Mathematical model acquisition method, related equipment and operation planning optimization method

Abstract

The embodiment of the invention discloses a mathematical model obtaining method and device and an operation planning optimization method, the method can use an artificial intelligence technology to obtain a mathematical model corresponding to an operation planning optimization problem, and the method comprises the following steps: outputting at least one problem, and obtaining an answer of each problem, the answer of each question is used for obtaining first description information, the first description information is description information used for describing an operation planning optimization question, and the first question is a question adopted for obtaining the description information of the operation planning optimization question; first information is obtained based on the first description information, the first information is input into a machine learning model, a mathematical model is obtained, the mathematical model is used for solving the operation planning optimization problem, and the mathematical model comprises an objective function and constraint conditions. Through the scheme, the manpower cost consumed in the process of obtaining the mathematical model is greatly reduced, and the method can be adapted to obtain mathematical models in various fields and has high generalization.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a method for acquiring a mathematical model and related equipment and an operations research optimization method. Background Art

[0002] Operational optimization problems refer to making the best decision that meets business objectives while considering certain constraints. When solving such problems in practice, it is necessary to establish a mathematical model corresponding to the operational optimization problem and solve the aforementioned mathematical model through a computer.

[0003] At present, in order to obtain the mathematical model corresponding to the operations research optimization problem, specifically, all operations research optimization problems can be divided into multiple fields, that is, multiple fields to which the mathematical model to be established can belong are obtained. In each field included in the aforementioned multiple fields, all objective functions of the field, the text description corresponding to each objective function, all constraints of the field and the text description corresponding to each constraint are abstracted. After obtaining the text description corresponding to the operations research optimization problem input by the user, the text description input by the user is matched with the text description of each objective function for similarity, and the text description input by the user is matched with the text description of each constraint for similarity, so as to obtain an objective function and at least one constraint condition that meet the similarity conditions with the text description input by the user, and the mathematical model corresponding to the operations research optimization problem includes the aforementioned objective function and constraints.

[0004] However, since the manpower cost of generating "all objective functions, text descriptions corresponding to each objective function, all constraints, and text descriptions corresponding to each constraint" corresponding to a single field is very high, the manpower cost of establishing "all objective functions, text descriptions corresponding to each objective function, all constraints, and text descriptions corresponding to each constraint" corresponding to multiple fields is even higher, which means that the generalization of this solution is relatively poor. Summary of the invention

[0005] The embodiments of the present application provide a method for obtaining a mathematical model and related equipment as well as an operations optimization method. In this solution, for different fields, there is no need to pre-generate multiple objective functions, text descriptions corresponding to each objective function, multiple constraints and text descriptions corresponding to each constraint in each field, which greatly reduces the manpower cost consumed in the process of obtaining the mathematical model, and can be adapted to obtain mathematical models in various fields, with high generalization. In addition, in this solution, the descriptive information is obtained in the form of answers to at least one of the aforementioned questions, which is conducive to obtaining more accurate descriptive information for the operations optimization problem, and further helps to improve the adaptability between the mathematical model output by the machine learning model and the operations optimization problem, that is, it is conducive to obtaining a more accurate mathematical model.

[0006] The embodiments of the present application provide the following technical solutions:

[0007] In the first aspect, the embodiment of the present application provides a method for obtaining a mathematical model, which can use artificial intelligence technology to obtain a mathematical model corresponding to an operations optimization problem. The method includes: in order to obtain the description information of the first operations optimization problem, the first device can output at least one first question to the user, use the content of the user's reply to each of the at least one first question as at least one answer, and obtain the at least one answer. The answer to each of the at least one question is used to obtain the first description information, and the first description information is the description information used to describe the first operations optimization problem. The first device determines the first information based on the first description information, and then inputs the first information into a machine learning model (for the convenience of description, it is referred to as the "first machine learning model" below) to obtain a first mathematical model, and the first mathematical model includes an objective function and constraints. Among them, the first mathematical model is generated by using the first machine learning model in order to solve the first operations optimization problem, that is, the solution result of the first mathematical model can solve the first operations optimization problem.

[0008] In this application, operations research optimization problems can be replaced by other descriptions, such as mathematical programming problems and decision optimization problems.

[0009] Exemplarily, the first information may include the first description information and other information, or the first information and the first description information may include the same information; Exemplarily, the other information may include prompt information, for example, the prompt information may include "please generate a mathematical model corresponding to the operations optimization problem based on the subsequent description information", and for example, the prompt information may include "please generate a mathematical model based on the first description information, the first description information includes:", etc. The prompt information may also be other information, etc. The first device may be a client device, for example, the client device may be a terminal device or an edge device; or the first device may also be a cloud device, for example, the cloud device may be a server or a server cluster, etc.

[0010] For example, when the first device is a client device, if the first machine learning model is not deployed in the first device, then “the first device inputs the first information into the machine learning model” can be understood as the first device sending the first information to the device on which the first machine learning model is deployed, and “the first device obtains the first mathematical model” can be understood as the first device receiving the first mathematical model sent by the device on which the first machine learning model is deployed.

[0011] In this implementation, in order to obtain the descriptive information of the operations optimization problem, at least one problem is abstracted, and the answer to the at least one problem is obtained. After the first information is obtained based on the first descriptive information, the first information is input into the machine learning model to obtain the first mathematical model corresponding to the first operations optimization problem. In this solution, for different fields, there is no need to pre-generate multiple objective functions, text descriptions corresponding to each objective function, multiple constraints, and text descriptions corresponding to each constraint in each field, which greatly reduces the manpower cost consumed in the process of obtaining the mathematical model corresponding to the operations optimization problem, and can be adapted to obtain the mathematical models corresponding to operations optimization problems in various fields, and has high generalization. In addition, in this solution, the descriptive information of the operations optimization problem is obtained by obtaining the answer to at least one of the aforementioned questions, which to a certain extent alleviates the problem that the user cannot accurately determine the first operations optimization problem, and is conducive to obtaining more accurate descriptive information, and then is conducive to improving the adaptability between the mathematical model output by the machine learning model and the operations optimization problem, that is, it is conducive to obtaining a more accurate mathematical model.

[0012] In one possible implementation, the method also includes: the first device displays the answer options corresponding to each first question through a display screen, and when the first device obtains a selection operation input for the first answer option, the correct answer corresponding to the first answer option can be obtained, that is, the correct answer to at least one of the first questions is obtained.

[0013] In a possible implementation, after the first device outputs the first question to the user, the method further includes: the first device obtains the first answer input by the user through the text box, and then obtains a first matching result between the first answer and all correct answers to the first question; the first matching result indicates which correct answers to the first question satisfy the first matching condition between the first answer and the first answer, or the first matching result indicates the similarity between the first answer and each correct answer among all correct answers to the first question. The first device may determine the correct answer to the first question displayed to the user based on the first matching result.

[0014] In one possible implementation, a first question set is pre-stored in the first device, and the at least one first question is determined based on the pre-stored first question set. In this implementation, the pre-stored first question set can be used to determine which first questions are output to the user, which is conducive to improving the speed of the process of "determining which first questions are output", and is conducive to obtaining the first mathematical model more efficiently.

[0015] In one possible implementation, the first question set is in the form of a directed graph data, and the directed graph data indicates the order of appearance of at least two different questions in the first question set. In this implementation, the questions included in the first question set are stored in the form of a directed graph data, which can indicate the order of appearance of at least two different questions in the first question set. Since different questions in at least one question corresponding to the first operations optimization question may have a correlation relationship, the use of the directed graph data can better reflect the logical relationship between different questions, making the process of asking questions to the user more logical, and is also conducive to avoiding the output of useless questions to the user, so as to improve the user stickiness of this solution.

[0016] In one possible implementation, in order to obtain the descriptive information of the first operations research optimization problem, the first problem set used may include multiple target problems, and the aforementioned multiple target problems may include at least one of the first problems output to the user. Optionally, the aforementioned multiple target problems may also include a second problem other than the first problem, that is, the second problem is also a problem used to obtain the descriptive information of the first operations research optimization problem, and the aforementioned second problem corresponds to the second descriptive information input by the user.

[0017] In a possible implementation, before the first device outputs the first question to the user for the first time, or in the process of the first device outputting the first question to the user multiple times, the second description information input by the user can also be obtained, and the second description information carries the description information of the first operations optimization problem; then the first device can also determine which of the multiple target questions included in the first question set are carried in the second description information. Answers to the multiple target questions and the second questions that have been answered can be determined, and the aforementioned questions that have not been answered are used as the first questions that need to be output to the user. Correspondingly, the first device determines the first information based on the first description information, which may include: the first device may merge the first description information and the second description information to obtain the union of the first description information and the second description information (hereinafter referred to as "updated first description information" for the convenience of description), and then the first information can be determined based on the aforementioned updated first description information, and the first information includes the updated first description information, that is, the first information includes the aforementioned first description information and the second description information.

[0018] In a possible implementation, after outputting at least one first question to the user, the first device may also obtain second description information input by the user, and the second description information may carry description information of the first operations optimization problem; then the first device determines the first information based on the first description information, which may include: the first device determines the first information based on the first description information and the second description information; optionally, the first device may merge the first description information and the second description information to obtain updated first description information, and then determine the first information based on the aforementioned updated first description information, the first information includes the updated first description information, that is, the first information includes the aforementioned first description information and the second description information.

[0019] In one possible implementation, the method is applied to a first device, in which at least one case is stored, and the target case is any one of the at least one case, and the target case includes description information and a mathematical model corresponding to the operations optimization problem; it should be understood that the concept of the aforementioned "description information" is similar to the concept of the aforementioned "first description information", both of which are description information used to describe the operations optimization problem, and the concept of the aforementioned "mathematical model" is similar to the concept of the aforementioned "first mathematical model", except that the description information and mathematical model included in the target case are already stored in the first device, the first description information is being obtained, and the first mathematical model is required to be generated by the first machine learning model. Among them, the first device determines the first information according to the first description information, including: the first device determines at least one first case from at least one case according to the similarity between the first description information and the description information included in each case in the at least one case; and then the first device can obtain the first information according to the first description information and the at least one first case.

[0020] Among them, the similarity between the first description information and the description information included in the first case satisfies a preset condition; exemplarily, at least one first case includes K first cases, and the preset condition may include: the similarity between the first description information and the description information included in each first case is greater than or equal to a similarity threshold, and / or, the K description information included in the K first cases are the K description information most similar to the first description information among all the description information included in at least one case.

[0021] In this implementation, at least one first case that best matches the first descriptive information is obtained from at least one pre-stored case, and the similarity between the first descriptive information and the descriptive information included in each first case meets a preset condition, and then each first case and the first descriptive information are used as input to the first machine learning model, that is, each first case is used as a reference case for the first machine learning model, which is beneficial to assisting the first machine learning model to generate a more accurate first mathematical model, and is also beneficial to improving the efficiency of the first machine learning model in the process of generating the first mathematical model.

[0022] In one possible implementation, the method further includes: the first device obtains second information, the second information includes at least one of the summary and keywords of the first descriptive information; and then determines the similarity between the second information and each descriptive information included in at least one case, wherein the similarity between the second information and each descriptive information included in at least one case is used as the similarity between the first descriptive information and each descriptive information included in at least one case. In this implementation, since the first descriptive information may include text information input by the user, and the text information input by the user may carry invalid information, first obtaining the summary and / or keywords of the first descriptive information, that is, filtering out the invalid information in the first descriptive information, and then generating the similarity between the summary and / or keywords of the first descriptive information and the fourth descriptive information is conducive to obtaining more accurate similarity information, and thus conducive to obtaining a more matching first case.

[0023] In one possible implementation, the method may further include: the first device may also obtain third information corresponding to each descriptive information included in at least one case (hereinafter referred to as "fourth descriptive information" for the convenience of description), that is, obtaining at least one third information corresponding one-to-one to at least one fourth descriptive information, and each third information includes a summary and / or keywords of the fourth descriptive information.

[0024] The first device determines the similarity between the second information and each fourth descriptive information included in at least one case, including: the first device obtains initial feature information of the second information and initial feature information of each third information, and then generates third similarity information; wherein the third similarity information includes the similarity between the initial feature information of the second information and the initial feature information of each third information, and the similarity between the initial feature information of the second information and the initial feature information of each third information can be used as the similarity between the second information and each fourth descriptive information, that is, it can be used as the similarity between the first descriptive information and each fourth descriptive information.

[0025] In one possible implementation, the first device obtains the first information based on the first description information and at least one first case, which may include: the first device may merge at least one first case and the first description information to obtain the first information, that is, the first information includes the first description information and each first case; exemplarily, the first information may include the first description information and prompt information, then the first device may combine at least one first case into the aforementioned prompt information to obtain the first information.

[0026] In one possible implementation, before the first device obtains the first description information corresponding to at least one question, the method further includes: the first device can determine the field to which the mathematical model to be established belongs from at least one field to which the mathematical model to be established can belong (for the convenience of description, the "field to which the mathematical model to be established belongs" will be referred to as the "first field" hereinafter); and then determine the first field to which the mathematical model to be established belongs. The first device can pre-store at least one set of questions corresponding to the aforementioned at least one field. After determining the first field to which the mathematical model to be established belongs, a first set of questions corresponding to the first field can be determined from the at least one set of questions, and then the at least one question can be determined based on the first set of questions.

[0027] In this implementation, all operations research and optimization problems are divided into multiple fields, that is, multiple fields to which the mathematical model to be established can belong are obtained. After determining the field to which the mathematical model to be established belongs, based on the field to which the aforementioned mathematical model to be established belongs, at least one problem used to obtain detailed description information of the first operations research and optimization problem is determined, that is, in order to obtain description information of operations research and optimization problems in different fields, different problems are used. It can be seen from the aforementioned description that in this solution, more refined management is carried out on the problems used to obtain description information of operations research and optimization problems, which is conducive to obtaining more accurate description information, and further conducive to obtaining more accurate mathematical models.

[0028] In one possible implementation, the first device determines the field to which the mathematical model to be established belongs, including: the first device provides the user with multiple fields to which the operations optimization problem can belong, that is, provides the user with multiple fields to which the mathematical model to be established can belong, and after obtaining the user's selection operation input for the first field among the aforementioned multiple fields, the first field to which the mathematical model to be established belongs can be determined.

[0029] In one possible implementation, the first device determines the field to which the mathematical model to be established belongs, including: the first device obtains third description information input by the user, and based on the aforementioned third description information, uses the second machine learning model to determine the first field to which the mathematical model to be established belongs; the "third description information" can be understood as the background description information of the first operations optimization problem.

[0030] In one possible implementation, the field to which the mathematical model to be established belongs includes any of the following: the field of site selection problems, the field of scheduling problems, the field of order fulfillment, the field of supply chain, the field of packing problems, the field of transportation, the field of resource allocation, the field of revenue management, or the field of production planning problems. In this implementation, multiple fields to which the mathematical model to be established can belong are listed, which greatly expands the application scenarios of this solution and is conducive to improving the implementation flexibility of this solution.

[0031] In the second aspect, the embodiment of the present application provides a method for obtaining a mathematical model, which can use artificial intelligence technology to obtain a mathematical model corresponding to an operations optimization problem. At least one case is stored in the first device, and any one of the at least one case includes description information and a mathematical model corresponding to the operations optimization problem. In this method, the first device obtains first description information, which is description information used to describe the first operations optimization problem; according to the similarity between the first description information and each description information included in the at least one case, at least one second mathematical model is determined from at least one mathematical model included in the at least one case, the second mathematical model belongs to the first case in the at least one case, and the similarity between the first description information and the description information included in the first case meets a preset condition; according to the first description information and the at least one second mathematical model, the first information is obtained; the first information is input into the machine learning model to obtain the first mathematical model, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations optimization problem.

[0032] In one possible implementation, the first device obtains first description information, including: the first device obtains first description information corresponding to at least one first question, the first description information includes an answer to each first question in the at least one first question, and the answer to each first question in the at least one first question is used to obtain the first description information, wherein the first question is a question used to obtain the description information of the first operations optimization problem.

[0033] In the second aspect of the present application, the first device can also be used to execute the steps performed by the first device in the first aspect and various possible implementation methods of the first aspect. The specific implementation methods of the steps in the second aspect, the meanings of the terms and the beneficial effects brought about can all be referred to the first aspect and will not be repeated here.

[0034] In a third aspect, an embodiment of the present application provides a device for acquiring a mathematical model, which can use artificial intelligence technology to acquire a mathematical model corresponding to an operations optimization problem. The device for acquiring a mathematical model includes: an output module, which is used to output at least one first question; a processing module, which is used to acquire at least one answer based on at least one first question, and the at least one answer is used to obtain first descriptive information, wherein the first descriptive information is descriptive information used to describe the first operations optimization problem, and the first question is a problem used to obtain the descriptive information of the first operations optimization problem; a determination module, which is used to determine the first information based on the first descriptive information; the processing module is also used to input the first information into a machine learning model to obtain a first mathematical model, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations optimization problem.

[0035] In the third aspect of the present application, the mathematical model acquisition device is also used to execute the steps performed by the first device in the first aspect and various possible implementation methods of the first aspect. The specific implementation methods of the steps in the third aspect, the meanings of the terms and the beneficial effects brought about can all be referred to the first aspect and will not be repeated here.

[0036] In a fourth aspect, an embodiment of the present application provides a device for acquiring a mathematical model, which can use artificial intelligence technology to acquire a mathematical model corresponding to an operations optimization problem. The device for acquiring a mathematical model is applied to a first device, and at least one case is stored in the first device. Any one of the at least one case includes descriptive information and a mathematical model corresponding to the operations optimization problem. The device for acquiring a mathematical model includes: an acquisition module, which is used to acquire first descriptive information, and the first descriptive information is descriptive information used to describe the first operations optimization problem; a determination module, which is used to determine at least one second mathematical model from at least one mathematical model included in at least one case based on the similarity between the first descriptive information and each descriptive information included in at least one case, and the second mathematical model belongs to the first case in the at least one case, and the similarity between the first descriptive information and the descriptive information included in the first case meets a preset condition; a processing module, which is used to obtain first information based on the first descriptive information and the at least one second mathematical model; the processing module is also used to input the first information into a machine learning model to obtain a first mathematical model, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations optimization problem.

[0037] In the fourth aspect of the present application, the mathematical model acquisition device is also used to execute the steps performed by the first device in the second aspect and various possible implementation methods of the second aspect. The specific implementation methods of the steps in the fourth aspect, the meanings of the terms and the beneficial effects brought about can all be referred to the second aspect and will not be repeated here.

[0038] In a fifth aspect, an embodiment of the present application provides a device, including a processor and a memory, wherein the processor is coupled to the memory, the memory is used to store programs; the processor is used to execute the programs in the memory, so that the device executes the method for obtaining the mathematical model of the first aspect or the second aspect mentioned above.

[0039] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method of the first aspect or the second aspect mentioned above.

[0040] In a seventh aspect, an embodiment of the present application provides a computer program product, which includes a program. When the program is run on a computer, the computer executes the method of the first aspect or the second aspect mentioned above.

[0041] In the eighth aspect, an embodiment of the present application provides an operations research optimization method, which includes: after acquiring the first mathematical model, the second device can solve the first mathematical model to obtain a solution result of the first mathematical model, wherein the first mathematical model includes an objective function and constraints, the first mathematical model is used to solve a first operations research optimization problem, and the first mathematical model is obtained based on the method provided in the first or second aspect above.

[0042] In a ninth aspect, the present application provides a chip system, which includes a processor for supporting the implementation of the functions involved in the above aspects, for example, sending or processing the data and / or information involved in the above methods. In one possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for a terminal device or a communication device. The chip system can be composed of a chip, or it can include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the structure of the artificial intelligence main framework provided in the embodiment of the present application;

[0044] Figure 2 A system architecture diagram of a data processing system provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a method for obtaining a mathematical model provided in an embodiment of the present application;

[0046] Figure 4 Another schematic flow chart of a method for obtaining a mathematical model provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of an interface for obtaining the “field to which the mathematical model to be established belongs” provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of obtaining second prediction information by using a large model provided in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of multiple target problems in the form of a directed graph provided in an embodiment of the present application;

[0050] Figure 8 A schematic diagram of “displaying a first question” and “obtaining an answer to the first question” provided in an embodiment of the present application;

[0051] Fig. 9 A schematic diagram of obtaining first description information corresponding to at least one question provided in an embodiment of the present application;

[0052] Fig.10A schematic diagram of a flow chart for determining at least one first case from at least one case provided in an embodiment of the present application;

[0053] Fig.11 A schematic diagram of a process for obtaining a first mathematical model based on first information provided in an embodiment of the present application;

[0054] Fig.12 A schematic diagram of outputting a first mathematical model through a display screen provided in an embodiment of the present application;

[0055] Fig.13 Another schematic diagram of a method for obtaining a mathematical model provided in an embodiment of the present application;

[0056] Fig.14 A schematic diagram of a structure of a device for acquiring a mathematical model provided in an embodiment of the present application;

[0057] Fig.15 Another schematic diagram of the structure of the device for acquiring the mathematical model provided in the embodiment of the present application;

[0058] Fig.16 A schematic diagram of the structure of the device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0059] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0060] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0061] "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. For example, "send information to XX device" can be understood as the destination of the information is XX device, which can include direct transmission through the air interface, and also include indirect transmission through the air interface by other units or modules. "Receive information from YY device" can be understood as the source of the information is YY device, which can include direct reception from YY device through the air interface, and can also include indirect reception from YY device through other units or modules through the air interface. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface. In other words, sending and receiving can be carried out between devices or within the device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, a wiring or an interface. It is understandable that the information may be processed as necessary between the source and the destination of the information transmission, such as encoding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood in a similar way and will not be repeated.

[0062] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated; it may also be possible to indicate only a part of the information to be indicated, while the other part of the information to be indicated is known or agreed in advance, for example, the indication of specific information can be realized by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0063] First, the overall workflow of the artificial intelligence system is described. Figure 1 , Figure 1The figure shows a structural diagram of the main framework of artificial intelligence. The following is an explanation of the above artificial intelligence theme framework from the two dimensions of "intelligent information chain" (horizontal axis) and "IT value chain" (vertical axis). Among them, the "intelligent information chain" reflects a series of processes from data acquisition to processing. For example, it can be a general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, intelligent execution and output. In this process, the data has undergone a condensation process of "data-information-knowledge-wisdom". The "IT value chain" reflects the value that artificial intelligence brings to the information technology industry from the underlying infrastructure of human intelligence, information (providing and processing technology implementation) to the industrial ecology process of the system.

[0064] (1) Infrastructure

[0065] The infrastructure provides computing power support for the artificial intelligence system, enables communication with the outside world, and is supported by the basic platform. It communicates with the outside world through sensors; computing power is provided by smart chips, which can specifically use hardware acceleration chips such as central processing units (CPU), embedded neural network processing units (NPU), graphics processing units (GPU), application specific integrated circuits (ASIC) or field programmable gate arrays (FPGA); the basic platform includes distributed computing frameworks and networks and other related platform guarantees and support, which can include cloud storage and computing, interconnected networks, etc. For example, sensors communicate with the outside world to obtain data, and these data are provided to the smart chips in the distributed computing system provided by the basic platform for calculation.

[0066] (2) Data

[0067] The data on the upper layer of the infrastructure is used to represent the data sources in the field of artificial intelligence. The data involves graphics, images, voice, text, and IoT data of traditional devices, including business data of existing systems and perception data such as force, displacement, liquid level, temperature, and humidity.

[0068] (3) Data processing

[0069] Data processing usually includes data training, machine learning, deep learning, search, reasoning, decision-making and other methods.

[0070] Among them, machine learning and deep learning can symbolize and formalize data for intelligent information modeling, extraction, preprocessing, and training.

[0071] Reasoning refers to the process of simulating human intelligent reasoning in computers or intelligent systems, using formalized information to perform machine thinking and solve problems based on reasoning control strategies. Typical functions are search and matching.

[0072] Decision-making refers to the process of making decisions after intelligent information is reasoned, usually providing functions such as classification, sorting, and prediction.

[0073] (4) General ability

[0074] After the data has undergone the data processing mentioned above, some general capabilities can be further formed based on the results of the data processing, such as an algorithm or a general system, for example, translation, text analysis, computer vision processing, speech recognition, image recognition, etc.

[0075] (5) Smart products and industry applications

[0076] Smart products and industry applications refer to the products and applications of artificial intelligence systems in various fields. They are the encapsulation of the overall artificial intelligence solution, which productizes intelligent information decision-making and realizes practical applications. Its application areas mainly include: smart terminals, smart manufacturing, smart transportation, smart homes, smart medical care, smart security, autonomous driving, smart cities, etc.

[0077] The method provided in the present application can be applied to various application fields of artificial intelligence technology. Specifically, it can be used to automatically generate mathematical models corresponding to operational optimization problems when there are operational optimization problems in various application fields. The method for obtaining the mathematical model provided in the present application is used to automatically generate mathematical models corresponding to operational optimization problems using machine learning models.

[0078] Exemplarily, the above mathematical model may include an objective function and constraints, and the aforementioned mathematical model may also include variables involved in the objective function and the constraints. In order to further understand the relationship between "operational optimization problem" and "mathematical model", the following is an example of a specific scenario. Here, the operation optimization problem is taken as an example of a production planning problem. The production planning problem is also called a high-level scheduling problem, which is a problem faced by a manufacturing enterprise when carrying out production and processing. When an enterprise is producing, it needs to decide how to optimally use resources to meet customer needs. The main task is to match supply with demand, and output the recommended processing decisions for each factory every day within a period of time, the recommended transportation decisions between factories, and the delivery decisions of demand. When making decisions, the goals are to maximize the delivery level and minimize the cost, and consider constraints such as production capacity not exceeding the upper limit, inventory not exceeding the upper limit, and raw materials being substitutable.

[0079] For example, the key components (raw materials) involved in the generation planning problem (i.e., a type of operations optimization problem) include structural parts 1 and structural parts 2; there are three demand codes corresponding to the generation planning problem, namely inverter A, inverter B, and inverter C; the demand corresponding to the generation planning problem includes 50 inverter A, 100 inverter B, and 150 inverter C, the virtual profit of inverter A is 1.3, the virtual profit of inverter B is 2.1, and the virtual profit of inverter C is 1.8. Among them, the inventory of structural parts 1 is 100 pieces, and the inventory of structural parts 2 is 90 pieces. Structural parts 1 can be used to produce inverter A and inverter B. Two structural parts 1 are required to process a unit of inverter A or inverter B; one structural part 2 is required to process a unit of inverter C, and structural part 2 can replace structural part 1, but structural part 1 cannot replace structural part 2. How should we decide the production quantity of inverters A, B, and C and the allocation of structural parts 1 and 2 so as to maximize the total virtual profit of the satisfied demand?

[0080] The mathematical model corresponding to the above generation plan problem can be as follows:

[0081] variable:

[0082] (Production quantity of inverter A) x

[0083] (Production quantity of inverter B)y

[0084] (Production quantity of inverter C)

[0085] (Number of replacements of structural component 2 for structural component 1)r

[0086] Objective function:

[0087] (Maximization of total virtual benefits) Maximization: 1.3x+2.1y+1.8z

[0088] Constraints:

[0089] (Maximum production quantity constraint of inverter A) x <= 50

[0090] (Maximum production quantity constraint of inverter B) y<=100

[0091] (Maximum production quantity constraint of inverter C) z <= 150

[0092] (The consumption quantity of structural part 1 cannot exceed the inventory) 2x+2y-r<=100

[0093] (The consumption quantity of structural part 2 cannot exceed the inventory) z+r<=90

[0094] (non-negative integer decision variable constraints)x,y,z,r\in\mathbb_{N}

[0095] It should be noted that the above examples of "operational optimization problems" and "mathematical models" are only for the convenience of understanding this solution and are not used to limit this solution. Since the method provided by this application can be used in intelligent manufacturing, intelligent transportation or other fields, the following examples are given of application scenarios in multiple application fields of this application.

[0096] Application field 1: Intelligent manufacturing

[0097] Exemplarily, there may be production planning problems in the field of intelligent manufacturing. For explanations and examples of generation planning problems, please refer to the above description, which will not be repeated here. The method provided in this application can be used to obtain a mathematical model corresponding to the generation planning problem in the field of intelligent manufacturing.

[0098] Application field 2: Intelligent transportation field

[0099] For example, in the field of intelligent transportation, traffic signals in a transportation network can be optimized in order to improve the efficiency of the transportation system while ensuring the safety of the transportation system. There may be operations research optimization problems corresponding to traffic signals, and the method provided in the present application can be used to obtain a mathematical model corresponding to the operations research optimization problem corresponding to traffic signals.

[0100] It should be noted that the method provided in the present application can also be applied to other application scenarios. The above examples of various application scenarios of the present application are only for the convenience of understanding the present solution and are not used to limit the present solution.

[0101] Before describing the method provided in this application in detail, please refer to Figure 2 , Figure 2 A system architecture diagram of a data processing system provided in an embodiment of the present application, in Figure 2In the embodiment, the data processing system 200 includes a training device 210 , a database 220 , an execution device 230 , a data storage system 240 and a client device 250 , and the execution device 230 includes a computing module 231 .

[0102] Among them, a training data set is stored in the database 220. In the training stage of the first machine learning model 201, the training device 210 generates the first machine learning model 201, and uses the training data set to iteratively train the first machine learning model 201 to obtain the first machine learning model 201 that has performed the training operation. The first machine learning model 201 can be specifically expressed as a neural network, or it can be expressed as a non-neural network model. In the embodiments of the present application, only the first machine learning model 201 expressed as a neural network is used as an example for explanation. Furthermore, when the first machine learning model 201 is expressed as a neural network, the first machine learning model 201 can be a large model, or it can also be other types of neural networks, etc., which are not limited in the embodiments of the present application.

[0103] The first machine learning model 201 that has performed the training operation obtained by the training device 210 can be deployed in the computing module 231 of the execution device 230. The execution device 230 can call the data, code, etc. in the data storage system 240, or store the data, instructions, etc. in the data storage system 240. The data storage system 240 can be placed in the execution device 230, or the data storage system 240 can be an external memory relative to the execution device 230. It should be noted that in the application stage of the first machine learning model 201, the first machine learning model 201 is used to generate a mathematical model corresponding to the operations optimization problem. The concepts of "operations optimization problem" and "mathematical model" can be referred to the above examples, and will not be described in detail here.

[0104] In some embodiments of this application, please refer to Figure 2 The execution device 230 and the client device 250 are independent devices. The execution device 230 is configured with an input / output (I / O) interface to exchange data with the client device 250. After obtaining the first description information corresponding to at least one problem, the client device 250 can obtain the first information based on the first description information, and then send the first information to the execution device 230 through the I / O interface. The first description information includes the description information of the first operations optimization problem; after receiving the first information, the execution device 230 can generate a first mathematical model corresponding to the first information through the first machine learning model 201 in the calculation module 231, and then send the aforementioned first mathematical model to the client device 250 through the I / O interface.

[0105] For example, the client device 250 can be a terminal device or an edge device, and the execution device 230 can be a cloud device, such as a server or a server cluster, etc. The product form of the client device 250 and the execution device 230 is not limited in the embodiments of the present application.

[0106] It is worth noting that Figure 2 It is only an architectural diagram of two data processing systems provided by an embodiment of the present invention, and the positional relationship between the devices, components, modules, etc. shown in the figure does not constitute any limitation. For example, in other embodiments of the present application, the execution device 230 and the client device 250 may be integrated in the same device, and the user can interact directly with the execution device 230. Exemplarily, when the client device 250 is a mobile phone or a tablet, the execution device 230 may be a module in the host processor (Host CPU) of the mobile phone or tablet that uses the first machine learning model to perform data processing, and the execution device 230 may also be a neural network processor (NPU) in the mobile phone or tablet. The NPU is mounted on the host processor as a coprocessor, and the tasks are assigned by the host processor.

[0107] For details, please refer to Figure 3 , Figure 3 A schematic diagram of a method for obtaining a mathematical model corresponding to an operations optimization problem provided in an embodiment of the present application. In 301, a first device outputs at least one first question, obtains at least one answer based on the at least one first question, and the at least one answer is used to obtain first description information, wherein the first description information is description information used to describe the first operations optimization problem, and the first problem is a problem used to obtain the description information of the first operations optimization problem.

[0108] In the embodiment of the present application, for example, the first device may be Figure 2 In the client device 250, in order to obtain detailed first description information of the first operations optimization problem, the first device can first determine at least one question used to obtain the description information of the first operations optimization problem (hereinafter referred to as the "target question" for the convenience of distinction), and then determine at least one question output to the user (hereinafter referred to as the "first question" for the convenience of distinction), use the user's reply to each of the at least one first question as at least one answer, and obtain the at least one answer.

[0109] For example, when the operations optimization problem is a production scheduling problem, the at least one target problem may include: Is the demand delivery mode back order or lost sale? Is the generated structure single-layer or multi-layer? Is it necessary to produce one item or multiple items or other issues, etc. For another example, when the operations optimization problem is a factory scheduling problem, the at least one target problem may include: How many machines are there? What is the relationship between tasks and machines or other issues, etc. It should be noted that the examples given here in conjunction with specific operations optimization problems for the at least one target problem are only for the convenience of understanding this solution. The at least one target problem may also be oriented to any operations optimization problem. The specific setting of the at least one target problem can be flexibly determined in combination with the actual application scenario, and is not limited in the embodiments of the present application.

[0110] 302. The first device determines first information according to the first description information.

[0111] In an embodiment of the present application, the first information may include the first description information and other information, or the first information and the first description information may include the same information; exemplarily, the other information may include prompt information, for example, the prompt information may include "Please generate a mathematical model corresponding to the operations optimization problem based on the subsequent description information", and for example, the prompt information may include "Please generate a mathematical model corresponding to the operations optimization problem based on the first description information, the first description information includes: " and so on. The prompt information may also be expressed as other information, etc.

[0112] 303. The first device inputs the first information into the machine learning model to obtain a first mathematical model, where the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

[0113] In an embodiment of the present application, the above-mentioned machine learning model (hereinafter referred to as the "first machine learning model" for the convenience of distinction) can be a large model. For example, the large model can be specifically expressed as a Pangu large model or other types of large models, etc.; or the first machine learning model can also be other forms of neural networks. For example, the first machine learning model can be a neural network based on an attention mechanism, etc., which is not limited in the present application.

[0114] The first mathematical model is a mathematical model corresponding to the first operations optimization problem generated by the first machine learning model. The first mathematical model is generated by the aforementioned first machine learning model in order to solve the first operations optimization problem, that is, the solution result of the first mathematical model can solve the aforementioned first operations optimization problem. Exemplarily, the first mathematical model includes an objective function and constraints corresponding to the first operations optimization problem. Optionally, the first mathematical model may also include variables corresponding to the first operations optimization problem. For examples of the first mathematical model, please refer to the above description, and no examples are given here.

[0115] In one case, "the first device inputs the first information into the first machine learning model" can be understood as the first device sending the first information to the device deploying the first machine learning model; "the first device obtains the first mathematical model corresponding to the first operations optimization problem" can be understood as the first device receiving the first mathematical model sent by the device deploying the first machine learning model.

[0116] In another case, step 303 can be understood as the first device inputting the first information into a locally deployed first machine learning model to obtain a first mathematical model output by the first machine learning model.

[0117] It should be noted that the “operations optimization problem” in this application may also be referred to as a “decision-making optimization problem” or other names, and the “mathematical model” in this application may also be referred to as a “mathematical programming model” or other names.

[0118] In an embodiment of the present application, in order to obtain descriptive information of an operations optimization problem, at least one problem is abstracted, and an answer to the at least one problem is obtained. After obtaining the first information based on the first descriptive information, the first information is input into the machine learning model to obtain a first mathematical model corresponding to the first operations optimization problem. In this solution, for different fields, there is no need to generate multiple objective functions, text descriptions corresponding to each objective function, multiple constraints, and text descriptions corresponding to each constraint in advance in each field, which greatly reduces the manpower cost consumed in the process of obtaining the mathematical model corresponding to the operations optimization problem, and can be adapted to obtain mathematical models corresponding to operations optimization problems in various fields, and has high generalization. In addition, in this solution, the descriptive information of the operations optimization problem is obtained by obtaining the answer to the aforementioned at least one question, which to a certain extent alleviates the problem that the user cannot accurately determine the first operations optimization problem, and is conducive to improving the adaptability between the mathematical model output by the machine learning model and the operations optimization problem, that is, it is conducive to obtaining a more accurate mathematical model.

[0119] In combination with the above description, the specific implementation process of the method provided in the embodiment of the present application is described in detail below. Figure 4 , Figure 4 Another flowchart of a method for obtaining a mathematical model provided in an embodiment of the present application is provided. The method for obtaining a mathematical model provided in an embodiment of the present application may include:

[0120] 401. Determine the field to which the mathematical model to be established belongs.

[0121] In the embodiment of the present application, step 401 is an optional step. For example, Figure 4 The first device in the corresponding embodiment may be Figure 2 The client device 250 shown in FIG. Before executing the step of generating the mathematical model corresponding to the first operations optimization problem, the first device may first determine the field to which the mathematical model to be established belongs (hereinafter referred to as the "first field" for the convenience of description), and then determine which questions (hereinafter referred to as the "target question" for the convenience of description) need to be answered according to the first field to which the mathematical model to be established belongs, so as to obtain detailed first description information of the first operations optimization problem; it should be understood that the relationship between the two concepts of "first description information" and "first operations optimization problem" can be referred to the above description, and will not be repeated here. Since the establishment of the mathematical model is used to solve the first operations optimization problem, the "field to which the mathematical model to be established belongs" can also be understood as the field to which the first operations optimization problem belongs.

[0122] Exemplarily, the first field to which the mathematical model to be established belongs can be any of the following: location problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource assignment field, revenue management field, production planning problem field or other fields, etc. It should be noted that the specific fields to which the mathematical model to be established can belong can be flexibly determined in combination with the actual application scenario. The examples given here are only for the convenience of understanding the concept of "the field to which the mathematical model to be established belongs" and are not used to limit this solution. The multiple fields to which the mathematical model to be established can belong are listed, which greatly expands the application scenarios of this solution and is conducive to improving the implementation flexibility of this solution.

[0123] For example, the "site selection problem domain" mainly involves selecting one or more optimal locations from a given set of candidate locations to meet specific goals and constraints. The site selection problem domain can include multiple sub-problems, such as facility site selection problems, warehouse site selection problems, network site selection problems, or other site selection problems. Exemplarily, the facility site selection problem refers to the need to determine which facility locations should be opened to meet the demand and minimize the overall cost or maximize the overall benefit given a set of potential facility locations and a set of demand points. This type of problem is common in application areas such as retail and logistics.

[0124] The "scheduling problem domain" refers to the reasonable arrangement of the order and time of tasks or work to maximize efficiency or meet specific constraints under limited resources. The scheduling problem domain can include multiple sub-problems, such as job scheduling problems, vehicle scheduling problems, project scheduling problems, or other scheduling problems. For example, the job scheduling problem refers to the need to determine the start time and completion time of each job given a set of jobs and a set of available resources to minimize the overall completion time or maximize the overall profit. This type of problem is common in application fields such as manufacturing and project management.

[0125] The "order fulfillment domain" refers to the reasonable arrangement of order processing and delivery given a set of orders and a set of available resources to meet customer needs and maximize efficiency. The order fulfillment problem involves many aspects, including order reception, order processing, inventory management, logistics distribution, etc. Its goal is to minimize order processing time, reduce inventory costs, and improve delivery on-time rate while meeting customer needs. In the order fulfillment problem, common challenges include order priority, resource constraints, delivery time windows, inventory management, etc. The key to solving these challenges is to reasonably arrange the order processing sequence, allocate resources, optimize logistics routes, etc. Methods and techniques in operations research can be applied to the solution of order fulfillment problems. For example, linear programming can be used to optimize resource allocation and order processing sequence, dynamic programming can be used to optimize logistics routes, and simulated annealing algorithms and genetic algorithms can be used to solve complex scheduling problems.

[0126] The "supply chain field" refers to optimizing logistics, inventory, production and other aspects of the supply chain network involving multiple links and participants through reasonable planning and decision-making to maximize the efficiency and profit of the entire supply chain. Supply chain issues involve multiple links, including suppliers, manufacturers, distributors, retailers, and logistics, inventory and order management. Its goal is to achieve the best resource allocation, inventory control, production planning and logistics distribution by optimizing the decisions of each link in the supply chain to meet customer needs and reduce costs.

[0127] The "packing problem domain" refers to putting a group of items into as few containers as possible to maximize the use of the container's space. Packing problems are often used to optimize the fields of logistics and transportation to reduce transportation costs and improve efficiency. The packing problem domain can include multiple sub-problems, such as one-dimensional packing problems and two-dimensional packing problems. Exemplarily, the one-dimensional packing problem refers to putting a group of items into containers on a straight line so that there is no overlap between the items. Each item has its own length, and the container has a certain length limit. The goal is to find a placement plan that minimizes the number of containers used. The two-dimensional packing problem refers to putting a group of items into a container on a two-dimensional plane so that there is no overlap between the items. Each item has its own length and width, and the container has a certain length and width limit. The goal is to find a placement plan that minimizes the number of containers used.

[0128] The "transportation field" refers to the problem of optimizing resource allocation and path selection in a transportation network. Operational optimization problems in the transportation field involve aspects such as traffic flow management, route planning, and traffic signal optimization, aiming to improve the efficiency and safety of the transportation system. The transportation field can include multiple sub-problems, such as traffic flow allocation problems, path selection problems, and traffic signal optimization problems. For example, the traffic flow allocation problem refers to how to reasonably allocate traffic flow to different paths to reduce congestion and improve traffic efficiency. This problem can be solved by establishing a traffic flow model and using optimization algorithms. The path selection problem refers to how to choose the best path to reach the destination in a given transportation network. This problem can be optimized by considering factors such as traffic flow, road conditions, and travel time.

[0129] The "resource allocation field" refers to how to reasonably allocate limited resources to maximize benefits or meet specific constraints. Resources can be manpower, materials, funds, equipment, etc., and the operational optimization problems in the resource allocation field can involve different fields, such as production, logistics, project management, etc. The resource allocation field can include multiple sub-problems, such as production resource allocation problems, logistics resource allocation problems, and project resource allocation problems. Exemplarily, the production resource allocation problem refers to how to reasonably allocate resources in the production process to maximize output or profit. This problem involves aspects such as production line optimization, job scheduling, and equipment configuration. The logistics resource allocation problem refers to how to reasonably allocate resources in the logistics process to minimize costs or improve efficiency. This problem involves aspects such as the transportation, warehousing, and distribution of goods. The project resource allocation problem refers to how to reasonably allocate resources in the project execution process to maximize the completion of the project or meet specific constraints. This problem involves aspects such as the allocation of project tasks, resource scheduling, and progress control.

[0130] The "revenue management field" refers to how to maximize the revenue of a company or organization, and optimize revenue through reasonable pricing, inventory management, and capacity control strategies. Revenue management problems are often applied to service industries, such as aviation, hotels, and tourism. The revenue management field can include multiple sub-problems, such as pricing problems, inventory management problems, and capacity control problems. For example, the pricing problem refers to how to determine the price of a product or service to maximize revenue, which involves factors such as market demand, competitive environment, and consumer behavior.

[0131] The "generative planning problem domain" refers to how to reasonably arrange resources and tasks in the production process to maximize production efficiency and meet customer needs. Production planning problems involve production scheduling, task allocation, resource utilization, etc., aiming to improve the efficiency and flexibility of the production system. The production planning domain can include multiple sub-problems, such as production scheduling problems, task allocation problems, and resource allocation problems. For example, the production scheduling problem refers to how to reasonably arrange the execution order and time of production tasks to minimize production time and cost. This problem involves aspects such as task priority, equipment utilization, and production line balance. The task allocation problem refers to how to reasonably allocate production tasks to different workstations or employees to maximize production efficiency and balance workloads. This problem involves aspects such as the characteristics of tasks, the capabilities of workstations, and the skills of employees. The resource allocation problem refers to how to reasonably allocate resources in the production process to maximize resource utilization and meet production needs. This problem involves aspects such as equipment scheduling, raw material procurement, and human resource allocation.

[0132] It should be noted that, in practical applications, the mathematical model to be established can also be divided into other fields, and the above explanations of the multiple problem fields to which the mathematical model to be established belongs are only for the convenience of understanding this solution and are not used to limit this solution.

[0133] The first device can implement step 401 in multiple ways. In one implementation, the first device can provide the user with multiple fields to which operations research optimization problems can belong, that is, provide the user with multiple fields to which the mathematical model to be established can belong. After obtaining the user's selection operation input for the first field among the aforementioned multiple fields, the first field to which the mathematical model to be established belongs can be determined.

[0134] For example, the first device may display multiple fields to which the operations optimization problem may belong through a display screen, and upon receiving a click operation input by the user for one of the multiple fields, it may be determined that a selection operation input by the user for the first field is obtained.

[0135] For a more intuitive understanding of this solution, please refer to Figure 5 , Figure 5A schematic diagram of an interface for obtaining the "field to which the mathematical model to be established belongs" provided in an embodiment of the present application. Figure 5 As shown, after the first device determines that a mathematical model needs to be automatically constructed for the operations optimization problem, the display screen can be used to display to the user multiple fields to which the operations optimization problem can be attributed, that is, Figure 5 The site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field, and planning problem field are shown in FIG. When the first device receives a click operation input by the user for one of the aforementioned multiple fields, it can be determined that the user's selection operation input for the first field (that is, the "planning problem field") is obtained. It should be understood that Figure 5 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0136] For another example, in another situation, the first device can play multiple fields to which operations research optimization problems can be classified to the user in the form of voice, and can obtain the selection operation input by the user in the form of voice for the first field among the multiple fields; illustratively, the first device can play in the form of voice "the fields to which operations research optimization problems belong include: site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field and production planning problem field. For which field of operations research optimization problems do you want to generate a mathematical model?" It should be understood that the examples given here are not used to limit this solution.

[0137] In another case, the first device can display the multiple fields to which the operations research optimization problem can be attributed through the display screen, and also play the multiple fields to which the operations research optimization problem can be attributed to the user in the form of voice. After obtaining the user's click operation input for one of the aforementioned multiple fields, the first field to which the mathematical model to be established belongs can be determined, and so on. It should be noted that the first device can also use other methods to achieve "providing the user with multiple fields to which the operations research optimization problem can be attributed, and then obtaining the user's selection operation input for the first field among the aforementioned multiple fields". The example here is only to prove the feasibility of this solution and is not used to limit this solution.

[0138] In another implementation, the first device can obtain third description information input by the user, and based on the aforementioned third description information, use the second machine learning model to determine the first field to which the mathematical model to be established belongs; the "third description information" can be understood as the background description information of the first operations optimization problem.

[0139] Exemplarily, the first device may display a text box on a display screen, and receive the third description information in the form of text input by the user through the aforementioned text box, or the first device may accept the third description information in the form of voice input by the user, or other methods may be used to obtain the third description information, etc. The present application does not limit the form in which the first device obtains the third description information. For example, the content of the third description information may be "Please generate a mathematical model for the operations optimization problem in the supply chain field", "Operations optimization problem in the field of packing problems" or other content, etc. It should be understood that the examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0140] For example, in one case, the second machine learning model can be specifically expressed as a large model. It should be noted that when the second machine learning model is expressed as a large model, the second machine learning model and the above-mentioned first machine learning model can be the same machine learning model or different machine learning models, which is not limited in this application.

[0141] After obtaining the third description information, the first device can input multiple fields to which the operations optimization problem can be attributed and the third description information into the large model to obtain second prediction information corresponding to the third description information. The second prediction information indicates the first field corresponding to the third description information, that is, the first field corresponding to the first operations optimization problem.

[0142] Exemplarily, the first device may determine the first prompt word based on “multiple fields to which operations research optimization problems can be attributed”; exemplary, the first prompt word may include content such as “for the given background description, select a field that best matches the background description of operations research optimization problems from the field of site selection problems, scheduling problems, order fulfillment, supply chain, packing problems, transportation, resource allocation, revenue management, and production planning problems”. It should be understood that the example here is only for the convenience of understanding the concept of “first prompt word” and is not used to limit this solution.

[0143] “The first device inputs multiple fields to which the operations optimization problem can be attributed and the third description information into the second machine learning model” can be understood as the first device can send the first prompt word and the third description information to the device where the second machine learning model is deployed. After receiving the first prompt word and the third description information, the device where the second machine learning model is deployed calls the second machine learning model to process the first prompt word and the third description information to obtain the above-mentioned second prediction information output by the second machine learning model. The device where the second machine learning model is deployed sends the second prediction information corresponding to the third description information to the first device, and correspondingly, the first device can obtain the above-mentioned second prediction information corresponding to the third description information.

[0144] For a more intuitive understanding of this solution, please refer to Figure 6 , Figure 6 A schematic diagram of using a large model to obtain second prediction information provided in an embodiment of the present application. Figure 6 As shown, it is possible to base on the multiple fields to which the mathematical model to be established can belong (i.e. Figure 6 The first prompt word can be determined by the site selection problem field, scheduling problem field, etc. shown in Figure 6 The “prompt word” shown in the figure is: “For the given background description, select a field that best matches the background description of the operations optimization problem from {site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field, and production planning problem field}”. The aforementioned background description of the operations optimization problem is also the third description information.

[0145] The first prompt word and the third description information are input into the second machine learning model in the form of a large model, and the second prediction information output by the second machine learning model in the form of a large model is obtained. The second prediction information indicates the first field corresponding to the third description information, that is, the first field corresponding to the first operations optimization problem. It should be understood that Figure 6 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0146] In another case, a second machine learning model that has performed a training operation may be deployed in the first device, and the second machine learning model is a machine learning model for performing text processing tasks; illustratively, the second machine learning model is used to determine a field that best matches the input description information among multiple fields to which the operations optimization problem can be attributed. The first device can input the first description information into the second machine learning model to obtain second prediction information corresponding to the first description information output by the second machine learning model, and the second prediction information indicates the first field corresponding to the first description information.

[0147] It should be noted that the first device can also determine the field to which the mathematical model to be established belongs in other ways. The examples in the embodiments of the present application are only for the convenience of understanding the present solution and are not used to limit the present solution.

[0148] In an embodiment of the present application, all operations research and optimization problems are divided into multiple fields, that is, multiple fields to which the mathematical model to be established can belong are obtained. After determining the field to which the mathematical model to be established belongs, based on the field to which the aforementioned first operations research and optimization problem belongs, at least one problem used to obtain detailed description information of the first operations research and optimization problem is determined, that is, in order to obtain description information of operations research and optimization problems in different fields, different problems are used. It can be seen from the aforementioned description that in this solution, more refined management of the problems used to obtain description information of operations research and optimization problems is carried out, which is conducive to obtaining more accurate description information, and further conducive to obtaining more accurate mathematical models.

[0149] 402. Output at least one first question, and obtain at least one answer based on the at least one first question, where the at least one answer is used to obtain first description information, wherein the first description information is description information used to describe the first operations research optimization problem, and the first question is a question used to obtain the description information of the first operations research optimization problem.

[0150] In the embodiment of the present application, step 402 is an optional step. It should be understood that the first description information is the description information used to describe the first operations optimization problem, and the first mathematical model is used to solve the first operations optimization problem. In the present application, the first description information is used to replace the specific first operations optimization problem in the prior art, which alleviates the problem that the user cannot accurately determine the first operations optimization problem to a certain extent. Before the first device executes the step of generating the mathematical model corresponding to the first operations optimization problem, in order to obtain the detailed first description information of the first operations optimization problem, the answer to each target problem in at least one problem corresponding to the first operations optimization problem (for the convenience of description, it is referred to as "target problem" later), the first description information may include the answer to the aforementioned at least one target problem, and then at least one target problem corresponding to the first operations optimization problem can be determined first.

[0151] Exemplarily, the at least one target question may include at least one first question output to the user, and optionally, the at least one target question may also include a second question; Exemplarily, the first question refers to a question output to the user by the first device, and the second question refers to other questions in the at least one target question other than the first question. For example, the answer to the second question may be obtained based on the second description information actively input by the user to the first device.

[0152] Regarding the specific implementation method of determining at least one problem corresponding to the first operations optimization problem by the first device, since step 401 is an optional step, in one implementation method, if step 401 is executed, the first device can determine at least one target problem corresponding to the first field to which the first operations optimization problem belongs based on the first field obtained in step 401, that is, obtain at least one target problem corresponding to the first operations optimization problem.

[0153] In one case, at least one problem set may be stored in the first device, and the aforementioned at least one problem set includes a problem set corresponding to each field in at least one field to which the operations optimization problem can be attributed, and each problem set in the aforementioned at least one problem set includes at least one problem; optionally, the aforementioned at least one problem set corresponds one-to-one with the at least one field to which the operations optimization problem can be attributed. Then the first device can obtain a first problem set corresponding to the first field to which the first operations optimization problem belongs from the aforementioned at least one problem set, that is, there is a first problem set corresponding to the first operations optimization problem in the aforementioned at least one problem set, and the first problem set includes at least one target problem.

[0154] In another case, after determining the first domain to which the first operations research optimization problem belongs, the first device may generate a first problem set corresponding to the first domain, that is, obtain a first problem set corresponding to the first operations research optimization problem.

[0155] In another implementation, if step 401 is not performed, the first device may store a second problem set including at least one problem, or the first device may generate a second problem set including at least one problem after determining that a mathematical model corresponding to the operations optimization problem needs to be established. The second problem set may correspond to operations optimization problems belonging to any field, that is, no matter which field the first device needs to establish a mathematical model for operations optimization problems, the problems in the second problem set may be used, and the at least one problem included in the second problem set may also be understood as at least one target problem corresponding to the first operations optimization problem.

[0156] Optionally, the first question set (or the second question set) may also include a correct answer to each target question, that is, the first question set (or the second question set) may also include an answer template for each target question.

[0157] For example, the data format used by each question set in the at least one question set mentioned above can be any of the following: directed graph, list, table, undirected graph or other data forms, etc. The specific data form to be used can be flexibly determined in combination with the actual application scenario, and is not limited in the embodiments of the present application.

[0158] Exemplarily, when the first question set (or the second question set) is expressed in the data form of a directed graph, each target question in the first question set (or the second question set) is used as a point in the directed graph, and the correct answer to each target question in the first question set (or the second question set) is used as an edge in the directed graph, and the connection relationship between different points in the directed graph is determined based on the association relationship between multiple target questions in the first question set (or the second question set).

[0159] In which, when the first question set (or the second question set) adopts the data form of a directed graph, the arrows in the directed graph can indicate the appearance order between at least two different target questions among the multiple target questions when multiple target questions in the first question set (or the second question set) are provided to the user.

[0160] For a more intuitive understanding of this solution, please refer to Figure 7 , Figure 7 A schematic diagram of multiple target problems in the form of a directed graph provided in an embodiment of the present application. Figure 7 In the example, the first problem set (or the second problem set) includes seven target problems from P1 to P7. Figure 7 In the above, each target question is regarded as a point in a directed graph, and the correct answer to each target question is regarded as an edge in the directed graph.

[0161] In accordance with Figure 7 When the seven target questions P1 to P7 are presented to the user in the order indicated by the arrows, there is a constraint between the presentation order of the four target questions P1, P3, P4 and P6, there is a constraint between the presentation order of the three target questions P2, P5 and P7, and there is no constraint between the presentation order of the four target questions P1, P3, P4 and P6 and the three target questions P2, P5 and P7. Figure 7 As shown, the display order of P1 is earlier than that of P3 and P4, and the display order of P4 is earlier than that of P6. When the answer obtained for the target question P1 is the correct answer 1, P3 is triggered to be displayed to the user, and P4 and P6 are no longer required to be displayed; when the answer obtained for the target question P1 is the correct answer 2, P4 is triggered to be displayed to the user, and P3 is no longer required to be displayed. The display order of P2 needs to be earlier than that of P5, and the display order of P5 needs to be earlier than that of P7. It should be understood that Figure 7 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0162] In an embodiment of the present application, a first question set is pre-stored, and the pre-stored first question set can be used to determine which first questions are output to the user, which is conducive to improving the speed of the process of "determining which first questions are output" and is conducive to more efficiently obtaining a mathematical model corresponding to the operations optimization problem.

[0163] Using the data form of a directed graph to store the questions included in the first question set can indicate the order of appearance between at least two different questions in the first question set. Since different questions in at least one question corresponding to the first operations optimization problem may have a correlation relationship, using the data form of a directed graph can better reflect the logical relationship between different questions, making the process of asking questions to the user more logical, and is also beneficial to avoid outputting useless questions to the user, so as to improve the user stickiness of this solution.

[0164] Exemplarily, step 402 may include: the first device may output the first question to the user, and then determine the content of the user's reply to each of the at least one first question as at least one answer, and determine the first description information according to the aforementioned at least one answer. Optionally, if the first device also obtains the second description information input by the user, and the second description information carries the description information of the first operations optimization problem; then the first device may also determine which answers of the second questions of the at least one target question are carried in the second description information, and then determine which first questions to output to the user according to the at least one target question and the aforementioned second question to which the answer has been obtained.

[0165] Furthermore, in one implementation, after the first device has determined the first problem set (or second problem set) corresponding to the first operations optimization problem, that is, after determining at least one target problem included in the first problem set (or second problem set), it can output at least one first question to the user for the first time to obtain an answer input by the user for each first question output for the first time.

[0166] Exemplarily, the specific implementation method of the above-mentioned "outputting the first question to the user" can be through display screen, voice playback or other output methods, etc. The specific output method can be flexibly determined in combination with the actual application scenario, and is not limited in this application. In the embodiment of this application, only "display through the display screen" is used as an example to illustrate the detailed time process of "displaying the first question and obtaining the answer to the first question". For the specific implementation methods using other output methods, please refer to it for understanding, and they will not be described one by one in the embodiment of this application.

[0167] In one case, if the data format used by the first question set (or the second question set) is a directed graph, the first device can determine each first question to be displayed to the user for the first time through the display screen according to the appearance order indicated by the directed graph. Alternatively, when the data format used by the first question set (or the second question set) is other data formats that can indicate the appearance order between at least two different target questions, the first device can also determine at least one first question to be displayed to the user for the first time based on the appearance order indicated by other data formats.

[0168] In order to understand this solution more intuitively, Figure 7 For example, at least one first question displayed to the user for the first time through the display screen may include two target questions P1 and P2, or at least one first question displayed to the user through the display screen may be a single target question P1, or at least one first question displayed to the user through the display screen may be a single target question P2. It should be understood that the combination of Figure 7 The examples are only provided to facilitate understanding of the present invention and are not intended to limit the present invention.

[0169] In another case, if the data format of the first question set (or the second question set) is a list, a table, an undirected graph, or other data format that does not indicate the order of appearance between different target questions, at least one first question displayed to the user for the first time may include any one or more target questions in the first question set (or the second question set).

[0170] Optionally, in one scenario, if the first device obtains the second description information input by the user before determining the first question to be displayed to the user for the first time, and the second description information carries the description information of the first operations optimization problem; then the first device can determine which of the second questions among the at least one target question are carried in the second description information, and then determine the first question to be displayed to the user for the first time based on the at least one target question and the aforementioned second question. And / or, in another scenario, during the process of the first device displaying the first question to the user multiple times, the first device obtains the second description information input by the user, then the first device can also determine which of the second questions among the at least one target question are carried in the second description information, and then determine the first question to continue to be displayed to the user based on the at least one target question and the aforementioned second question.

[0171] Specifically, the first device may use a variety of implementation methods to implement "outputting the first question and obtaining the answer to the first question". Exemplarily, in one implementation method, the first device not only displays each first question to the user through a display screen, but also displays the answer options corresponding to each first question. When the first device obtains the selection operation input by the user through the display screen for a certain answer option (hereinafter referred to as the "first answer option" for the convenience of description), the correct answer corresponding to the aforementioned first answer option can be obtained, that is, the correct answer to a certain first question (hereinafter referred to as the "third question" for the convenience of distinction) among all the first questions displayed for the first time is obtained.

[0172] For a more intuitive understanding of this solution, please refer to Figure 8 , Figure 8 A schematic diagram of "displaying a first question" and "obtaining an answer to the first question" provided in an embodiment of the present application. Figure 8 As shown, after the first device determines that the field to which the first operations optimization problem belongs is the "planning problem field", it displays three first questions to the user through the display screen. The three first questions respectively involve the granularity of the plan, the number of codes included in the plan, and the total length of the plan cycle. When the user selects a first question (for example, hovers the cursor over a first question), the first device can be triggered to display the answer options of the aforementioned first question, such as Figure 8 As shown, when the first device determines that the cursor is hovering over the first question "Is this a plan with a granularity of {days}", it can trigger the display of three answer options (i.e., Figure 8 The user can click on one of the three answer options to enter a selection operation for the answer option.

[0173] Optionally, see Figure 8 After the first device determines the first field, it can also display at least one historical case in the planning problem field to the user, and the aforementioned at least one historical case is a mathematical model established for the operations optimization problem in the planning problem field.

[0174] It should be noted that this is combined with Figure 8 The examples are given only to facilitate understanding of the present solution. "Triggering the display of answer options" and "inputting a selection operation for a certain answer option through the display screen" may also be implemented in other ways. For example, the first device may directly display all answer options for each first question while displaying each first question; for example, the user may drag and drop a certain answer option to the area where the first question is located to input a selection operation for a certain answer option, etc. The specific implementation method may be flexibly determined in combination with the actual application scenario, and is not limited in the present application.

[0175] In another implementation, after the first device displays at least one first question to the user for the first time through the display screen, it may obtain a first answer input by the user through a text box, and the first device may obtain a first matching result between the first answer and all correct answers to all first questions displayed for the first time. The first device may determine the correct answers to one or more third questions among all first questions displayed for the first time based on the first matching result, or the first device may determine that the correct answer to any first question among all first questions displayed for the first time has not been obtained based on the first matching result.

[0176] Optionally, the first device may obtain a first matching result between the first answer and all correct answers to all first questions displayed for the first time through a third machine learning model. Exemplarily, in one case, if the third machine learning model is specifically expressed as a large model, the third machine learning model and the above-mentioned second machine learning model and first machine learning model may be the same machine learning model or different machine learning models. The first device may send the first answer, all first questions displayed for the first time, and all correct answers to all first questions displayed for the first time to a device deployed with a third machine learning model in the form of a large model, and the aforementioned device calls the third machine learning model in the form of a large model to process the first answer, all first questions displayed for the first time, and all answers to all first questions displayed for the first time, and obtains a first matching result generated by the third machine learning model in the form of a large model. The device deployed with the third machine learning model in the form of a large model sends the first matching result to the first device.

[0177] For example, the above-mentioned device can input all the first questions displayed for the first time and all the answers to all the first questions displayed for the first time into the third machine learning model in the form of a large model, and input "Please ask which questions among all the first questions displayed for the first time are included in the first answer and which correct answers are included in the first answer" into the third machine learning model in the form of a large model, triggering the third machine learning model in the form of a large model to process the first answer, all the first questions displayed for the first time, and all the answers to all the first questions displayed for the first time to obtain a first matching result. It should be understood that the examples given here are only to prove the feasibility of this solution and are not used to limit this solution.

[0178] If the first matching result is used to inform the first device that the first answer and which correct answer to the one or more third questions (hereinafter referred to as the "first correct answer" for convenience of description) meet the first matching condition, the first device can determine the first answer as the correct answer to the one or more third questions. If the first matching result is used to inform the first device that the first answer and the correct answer to any of all the first questions displayed for the first time do not meet the first matching condition, the first device can determine that the correct answer to any of the first questions displayed for the first time has not been obtained.

[0179] In another case, a third machine learning model that has performed training operations may be deployed in the first device, and the first device inputs the first answer and all correct answers to all first questions displayed for the first time into the third machine learning model to obtain a first matching result output by the third machine learning model, and the first matching result indicates the similarity between the first answer and each correct answer of all correct answers to all first questions displayed for the first time.

[0180] The first device may determine, based on the first matching result, whether the similarity between the aforementioned first answer and any correct answer to at least one of all first questions displayed for the first time satisfies the first similarity. If the first device determines that the similarity between the first answer and the first correct answer to the one or more third questions satisfies the first similarity, the first answer may be determined as the correct answer to the one or more third questions. Alternatively, if the first device determines that the similarity between the aforementioned first answer and all correct answers to all first questions displayed for the first time does not satisfy the first similarity, the first device may determine that the correct answer to any of all first questions displayed for the first time has not been obtained.

[0181] Alternatively, the first device may also adopt other algorithms to calculate the similarity between the first answer and all the answers to all the first questions displayed for the first time, for example, by calculating the cosine similarity, Euclidean distance or L1 distance between the first answer and all the answers to all the first questions displayed for the first time, to determine the similarity between the first answer and all the answers to all the first questions displayed for the first time, and then determine which answers to which questions are included in the first answer. The above-mentioned examples of various implementation methods are only to prove the feasible methods of this scheme and are not used to limit this scheme.

[0182] In another implementation, the first device not only displays at least one first question to the user for the first time through the display screen, but also displays the answer options corresponding to each first question, and displays a text box for obtaining the answer to the first question to the user through the display screen. The user can then input a selection operation for a certain answer option, or the user can also input the answer through the text box, and then the first device obtains the answer to at least one third question among all the first questions displayed for the first time based on the user's operation; it should be noted that the specific implementation methods of "determining the answer to the third question based on the selection operation input by the user for the answer option" or "determining the answer to the third question based on the text description input by the user" can refer to the description of the above two implementation methods, which will not be repeated here.

[0183] After the first device outputs at least one first question to the user for the first time and then determines that the answer to at least one third question has been obtained (or determines that the answer to any question has not been obtained), it can determine which of all the target questions included in the first question set (or the second question set) have not yet obtained the correct answer, and then determine which first questions need to continue to be output to the user; the first device repeats the above steps at least once until the correct answers to all the target questions in the first question set (or the second question set) are obtained, and then stops outputting the first question to the user; that is, the first device obtains the first description information, and the first description information includes the correct answer to at least one target question. Among them, "first question" and "third question" are both questions among multiple target questions, the difference is that "first question" represents the question output to the user, and "third question" represents the question that has obtained the correct answer.

[0184] It should be noted that the specific implementation methods of "the first device determines the first question to be output to the user each time", "how to output the first question to the user" and "determine which answers to the third questions have been obtained" can refer to the above description of the specific implementation method of "outputting the first question to the user for the first time and obtaining the answer", which will not be repeated here.

[0185] For a more intuitive understanding of this solution, please refer to Fig. 9 , Fig. 9 A schematic diagram of obtaining first description information corresponding to at least one question provided in an embodiment of the present application, such as Fig. 9 As shown, in stage one: after the first device determines that a first mathematical model corresponding to a first operations optimization problem needs to be generated, it can first determine a first field to which the mathematical model to be established belongs, and then determine at least one target problem corresponding to the operations optimization problem in the first field.

[0186] In stage two: the first device can obtain the answer to the at least one target question mentioned above, that is, obtain the first description information. Fig. 9 Taking the example of storing the aforementioned at least one target question in the form of a directed graph, the first device can determine at least one first question output to the user for the first time based on the indication of the directed graph to obtain the correct answer to at least one third question. The first device determines which third questions in the at least one target question have been answered (that is, the correct answers to which third questions have been obtained), and then determines which answers to the at least one target question have not been obtained, and continues to determine at least one first question output to the user again based on the indication of the directed graph. The first device can repeat the aforementioned operation at least once to obtain the first description information. It should be understood that Fig. 9 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0187] In an embodiment of the present application, at least one first question is output to the user to obtain an answer to each question, and then the first description information is determined based on the answer to each first question, that is, a guided manner is adopted to obtain the answer to at least one question corresponding to the first operations optimization problem, which is conducive to more efficiently obtaining the description information of the first operations optimization problem, and is also conducive to obtaining more accurate first description information, thereby facilitating obtaining a more accurate mathematical model.

[0188] Optionally, before the first device outputs the first question to the user for the first time, or while the first device outputs the first question to the user multiple times, the first device can also obtain second description information actively input by the user; or, after the first device outputs the first question to the user, the second description information carries the description information of the first operations optimization problem.

[0189] The first device can also merge the first description information and the second description information to obtain the union of the first description information and the second description information (hereinafter referred to as "updated first description information" for the convenience of description), and then determine the first information based on the aforementioned updated first description information in a subsequent step. The first information includes the updated first description information, that is, the first information includes the aforementioned first description information and the second description information.

[0190] 403. Determine at least one first case from at least one case based on the similarity between the first description information and each description information included in at least one case, wherein at least one case is stored in the first device, any one of the at least one case includes description information and a mathematical model corresponding to the operations optimization problem, and the similarity between the first description information and the description information included in the first case meets a preset condition.

[0191] In the embodiment of the present application, step 403 is an optional step. It should be understood that the concept of "descriptive information" included in the target case is similar to the concept of "first descriptive information", both of which are descriptive information used to describe the operations optimization problem, and the concept of "mathematical model" included in the target case is similar to the concept of "first mathematical model", except that the descriptive information and mathematical model included in the target case are already stored in the first device, the first descriptive information is being obtained, and the first mathematical model needs to be generated by the first machine learning model.

[0192] After obtaining the first description information (or the updated first description information), the first device can also obtain the description information included in each case in at least one case stored in the first device (hereinafter referred to as "fourth description information" for the convenience of description), that is, it can obtain at least one fourth description information corresponding to at least one case, and then obtain the similarity between the first description information (or the updated first description information) and the fourth description information included in each case in at least one case. Exemplarily, each case in the aforementioned at least one case (that is, including the target case) can be expressed in the form of <description information, mathematical model>, and the "description information" and "mathematical model" included in each case correspond to the same operations research optimization problem.

[0193] Since steps 401 and 402 are both optional steps, if steps 401 and 402 are performed, the first description information used in step 403 can be obtained through steps 401 and 402. If steps 401 and 402 are not performed, there may not be at least one target problem corresponding to the first operations research problem, and the first description information may also include a piece of description information input by the user, and the first description information at least indicates that a mathematical model corresponding to the operations research optimization problem needs to be generated.

[0194] The first device may use a variety of methods to implement "obtaining the similarity between the first description information and the fourth description information included in each case in at least one case". In one implementation method, the first device may obtain the initial feature information of the first description information (or the updated first description information) and the initial feature information of the fourth description information included in each case in at least one case; for example, the initial feature information of the first description information is obtained by vectorizing (embedding) the first description information (or the updated first description information), and the initial feature information of the fourth description information is obtained by vectorizing the fourth description information. The first device may generate first similarity information, and the first similarity information includes the similarity between the initial feature information of the first description information and the initial feature information of each fourth description information in at least one fourth description information.

[0195] Optionally, the initial feature information of the first description information and the initial feature information of the fourth description information can be obtained through a fourth machine learning model. In one case, if the fourth machine learning model is specifically manifested as a large model, the fourth machine learning model, the third machine learning model, the third machine learning model and the first machine learning model can be the same machine learning model, or can also be different machine learning models. Exemplarily, the first device can send the first description information (or the updated first description information) and all the fourth description information included in at least one case to a device deployed with the fourth machine learning model in the form of a large model, so as to process the first description information (or the updated first description information) and each fourth description information through the fourth machine learning model in the form of a large model, and obtain the initial feature information of the first description information output by the fourth machine learning model in the form of a large model and the initial feature information of each fourth description information.

[0196] For example, a device deployed with the fourth machine learning model in the form of a large model can input "Please vectorize the first description information" to the aforementioned fourth machine learning model, triggering the fourth machine learning model in the form of a large model to process the first description information, and obtain the initial feature information of the aforementioned first description information; the method for obtaining the "initial feature information of the fourth description information" can refer to the aforementioned description and will not be repeated here. It should be understood that the examples given here are only for demonstrating the feasibility of this solution and are not used to limit this solution.

[0197] In another case, a fourth machine learning model may also be deployed in the first device. The first device inputs the first description information (or the updated first description information) and each fourth description information into the fourth machine learning model respectively, and vectorizes the first description information (or the updated first description information) and each fourth description information through the fourth machine learning model to obtain initial feature information of the first description information and initial feature information of each fourth description information.

[0198] The first device may obtain the first similarity information in a variety of ways. For example, the first device may determine the first similarity information based on the cosine similarity between the initial feature information of the first description information and the initial feature information of each fourth description information. For another example, the aforementioned "cosine similarity" may be replaced by "Euclidean distance", "Mahalanobis distance", "L1 distance" or other algorithms for calculating similarity, etc., which may be determined in combination with actual application scenarios, and are not exhaustively listed in the embodiments of the present application.

[0199] In another implementation, the first device may obtain second information, and the second information includes at least one of a summary and keywords of the first descriptive information (or the updated first descriptive information); exemplarily, a fifth machine learning model may be deployed in the first device, and the first device inputs the first descriptive information into the fifth machine learning model, and extracts the summary and / or keywords of the first descriptive information through the fifth machine learning model to obtain the second information output by the fifth machine learning model. It should be noted that the examples given here are only to prove the feasibility of this solution, and the aforementioned steps can also be implemented through a large model, and other implementation methods are not listed one by one here.

[0200] After acquiring the second information, the first device can determine the similarity between the second information and the fourth descriptive information included in each case in at least one case, wherein the similarity between the second information and the fourth descriptive information included in each case in at least one case is taken as the similarity between the first descriptive information and the fourth descriptive information included in each case in at least one case.

[0201] Exemplarily, in one case, the first device can obtain the initial feature information of the second information and the initial feature information of each fourth description information, and then generate the second similarity information, the second similarity information includes the similarity between the initial feature information of the second information (that is, the summary and / or keywords of the first description information) and the initial feature information of each fourth description information, that is, the similarity between the first description information and the fourth description information included in each case in at least one case is obtained. It should be noted that the specific method for obtaining the "initial feature information of the second information" can refer to the above description of the specific method for obtaining the "initial feature information of the first description information" and the "initial feature information of the fourth description information", and the specific method for obtaining the "second similarity information" can be generated by using the above description of the specific method for obtaining the "first similarity information", which will not be repeated here.

[0202] In another case, the first device may also obtain third information corresponding to each fourth description information, that is, obtain at least one third information corresponding to at least one fourth description information, and each third information includes a summary and / or keywords of the fourth description information. The first device obtains the initial feature information of the second information and the initial feature information of each third information, and then generates third similarity information; wherein the third similarity information includes the similarity between the initial feature information of the second information and the initial feature information of each third information, and the similarity between the initial feature information of the second information and the initial feature information of each third information can be used as the similarity between the second information and each fourth description information, that is, it can be used as the similarity between the first description information and each fourth description information.

[0203] In an embodiment of the present application, since the first description information may include text information entered by the user, and the text information entered by the user may carry invalid information, first obtaining the summary and / or keywords of the first description information, that is, filtering out the invalid information in the first description information, and then generating the similarity between the summary and / or keywords of the first description information and the fourth description information, is conducive to obtaining more accurate similarity information, and further conducive to obtaining a more matching first case.

[0204] In another implementation, after obtaining the first description information in text form (or the updated first description information), the first device may also convert the aforementioned text form into a graph form, wherein the aforementioned at least one target question is used as a point of the first description information in graph form (or the updated first description information), and the correct answer of each target question included in the first description information (or the updated first description information) is used as an edge of the first description information in graph form (or the updated first description information). The first device may obtain the initial feature information of the first description information in graph form and the initial feature information of each fourth description information, and then generate fourth similarity information, wherein the fourth similarity information includes the similarity between the initial feature information of the first description information in graph form and the initial feature information of each fourth description information, and the similarity between the initial feature information of the first description information in graph form and the initial feature information of each fourth description information is used as the similarity between the first description information and each fourth description information, etc.

[0205] It should be noted that the first device can also use other methods to obtain the similarity between the first description information (or the updated first description information) and the fourth description information included in each case in at least one case. The above examples of various specific implementation methods of the aforementioned steps are only for the convenience of understanding this solution and are not used to limit this solution.

[0206] Exemplarily, after determining the similarity between the first description information (or the updated first description information) and the fourth description information included in each case in at least one case, the first device can determine K fourth description information whose similarity with the first description information (or the updated first description information) satisfies a preset condition from at least one fourth description information corresponding one-to-one to at least one case, and then obtain a first case to which each of the K fourth description information belongs, that is, screening out K first cases from at least one case, and the similarity between the first description information and the fourth description information included in each of the K first cases satisfies the preset condition, and K is an integer greater than or equal to 1.

[0207] The similarity between each of the K fourth description information and the first description information (or the updated first description information) is greater than or equal to a similarity threshold, and / or the K fourth description information include at least one of the fourth description information that is most similar to the first description information (or the updated first description information).

[0208] Exemplarily, the similarity threshold may be eighty percent, eighty-five percent, ninety percent or other values, etc., which are not limited in the embodiments of the present application.

[0209] For a more intuitive understanding of this solution, please refer to Fig.10 , Fig.10 A schematic diagram of a flow chart for determining at least one first case from at least one case provided in an embodiment of the present application. Fig.10 The icon in represents a machine learning model in the form of a large model. The machine learning model in the form of a large model can be used to generate a summary and keywords for each fourth description information in the case library, and then the machine learning model in the form of a large model can be used to generate the initial feature information of the summary and keywords of each fourth description information.

[0210] It is also possible to generate a summary and keywords of the first description information through a machine learning model in the form of a large model, and then generate initial feature information of the summary and keywords of the first description information through a machine learning model in the form of a large model.

[0211] Based on the initial feature information of the summary and keywords of the first description information, and the initial feature information of the summary and keywords of each fourth description information, K fourth description information with the most similarity to the first description information is determined. Since each of the K fourth description information belongs to a case in the case library, the K first cases that best match the first description information are obtained. It should be understood that Fig.10 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0212] 404. Obtain first information according to the first description information and at least one first case.

[0213] In the embodiment of the present application, step 404 is an optional step. If the first device determines K first cases from at least one case through step 403, the first description information (or the updated first description information) and the K first cases can be combined to obtain the first information, so as to process the first information through the first machine learning model in the subsequent steps. If the first device does not obtain any first case from at least one case through step 403, step 404 may not be executed. Alternatively, if step 403 is not executed, step 404 may not be executed.

[0214] In an embodiment of the present application, at least one first case that best matches the first descriptive information is obtained from at least one pre-stored case, and the similarity between the first descriptive information and the descriptive information included in each first case satisfies a preset condition, and then each first case and the first descriptive information are used as input to the first machine learning model, that is, each first case is used as a reference case for the first machine learning model, which is beneficial to assisting the first machine learning model to generate a more accurate first mathematical model, and is also beneficial to improving the efficiency of the first machine learning model in the process of generating the first mathematical model.

[0215] 405. Input the first information into the first machine learning model to obtain a first mathematical model corresponding to the first operations research optimization problem, wherein the first information is obtained based on the first description information, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

[0216] In an embodiment of the present application, steps 403 and 404 are both optional steps. In one case, if steps 403 and 404 are executed and K first cases are determined by step 403, the first information may include K first cases and first description information. In one implementation, the first machine learning model is a large model. Optionally, the first information includes first description information (or updated first description information) and prompt information (hereinafter referred to as "second prompt word" for the convenience of distinction), and the first device can combine K first cases into the second prompt word in the form of <problem description, mathematical model> to obtain the first information.

[0217] Exemplarily, the K first cases include: {problem description 1, mathematical model 1}, {problem description 2, mathematical model 2}, …, {problem description K, mathematical model K}, and the first information can be: "Prompt word (instruction)": "Please give a mathematical model under the new operations optimization problem description based on the provided <problem description, mathematical model> example. Example: {problem description 1, mathematical model 1}, {problem description 2, mathematical model 2}, …, {problem description K, mathematical model K}", "Input (input)": "First description information", it should be understood that the examples given here are only for the convenience of understanding this solution and are not used to limit this solution.

[0218] “The first device inputs the first information into the first machine learning model” can be understood as the first device sending the first information to the device on which the first machine learning model is deployed, and then the device capable of deploying the first machine learning model inputs the first information into the first machine learning model in the form of a large model. “The first device obtains the first mathematical model corresponding to the first operations optimization problem” can be understood as the first device receiving the first mathematical model corresponding to the first operations optimization problem.

[0219] For a more intuitive understanding of this solution, please refer to Fig.11 , Fig.11 A schematic diagram of a process for obtaining a first mathematical model based on first information provided in an embodiment of the present application. Fig.11 As shown, after obtaining K first cases, a second prompt word can be obtained based on the K first cases, and the second prompt word and the first description information both belong to the first information; the second prompt word and the first description information can be input into the first machine learning model in the form of a large model to obtain the first mathematical model corresponding to the first operations optimization problem output by the first machine learning model in the form of a large model. It should be understood that Fig.11 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0220] In another case, if steps 403 and 404 are not executed, step 405 can be executed directly after step 402 is executed. Alternatively, if steps 403 and 404 are executed and no first case is determined by step 403, the first information and the first descriptive information can be the same information, and "the first device inputs the first information into the first machine learning model" can be understood as the first device sending the first information (which can also be understood as the first descriptive information) to the device on which the first machine learning model is deployed, and then the device capable of deploying the first machine learning model inputs the first information (which can also be understood as the first descriptive information) into the first machine learning model in the form of a large model.

[0221] After obtaining the first mathematical model corresponding to the first operations research optimization problem, the first device can output the first mathematical model to the user through a display screen; the aforementioned output method includes but is not limited to: displaying the first mathematical model through a display screen, outputting a file containing the first mathematical model through a display screen, or other output methods, etc.

[0222] For a more intuitive understanding of this solution, please refer to Fig.12 , Fig.12 A schematic diagram of outputting a first mathematical model through a display screen provided in an embodiment of the present application. Fig.12 In the example, the first mathematical model is displayed on the display screen. Fig.12 As shown, not only the objective function and constraint conditions included in the first mathematical model are shown, but also the variables involved in the first mathematical model are shown. Optionally, the first case matched based on the first description information can also be displayed on the display screen, or the user can be informed on the display screen that no case is matched. It should be understood that Fig.12 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0223] In addition, for a more intuitive understanding of this solution, please refer to Fig.13 , Fig.13Another schematic diagram of the method for obtaining the mathematical model provided in the embodiment of the present application. Fig.13 As shown, stage one: obtain the first field to which the mathematical model to be established belongs. Stage two: at least one target problem corresponding to the first field adopts a directed graph data structure, and based on at least one target problem in the form of the aforementioned directed graph, obtain the answer to each first target problem in a guided manner, that is, obtain the first descriptive information in a guided manner. Stage three: based on the first descriptive information, determine K first cases from at least one case. Stage four: determine the second prompt word based on the K first cases, that is, combine the K first cases into the second prompt word. Then, the second prompt word and the first descriptive information can be input into the first machine learning model in the form of a large model to obtain the first mathematical model generated by the first machine learning model. It should be understood that the first mathematical model includes the objective function and the constraints. Fig.13 The examples are only for facilitating the understanding of this solution and are not intended to limit this solution.

[0224] Optionally, after the first device obtains the first mathematical model, the second device may also solve the first mathematical model to obtain a solution result of the first mathematical model, and the solution result of the first mathematical model is used to solve the first operations optimization problem.

[0225] Exemplarily, the second device and the first device may be the same device; or, the second device and the first device may be different devices. After obtaining the first mathematical model, the first device may send the first mathematical model to the second device.

[0226] For example, when the first field to which the first operations optimization problem to be solved belongs is the field of site selection, the solution result of the first mathematical model can indicate which one or several locations in the set of candidate locations are the best locations. For another example, when the first field to which the first operations optimization problem to be solved belongs is the field of scheduling problems, the solution result of the first mathematical model is used to inform how to arrange tasks under the constraints of limited resources to maximize work efficiency. For another example, when the first field to which the first operations optimization problem to be solved belongs is the field of order fulfillment, the solution result of the first mathematical model is used to inform how to arrange the processing and delivery of orders under the premise of given orders, so as to optimally use resources to meet customer needs, etc. It should be noted that the examples given here are only for the convenience of understanding the relationship between the "solution result of the first mathematical model" and the "first operations optimization problem", and are not used to limit this solution.

[0227] In order to have a more intuitive understanding of the beneficial effects brought about by the method provided by the present application, the beneficial effects brought about by the present application are explained below in combination with experimental data, and the experimental data are shown in the following Table 1.

[0228]

[0229] Table 1

[0230] Referring to the above experimental data, it can be seen that the method provided in this application is used to obtain a mathematical model corresponding to the operations optimization problem, which can obtain a mathematical model that is more suitable for the operations optimization problem, that is, improve the accuracy of the obtained mathematical model and increase the speed of the process of obtaining the mathematical model.

[0231] exist Figures 1 to 13 On the basis of the corresponding embodiments, in order to better implement the above solutions of the embodiments of the present application, the following also provides related devices for implementing the above solutions. Fig.14 , Fig.14 A structural schematic diagram of a device for acquiring a mathematical model provided in an embodiment of the present application, the device 1400 for acquiring a mathematical model includes: an output module 1401, used to output at least one first question; an acquisition module 1402, used to acquire at least one answer based on the at least one first question, and the at least one answer is used to obtain first description information, wherein the first description information is description information used to describe a first operations optimization problem, and the first problem is a problem used to obtain the description information of the first operations optimization problem; a determination module 1403, used to determine the first information based on the first description information; an input module 1404, used to input the first information into a machine learning model to obtain a first mathematical model, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations optimization problem.

[0232] Optionally, the at least one first question is determined based on a pre-stored first question set.

[0233] Optionally, the first question set is expressed in a data form of a directed graph, and the data form of the directed graph indicates an appearance order between at least two different questions in the first question set.

[0234] Optionally, the acquisition module 1402 is also used to obtain second descriptive information, wherein the first question set includes at least one first question and a second question corresponding to the second descriptive information, the second descriptive information is obtained through user input, the second question is a question used to obtain the descriptive information of the first operations optimization problem, and the second descriptive information includes an answer to the second question; the determination module 1403 is specifically used to determine the first information based on the first descriptive information and the second descriptive information, and the first information includes the first descriptive information and the second descriptive information.

[0235] Optionally, the mathematical model acquisition device 1400 is applied to the first device, and at least one case is stored in the first device. Any one of the at least one case includes descriptive information corresponding to the operations optimization problem and a mathematical model. The determination module 1403 is specifically used to: determine at least one first case from at least one case based on the similarity between the first descriptive information and each descriptive information included in the at least one case, wherein the similarity between the first descriptive information and the descriptive information included in the first case satisfies a preset condition; and obtain first information based on the first descriptive information and the at least one first case.

[0236] Optionally, the acquisition module 1402 is also used to obtain second information, the second information including a summary of the first descriptive information and at least one of the keywords of the first descriptive information; the determination module 1403 is also used to determine the similarity between the second information and each descriptive information included in at least one case, wherein the similarity between the second information and each descriptive information included in at least one case is taken as the similarity between the first descriptive information and each descriptive information included in at least one case.

[0237] Optionally, the determination module 1403 is also used to determine the field to which the mathematical model to be established belongs; the determination module 1403 is also used to determine a first set of questions from at least one pre-stored set of questions based on the field to which the mathematical model to be established belongs, and the at least one pre-stored set of questions corresponds one-to-one to at least one field; the determination module 1403 is also used to determine at least one first question based on the first set of questions.

[0238] Optionally, the field to which the mathematical model to be established belongs includes any of the following: site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field or production planning problem field.

[0239] It should be noted that the information interaction, execution process, etc. between the modules / units in the mathematical model acquisition device 1400 are the same as those in the present application. Figures 1 to 13 The corresponding method embodiments are based on the same concept. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0240] Please continue reading Fig.15 , Fig.15Another structural schematic diagram of a mathematical model acquisition device provided in an embodiment of the present application, the mathematical model acquisition device 1500 is applied to a first device, at least one case is stored in the first device, any one of the at least one case includes description information and a mathematical model corresponding to an operations optimization problem, and the mathematical model acquisition device 1500 includes: an acquisition module 1501, used to acquire first description information, the first description information is description information used to describe the first operations optimization problem; a determination module 1502, used to determine at least one second mathematical model from at least one mathematical model included in at least one case according to the similarity between the first description information and each description information included in the at least one case, the second mathematical model belongs to the first case in the at least one case, and the similarity between the first description information and the description information included in the first case meets a preset condition; a processing module 1503, used to obtain first information according to the first description information and the at least one second mathematical model; an input module 1504, used to input the first information into a machine learning model to obtain a first mathematical model, wherein the first mathematical model is output through a display screen, the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations optimization problem.

[0241] Optionally, the acquisition module 1501 is specifically used to obtain first descriptive information corresponding to at least one first question, the first descriptive information includes an answer to each first question in the at least one first question, and the answer to each first question in the at least one first question is used to obtain the first descriptive information, wherein the first question is a question used to obtain the descriptive information of the first operations optimization problem.

[0242] It should be noted that the information interaction, execution process, etc. between the modules / units in the mathematical model acquisition device 1500 are the same as those in the present application. Figures 1 to 13 The corresponding method embodiments are based on the same concept. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0243] Next, a device provided by an embodiment of the present application is introduced. Please refer to Fig.16 , Fig.16 A schematic diagram of a structure of a device provided in an embodiment of the present application, specifically, the device 1600 includes: a receiver 1601, a transmitter 1602, a processor 1603 and a memory 1604 (wherein the number of processors 1603 in the device 1600 can be one or more, Fig.16 In the example of FIG. 1603 , the processor 1603 may include an application processor 16031 and a communication processor 16032. In some embodiments of the present application, the receiver 1601, the transmitter 1602, the processor 1603 and the memory 1604 may be connected via a bus or other means.

[0244] The memory 1604 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1603. A portion of the memory 1604 may also include a non-volatile random access memory (NVRAM). The memory 1604 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, wherein the operation instructions may include various operation instructions for implementing various operations.

[0245] The processor 1603 controls the operation of the device. In a specific application, the various components of the device are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are referred to as bus systems in the figure.

[0246] The method disclosed in the above embodiment of the present application can be applied to the processor 1603, or implemented by the processor 1603. The processor 1603 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 1603. The above processor 1603 can be a general processor, a digital signal processor (digital signal processing, DSP), a microprocessor or a microcontroller, and can further include an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 1603 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 1604, and the processor 1603 reads the information in the memory 1604 and completes the steps of the above method in combination with its hardware.

[0247] The receiver 1601 can be used to receive input digital or character information and generate signal input related to the relevant settings and function control of the device. The transmitter 1602 can be used to output digital or character information through the first interface; the transmitter 1602 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 1602 can also include a display device such as a display screen.

[0248] In the embodiment of the present application, the processor 1603 is used to execute Figures 1 to 13 The method for obtaining the mathematical model executed by the first device in the corresponding embodiment. It should be noted that the specific manner in which the application processor 16031 in the processor 1603 executes the above steps is the same as that in the present application. Figures 1 to 13 The corresponding method embodiments are based on the same concept, and the technical effects they bring are the same as those in this application. Figures 1 to 13 The corresponding method embodiments are the same. For specific contents, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0249] The present application also provides a computer-readable storage medium in which a program for signal processing is stored. When the program is run on a computer, the computer executes the above-mentioned Figures 1 to 13 The illustrated embodiment describes the steps performed by the first device in the method.

[0250] The present application also provides a computer program product, which includes a program, and when the program is run on a computer, the computer executes the above Figures 1 to 13 The illustrated embodiment describes the steps performed by the first device in the method.

[0251] The first device provided in the embodiment of the present application may be a chip, which includes: a processing unit and a communication unit. The processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, a pin or a circuit. The processing unit may execute the computer execution instructions stored in the storage unit to enable the chip to execute the above Figures 1 to 13 The method for obtaining the mathematical model described in the illustrated embodiment. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc. The storage unit may also be a storage unit located outside the chip in the wireless access device, such as a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), etc.

[0252] The processor mentioned in any of the above places may be a general-purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned first aspect method.

[0253] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0254] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a first device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0255] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0256] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a first device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, first device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a first device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for obtaining a mathematical model, characterized in that: The method comprises: Outputting at least one first question, obtaining at least one answer based on the at least one first question, wherein the at least one answer is used to obtain first description information, wherein the first description information is description information used to describe a first operations research optimization problem, and the first question is a question used to obtain the description information of the first operations research optimization problem; Determine first information according to the first description information; The first information is input into a machine learning model to obtain a first mathematical model, wherein the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

2. The method according to claim 1, characterized in that The at least one first question is determined based on a pre-stored first question set.

3. The method according to claim 2, characterized in that The first question set is expressed in a data form of a directed graph, and the data form of the directed graph indicates an appearance order between at least two different questions in the first question set.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: Obtaining second description information, wherein the first question set includes the at least one first question and a second question corresponding to the second description information, the second description information is obtained through user input, the second question is a question used to obtain the description information of the first operations research optimization problem, and the second description information includes an answer to the second question; The determining the first information according to the first description information includes: The first information is determined according to the first description information and the second description information, where the first information includes the first description information and the second description information.

5. The method according to any one of claims 1 to 3, characterized in that: The method is applied to a first device, wherein at least one case is stored in the first device, and any one of the at least one case includes description information and a mathematical model corresponding to an operations research optimization problem, and determining first information according to the first description information includes: Determine at least one first case from the at least one case according to the similarity between the first description information and each description information included in the at least one case, wherein the similarity between the first description information and the description information included in the first case satisfies a preset condition; The first information is obtained according to the first description information and the at least one first case.

6. The method according to claim 5, characterized in that The method further comprises: Acquire second information, where the second information includes at least one of a summary of the first description information and a keyword of the first description information; Determine the similarity between the second information and each descriptive information included in the at least one case, wherein the similarity between the second information and each descriptive information included in the at least one case is taken as the similarity between the first descriptive information and each descriptive information included in the at least one case.

7. The method according to claim 2 or 3, characterized in that: Before outputting at least one first question, the method further includes: Determine the field to which the mathematical model to be established belongs; According to the field to which the mathematical model to be established belongs, determining the first set of questions from at least one set of questions stored in advance, wherein the at least one set of questions stored in advance corresponds to at least one field in a one-to-one manner; The at least one first question is determined based on the first question set.

8. The method according to claim 7, characterized in that The field to which the mathematical model to be established belongs includes any of the following: site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field or production planning problem field.

9. A method for obtaining a mathematical model, characterized in that: The method is applied to a first device, wherein at least one case is stored in the first device, and any one of the at least one case includes description information and a mathematical model corresponding to an operations research optimization problem. The method includes: Acquire first description information, where the first description information is description information used to describe a first operations optimization problem; Determining at least one second mathematical model from at least one mathematical model included in the at least one case according to the similarity between the first description information and each description information included in the at least one case, the second mathematical model belonging to a first case in the at least one case, and the similarity between the first description information and the description information included in the first case meets a preset condition; Obtaining first information according to the first description information and the at least one second mathematical model; The first information is input into a machine learning model to obtain a first mathematical model, wherein the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

10. The method according to claim 9, characterized in that The obtaining of the first description information includes: Obtain first description information corresponding to at least one first question, the first description information including an answer to each of the at least one first question, the answer to each of the at least one first question being used to obtain first description information, wherein the first question is a question used to obtain description information of the first operations optimization problem.

11. A device for acquiring a mathematical model, characterized in that: The device comprises: An output module, configured to output at least one first question; an acquisition module, configured to acquire at least one answer based on the at least one first question, wherein the at least one answer is used to obtain first description information, wherein the first description information is description information used to describe a first operations research optimization problem, and the first question is a question used to acquire the description information of the first operations research optimization problem; A determination module, configured to determine first information according to the first description information; An input module is used to input the first information into a machine learning model to obtain a first mathematical model, wherein the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

12. The device according to claim 11, characterized in that The at least one first question is determined based on a pre-stored first question set.

13. The device according to claim 12, characterized in that The first question set is expressed in a data form of a directed graph, and the data form of the directed graph indicates an appearance order between at least two different questions in the first question set.

14. The device according to claim 12 or 13, characterized in that The acquisition module is further used to acquire second description information, wherein the first problem set includes the at least one first problem and the second problem corresponding to the second description information, the second description information is obtained through user input, the second problem is a problem used to acquire the description information of the first operations optimization problem, and the second description information includes an answer to the second problem; The determination module is specifically configured to determine the first information according to the first description information and the second description information, where the first information includes the first description information and the second description information.

15. The device according to any one of claims 11 to 13, characterized in that The apparatus is applied to a first device, wherein at least one case is stored in the first device, and any one of the at least one case includes description information and a mathematical model corresponding to an operations optimization problem, and the determination module is specifically used to: Determine at least one first case from the at least one case according to the similarity between the first description information and each description information included in the at least one case, wherein the similarity between the first description information and the description information included in the first case satisfies a preset condition; The first information is obtained according to the first description information and the at least one first case.

16. The device according to claim 15, characterized in that The acquisition module is further configured to acquire second information, wherein the second information includes at least one of a summary of the first description information and a keyword of the first description information; The determination module is also used to determine the similarity between the second information and each descriptive information included in the at least one case, wherein the similarity between the second information and each descriptive information included in the at least one case is taken as the similarity between the first descriptive information and each descriptive information included in the at least one case.

17. The device according to claim 12 or 13, characterized in that The determination module is also used to determine the field to which the mathematical model to be established belongs; The determination module is further used to determine the first set of questions from at least one pre-stored set of questions according to the field to which the mathematical model to be established belongs, wherein the at least one pre-stored set of questions corresponds to at least one field in a one-to-one manner; The determination module is further used to determine the at least one first question according to the first question set.

18. The device according to claim 17, characterized in that The field to which the mathematical model to be established belongs includes any of the following: site selection problem field, scheduling problem field, order fulfillment field, supply chain field, packing problem field, transportation field, resource allocation field, revenue management field or production planning problem field.

19. A device for acquiring a mathematical model, characterized in that: The mathematical model acquisition device is applied to a first device, wherein at least one case is stored in the first device, and any one of the at least one case includes description information and a mathematical model corresponding to an operations research optimization problem, and the device includes: An acquisition module, used to acquire first description information, where the first description information is description information used to describe a first operations optimization problem; a determination module, configured to determine at least one second mathematical model from at least one mathematical model included in the at least one case according to the similarity between the first description information and each description information included in the at least one case, the second mathematical model belonging to a first case in the at least one case, and the similarity between the first description information and the description information included in the first case meeting a preset condition; A processing module, configured to obtain first information according to the first description information and the at least one second mathematical model; An input module is used to input the first information into a machine learning model to obtain a first mathematical model, wherein the first mathematical model includes an objective function and constraints, and the first mathematical model is used to solve the first operations research optimization problem.

20. The device according to claim 19, characterized in that The acquisition module is specifically used to obtain first descriptive information corresponding to at least one first problem, the first descriptive information includes the answer to each first question in the at least one first problem, and the answer to each first question in the at least one first problem is used to obtain the first descriptive information, wherein the first problem is the problem used to obtain the descriptive information of the first operations optimization problem.

21. A device, characterized in that comprising a processor and a memory, the processor being coupled to the memory, The memory is used to store programs; The processor is configured to execute the program in the memory so that the device performs the method according to any one of claims 1 to 10.

22. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 10.

23. A computer program product, characterized in that The computer program product comprises a program, and when the program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 10 .

24. An operations optimization method, characterized in that: The method includes: solving a first mathematical model to obtain a solution result of the first mathematical model, wherein the first mathematical model includes an objective function and constraints, the first mathematical model is used to solve a first operations research optimization problem, and the first mathematical model is obtained based on the method described in any one of claims 1 to 10.

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