data management method and device, which can solve at least one of the technical problems mentioned above. The specific solution is as follows: According to the specific implementation of the present application, in the first aspect, the present application provides an intelligent ore distribution data management method, including: Monitor the mineral raw material information of the intelligent ore blending warehouse, the mineral raw material information includes the type of mineral raw material, the price cost of each mineral raw material, the upper limit of the proportion of each mineral raw material, the lower limit of the proportion of each mineral raw material, the composition of each mineral raw material, and the allowed configuration quantity of each mineral raw material at the current moment; in response to receiving a new order, determine the product information required for the new order, the product information includes the demand quantity of the product, the composition of the product, and the composition ratio of the product; use the mineral raw material information and the product information as input to the pre-trained ore blending model to obtain the ore blending result output by the ore blending model, the ore blending result includes the selected target mineral raw material, the proportion of each target mineral raw material, and the configuration quantity of each target mineral raw material; according to the ore blending result, control the production workshop of the intelligent ore blending warehouse to perform ore blending.

[0006] In one embodiment, the ore blending model includes a combination model, a sintering model, and an ironmaking model; the ore raw material information and the product information are input as inputs to a pre-trained ore blending model to obtain an ore blending result output by the ore blending model, including: inputting the ore raw material information and the product information as inputs to the combination model to obtain a plurality of different ore blending schemes output by the combination model, which use different ore raw materials and / or use different blending ratios; inputting the plurality of different ore blending schemes as inputs to the sintering model to obtain the sintering process costs corresponding to the plurality of different ore blending schemes output by the sintering model; inputting the plurality of different ore blending schemes as inputs to the sintering model to obtain the ironmaking process costs corresponding to the plurality of different ore blending schemes output by the sintering model; respectively determining the price cost of ore blending according to each of the ore blending schemes, and respectively determining the total cost of each of the ore blending schemes, the total cost being the sum of the price cost, the sintering process cost, and the ironmaking process cost; outputting the ore blending scheme with the smallest cost sum as the ore blending result of the ore blending model.

[0007] In one embodiment, the following method is used to determine the allowed configuration quantity of a specified mineral raw material at the current moment, where the specified mineral raw material is any one of the mineral raw materials: obtaining the actual storage quantity of the specified mineral raw material at the current moment, and the actual storage quantity of the specified mineral raw material at the previous moment; determining the predicted net increase in storage quantity of the specified mineral raw material at the next moment; and taking the sum of the actual storage quantity and the predicted net increase in storage quantity as the allowed configuration quantity of the specified mineral raw material at the current moment.

[0008] In one implementation, the following formula is used to determine the predicted net increase in storage volume of the specified ore material at the next moment: ;in, is the predicted net increase in storage volume of the designated ore material at the next moment, For the current moment, The next moment, -1 is the previous moment, is the actual storage volume of the specified mineral material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified mineral material at the current moment, is the predicted net increase in storage volume of the specified mineral material at the previous moment. Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each of the mineral material collecting robots collecting the specified mineral material, The quantitative parameters of the currently available mining raw material storage robots are: is the efficiency influencing factor of each of the mineral material storage robots for storing the specified mineral material, The predicted net increase in storage capacity at the current moment.

[0009] In one embodiment, the method further includes: in response to receiving an instruction to modify the ore blending result, controlling a production workshop of the intelligent ore blending warehouse to execute ore blending based on the modified ore blending result.

[0010] According to the specific implementation of the present application, in the second aspect, the present application provides an intelligent ore distribution data management device, including: A monitoring unit is used to monitor the mineral raw material information of the intelligent ore blending warehouse, wherein the mineral raw material information includes the type of mineral raw material, the price cost of each of the mineral raw materials, the upper limit of the proportion of each of the mineral raw materials, the lower limit of the proportion of each of the mineral raw materials, the composition of each of the mineral raw materials, and the allowed configuration quantity of each of the mineral raw materials at the current moment; a determination unit is used to determine the product information required for the new order in response to receiving a new order, wherein the product information includes the demand quantity of the product, the composition of the product, and the composition ratio of the product; an input-output unit is used to input the mineral raw material information and the product information as input to a pre-trained ore blending model to obtain the ore blending result output by the ore blending model, wherein the ore blending result includes the selected target mineral raw materials, the proportion of each of the target mineral raw materials, and the configuration quantity of each of the target mineral raw materials; a control unit is used to control the production workshop of the intelligent ore blending warehouse to perform ore blending according to the ore blending result.

[0011] In one embodiment, the ore blending model includes a combination model, a sintering model, and an ironmaking model; the input-output unit uses the following method to input the ore raw material information and the product information as input to the pre-trained ore blending model to obtain the ore blending result output by the ore blending model: the ore raw material information and the product information are input to the combination model to obtain a plurality of different ore blending schemes using different ore raw materials and / or different proportions output by the combination model; the plurality of different ore blending schemes are respectively input to the sintering model to obtain the The sintering model outputs the sintering process costs corresponding to each of the multiple different ore blending schemes; the multiple different ore blending schemes are respectively input as inputs to the sintering model to obtain the ironmaking process costs corresponding to each of the multiple different ore blending schemes output by the sintering model; the price cost of ore blending according to each of the ore blending schemes is respectively determined, and the total cost of each ore blending scheme is respectively determined, the total cost being the sum of the price cost, the sintering process cost, and the ironmaking process cost; the ore blending scheme with the smallest cost sum is output as the ore blending result of the ore blending model.

[0012] In one embodiment, the determination unit determines the allowed configuration quantity of a specified mineral material at the current moment in the following manner, where the specified mineral material is any one of the mineral materials: obtaining the actual storage quantity of the specified mineral material at the current moment, and the actual storage quantity of the specified mineral material at the previous moment; determining the predicted net increase in storage quantity of the specified mineral material at the next moment; and taking the sum of the actual storage quantity and the predicted net increase in storage quantity as the allowed configuration quantity of the specified mineral material at the current moment.

[0013] In one embodiment, the determination unit determines the predicted net increase in storage volume of the designated mineral material at the next moment using the following formula: ;in, is the predicted net increase in storage volume of the designated ore material at the next moment, For the current moment, The next moment, -6 is the previous moment, is the actual storage volume of the specified mineral material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified mineral material at the current moment, is the predicted net increase in storage volume of the specified mineral material at the previous moment. Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each of the mineral material collecting robots collecting the specified mineral material, The quantitative parameters of the currently available mining raw material storage robots are: is the efficiency influencing factor of each of the mineral material storage robots for storing the specified mineral material, The predicted net increase in storage capacity at the current moment.

[0014] In one embodiment, the control unit is further used to: in response to receiving an instruction to modify the ore matching result, control the production workshop of the intelligent ore matching warehouse to perform ore matching based on the modified ore matching result.

[0015] Compared with the prior art, the above scheme of the embodiment of the present application has at least the following beneficial effects: the present application provides an intelligent ore blending data management method and device. The intelligent ore blending data management method realizes the input, analysis, modeling and optimization of ore raw material information by establishing a digital ore blending platform, thereby reducing production costs, avoiding human intervention in ore blending, and realizing unified management of mining, ore blending and production. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of an intelligent ore blending data management method according to an embodiment of the present application is shown; Figure 2 A flow chart of a method for obtaining an ore blending result according to an embodiment of the present application is shown; Figure 3 A flow chart of a method for determining the allowed configuration amount of a specified mineral material at the current moment according to an embodiment of the present application is shown; Figure 4 A logical schematic diagram of intelligent ore matching according to an embodiment of the present application is shown; Figure 5 A unit block diagram of an intelligent ore blending data management device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0018] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0019] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0020] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0021] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0022] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0023] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0024] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0025] The embodiment provided in this application is an embodiment of an intelligent ore blending data management method.

[0026] Combine the following Figure 1 The embodiments of the present application are described in detail.

[0027] Figure 1 A flow chart of the intelligent ore distribution data management method according to an embodiment of the present application is shown as follows: Figure 1 As shown, the process includes the following steps S101 to S104.

[0028] In step S101, the ore material information of the intelligent ore distribution warehouse is monitored.

[0029] In this application, the intelligent ore distribution warehouse is used to control the ore raw material information, including the collection, storage, and output of ore raw materials. The ore raw material information includes the type of ore raw materials, the price cost of each ore raw material, the upper limit of the ratio of each ore raw material, the lower limit of the ratio of each ore raw material, the composition of each ore raw material, and the allowed configuration amount of each ore raw material at the current moment.

[0030] In step S102, in response to receiving a new order, product information required for the new order is determined.

[0031] Among them, product information includes product demand, product ingredients, and product ingredient ratio.

[0032] In step S103, the ore raw material information and product information are input into the pre-trained ore blending model to obtain the ore blending result output by the ore blending model.

[0033] The ore blending results include the selected target ore raw materials, the blending ratio of each target ore raw material, and the blending amount of each target ore raw material.

[0034] In step S104, according to the ore matching result, the production workshop of the intelligent ore matching warehouse is controlled to execute ore matching.

[0035] The intelligent ore matching data management method provided in this application controls the ore raw material information through the intelligent ore matching library, monitors the ore raw material information in real time, and can quickly obtain the detailed information such as the type, price cost, upper limit, lower limit, and composition of the ore raw materials, so as to provide accurate data support for the subsequent ore matching. When a new order is received, the product information required for the order can be quickly determined. Combined with the pre-trained ore matching model, the ore matching result is quickly calculated, which greatly improves the efficiency of ore matching. At the same time, since the entire ore matching process is completed by the intelligent ore matching library, it can not only avoid human errors, but also dynamically adjust the ore matching results according to the real-time information of the ore raw materials, such as price cost, composition, etc. The intelligent ore matching library can also select the ore raw materials with the lowest cost for matching according to the order requirements, on the premise of meeting the product quality requirements, and effectively reduce the production cost. In addition, since the ore matching model can be based on the product demand, composition, and the matching requirements of each target ore raw material, the type and quality of the required ore raw materials can be accurately calculated. By controlling the production workshop of the intelligent ore matching library, the ore matching is strictly carried out according to the ore matching results, reducing human errors and further ensuring that the product quality meets the requirements. In addition, the intelligent ore distribution warehouse can grasp the allowed allocation quantity of each ore material at the current moment in real time, and reasonably allocate and utilize ore material resources according to order requirements to avoid resource waste and inventory backlog. At the same time, through accurate ore distribution calculation, it ensures that various ore materials are fully utilized and realizes the optimal allocation of resources.

[0036] Based on the above advantages, this application can deliver high-quality products that meet customer needs on time and improve customer satisfaction. At the same time, the efficient ore blending process can shorten the production cycle, quickly respond to market demand, and enhance the market competitiveness of the enterprise.

[0037] In one embodiment of the present application, the ore matching model includes three sub-models, namely a combination model, a sintering model, and an ironmaking model. The ore matching model takes ore raw material information and product information as input, outputs corresponding results through the three sub-models, and then obtains the final output ore matching result through a comprehensive analysis of the results.

[0038] Figure 2 A flow chart of a method for obtaining an ore blending result according to an embodiment of the present application is shown. Figure 2 As shown, the process includes the following steps S201 to S204.

[0039] In step S201, the ore raw material information and product information are input into the combination model as input, and a plurality of different ore blending schemes using different ore raw materials and / or different blending ratios are output by the combination model.

[0040] In step S202a, a plurality of different ore blending schemes are respectively input as inputs to the sintering model, and the sintering process costs corresponding to the plurality of different ore blending schemes are obtained as outputs of the sintering model.

[0041] In step S202b, a plurality of different ore blending schemes are respectively input as inputs to the sintering model, and the ironmaking process costs corresponding to the plurality of different ore blending schemes output by the sintering model are obtained.

[0042] In the present application, the execution order of step S202a and step S202b is not limited. For example, in some embodiments, step S202a and step S202b can be executed synchronously, or can be executed in a distributed order.

[0043] In step S203, the price cost of ore blending according to each ore blending scheme is determined respectively, and the total cost of each ore blending scheme is determined respectively, and the total cost is the sum of the price cost, the sintering process cost, and the ironmaking process cost.

[0044] In step S204, the cost and the minimum ore allocation plan are output as the ore allocation result of the ore allocation model.

[0045] In this application, the process cost is taken into consideration for the sintering link and the ironmaking link, respectively, where the process cost is a predefined parameter value, which is obtained by weighting the cost factors such as the process duration, the labor cost required for the process, and the machinery cost required for the project based on labor or machine. In order to adapt to the sum of the process cost and the price cost, the price cost is not completely consistent with the price of the ore raw material, but is scaled according to the ratio between the process cost and the price cost and the importance of the entire ore distribution, so that the final sum value is stable in the range of 0-100, so that the final output cost and is easy to reference and authoritative.

[0046] In the present application, any one of the mineral raw materials can be regarded as a designated mineral raw material. Furthermore, the method of determining the configuration amount of the designated mineral raw material allowed at the current moment is as follows.

[0047] Figure 3 A flow chart of a method for determining the allowed configuration amount of a specified mineral material at the current moment according to an embodiment of the present application is shown, as follows: Figure 3As shown, the process includes the following steps S301 to S304.

[0048] In step S301, the actual storage amount of the specified mineral material at the current moment and the actual storage amount of the specified mineral material at the previous moment are obtained.

[0049] In this application, the intelligent ore distribution warehouse uses the same frame interval for sampling, and the frame intervals under the continuous time frame are of the same time length. On this basis, the intelligent ore distribution warehouse obtains the fixed value of the actual storage amount of each type of ore raw material after completing the reading of each frame interval.

[0050] In step S302, the predicted net increase in storage volume of the designated mineral material at the next moment is determined.

[0051] In step S303, the sum of the actual storage volume and the predicted net increase in storage volume is used as the allowed configuration volume of the designated mineral material at the current moment.

[0052] As a feasible implementation method, the following formula can be used Determine the predicted net increase in storage volume of the specified ore material at the next moment. The predicted net increase in storage volume of the specified ore material at the next moment. For the current moment, The next moment, -1 is the previous moment, is the actual storage volume of the specified ore material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified ore material at the current moment, is the predicted net increase in storage volume of the specified ore material at the previous moment, Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each ore material collection robot collecting the specified ore material, The quantitative parameters of the currently available mining raw material storage robots are: The efficiency influencing factor of each ore raw material storage robot for storing the specified ore raw materials, The predicted net increase in storage capacity at the current moment.

[0053] In this application, for the machine collection and warehousing scenarios, the efficiency impact of each ore raw material warehousing robot on the collection of designated ore raw materials, the predicted net increase in storage between the current moment and the previous moment, and the efficiency impact of each ore raw material warehousing robot on the warehousing of designated ore raw materials are taken into consideration to ensure the accuracy of the estimate. At the same time, the actual storage volume of the designated ore raw materials at the previous moment and the current moment is introduced as the standard value to ensure the accuracy of the estimate while further ensuring that the difference between the estimated value and the actual value obtained later can be as small as possible, and on the basis of achieving accurate prediction, further ensuring the cost minimization of the selected ore allocation scheme.

[0054] As a feasible embodiment, when an instruction to modify the ore matching result is received, the production workshop of the intelligent ore matching warehouse can be controlled to execute the ore matching based on the modified ore matching result.

[0055] For example, a user interface or interface can be provided to allow relevant personnel (such as production management personnel, technical personnel, etc.) to modify the ore matching results. These personnel can adjust the ore matching results according to actual conditions, such as the availability of raw materials, the status of production equipment, changes in market demand, etc. After receiving the instruction to modify the ore matching results, the system will recalculate and verify the modified ore matching results to ensure their rationality and feasibility. This may involve re-evaluation of the demand, cost, quality indicators, etc. of raw materials. According to the modified ore matching results, the system will generate corresponding control instructions and send them to the production workshop of the intelligent ore matching library. The equipment and control system of the production workshop will be adjusted and operated according to these instructions to implement the new ore matching plan. In the process of implementing the new ore matching plan, the system will monitor and feedback the data and information of the production process in real time so as to timely discover and solve possible problems. At the same time, the system can also further optimize and adjust the ore matching results to improve production efficiency and product quality. In addition, in order to ensure that the process of modifying the ore matching results is safe and reliable, the system can set corresponding permissions and approval mechanisms. Only authorized and approved personnel can modify the ore matching results to avoid production problems caused by misoperation or improper modification.

[0056] Through the above-mentioned embodiments of the present application, the demand for flexible adjustment of ore blending results in actual production can be better met, the efficiency and quality of ore blending can be improved, and the safety and reliability of the production process can be ensured.

[0057] The technical solution aims to provide an ironmaking method based on a variety of different ore blending schemes. The method analyzes and compares a variety of ore blending schemes and selects the optimal ore blending scheme to improve the efficiency and quality of ironmaking.

[0058] Figure 4 A logical schematic diagram of intelligent ore matching according to an embodiment of the present application is shown.

[0059] For example, Figure 4 As shown, first, a variety of different ore distribution schemes are prepared by combining models, and then a variety of different ore distribution schemes are collected through model output, and they are organized into formatted data. On this basis, data preprocessing is performed to check the input data to ensure that it meets the requirements. If the data does not meet the requirements, data preprocessing is performed, including data cleaning, conversion, and normalization. At the same time, data analysis is performed on the processed data according to the sintering model and the ironmaking model, so as to determine the sintering process cost and the ironmaking process cost respectively through the sintering model and the ironmaking model. Of course, it also includes the verification of the model. For example, for the sintering model, the properties of the established model are judged to determine whether it is a linear model. If the model is a linear model, a suitable optimization algorithm is selected, such as pvthc, pup or ipproble. In addition, for the ironmaking model, the present application can relax the original model to obtain a relaxed model and use it as an updated ironmaking model.

[0060] Furthermore, when the sintering model outputs the sintering process cost and the ironmaking model outputs the ironmaking model cost, the optimal ore blending scheme is selected by analyzing and comparing multiple ore blending schemes to improve the efficiency of ironmaking. Under this scheme, the quality of ironmaking can be improved, the content of impurities can be reduced, and the cost of ironmaking can be reduced, thereby improving the economic benefits of the enterprise.

[0061] This application selects the optimal ore blending scheme by analyzing and comparing a variety of different ore blending schemes to improve the efficiency and quality of ironmaking and reduce costs. This method has high practical value and promotion value.

[0062] The present application also provides an apparatus embodiment that is consistent with the above embodiment, which is used to implement the method steps of the above embodiment. The explanation based on the same name meaning is the same as the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0063] like Figure 5 As shown, the present application provides an intelligent ore blending data management device 500, comprising: The monitoring unit 501 is used to monitor the ore raw material information of the intelligent ore matching warehouse. The ore raw material information includes the type of ore raw material, the price cost of each ore raw material, the upper limit of the proportion of each ore raw material, the lower limit of the proportion of each ore raw material, the composition of each ore raw material, and the configuration amount of each ore raw material allowed at the current moment. The determination unit 502, in response to receiving a new order, determines the product information required for the new order. The product information includes the demand for the product, the composition of the product, and the composition ratio of the product. The input and output unit 503 is used to input the ore raw material information and product information as input to the pre-trained ore matching model to obtain the ore matching results output by the ore matching model. The ore matching results include the selected target ore raw materials, the proportions of each target ore raw materials, and the configuration amount of each target ore raw material. The control unit 504 is used to control the production workshop of the intelligent ore matching warehouse to perform ore matching according to the ore matching results.

[0064] In one embodiment, the ore matching model includes a combination model, a sintering model, and an ironmaking model. The input-output unit 503 uses the following method to input the ore raw material information and product information as input to the pre-trained ore matching model to obtain the ore matching result output by the ore matching model: the ore raw material information and product information are input to the combination model to obtain a plurality of different ore matching schemes output by the combination model, which use different ore raw materials and / or use different matching ratios. The plurality of different ore matching schemes are respectively input as input to the sintering model to obtain the sintering process costs corresponding to the plurality of different ore matching schemes output by the sintering model. The plurality of different ore matching schemes are respectively input as input to the sintering model to obtain the ironmaking process costs corresponding to the plurality of different ore matching schemes output by the sintering model. The price cost of ore matching according to each ore matching scheme is determined respectively, and the total cost of each ore matching scheme is determined respectively, and the total cost is the sum of the price cost, the sintering process cost, and the ironmaking process cost. The ore matching scheme with the smallest cost sum is output as the ore matching result of the ore matching model.

[0065] In one embodiment, the determination unit 502 determines the allowed configuration amount of the specified mineral material at the current moment in the following manner, where the specified mineral material is any one of each mineral material: obtaining the actual storage amount of the specified mineral material at the current moment and the actual storage amount of the specified mineral material at the previous moment. Determine the predicted net increase storage amount of the storage amount of the specified mineral material at the next moment. The sum of the actual storage amount and the predicted net increase storage amount is used as the allowed configuration amount of the specified mineral material at the current moment.

[0066] In one implementation, the determination unit 502 uses the following formula to determine the predicted net increase in storage volume of the specified mineral material at the next moment: .in, The predicted net increase in storage volume of the specified ore material at the next moment. For the current moment, The next moment, -6 is the previous moment, is the actual storage volume of the specified ore material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified ore material at the current moment, is the predicted net increase in storage volume of the specified ore material at the previous moment, Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each ore material collection robot collecting the specified ore material, The quantitative parameters of the currently available mining raw material storage robots are: The efficiency influencing factor of each ore raw material storage robot for storing the specified ore raw materials, The predicted net increase in storage capacity at the current moment.

[0067] In one embodiment, the control unit 504 is further used to: in response to receiving an instruction to modify the ore matching result, control the production workshop of the intelligent ore matching warehouse to execute ore matching based on the modified ore matching result.

[0068] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0069] Although operations are described in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in serial order, or that all shown operations be performed to achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0070] The methods and apparatus of the present disclosure can be implemented using standard programming techniques, using rule-based logic or other logic to implement various method steps. It should also be noted that the words "apparatus" and "module" used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0071] Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations or procedures.

[0072] The foregoing description of the implementation of the present disclosure has been given for the purpose of illustration and description. The foregoing description is not exhaustive nor is it intended to limit the present disclosure to the exact form disclosed, and various variations and modifications may exist in accordance with the above teachings or may be obtained from the practice of the present disclosure. These embodiments are selected and described in order to illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can utilize the present disclosure in various embodiments and various modifications suitable for the specific purpose contemplated.

[0073] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0074] It is to be understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The singular forms "a", "the" and "the" are also intended to include plural forms, unless the context clearly indicates other meanings.

[0075] It is further understood that the terms "first", "second", etc. are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not indicate a specific order or degree of importance. In fact, the expressions "first", "second", etc. can be used interchangeably. For example, without departing from the scope of the present disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

[0076] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0077] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood as requiring the operations to be performed in the specific order shown or in a serial order, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0078] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following scope of rights.

[0079] It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the scope of the appended claims.

Claims

1. An intelligent ore blending data management method, characterized in that: include: Monitor the ore raw material information of the intelligent ore blending warehouse, the ore raw material information includes the type of ore raw material, the price cost of each ore raw material, the upper limit of the proportion of each ore raw material, the lower limit of the proportion of each ore raw material, the composition of each ore raw material, and the allowed configuration amount of each ore raw material at the current moment; In response to receiving a new order, determining product information required for the new order, the product information including a required quantity of the product, ingredients of the product, and a composition ratio of the product; The ore raw material information and the product information are used as inputs to a pre-trained ore blending model to obtain an ore blending result output by the ore blending model, wherein the ore blending result includes the selected target ore raw materials, the blending ratio of each target ore raw material, and the configuration amount of each target ore raw material; According to the ore matching results, the production workshop of the intelligent ore matching warehouse is controlled to execute ore matching.

2. The method according to claim 1, characterized in that The ore blending model includes a combination model, a sintering model, and an ironmaking model; The method of inputting the ore raw material information and the product information as input into a pre-trained ore blending model to obtain an ore blending result output by the ore blending model includes: The ore raw material information and the product information are used as inputs to the combination model to obtain a plurality of different ore blending schemes using different ore raw materials and / or different blending ratios output by the combination model; The plurality of different ore blending schemes are respectively used as inputs and input into the sintering model to obtain the sintering process costs corresponding to the plurality of different ore blending schemes output by the sintering model; The plurality of different ore blending schemes are respectively used as inputs and input into the sintering model to obtain the ironmaking process costs corresponding to the plurality of different ore blending schemes output by the sintering model; Determine the price cost of ore blending according to each ore blending scheme, and determine the total cost of each ore blending scheme, wherein the total cost is the sum of the price cost, the sintering process cost, and the ironmaking process cost; The cost and the minimum ore allocation plan are output as the ore allocation result of the ore allocation model.

3. The method according to claim 1 or 2, characterized in that: The allowed configuration amount of the designated mineral raw material at the current moment is determined in the following manner, wherein the designated mineral raw material is any one of the mineral raw materials: Obtaining the actual storage amount of the specified mineral material at the current moment and the actual storage amount of the specified mineral material at the previous moment; Determine the predicted net increase in storage volume of the designated mineral material at the next moment; The sum of the actual storage amount and the predicted net increase in storage amount is used as the allowed configuration amount of the designated mineral material at the current moment.

4. The method according to claim 3, characterized in that The following formula is used to determine the predicted net increase in storage volume of the specified ore raw material at the next moment: ； in, is the predicted net increase in storage volume of the designated ore material at the next moment, For the current moment, The next moment, -1 is the previous moment, is the actual storage volume of the specified mineral material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified mineral material at the current moment, is the predicted net increase in storage volume of the specified mineral material at the previous moment. Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each of the mineral material collecting robots collecting the specified mineral material, The quantitative parameters of the currently available mining raw material storage robots are: is the efficiency influencing factor of each of the mineral material storage robots for storing the specified mineral material, The predicted net increase in storage capacity at the current moment.

5. The method according to claim 1, characterized in that The method further comprises: In response to receiving an instruction to modify the ore matching result, the production workshop of the intelligent ore matching warehouse is controlled to execute ore matching based on the modified ore matching result.

6. An intelligent ore blending data management device, characterized in that: include: A monitoring unit, used to monitor the ore material information of the intelligent ore blending warehouse, wherein the ore material information includes the type of ore material, the price cost of each ore material, the upper limit of the proportion of each ore material, the lower limit of the proportion of each ore material, the composition of each ore material, and the allowed configuration amount of each ore material at the current moment; a determination unit, in response to receiving a new order, determining product information required for the new order, the product information including a required quantity of the product, a composition of the product, and a composition ratio of the product; An input-output unit, used to input the ore raw material information and the product information as input to a pre-trained ore blending model, to obtain an ore blending result output by the ore blending model, wherein the ore blending result includes the selected target ore raw materials, the blending ratio of each target ore raw material, and the configuration amount of each target ore raw material; A control unit is used to control the production workshop of the intelligent ore blending warehouse to execute ore blending according to the ore blending result.

7. The device according to claim 6, characterized in that The ore blending model includes a combination model, a sintering model, and an ironmaking model; The input-output unit uses the following method to input the ore raw material information and the product information into the pre-trained ore blending model to obtain the ore blending result output by the ore blending model: The ore raw material information and the product information are used as inputs to the combination model to obtain a plurality of different ore blending schemes using different ore raw materials and / or different blending ratios output by the combination model; The plurality of different ore blending schemes are respectively used as inputs and input into the sintering model to obtain the sintering process costs corresponding to the plurality of different ore blending schemes output by the sintering model; The plurality of different ore blending schemes are respectively used as inputs and input into the sintering model to obtain the ironmaking process costs corresponding to the plurality of different ore blending schemes output by the sintering model; Determine the price cost of ore blending according to each ore blending scheme, and determine the total cost of each ore blending scheme, wherein the total cost is the sum of the price cost, the sintering process cost, and the ironmaking process cost; The cost and the minimum ore allocation plan are output as the ore allocation result of the ore allocation model.

8. The device according to claim 6 or 7, characterized in that The determination unit determines the allowed configuration amount of the specified mineral material at the current moment in the following manner, wherein the specified mineral material is any one of the mineral materials: Obtaining the actual storage amount of the specified mineral material at the current moment and the actual storage amount of the specified mineral material at the previous moment; Determine the predicted net increase in storage volume of the designated mineral material at the next moment; The sum of the actual storage amount and the predicted net increase in storage amount is used as the allowed configuration amount of the designated mineral material at the current moment.

9. The device according to claim 8, characterized in that The determination unit determines the predicted net increase in storage volume of the designated mineral material at the next moment using the following formula: ； in, is the predicted net increase in storage volume of the designated ore material at the next moment, For the current moment, The next moment, -6 is the previous moment, is the actual storage volume of the specified mineral material at the current moment, is the actual storage volume of the specified ore material at the last moment, is the predicted net increase in storage volume of the specified mineral material at the current moment, is the predicted net increase in storage volume of the specified mineral material at the previous moment. Quantitative parameters of currently available mining material collection robots, The efficiency influencing factor of each of the mineral material collecting robots collecting the specified mineral material, The quantitative parameters of the currently available mining raw material storage robots are: is the efficiency influencing factor of each of the mineral material storage robots for storing the specified mineral material, The predicted net increase in storage capacity at the current moment.

10. The device according to claim 6, characterized in that The control unit is also used for: In response to receiving an instruction to modify the ore matching result, the production workshop of the intelligent ore matching warehouse is controlled to execute ore matching based on the modified ore matching result.

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