A method, device and equipment for comprehensive analysis and optimization of three-dose use data

Abstract

The present specification relates to the technical field of petrochemical production, and particularly relates to a three-agent use data comprehensive analysis and optimization method, device and equipment. Including: obtaining historical production data of three-agent related materials in a production line, classifying the obtained historical production data according to the categories of the three agents; then establishing the historical production data into multiple data models according to the categories, wherein the data models include: a price ratio model, a cost model and a device model; the data models are subjected to multi-condition constraints according to actual conditions, and an overall optimization model is generated through cross-training, the production demand in actual production is input into the optimization model to generate optimization suggestions, so that the operating personnel can purchase, produce and other processes according to the optimization suggestions. Thus, an optimization model capable of generating an optimization suggestion covering the whole process of three-agent production is realized.

Description

Technical Field

[0001] This manual belongs to the field of petrochemical production technology, and in particular relates to a method, apparatus and equipment for comprehensive analysis and optimization of three-agent usage data. Background Technology

[0002] Currently, in the production processes of oil refining / chemical enterprises, the entire process of using and filling production and auxiliary equipment with catalysts, additives, and solvents (APCs) encompasses business data including, but not limited to, demand, procurement, warehousing, requisition, consumption, reporting, analysis, evaluation, and early warning. This is a comprehensive management system based on the collection, analysis, management, and business optimization of data related to APCs and auxiliary raw materials, which can directly or indirectly improve enterprise efficiency. Currently, the specialized and comprehensive management of APCs in refining and chemical production enterprises is a demand arising from the need to continuously improve quality and efficiency in accordance with enterprise development plans. Specific needs include: strictly controlling operations, enhancing circulation efficiency, strengthening professional and refined management, improving benchmarking management, reducing auxiliary material costs, monitoring and early warning of procurement and operational risks, multi-dimensional early warning and analysis of equipment process status, and ensuring data standardization through integrated business and financial management, among other areas.

[0003] In existing technologies, domestic refining and chemical enterprises employ traditional methods or software for managing reagents, additives, and chemical reagents, as well as data analysis, which suffer from numerous problems. They lack effective methods for collecting equipment and consumption data, hindering sustainable analysis at the equipment level and preventing defect analysis and early warning. Furthermore, the classification and storage of reagent data is disorganized, lacking effective and scientific methods for data categorization and identification, ultimately preventing effective analysis and comparison of business data and accurate data evaluation. Moreover, existing data analysis methods are not advanced enough, often relying on simple linear formulas and outdated approaches, making it difficult to analyze and capture key points and issues across the entire business, and hindering the identification of optimization directions. This also results in a lack of full lifecycle analysis of reagents, often limited to a single stage or a small number of processes, with poor coordination between processes, leading to reduced overall operational efficiency.

[0004] Therefore, how to design a comprehensive management method for the three agents by strengthening the professional analysis of the three agents is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To address the lack of effective methods for collecting equipment and consumption data during the use of the three agents in existing technologies, this specification provides a comprehensive analysis and optimization method, apparatus, and equipment for the use of the three agents. It proposes a method for production optimization based on models built from the usage data of the three agents and describes the relationship between the production data of the three agents and related materials. The specific technical solutions of this specification's embodiments are as follows:

[0006] On the one hand, the embodiments of this specification provide a method for comprehensive analysis and optimization of three-dose usage data, the method including:

[0007] Obtain historical production data of the three agents and related materials in the production line, and classify the historical production data according to the categories of the three agents;

[0008] Multiple data models are established based on the historical production data, including: a price comparison model, a cost model, and a device model.

[0009] The multiple data models are subjected to multiple constraints and cross-generate an optimization model. Production requirements are then input into the optimization model to generate optimization suggestions.

[0010] Furthermore, the three agents include a catalyst, an additive, and a solvent.

[0011] Furthermore, the historical production data includes at least: basic equipment data, historical consumption data of the three agents in the equipment, classification data of the three agents, processing volume data of the equipment, and material prices.

[0012] Furthermore, classifying the historical production data according to the categories of the three agents further includes,

[0013] The historical production data of the three-agent related materials are preprocessed, and the data of each three-agent related material is input into a data matrix according to the type of three-agent used in the three-agent related materials, and classified by K-means clustering method.

[0014] Furthermore, establishing multiple data models based on the historical production data further includes,

[0015] The device model is generated based on the basic device data in the historical production data.

[0016] The cost model is generated based on the historical consumption data of the three agents and the processing volume data of the unit in the historical production data.

[0017] The price comparison model is generated based on the material prices in the historical production data.

[0018] Furthermore, the process of applying multiple constraints to the data models and cross-generating an optimization model further includes:

[0019] The upper and lower limits of the production of the three-agent related materials are generated based on the data from the basic equipment.

[0020] Generate the upper and lower price limits for the three related materials based on the material prices;

[0021] The upper and lower limits of output and the upper and lower limits of price are used as constraints to impose multi-condition constraints on the multiple data models.

[0022] Furthermore, the process of applying multiple constraints to the data models and cross-generating an optimization model further includes:

[0023] Training data is generated based on the historical production data, and the multiple data models with multiple conditions and constraints are cross-trained based on the training data.

[0024] The optimized model is generated based on the cross-training results.

[0025] Furthermore, inputting production requirements into the optimization model to generate optimization suggestions further includes,

[0026] Obtain the production requirements of the three related materials and input the production requirements into the optimization model;

[0027] The optimization model generates the estimated production cost of the three agents and related materials, the estimated total consumption of the three agents, and the suggested purchase price based on the production demand.

[0028] The optimization recommendations are generated based on the estimated production costs of the three related materials, the estimated total consumption of the three agents, and the suggested purchase price.

[0029] On the other hand, embodiments of this specification also provide a device for comprehensive analysis and optimization of three-dose usage data, the device comprising:

[0030] The data classification module is used to acquire historical production data of the three agents-related materials in the production line and classify the historical production data according to the categories of the three agents.

[0031] The model building module is used to build multiple data models based on the historical production data, including: a price comparison model, a cost model, and a device model.

[0032] The optimization suggestion generation module is used to apply multiple constraints to the multiple data models and cross-generate an optimization model, and input production requirements into the optimization model to generate optimization suggestions.

[0033] On the other hand, embodiments of this specification also provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-described method.

[0034] On the other hand, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0035] Finally, this specification also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method.

[0036] Using the embodiments in this specification, product data is first collected and organized. Historical production data of the three agents-related materials are obtained from the "Equipment Consumption" management item in the production line. This historical production data is then categorized according to the three agents. Multiple data models are then established based on these categories, including a price comparison model, a cost model, and an equipment model. This allows for the calculation of how to allocate the consumption quantity and cost of the three agents without changing the equipment or production process to maximize efficiency. Finally, the multiple data models are subjected to multi-condition constraints based on actual production conditions, and an optimization model is generated through cross-validation to obtain a comprehensive optimization model for the production of the three agents-related materials. Finally, the actual production needs are input into the optimization model to generate optimization suggestions. This achieves the realization of an optimization model capable of generating optimization suggestions covering the entire production process of the three agents. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 The diagram shown is a schematic representation of an implementation system for a comprehensive analysis and optimization method for the use data of three agents, as described in an embodiment of this specification.

[0039] Figure 2 The diagram shown is a flowchart illustrating a method for comprehensive analysis and optimization of three-drug usage data according to an embodiment of this specification.

[0040] Figure 3 The diagram shown is a flowchart illustrating the process of establishing multiple data models based on the historical production data in an embodiment of this specification.

[0041] Figure 4 The diagram shown is a flowchart illustrating how multiple data models are subjected to multiple constraints and cross-generate an optimized model in an embodiment of this specification.

[0042] Figure 5 The diagram shown is a schematic representation of the profit parallel lines in an embodiment of this specification.

[0043] Figure 6The diagram shown is a schematic representation of a device for comprehensive analysis and optimization of three-agent usage data in an embodiment of this specification.

[0044] Figure 7 The diagram shown is a structural schematic of the computer device in an embodiment of this specification.

[0045] [Explanation of Labels in the Attached Image]

[0046] 101. Terminal;

[0047] 102. Server;

[0048] 601. Data Classification Module;

[0049] 602. Model Building Module;

[0050] 603. Optimization suggestion generation module;

[0051] 702. Computer equipment;

[0052] 704. Processing equipment;

[0053] 706. Storage resources;

[0054] 708. Drive system;

[0055] 710. Input / Output Module;

[0056] 712. Input devices;

[0057] 714. Output devices;

[0058] 716. Presentation equipment;

[0059] 718. Graphical User Interface;

[0060] 720. Network interface;

[0061] 722. Communication link;

[0062] 724. Communication bus. Detailed Implementation

[0063] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the embodiments of this specification.

[0064] It should be noted that the terms "first," "second," etc., in the description, claims, and accompanying drawings of the embodiments herein 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 embodiments of the embodiments 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, apparatus, product, or device that includes 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 devices.

[0065] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification all comply with the relevant provisions of national laws and regulations.

[0066] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0067] like Figure 1 The diagram illustrates a system implementation of a comprehensive analysis and optimization method for three-drug usage data in an embodiment of this specification, including a terminal 101 and a server 102. The terminal 101 and server 102 can communicate via a network, which may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, user equipment (e.g., computing devices), and a backend system.

[0068] The project manager can input historical production data of the three related materials in the production line into the server 102 via terminal 101. Server 102 builds multiple data models based on the historical production data and integrates and cross-trains them into an optimization model. Then, it generates optimization suggestions based on production needs and displays these suggestions to the project manager via terminal 101. Optionally, server 102 can be a node in a cloud computing system (not shown in the diagram), or each server can be a separate cloud computing system, including multiple computers interconnected by a network and operating as a distributed processing system.

[0069] In addition, it should be noted that, Figure 1 The examples shown are merely one application environment provided by the embodiments in this specification. In practical applications, other application environments may also be included, and this specification does not impose any limitations.

[0070] To address the problems existing in the prior art, this specification provides a comprehensive analysis and optimization method for the usage data of three agents. Through system integration and data sharing, it can extract existing data in a timely manner and call data of the three agents at different stages. Based on the data analysis, it provides optimization suggestions, which can help enterprises take effective measures in a timely manner to reduce usage costs and improve economic efficiency. Figure 2 The diagram shown is a flowchart illustrating the comprehensive analysis and optimization method for the usage data of the three agents in an embodiment of this specification. This diagram describes the process of comprehensive analysis and optimization of the usage data of the three agents. The order of steps listed in the embodiment is merely one possible execution order among many steps and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or accompanying drawings can be executed sequentially or in parallel.

[0071] As shown in the figure, the method may include:

[0072] Step 201: Obtain historical production data of the three agents and related materials in the production line, and classify the historical production data according to the categories of the three agents;

[0073] Step 202: Establish multiple data models based on the historical production data, including: a price comparison model, a cost model, and a device model;

[0074] Step 203: Apply multi-condition constraints to the multiple data models and cross-generate an optimization model. Input the production requirements into the optimization model to generate optimization suggestions.

[0075] Using the embodiments in this specification, product data is first collected and organized. Historical production data of the three agents-related materials are obtained from the "Equipment Consumption" management item in the production line. This historical production data is then categorized according to the three agents. Multiple data models are then established based on these categories, including a price comparison model, a cost model, and an equipment model. This allows for the calculation of how to allocate the consumption quantity and cost of the three agents without changing the equipment or production process to maximize efficiency. Finally, the multiple data models are subjected to multi-condition constraints based on actual production conditions, and an optimization model is generated through cross-validation to obtain a comprehensive optimization model for the production of the three agents-related materials. Finally, the actual production needs are input into the optimization model to generate optimization suggestions. This achieves the realization of an optimization model capable of generating optimization suggestions covering the entire production process of the three agents.

[0076] This specification's embodiments enhance specialized analysis methods for the three agents (pharmaceutical, pharmaceutical, and pharmaceutical) and establish scientific classifications. Through big data information, it integrates basic data management, approval management, and monitoring of fixed-bed three-agent amortization cost management and usage cycle tracking. Each process will serve the characteristics of its respective business, utilizing advanced intelligent analysis methods to achieve multi-dimensional benchmarking analysis for enterprises. The automatic analysis results provide reasonable suggestions, bridging the gap between different companies. Simultaneously, by achieving dual control over unit consumption and cost, and the integrated business and financial accounting of the three agents, it supports centralized specialized management of the three agents, effectively reducing consumption costs and standardizing the material requisition and consumption operations of commonly used agents in various regional companies, achieving consistent and synchronized management of three-agent materials. Furthermore, through data integration and intelligent data analysis, it also improves the level of synchronized management of physical inventory and book inventory, enhancing the efficiency of cost-per-ton analysis; it improves the analytical capabilities of business personnel, enabling analysis of procurement, consumption, and costs from multiple perspectives such as monthly completion, actual vs. budget comparison, and inventory, identifying problems, providing adjustments to procurement and agent usage strategies, and reducing equipment costs.

[0077] According to the embodiments in this specification, the three agents include a catalyst, an additive, and a solvent.

[0078] Specifically, in the production process of oil refining / chemical enterprises, it is necessary to collect and analyze business data based on catalysts, additives, and solvents, and to manage this data. Furthermore, in the embodiments of this specification, the entire process of using and filling these three auxiliary materials in production and auxiliary equipment covers business data including, but not limited to, demand, procurement, warehousing, requisition, consumption, reporting, analysis, evaluation, and early warning.

[0079] According to the embodiments of this specification, the historical production data includes at least: basic equipment data, historical consumption data of the three agents in the equipment, classification data of the three agents, processing volume data of the equipment, and material prices.

[0080] Specifically, in the production line of materials related to the three agents, it is necessary to collect statistics on the consumption and inventory of the three agents, the procurement and quality of the three agents, and relevant information about the equipment. First, product data is collected and organized, and materials with similar three agents are extracted and analyzed. The data comes from basic equipment collection tables, historical three agent consumption tables of the equipment, three agent classification data, equipment processing volume data, material price data, and other data tables.

[0081] In one embodiment of this specification, classifying the historical production data according to the categories of the three agents further includes preprocessing the historical production data of the materials related to the three agents, and inputting the data of each type of material related to the three agents into a data matrix according to the type of three agents used in the materials related to the three agents, and classifying them using the K-means clustering method.

[0082] Specifically, the consumption and inventory of the three agents (powder, chemicals, and chemicals) include: unit processing volume management, three agent consumption management, and inventory monitoring; the procurement and quality of the three agents include: purchase order data, incoming quality inspection data, supplier numbers, and price data analysis and management; relevant unit information includes: unit start-up and shutdown, unit classification, and unit capacity management. Therefore, relevant unit information is summarized into basic unit data and unit processing volume data; the consumption and inventory of the three agents are summarized into historical three agent consumption data; and the procurement and quality of the three agents are summarized into material price data. Through system integration and data sharing, timely extraction of existing data, external data, data from other sources, and synchronization of business and process data, as well as business and financial data, data on the three agents in planning, demand, procurement, quality, material requisition, replacement, usage, and inventory are accessed to ensure accuracy and timeliness, providing a guarantee for subsequent analysis and optimization.

[0083] According to the embodiments in this specification, in order to establish an optimization model, such as Figure 3 As shown, establishing multiple data models based on the historical production data further includes,

[0084] Step 301: Generate the device model based on the basic device data in the historical production data;

[0085] Step 302: Generate the cost model based on the historical consumption data of the three agents and the processing volume data of the equipment in the historical production data;

[0086] Step 303: Generate the price comparison model based on the material prices in the historical production data.

[0087] Specifically, data models and algorithms are used to process the large amount of scattered and categorized data in the historical production data. Model algorithms are created from historical data to predict future data. Equipment models are generated based on historical basic equipment data; cost models are generated from historical three-agent consumption data and equipment processing volume data; and price comparison models are generated from historical material prices. In this way, the various situations of the enterprise in each stage of the use of the three agents are analyzed, and different dimensions of the situation are classified, summarized and extracted. Low-quality business situations and high-quality business situations are accurately analyzed and optimized.

[0088] According to the embodiments in this specification, in order to ensure that the generated model conforms to the actual situation in actual production needs, such as... Figure 4 As shown, the process of interleaving the multiple data models and applying multiple constraints to generate an optimization model further includes,

[0089] Step 401: Generate the upper and lower limits of the production of the three-agent related materials based on the basic device data;

[0090] Step 402: Generate the upper and lower price limits of the three related materials based on the material prices;

[0091] Step 403: Apply multi-condition constraints to the multiple data models by using the upper and lower limits of output and the upper and lower limits of price as constraints.

[0092] Step 404: Generate training data based on the historical production data, and perform cross-training on the multiple data models with multiple constraints based on the training data;

[0093] Step 405: Generate the optimized model based on the cross-training results.

[0094] According to embodiments of this specification, in order to obtain optimization suggestions, inputting production requirements into the optimization model to generate optimization suggestions further includes:

[0095] Obtain the production requirements of the three related materials and input the production requirements into the optimization model;

[0096] The optimization model generates the estimated production cost of the three agents and related materials, the estimated total consumption of the three agents, and the suggested purchase price based on the production demand.

[0097] The optimization recommendations are generated based on the estimated production costs of the three related materials, the estimated total consumption of the three agents, and the suggested purchase price.

[0098] Specifically, based on the results of intelligent data analysis, risk assessments are conducted, triggering corresponding early warnings. The system generates estimated production costs for the three agents and related materials, estimated total consumption of the three agents, and suggested purchase prices. Furthermore, if the intelligent data analysis identifies problems or risks at any stage of operation, it will display alerts through notifications or graphical representations. Based on the data analysis, the system provides reasons for deviations and suggests optimization directions or values, enabling enterprises to take timely and effective measures to reduce operating costs and improve economic efficiency.

[0099] For example, in practical applications, the optimization model establishment process for the use of three auxiliary agents further includes: first, collecting and organizing historical production data of materials related to the three agents in the production line; then, extracting and analyzing materials produced by similar three agents. The data comes from basic equipment collection tables, historical three agent consumption tables, three agent classification data, equipment processing volume data, material price data, and other data tables. Assume two similar equipment processes (using similar types of three agents) are extracted, which can produce two marketable materials, A and B (raw data on product quantity, raw data on price, etc.), with quantities X and Y respectively. The production process consumes three catalysts EFG (EFG is determined by material coding data, three agent classification management data (large, medium, small, etc.)). According to market calculations, the per-ton benefits of A and B are N1: 600 yuan and N2: 1000 yuan, respectively. The total purchase amount and process condition limitations are derived from the three agent container capacity data, manually corrected data, and equipment load data. The EFG limits are less than N3: 300 tons, N4: 200 tons, and N5: 360 tons, respectively.

[0100] Without altering the equipment or production process, the allocation and consumption of EFG are calculated using an optimization model to maximize efficiency. The results are then fed back for use in consumption planning, or the corresponding amount of EFG can be directly added to existing inventory for processing. Based on the equipment model, the consumption C of output materials A and B, and the processing quantity D are obtained within the time range Q. The average value Cn / Dn is taken, and the unit consumption is calculated as follows: E unit consumption N6:10 at T1, E unit consumption N7:4 at T2, F unit consumption N8:5 at T1, F unit consumption N8:4 at T2, G unit consumption N10:4 at T1, and G unit consumption N11:9 at T2. The raw data comes from historical consumption data of the three agents, classification management data of the three agents, and basic equipment data.

[0101] Using the principles of linear programming, establish multiple data models:

[0102] N6*x + N7*y = <N3,L1;N8*x+N9*y=<N4,L2;N10*x+N11*y=<N5,L3;x> =0;

[0103] y>=0; T=N1*X+N2*Y;

[0104] Substitute the production demand data N1 to N11 into the following:

[0105] 10x+4y=<300, L1; 5x+4y=<200, L2; 4x+9y=<360, L3; x>=0; y>=0;

[0106] Make feasible areas, such as Figure 5As shown, a profit parallel line T = 600X + 1000Y is drawn, passing through the feasible region point M. The objective function T has the largest y-intercept. From 5x + 4y = 200 and 4x + 9y = 360, the T point obtained using linear planning is (12.4, 34.4), and the optimal profit T is calculated. Finally, the EFG usage is obtained as follows: E = 10 * 12.4 + 4 * 34.4 = 261.6; F = 5 * 12.4 + 4 * 34.4 = 199.6; G = 4 * 12.4 + 9 * 34.4 = 359.2. Then, the three catalysts EFG are planned or actually filled and procured according to the above quantities.

[0107] Simultaneously, the procurement price budget can be further combined with a price comparison model. Based on historical procurement data from other companies and supplier data, the EFG procurement prices of different suppliers can be obtained. Then, the total consumption cost of the three auxiliary agents can be optimized by extracting the previous period's cost of the three auxiliary agents and the "theoretical optimal cost" and conducting horizontal comparisons with different companies and vertical comparisons within the same company. The final optimization suggestions are based on theoretical data and may deviate due to on-site process conditions, transportation conditions, and customer conditions. The model also considers the comparative analysis of calculated and actual data, and imposes proportional limits on the maximum data fluctuation to improve data accuracy and foresight. This achieves strict and refined control over the three agents, improves benchmarking, reduces material costs, and enables business monitoring and early warning of the three agents data, yielding significant results in practical applications.

[0108] Based on the same inventive concept, embodiments of this specification also provide a device for comprehensive analysis and optimization of three-dose usage data, such as... Figure 6 As shown, the device includes:

[0109] The data classification module 601 is used to acquire historical production data of the three agents-related materials in the production line and classify the historical production data according to the categories of the three agents.

[0110] The model building module 602 is used to build multiple data models based on the historical production data, including: a price comparison model, a cost model, and a device model.

[0111] The optimization suggestion generation module 603 is used to apply multiple constraints to the multiple data models and cross-generate an optimization model, and input production requirements into the optimization model to generate optimization suggestions.

[0112] like Figure 7 The diagram shown is a structural schematic of a computer device according to an embodiment of this specification. The methods described in this specification can be applied to the computer device of this embodiment.

[0113] Specifically, such as Figure 7As shown, computer device 702 may include one or more processing devices 704, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 702 may also include any storage resource 706 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, storage resource 706 may include any combination of one or more of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage resource can use any technology to store information.

[0114] Furthermore, any storage resource can provide volatile or non-volatile retention of information.

[0115] Furthermore, any storage resource can represent a fixed or removable component of the computer device 702. In one case, when the processing device 704 executes associated instructions stored in any storage resource or combination of storage resources, the computer device 702 can perform any operation of the associated instructions. The computer device 702 also includes one or more drive systems 708 for interacting with any storage resource, such as a hard disk drive system, an optical disk drive system, etc.

[0116] Computer device 702 may also include an input / output module 710 (I / O) for receiving various inputs (via input device 712) and providing various outputs (via output device 714). A specific output mechanism may include a presentation device 716 and an associated graphical user interface (GUI) 718. In other embodiments, the input / output module 710 (I / O), input device 712, and output device 714 may be omitted, and the device may function solely as a computer device within a network. Computer device 702 may also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the components described above together.

[0117] Communication link 722 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0118] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0119] This specification also provides computer-readable instructions, wherein when a processor executes the instructions, the program therein causes the processor to perform the above-described method.

[0120] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0121] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the embodiments in this specification.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only 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 couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.

[0126] Furthermore, the functional units in the various embodiments of this specification 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.

[0127] 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 solutions of the embodiments of this specification, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] This specification describes the principles and implementation methods of the embodiments using specific examples. The above descriptions of the embodiments are only for the purpose of helping to understand the methods and core ideas of the embodiments in this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments in this specification. Therefore, the content of this specification should not be construed as a limitation on the embodiments in this specification.

Claims

1. A method for comprehensive analysis and optimization of three-agent usage data, characterized in that, The method includes: Obtain historical production data of the three agents and related materials in the production line, and classify the historical production data according to the categories of the three agents; Multiple data models are established based on the historical production data, including: a price comparison model, a cost model, and a device model. The multiple data models are subjected to multiple constraints and cross-generate an optimization model. Production requirements are then input into the optimization model to generate optimization suggestions.

2. The method for comprehensive analysis and optimization of three-agent usage data according to claim 1, characterized in that, The historical production data includes at least: basic equipment data, historical consumption data of the three agents in the equipment, classification data of the three agents, processing volume data of the equipment, and material prices.

3. The method for comprehensive analysis and optimization of three-agent usage data according to claim 2, characterized in that, The classification of the historical production data according to the categories of the three agents further includes, The historical production data of the three-agent related materials are preprocessed, and the data of each three-agent related material is input into a data matrix according to the type of three-agent used in the three-agent related materials, and classified by K-means clustering method.

4. The method for comprehensive analysis and optimization of three-agent usage data according to claim 2, characterized in that, The establishment of multiple data models based on the historical production data further includes, The device model is generated based on the basic device data in the historical production data. The cost model is generated based on the historical consumption data of the three agents and the processing volume data of the unit in the historical production data. The price comparison model is generated based on the material prices in the historical production data.

5. The method for comprehensive analysis and optimization of three-agent usage data according to claim 4, characterized in that, The process of applying multiple constraints to the data models and generating an optimized model through cross-constraints further includes... The upper and lower limits of the production of the three-agent related materials are generated based on the data from the basic equipment. Generate the upper and lower price limits for the three related materials based on the material prices; The upper and lower limits of output and the upper and lower limits of price are used as constraints to impose multi-condition constraints on the multiple data models.

6. The method for comprehensive analysis and optimization of three-agent usage data according to claim 5, characterized in that, The process of applying multiple constraints to the data models and generating an optimized model through cross-constraints further includes... Training data is generated based on the historical production data, and the multiple data models with multiple conditions and constraints are cross-trained based on the training data. The optimized model is generated based on the cross-training results.

7. The method for comprehensive analysis and optimization of three-agent usage data according to claim 1, characterized in that, The process of inputting production requirements into the optimization model to generate optimization suggestions further includes... Obtain the production requirements of the three related materials and input the production requirements into the optimization model; The optimization model generates the estimated production cost of the three agents and related materials, the estimated total consumption of the three agents, and the suggested purchase price based on the production demand. The optimization recommendations are generated based on the estimated production costs of the three related materials, the estimated total consumption of the three agents, and the suggested purchase price.

8. The method for comprehensive analysis and optimization of three-agent usage data according to claim 1, characterized in that, The three agents include a catalyst, an additive, and a solvent.

9. A device for comprehensive analysis and optimization of three-agent usage data, characterized in that, The device includes: The data classification module is used to acquire historical production data of the three agents-related materials in the production line and classify the historical production data according to the categories of the three agents. The model building module is used to build multiple data models based on the historical production data, including: a price comparison model, a cost model, and a device model. The optimization suggestion generation module is used to apply multiple constraints to the multiple data models and cross-generate an optimization model, and input production requirements into the optimization model to generate optimization suggestions.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 8.

Images